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PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: An apparatus for generating the weight estimation model includes a training data collection unit that collects training data, the training data including skin spectrum information and weight information of a plurality of objects, and a model generation unit that generates the weight estimation model, used for a spectrum-based weight estimation, through machine learning based on the collected training data.
G06N3086		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An apparatus for generating the weight estimation model includes a training data collection unit that collects training data, the training data including skin spectrum information and weight information of a plurality of objects, and a model generation unit that generates the weight estimation model, used for a spectrum-based weight estimation, through machine learning based on the collected training data.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a larger neural network from a smaller neural network. In one aspect, a method includes obtaining data specifying an original neural network; generating a larger neural network from the original neural network, wherein the larger neural network has a larger neural network structure including the plurality of original neural network units and a plurality of additional neural network units not in the original neural network structure; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same outputs from the same inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a larger neural network from a smaller neural network, the method comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 2. The method of claim 1, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 3. The method of claim 2, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 4. The method of claim 3, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit. 5. The method of claim 4, wherein selecting an original neural network unit in the larger neural network layer comprises: randomly selecting an original neural network unit from the original neural network units in the original neural network layer. 6. The method of claim 4, wherein: in the original neural network structure, a second original neural network layer is configured to receive as input outputs generated by the first original neural network layer; in the larger neural network structure, the second original neural network layer is configured to receive as input outputs generated by the larger neural network layer; and initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the second original neural network layer so that, for a given neural network input, the second neural network layer generates the same output in both the original neural network structure and the larger neural network structure. 7. The method of claim 6, wherein the original neural network structure comprises a third original neural network layer configured to receive a third original layer input and generate a third original layer output from the third layer input, and wherein generating the larger neural network comprises: replacing the third original neural network layer with a first additional neural network layer having additional neural network units and a second additional neural network layer having additional neural network units, wherein: the first additional neural network layer is configured to receive the third original layer input and generate a first additional layer output from the third original layer input, and the second additional neural network layer is configured to receive the first additional layer output and generate a second additional layer output from the first additional layer output. 8. The method of claim 7, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the additional neural network units in the first additional neural network layer and in the second additional neural network layer so that, for the same neural network input, the second additional layer output is the same as the third original layer output. 9. The method of claim 7, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the additional neural network units in the first additional neural network layer using the respective original values for the parameters of the original neural network units in the third original neural network layer. 10. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 11. The system of claim 10, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 12. The system of claim 11, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 13. The system of claim 12, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit. 14. The system of claim 13, wherein selecting an original neural network unit in the larger neural network layer comprises: randomly selecting an original neural network unit from the original neural network units in the original neural network layer. 15. The system of claim 13, wherein: in the original neural network structure, a second original neural network layer is configured to receive as input outputs generated by the first original neural network layer; in the larger neural network structure, the second original neural network layer is configured to receive as input outputs generated by the larger neural network layer; and initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the second original neural network layer so that, for a given neural network input, the second neural network layer generates the same output in both the original neural network structure and the larger neural network structure. 16. The system of claim 15, wherein the original neural network structure comprises a third original neural network layer configured to receive a third original layer input and generate a third original layer output from the third layer input, and wherein generating the larger neural network comprises: replacing the third original neural network layer with a first additional neural network layer having additional neural network units and a second additional neural network layer having additional neural network units, wherein: the first additional neural network layer is configured to receive the third original layer input and generate a first additional layer output from the third original layer input, and the second additional neural network layer is configured to receive the first additional layer output and generate a second additional layer output from the first additional layer output. 17. A computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining data specifying an original neural network, the original neural network being configured to generate neural network outputs from neural network inputs, the original neural network having an original neural network structure comprising a plurality of original neural network units, each original neural network unit having respective parameters, and each of the parameters of each of the original neural network units having a respective original value; generating a larger neural network from the original neural network, the larger neural network having a larger neural network structure comprising: (i) the plurality of original neural network units, and (ii) a plurality of additional neural network units not in the original neural network structure, each additional neural network unit having respective parameters; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values. 18. The computer storage medium of claim 17, further comprising: training the original neural network to determine the original values of the parameters of the original neural network. 19. The computer storage medium of claim 18, wherein the original neural network structure comprises a first original neural network layer having a first number of original units, and wherein generating the larger neural network comprises: adding a plurality of additional neural network units to the first original neural network layer to generate a larger neural network layer. 20. The computer storage medium of claim 19, wherein initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same neural network outputs from the same neural network inputs as the original neural network comprises: initializing the values of the parameters of the original neural network units in the larger neural network layer to the respective original values for the parameters; and for each additional neural network unit in the larger neural network layer: selecting an original neural network unit in the original neural network layer, and initializing the values of the parameters of the additional neural network unit to be the same as the respective original values for the selected original neural network unit.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a larger neural network from a smaller neural network. In one aspect, a method includes obtaining data specifying an original neural network; generating a larger neural network from the original neural network, wherein the larger neural network has a larger neural network structure including the plurality of original neural network units and a plurality of additional neural network units not in the original neural network structure; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same outputs from the same inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values.
G06N3082		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a larger neural network from a smaller neural network. In one aspect, a method includes obtaining data specifying an original neural network; generating a larger neural network from the original neural network, wherein the larger neural network has a larger neural network structure including the plurality of original neural network units and a plurality of additional neural network units not in the original neural network structure; initializing values of the parameters of the original neural network units and the additional neural network units so that the larger neural network generates the same outputs from the same inputs as the original neural network; and training the larger neural network to determine trained values of the parameters of the original neural network units and the additional neural network units from the initialized values.
A computer-implemented system for real time topic detection in a social media message includes a knowledge base of keywords used to ingest the social media message and a partial parser deriving a syntax-semantic parse tree. The system also includes a topic calculator compositionally deriving the topic of the social media message by computing a topic value for given entities in the event described by the social media message. The topic value is derived from a first set of rules assigning Restrictor R-value to prominent R-expressions compositionally in the syntax-semantic parse tree and a second set of rules assigning a numeric Strength S-value to the R-expressions according to whether or not they are part of anaphoric chains in the social media message, and whether or not the R-expressions include name entities that are part of the knowledge base of keywords.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented system for real time topic detection in a social media message, wherein information structure of the social media message includes a topic and a comment, and wherein the topic is an R-expression (Referential expression) that restricts the information structure of an event described by the social media message, comprising: a knowledge base of keywords used to ingest the social media message; a partial parser deriving a syntax-semantic parse tree; a topic calculator compositionally deriving the topic of the social media message by computing a topic value for given entities in the event described by the social media message, wherein the topic value is derived from a first set of rules assigning a Restrictor R-value to prominent R-expressions compositionally in the syntax-semantic parse tree and a second set of rules assigning a numeric Strength S-value to the R-expressions according to whether or not they are part of anaphoric chains in the social media message, and whether or not the R-expressions include name entities that are part of the knowledge base of keywords. 2. The computer-implemented system for real time topic detection in the social media message according to claim 1, further including an inference engine reducing uncertainty in results of the topic calculator. 3. The computer-implemented system for real time topic detection in the social media message according to claim 2, wherein the inference engine includes a data structure and a set of inference rules. 4. The computer-implemented system for real time topic detection in the social media message according to claim 1, wherein the topic value of the social media message is associated with a strength value 1 to 3, where 1 is the lowest strength and 3 is the highest strength. 5. The computer-implemented system for real time topic detection in the social media message according to claim 4, wherein the strength value is the sum of the numeric Strength S-value of the second set of rates.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A computer-implemented system for real time topic detection in a social media message includes a knowledge base of keywords used to ingest the social media message and a partial parser deriving a syntax-semantic parse tree. The system also includes a topic calculator compositionally deriving the topic of the social media message by computing a topic value for given entities in the event described by the social media message. The topic value is derived from a first set of rules assigning Restrictor R-value to prominent R-expressions compositionally in the syntax-semantic parse tree and a second set of rules assigning a numeric Strength S-value to the R-expressions according to whether or not they are part of anaphoric chains in the social media message, and whether or not the R-expressions include name entities that are part of the knowledge base of keywords.
G06N5025		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A computer-implemented system for real time topic detection in a social media message includes a knowledge base of keywords used to ingest the social media message and a partial parser deriving a syntax-semantic parse tree. The system also includes a topic calculator compositionally deriving the topic of the social media message by computing a topic value for given entities in the event described by the social media message. The topic value is derived from a first set of rules assigning Restrictor R-value to prominent R-expressions compositionally in the syntax-semantic parse tree and a second set of rules assigning a numeric Strength S-value to the R-expressions according to whether or not they are part of anaphoric chains in the social media message, and whether or not the R-expressions include name entities that are part of the knowledge base of keywords.
The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training an encoder and a decoder, the method comprising: obtaining a set of training content; and for a compression model including an encoding portion, a decoding portion, and a discriminator portion: repeatedly backpropagating one or more error terms obtained from a loss function to update a set of parameters of the encoding portion and the decoding portion, wherein the loss function includes: a reconstruction loss indicating a dissimilarity between the training content and reconstructed content, wherein the reconstructed content is generated by applying the encoding portion to the training content to generate tensors for the training content, and applying the decoding portion to the tensors to generate the reconstructed content, and a discriminator loss indicating a cost of generating incorrect discrimination predictions generated by applying the discriminator portion to input content, wherein the input content includes the training content and the reconstructed content, and wherein the discrimination predictions indicate likelihoods of whether the input content is a reconstructed version of the training content; and stopping the backpropagation after the loss function satisfies a predetermined criteria. 2. The method of claim 1, wherein the discriminator portion is coupled to receive each of the training content and the reconstructed content individually as the input content, and wherein the discrimination predictions indicate likelihoods that the input content is the reconstructed version of the training content. 3. The method of claim 1, wherein the discriminator portion is coupled to receive ordered pairs of the training content and the corresponding reconstructed content as the input content, and wherein the discrimination predictions indicate which content in the ordered pairs is the reconstructed version of the training content. 4. The method of claim 3, wherein the ordered pairs of input content include a first pair having first training content as a first element and first reconstructed content as a second element, and a second pair having second reconstructed content as the first element and second training content as the second element. 5. The method of claim 1, wherein the discriminator portion includes a neural network model, and the discrimination predictions are generated by combining outputs from one or more intermediate layers of the neural network model. 6. The method of claim 1, wherein the loss function further includes: a codelength regularization loss indicating a cost of code lengths for compressed codes generated by applying an entropy coding technique to the tensors, wherein the codelength regularization loss is determined based on magnitudes of elements of the tensors for the training content. 7. The method of claim 1, further comprising: repeatedly backpropagating one or more error terms obtained from the discriminator loss to update a set of parameters of the discriminator portion while fixing the set of parameters of the encoding portion and the decoding portion; and stopping the backpropagation after the discriminator loss satisfies a predetermined criteria. 8. The method of claim 7, further comprising: responsive to determining that an accuracy of the discrimination predictions are above a first threshold, repeatedly backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion for one or more iterations, responsive to determining that the accuracy of the discrimination predictions are below a second threshold, repeatedly backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion for one or more iterations, and responsive to determining that the accuracy of the discrimination predictions are between the first threshold and the second threshold, alternating between backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion and backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion. 9. A decoder stored on a computer readable storage medium, wherein the decoder is manufactured by a process comprising: obtaining a set of training content; for a compression model including an encoding portion, a decoding portion, and a discriminator portion, repeatedly backpropagating one or more error terms obtained from a loss function to update a set of parameters of the encoding portion and the decoding portion, wherein the loss function includes: a reconstruction loss indicating a dissimilarity between the training content and reconstructed content, wherein the reconstructed content is generated by applying the encoding portion to the training content to generate tensors for the training content, and applying the decoding portion to the tensors to generate the reconstructed content, and a discriminator loss indicating a cost of generating incorrect discrimination predictions generated by applying the discriminator portion to input content, wherein the input content includes the training content and the reconstructed content, and wherein the discrimination predictions indicate likelihoods of whether the input content is a reconstructed version of the training content; and stopping the backpropagation after the loss function satisfies a predetermined criteria; and storing the set of parameters of the decoding portion on the computer readable storage medium as the parameters of the decoder, wherein the decoder is coupled to receive a compressed code for content and output a reconstructed version of the content using the stored parameters. 10. The decoder of claim 9, wherein the discriminator portion is coupled to receive each of the training content and the reconstructed content individually as the input content, and wherein the discrimination predictions indicate likelihoods that the input content is the reconstructed version of the training content. 11. The decoder of claim 9, wherein the discriminator portion is coupled to receive ordered pairs of the training content and the corresponding reconstructed content as the input content, and wherein the discrimination predictions indicate which content in the ordered pairs is the reconstructed version of the training content. 12. The decoder of claim 11, wherein the ordered pairs of input content include a first pair having first training content as a first element and first reconstructed content as a second element, and a second pair having second reconstructed content as the first element and second training content as the second element. 13. The decoder of claim 9, wherein the discriminator portion includes a neural network model, and the discrimination predictions are generated by combining outputs from one or more intermediate layers of the neural network model. 14. The decoder of claim 9, wherein the loss function further includes: a codelength regularization loss indicating a cost of code lengths for compressed codes generated by applying an entropy coding technique to the tensors, wherein the codelength regularization loss is determined based on magnitudes of elements of the tensors for the training content. 15. The decoder of claim 9, further comprising: repeatedly backpropagating one or more error terms obtained from the discriminator loss to update a set of parameters of the discriminator portion while fixing the set of parameters of the encoding portion and the decoding portion; and stopping the backpropagation after the discriminator loss satisfies a predetermined criteria. 16. The decoder of claim 15, further comprising: responsive to determining that an accuracy of the discrimination predictions are above a first threshold, repeatedly backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion for one or more iterations, responsive to determining that the accuracy of the discrimination predictions are below a second threshold, repeatedly backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion for one or more iterations, and responsive to determining that the accuracy of the discrimination predictions are between the first threshold and the second threshold, alternating between backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion and backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion. 17. An encoder stored on a computer readable storage medium, wherein the encoder is manufactured by a process comprising: obtaining a set of training content; for a compression model including an encoding portion, a decoding portion, and a discriminator portion, repeatedly backpropagating one or more error terms obtained from a loss function to update a set of parameters of the encoding portion and the decoding portion, wherein the loss function includes: a reconstruction loss indicating a dissimilarity between the training content and reconstructed content, wherein the reconstructed content is generated by applying the encoding portion to the training content to generate tensors for the training content, and applying the decoding portion to the tensors to generate the reconstructed content, and a discriminator loss indicating a cost of generating incorrect discrimination predictions generated by applying the discriminator portion to input content, wherein the input content includes the training content and the reconstructed content, and wherein the discrimination predictions indicate likelihoods of whether the input content is a reconstructed version of the training content; and stopping the backpropagation after the loss function satisfies a predetermined criteria; and storing the set of parameters of the encoding portion on the computer readable storage medium as the parameters of the encoder, wherein the decoder is coupled to receive content and output a compressed code for the content using the stored parameters. 18. The encoder of claim 17, wherein the discriminator portion is coupled to receive each of the training content and the reconstructed content individually as the input content, and wherein the discrimination predictions indicate likelihoods that the input content is the reconstructed version of the training content. 19. The encoder of claim 17, wherein the discriminator portion is coupled to receive ordered pairs of the training content and the corresponding reconstructed content as the input content, and wherein the discrimination predictions indicate which content in the ordered pairs is the reconstructed version of the training content. 20. The encoder of claim 19, wherein the ordered pairs of input content include a first pair having first training content as a first element and first reconstructed content as a second element, and a second pair having second reconstructed content as the first element and second training content as the second element. 21. The encoder of claim 17, wherein the discriminator portion includes a neural network model, and the discrimination predictions are generated by combining outputs from one or more intermediate layers of the neural network model. 22. The encoder of claim 17, wherein the loss function further includes: a codelength regularization loss indicating a cost of code lengths for compressed codes generated by applying an entropy coding technique to the tensors, wherein the codelength regularization loss is determined based on magnitudes of elements of the tensors for the training content. 23. The encoder of claim 17, further comprising: repeatedly backpropagating one or more error terms obtained from the discriminator loss to update a set of parameters of the discriminator portion while fixing the set of parameters of the encoding portion and the decoding portion; and stopping the backpropagation after the discriminator loss satisfies a predetermined criteria. 24. The encoder of claim 23, further comprising: responsive to determining that an accuracy of the discrimination predictions are above a first threshold, repeatedly backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion for one or more iterations, responsive to determining that the accuracy of the discrimination predictions are below a second threshold, repeatedly backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion for one or more iterations, and responsive to determining that the accuracy of the discrimination predictions are between the first threshold and the second threshold, alternating between backpropagating the error terms obtained from the loss function to update the set of parameters of the encoding portion and the decoding portion and backpropagating the error terms obtained from the discriminator loss to update the set of parameters of the discriminator portion. 25. A method for training an encoder and a decoder, comprising: obtaining training content and downsampling the training content to generate a set of training content each associated with a corresponding scale in a set of scales; and for a compression model including a set of autoencoder and discriminator pairs: for each autoencoder and discriminator pair associated with a corresponding scale, repeatedly backpropagating one or more error terms obtained from a loss function to update a set of parameters of the autoencoder in the pair, wherein the loss function includes: a reconstruction loss indicating a dissimilarity between the training content and reconstructed content, wherein the reconstructed content is generated by combining a set of content generated by applying the autoencoders of the set of pairs to the corresponding set of training content, and a discriminator loss indicating a cost of incorrect discrimination predictions generated by applying the discriminator of the pair to input content, wherein the input content includes the training content associated with the scale and content generated by applying the autoencoder of the pair to the training content associated with the scale, and wherein the discrimination predictions indicate likelihoods of whether the input content is a reconstructed version of the training content; and stopping the backpropagation after the loss function satisfies a predetermined criteria.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.
G06N3084		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.
An online system receives a request from a user of the online system to generate a content item specifying content (e.g., an image) received from the user and one or more modifications to the appearance of the content to be included in the content item. The online system generates multiple instances of the content item based on the request, in which each instance includes a different set of the specified modifications. Using an identifier that identifies each instance based on the set of modifications to the appearance of the included content (e.g., using an image fingerprint), the online system tracks a performance metric associated with each instance. By comparing the performance metrics associated with the instances, the online system identifies one or more modifications responsible for one or more differences between the performance metrics and predicts an effect on the performance metrics associated with content item instances including the identified modifications.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving a request from a content-providing user of an online system to generate a content item to be presented to one or more viewing users of the online system, the request specifying one or more modifications to an appearance of content received from the content-providing user; generating a plurality of content item instances of the content item, each of the plurality of content item instances including a different set of the one or more modifications to the appearance of the content specified in the request; generating an identifier for each of the plurality of content item instances, each identifier associated with the set of the one or more modifications to the appearance of the content included in the content item instance; presenting the plurality of content item instances to a subset of the one or more viewing users of the online system; tracking a performance metric associated with impressions of each of the plurality of content item instances using the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances; identifying one or more pairs of the plurality of content item instances; and for each of the one or more pairs of the plurality of content item instances: comparing a first value of the performance metric associated with a first content item instance of the pair to a second value of the performance metric associated with a second content item instance of the pair; determining a difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance based at least in part on the comparing; identifying a subset of the one or more modifications to the appearance of the content to which the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance is attributable; and predicting an improvement in a value of the performance metric associated with content item instances including the subset of the one or more modifications, based at least in part on the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance. 2. The method of claim 1, wherein the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances comprises a digital watermark, an image fingerprint, or an image hash. 3. The method of claim 1, wherein the one or more modifications to the appearance of the content are selected from a group consisting of: modifying one or more colors of the content, modifying a placement of an element of the content, modifying a size of the content, modifying a size of an element of the content, modifying a color of an element of the content, and any combination thereof. 4. The method of claim 1, wherein the improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications is predicted by a machine-learned model. 5. The method of claim 1, wherein the request is received from the content-providing user via a tool provided by the online system. 6. The method of claim 5, wherein the one or more modifications are specified using one or more features of the tool. 7. The method of claim 6, further comprising: identifying a feature of the tool used to specify the subset of the one or more modifications to the appearance of the content to which the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance is attributable; and predicting the improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications based at least in part on the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance. 8. The method of claim 1, wherein the content comprises one or more selected from a group consisting of: an image, a photograph, text, and any combination thereof. 9. The method of claim 1, wherein the performance metric describes a number of times a content item instance is accessed, a number of times a preference for the content item instance is indicated, a number of installations of an application associated with the content item instance, a number of times an application associated with the content item instance is accessed, a number of purchases of a product associated with the content item instance, a number of purchases of a service associated with the content item instance, a number of views of data associated with the content item instance, a number of conversions associated with the content item instance, a number of subscriptions associated with the content item instance, or a number of interactions with the content item instance. 10. The method of claim 1, further comprising: ranking the plurality of content item instances based at least in part on the value of the performance metric associated with each content item instance of the plurality of content item instances; determining an amount of variation in values of the performance metric associated with the plurality of content item instances; responsive to determining the amount of variation in values of the performance metric associated with the plurality of content item instances is at least a threshold amount, identifying an additional subset of the one or more modifications to the appearance of the content to which the amount of variation in values of the performance metric associated with the plurality of content item instances is attributable; and predicting the improvement in the value of the performance metric associated with content item instances including the additional subset of the one or more modifications based at least in part on the ranking and the amount of variation in values of the performance metric associated with the plurality of content item instances. 11. The method of claim 1, further comprising: storing the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances in association with each of the plurality of content item instances including the set of the one or more modifications to the appearance of the content. 12. The method of claim 1, further comprising: communicating the predicted improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications to the content-providing user of the online system. 13. The method of claim 1, further comprising: receiving the content from the content-providing user of the online system. 14. A computer program product comprising a computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: receive a request from a content-providing user of an online system to generate a content item to be presented to one or more viewing users of the online system, the request specifying one or more modifications to an appearance of content received from the content-providing user; generate a plurality of content item instances of the content item, each of the plurality of content item instances including a different set of the one or more modifications to the appearance of the content specified in the request; generate an identifier for each of the plurality of content item instances, each identifier associated with the set of the one or more modifications to the appearance of the content included in the content item instance; present the plurality of content item instances to a subset of the one or more viewing users of the online system; track a performance metric associated with impressions of each of the plurality of content item instances using the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances; identify one or more pairs of the plurality of content item instances; and for each of the one or more pairs of the plurality of content item instances: compare a first value of the performance metric associated with a first content item instance of the pair to a second value of the performance metric associated with a second content item instance of the pair; determine a difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance based at least in part on the comparing; identify a subset of the one or more modifications to the appearance of the content to which the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance is attributable; and predict an improvement in a value of the performance metric associated with content item instances including the subset of the one or more modifications, based at least in part on the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance. 15. The computer program product of claim 14, wherein the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances comprises a digital watermark, an image fingerprint, or an image hash. 16. The computer program product of claim 14, wherein the one or more modifications to the appearance of the content are selected from a group consisting of: modifying one or more colors of the content, modifying a placement of an element of the content, modifying a size of the content, modifying a size of an element of the content, modifying a color of an element of the content, and any combination thereof. 17. The computer program product of claim 14, wherein the improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications is predicted by a machine-learned model. 18. The computer program product of claim 14, wherein the request is received from the content-providing user via a tool provided by the online system. 19. The computer program product of claim 18, wherein the one or more modifications are specified using one or more features of the tool. 20. The computer program product of claim 18, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: identifying a feature of the tool used to specify the subset of the one or more modifications to the appearance of the content to which the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance is attributable; and predicting the improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications based at least in part on the difference between the first value of the performance metric associated with the first content item instance and the second value of the performance metric associated with the second content item instance. 21. The computer program product of claim 14, wherein the content comprises one or more selected from a group consisting of: an image, a photograph, text, and any combination thereof. 22. The computer program product of claim 14, wherein the performance metric describes a number of times a content item instance is accessed, a number of times a preference for the content item instance is indicated, a number of installations of an application associated with the content item instance, a number of times an application associated with the content item instance is accessed, a number of purchases of a product associated with the content item instance, a number of purchases of a service associated with the content item instance, a number of views of data associated with the content item instance, a number of conversions associated with the content item instance, a number of subscriptions associated with the content item instance, or a number of interactions with the content item instance. 23. The computer program product of claim 14, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: rank the plurality of content item instances based at least in part on the value of the performance metric associated with each content item instance of the plurality of content item instances; determine an amount of variation in values of the performance metric associated with the plurality of content item instances; responsive to determine the amount of variation in values of the performance metric associated with the plurality of content item instances is at least a threshold amount, identify an additional subset of the one or more modifications to the appearance of the content to which the amount of variation in values of the performance metric associated with the plurality of content item instances is attributable; and predict the improvement in the value of the performance metric associated with content item instances including the additional subset of the one or more modifications based at least in part on the ranking and the amount of variation in values of the performance metric associated with the plurality of content item instances. 24. The computer program product of claim 14, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: store the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances in association with each of the plurality of content item instances including the set of the one or more modifications to the appearance of the content. 25. The computer program product of claim 14, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: communicate the predicted improvement in the value of the performance metric associated with content item instances including the subset of the one or more modifications to the content-providing user of the online system. 26. The computer program product of claim 14, wherein the computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: receive the content from the content-providing user of the online system. 27. A method comprising: receiving a request from a content-providing user of an online system to generate a content item to be presented to one or more viewing users of the online system, the request specifying one or more modifications to an appearance of content received from the content-providing user; generating a plurality of content item instances of the content item, each of the plurality of content item instances including a different set of the one or more modifications to the appearance of the content specified in the request; generating an identifier for each of the plurality of content item instances, each identifier associated with the set of the one or more modifications to the appearance of the content included in the content item instance; presenting the plurality of content item instances to a subset of the one or more viewing users of the online system; tracking a performance metric associated with impressions of each of the plurality of content item instances using the identifier associated with the set of the one or more modifications to the appearance of the content included in each of the plurality of content item instances; ranking the plurality of content item instances based at least in part on a value of the performance metric associated with each content item instance of the plurality of content item instances; determining an amount of variation in values of the performance metric associated with the plurality of content item instances; responsive to determining the amount of variation in values of the performance metric associated with the plurality of content item instances is at least a threshold amount, identifying a subset of the one or more modifications to the content to which the amount of variation in values of the performance metric associated with the plurality of content item instances is attributable; and predicting an effect on the value of the performance metric associated with content item instances as a result of including the subset of the one or more modifications in the content item instances, the effect predicted based at least in part on the ranking and the amount of variation in values of the performance metric associated with the plurality of content item instances.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: An online system receives a request from a user of the online system to generate a content item specifying content (e.g., an image) received from the user and one or more modifications to the appearance of the content to be included in the content item. The online system generates multiple instances of the content item based on the request, in which each instance includes a different set of the specified modifications. Using an identifier that identifies each instance based on the set of modifications to the appearance of the included content (e.g., using an image fingerprint), the online system tracks a performance metric associated with each instance. By comparing the performance metrics associated with the instances, the online system identifies one or more modifications responsible for one or more differences between the performance metrics and predicts an effect on the performance metrics associated with content item instances including the identified modifications.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An online system receives a request from a user of the online system to generate a content item specifying content (e.g., an image) received from the user and one or more modifications to the appearance of the content to be included in the content item. The online system generates multiple instances of the content item based on the request, in which each instance includes a different set of the specified modifications. Using an identifier that identifies each instance based on the set of modifications to the appearance of the included content (e.g., using an image fingerprint), the online system tracks a performance metric associated with each instance. By comparing the performance metrics associated with the instances, the online system identifies one or more modifications responsible for one or more differences between the performance metrics and predicts an effect on the performance metrics associated with content item instances including the identified modifications.
The disclosed embodiments provide a system for processing data. During operation, the system obtains validated training data containing a first set of content items and a first set of classification tags for the first set of content items. Next, the system uses the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags. The system then uses the statistical model to generate a second set of classification tags for a second set of content items. Finally, the system outputs one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: obtaining validated training data comprising a first set of content items and a first set of classification tags for the first set of content items; using the validated training data to produce, by one or more computer systems, a statistical model for classifying content using a set of dimensions represented by the first set of classification tags; using the statistical model to generate, by the one or more computer systems, a second set of classification tags for a second set of content items; and outputting, by the one or more computer systems, one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items. 2. The method of claim 1, further comprising: obtaining a validated subset of the second set of classification tags for the second set of content items. 3. The method of claim 2, further comprising: providing the validated subset as additional training data to the statistical model to produce an update to the statistical model; and using the update to generate a third set of classification tags for a third set of content items. 4. The method of claim 2, wherein obtaining the validated subset of the second set of classification tags comprises: displaying the second set of content items and the second set of classification tags in a user interface; and obtaining one or more corrections to the second set of classification tags through the user interface. 5. The method of claim 1, wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises: generating a set of features from a content item in the first set of content items; and providing the set of features as input to the statistical model. 6. The method of claim 5, wherein the set of features comprises at least one of: one or more n-grams from the content items; a number of characters; a number of units of speech; an average number of units of speech; and a percentage of a character type. 7. The method of claim 5, wherein the set of features comprises profile data for a creator of the content item. 8. The method of claim 1, wherein the set of dimensions comprises a sentiment. 9. The method of claim 1, wherein the set of dimensions comprises a product associated with an online professional network. 10. The method of claim 1, wherein the set of dimensions comprises a value proposition. 11. An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: obtain validated training data comprising a first set of content items and a first set of classification tags for the first set of content items; use the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags; use the statistical model to generate a second set of classification tags for a second set of content items; and output one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items. 12. The apparatus of claim 11, wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to: obtain a validated subset of the second set of classification tags for the first set of content items; provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and use the update to generate a third set of classification tags for a third set of content items. 13. The apparatus of claim 12, wherein obtaining the validated subset of the first set of classification tags comprises: displaying the second set of content items and the second set of classification tags in a user interface; and obtaining one or more corrections to the second set of classification tags through the user interface. 14. The apparatus of claim 11, wherein using the training data to produce the statistical model for classifying the relevance of content to the one or more topics comprises: generating a set of features from a content item in the first set of content items; and providing the set of features as input to the statistical model. 15. The apparatus of claim 14, wherein the set of features comprises at least one of: one or more n-grams from the content items; a number of characters; a number of units of speech; an average number of units of speech; a percentage of a character type; and profile data for a creator of the content item. 16. The apparatus of claim 11, wherein the set of dimensions comprises a sentiment. 17. The apparatus of claim 11, wherein the set of dimensions comprises a product associated with an online professional network. 18. The apparatus of claim 11, wherein the set of dimensions comprises a value proposition. 19. A system, comprising: an analysis non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to: obtain validated training data comprising a first set of content items and a first set of classification tags for the first set of content items; use the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags; and use the statistical model to generate a second set of classification tags for a second set of content items; and a management non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to output one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items. 20. The system of claim 19, wherein the analysis non-transitory computer-readable medium further instructions that, when executed by the one or more processors, cause the system to: obtain a validated subset of the second set of classification tags for the first set of content items; provide the validated subset as additional training data to the statistical model to produce an update to the statistical model; and use the update to generate a third set of classification tags for a third set of content items.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: The disclosed embodiments provide a system for processing data. During operation, the system obtains validated training data containing a first set of content items and a first set of classification tags for the first set of content items. Next, the system uses the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags. The system then uses the statistical model to generate a second set of classification tags for a second set of content items. Finally, the system outputs one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The disclosed embodiments provide a system for processing data. During operation, the system obtains validated training data containing a first set of content items and a first set of classification tags for the first set of content items. Next, the system uses the validated training data to produce a statistical model for classifying content using a set of dimensions represented by the first set of classification tags. The system then uses the statistical model to generate a second set of classification tags for a second set of content items. Finally, the system outputs one or more groupings of the second set of content items by the second set of classification tags to improve understanding of content related to the set of dimensions without requiring a user to manually analyze the second set of content items.
A method of using artificial intelligence (e.g., SMILE) to assist users, such as pathologists and pathologist assistants, in pathology report preparation is described. The method includes the steps of (1) specimen gross examination, submission and dictation, (2) final diagnosis dictation, and (3) Cancer Protocol Templates construction. SMILE “listens” to the voice commands, “reads” case/slide information, goes through algorithms, and engages in report preparation. SMILE performs secretarial tasks, such as typing, error checking, important information announcing, and inputting commands by simulating keystrokes and mouse clicks, thus enabling the user to focus on the professional tasks at hand. This results in an increase in the efficiency for the user, and a decrease in reporting errors. Human-SMILE interaction is very much human-to-human like, mediated by voice recognition technology and text-to-speech. There is a significant reduction in the keyboard and mouse usage in comparison to either human transcription or voice recognition without SMILE.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of computer-assisted pathology report preparation, wherein a computer displays a cursor at a current cursor location in an active window, the method comprising: in response to determining a voice input regarding a report document comprises a command for the computer: determining a current context of the computer, wherein the current context is based at least in part on situational knowledge, the active window and the current cursor location, wherein the situational knowledge includes information regarding at least one of a current user, a current patient, a current case, a current specimen and a current slide; determining at least one instruction based on information stored in long-term knowledge data files, the command and the current context, wherein the information stored in the long-term knowledge data files includes program defined instructions and user taught instructions; and executing the at least one instruction on the computer. 2. The method of claim 1, further comprising in response to determining the voice input comprises dictation, inputting text into the active window at the current cursor location based on the dictation. 3. The method of claim 1, wherein the voice input comprises a combination of dictation and at least one command. 4. The method of claim 1, wherein the current specimen is a first specimen, the method further comprising: receiving a scanned barcode associated with a second specimen; and in response to determining that the second specimen is a different slide of the first specimen, ensuring a text editor is the active window. 5. The method of claim 4, the method comprising, in response to determining the second specimen is a different from the first specimen: accessing case information for the second specimen; parsing gross description text; and placing the cursor at a new cursor location in the report document based at least in part on gross description text. 6. The method of claim 1, further comprising: accessing case information for a specimen; and generating the report document by loading specimen information and the case information into a report template. 7. The method of claim 6, wherein generating the report document comprises: accessing a specimen list; and for each specimen in the specimen list, adding a specimen header and a diagnosis placeholder into the report document. 8. The method of claim 7, wherein generating the report document comprises automatically typing a diagnosis to replace a diagnosis placeholder based at least in part on a label for a specimen in the specimen list and a gross description. 9. The method of claim 1, further comprising: accessing case information for the report document; and automatically verifying consistency of case information and gross description. 10. The method of claim 9, wherein automatically verifying consistency comprises determining whether a potential gender error. 11. The method of claim 9, further comprising, in response to detecting a potential inconsistency, notifying a user of the potential inconsistency. 12. The method of claim 1, wherein the command is a request to finalize and release the report document. 13. The method of claim 12, wherein the at least one instruction comprises instructions to determine that all slides described in a gross description have been scanned. 14. The method of claim 1, wherein the current specimen is a first specimen, the method further comprising: receiving a scanned barcode associated with a second specimen; and in response to receiving the scanned barcode, updating the situational knowledge regarding the second specimen. 15. The method of claim 14, wherein updating the situational knowledge comprises updating at least one of: the current patient, the current case, the current specimen and the current slide. 16. The method of claim 1, wherein determining at least one instruction includes accessing at least one dictionary file, wherein entries in the at least one dictionary file describe the program defined instructions and the user taught instructions. 17. The method of claim 16, wherein the user taught instructions include instructions to automatically replace entered text with alternative language. 18. The method of claim 17, wherein automatically replacing the entered text includes: automatically expanding abbreviations; automatically rearranging an order of words in the entered text; automatically adding a tissue source in front of a label; and automatically putting in correct procedures for each header in the report document. 19. A computer readable medium tangibly encoded with a computer program executable by a processor to perform actions comprising: in response to determining a voice input regarding a report document comprises a command for the computer: determining a current context of the computer, wherein the current context is based at least in part on situational knowledge, an active window and a current cursor location, wherein the situational knowledge includes information regarding at least one of a current user, a current patient, a current case, a current specimen and a current slide; determining at least one instruction based on information stored in long-term knowledge data files, the command and the current context, wherein the information stored in the long-term knowledge data files includes program defined instructions and user taught instructions; and executing the at least one instruction on the computer. 20. A computer readable medium of claim 19, wherein the actions further comprise, in response to determining the voice input comprises dictation, inputting text into the active window at the current cursor location based on the dictation.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A method of using artificial intelligence (e.g., SMILE) to assist users, such as pathologists and pathologist assistants, in pathology report preparation is described. The method includes the steps of (1) specimen gross examination, submission and dictation, (2) final diagnosis dictation, and (3) Cancer Protocol Templates construction. SMILE “listens” to the voice commands, “reads” case/slide information, goes through algorithms, and engages in report preparation. SMILE performs secretarial tasks, such as typing, error checking, important information announcing, and inputting commands by simulating keystrokes and mouse clicks, thus enabling the user to focus on the professional tasks at hand. This results in an increase in the efficiency for the user, and a decrease in reporting errors. Human-SMILE interaction is very much human-to-human like, mediated by voice recognition technology and text-to-speech. There is a significant reduction in the keyboard and mouse usage in comparison to either human transcription or voice recognition without SMILE.
G06N3006		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method of using artificial intelligence (e.g., SMILE) to assist users, such as pathologists and pathologist assistants, in pathology report preparation is described. The method includes the steps of (1) specimen gross examination, submission and dictation, (2) final diagnosis dictation, and (3) Cancer Protocol Templates construction. SMILE “listens” to the voice commands, “reads” case/slide information, goes through algorithms, and engages in report preparation. SMILE performs secretarial tasks, such as typing, error checking, important information announcing, and inputting commands by simulating keystrokes and mouse clicks, thus enabling the user to focus on the professional tasks at hand. This results in an increase in the efficiency for the user, and a decrease in reporting errors. Human-SMILE interaction is very much human-to-human like, mediated by voice recognition technology and text-to-speech. There is a significant reduction in the keyboard and mouse usage in comparison to either human transcription or voice recognition without SMILE.
A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus which can communicate with a winding machine and which learns an operation for forming a coil by the winding machine, comprising: a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, and a wire rod used amount of a coil formed by the winding machine, and a program execution time actual value, and at least one of a dimension command value, a resistance command value, a turn number command value, a winding speed command value, and a tension command value for the coil, which are instructed by a program for the winding machine, and an execution time command value for the program; and a learning unit for learning by linking at least one of an actual dimension value, a resistance actual value, and a wire rod used amount of the coil observed by the state observing unit, and a program execution time actual value to at least one of a dimension command value, a resistance command value, a turn number command value, a winding speed command value, and a tension command value for the coil observed by the state observing unit, and an execution time command value for the program. 2. The machine learning apparatus according to claim 1, wherein the learning unit comprises: a reward computing unit for computing a reward based on at least one of an actual dimension value, a resistance actual value, and a wire rod used amount of the coil observed by the state observing unit, and a program execution time actual value; and a function updating unit for updating a function for deciding, from the state variable at present, based on the reward computed by the reward computing unit, at least one of a dimension command value, a resistance command value, a turn number command value, a winding speed command value, and a tension command value for the coil, and an execution time command value for the program. 3. The machine learning apparatus according to claim 1, comprising a decision-making unit for deciding, from the state variable at present, based on the result of learning of the learning unit, an optimal value of at least one of a dimension command value, a resistance command value, a turn number command value, a winding speed command value, and a tension command value for the coil, and an execution time command value for the program. 4. The machine learning apparatus according to claim 1, wherein the reward computing unit increases a reward when an actual dimension value, a resistance actual value, and a wire rod used amount of a coil, and a program execution time actual value remain within their respective allowable ranges, and decreases a reward when the same are outside of the allowable ranges. 5. The machine learning apparatus according to claim 1, wherein the learning unit computes a state variable observed by the state observing unit in a multilayer structure, to update the function on a real-time basis. 6. The machine learning apparatus according to claim 1, wherein the function of the function updating unit is updated using a function updated by a function updating unit of another machine learning apparatus. 7. A coil producing apparatus comprising the machine learning apparatus according to claim 1.
ACCEPTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.
A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A semi- or fully automated, integrated learning and labeling and classification learning system with closed, self-sustaining pattern recognition, labeling and classification operation, comprising: circuitry configured to implement a machine learning classifier; select unclassified data sets and convert the unclassified data sets into an assembly of graphic and text data forming compound data sets to be classified, wherein, by generated feature vectors of training data sets, the machine learning classifier is trained for improving the classification operation of the automated system generically during training as a measure of the classification performance, if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified by applying the machine learning classifier of the system to the compound data set of the unclassified data sets; generate training data sets, wherein for each data set of selected test data sets, a feature vector is generated comprising a plurality of labeled features associated with the different selected test data sets; generate a two-dimensional confusion matrix based on the feature vector of the test data sets, wherein a first dimension of the two-dimensional confusion matrix comprises pre-processed labeled features of the feature vectors of the test data sets and a second dimension of the two-dimensional confusion matrix comprises classified and verified features of the feature vectors of the test data sets by applying the machine learning classifier to the test data sets; and in case an inconsistently or wrongly classified test data set and/or feature of a test data set is detected, assign the inconsistently or wrongly classified test data set and/or feature of the test data set to the training data sets, and generate additional training data sets based on the confusion matrix, which are added to the training data sets for filling in the gaps in the training data sets and improving the measurable performance of the system. 2. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that the machine learning classifier comprises at least a scalable Naive Bayes classifier based on a linear number of parameters in the number of features and predictors, respectively. 3. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that the machine learning classifier comprises a non-probabilistic, binary, linear, support vector machines classifier and/or a non-parametric k-Nearest Neighbors classifier, and/or an exponential, probabilistic, max entropy classifier, and/or decision tree classifier based on a finite set of values, and/or Balanced Winnow classifier, and/or deep learning classifiers using multiple processing layers composed of multiple linear and non-linear transformations. 4. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that the machine learning classifier applies unigrams and bigrams, and/or a combination of unigrams and bigrams or n-grams to the machine learning classifier. 5. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to apply distribution scaling to the data sets scaling word counts so that pages with a small number of words are not underrepresented. 6. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to boost a probability of words that are unique for a certain class as compared to other words that occur relatively frequently in other classes. 7. The automated learning and labeling and classification system according to claim 1, wherein the circuitry is configured to ignore a given page of a data set if the given page comprises only little or non-relevant text compared to average pages, and the label of the previous page is assigned during inference. 8. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to filter out data sets with spikes as representing unlikely scenarios. 9. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to accept a selection of defined features to be ignored by the machine learning classifier. 10. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that the machine learning classifier comprises at least a population of separate rule sets, such that the learning operation recombines and reproduces the best of these rule sets, and/or the machine learning classifier comprises a single set of rules in a defined population, and such that the generic learning operation selects the best classifiers within that set. 11. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to have a predefined threshold value for a performance strength-based and/or accuracy-based classification of the operation performance. 12. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to convert the selected unclassified data sets to an assembly of graphic and text data forming a compound data set to be classified, and to pre-process the unclassified data sets by optical character recognition converting images of typed, handwritten or printed text into machine-encoded text. 13. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured to convert the selected unclassified data sets to an assembly of graphic and text data forming a compound data set to be classified, to pre-process and store the graphic data as raster graphics images in tagged image file format, and to store the text data in plain text format or rich text format. 14. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that each feature vector comprises a plurality of invariant features associated with a specific data set or an area of interest of a data set. 15. The automated learning, labeling and classification system according to claim 14, wherein the circuitry is configured such that the invariant features of the graphic data of the compound data set of the specific data set comprise scale invariant, rotation invariant, and position invariant features. 16. The automated learning, labeling and classification system according to claim 14, wherein the circuitry is configured such that the area of interest comprises a representation of at least a portion of a subject object within the image or graphic data of the compound data set of the specific data set, the representation comprising at least one of an object axis, an object base point, or an object tip point, and wherein the invariant features comprise at least one of a normalized object length, a normalized object width, a normalized distance from an object base point to a center of a portion of the image or graphic data, an object or portion radius, a number of detected distinguishable parts of the portion or the object, a number of detected features pointing in the same direction, a number of features pointing in the opposite direction of a specified feature, or a number of detected features perpendicular to a specified feature. 17. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured such that the pre-processed labeled features of the feature vectors of the test data sets comprise manually labeled pre-processed features of the feature vectors of the test data sets as a verified gold standard. 18. The automated learning, labeling and classification system according to claim 1, wherein the circuitry is configured, in case that an inconsistently or wrongly classified test data set and/or feature of a test data set is detected, to assign the inconsistently or wrongly classified test data set and/or feature of the test data set to the training data sets if comparable training data sets are triggered within the training data sets based on the confusion matrix, and to create a new labeling feature of the recognizable feature vector if no comparable training data sets are triggered within the training data sets. 19. A fully or partially automated, integrated learning, labeling and classification learning method for closed, self-sustaining pattern recognition, labeling and classification operation, comprising: circuitry configured to implement a machine learning classifier; select unclassified data sets and convert the unclassified data sets into an assembly of graphic and text data forming a compound data set to be classified, wherein, by feature vectors of training data sets, the machine learning classifier is trained for generically improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unclassified data sets, and to classify unclassified data sets by applying the machine learning classifier of the system to the compound data set; generate training data sets, wherein for each data set of selected test data sets, a feature vector is generated comprising a plurality of labeled features associated with the different selected test data sets; generate a two-dimensional confusion matrix based on the feature vector of the test data sets, wherein a first dimension of the two-dimensional confusion matrix comprises pre-processed labeled features of the feature vectors of the test data sets, and a second dimension of the two-dimensional confusion matrix comprises classified and verified features of the feature vectors of the test data sets by applying the machine learning classifier to the test data sets; and in case that an inconsistently or wrongly classified test data set and/or feature of a test data set is detected, assign the inconsistently or wrongly classified test data set and/or feature of the test data set to the training data sets, and generate additional training data sets, based on the confusion matrix by means of the system, which are added to the training data sets, thereby filling in the gaps in the training data sets and improving the measurable performance of the system. 20. The fully or partially automated, integrated learning, labeling and classification method for closed, self-sustaining pattern recognition, labeling and classification operation according to claim 19, wherein the circuitry is configured, in case that an inconsistently or wrongly classified test data set and/or feature of a test data set is detected, to assign, based on the confusion matrix, the inconsistently or wrongly classified test data set and/or feature of the test data set to the training data sets if comparable training data sets are triggered within the training data sets, and to create a new labeling feature of the recognizable feature vector if no comparable training data sets are triggered within the training data sets. 21. The fully or partially automated, integrated learning, labeling and classification method for closed, self-sustaining pattern recognition, labeling and classification operation according to claim 19, wherein the circuitry is configured to extend the confusion matrix and/or the recognizable feature vector correspondingly by the triggered new labeling feature if no comparable training data sets are triggered within the training data sets.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.
An approach is provided for providing anonymous crowd sourced software tuning. The approach operates by anonymously receiving usage data from a number of software customer systems. The usage data that is received pertains to a software product. The received usage data is analyzed to identify healthy system patterns. The usage data received from each customer system is compared to at least one of the healthy system patterns. In one embodiment, the usage data from a customer system is compared to healthy system patterns from systems with similar configurations as the customer system. Sets of recommendations are generated based on the comparison with each set of recommendations corresponds to one of the software customers. The generated recommendations are provided to the respective software customers.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in an information handling system comprising one or more processors and a memory, of anonymous crowd sourced software tuning, the method comprising: anonymously receiving usage data from a plurality of customer systems, wherein the usage data pertains to a software product and includes at least one unique identifier generated by a selected one of the plurality of customer systems; analyzing the received usage data, wherein the analysis identifies one or more healthy system patterns; comparing the usage data received from each of the plurality of customer systems to at least one of the healthy system patterns; generating a plurality of sets of one or more recommendations based on the comparison, wherein each set of recommendations corresponds to one of the plurality of customer systems; assigning the unique identifier to a selected one set of the one or more recommendations that correspond to the selected customer system; and providing the selected set of the one or more recommendations to the selected customer system, wherein the selected customer system is adapted to identify the selected set of the one or more recommendations based upon the unique identifier. 2. The method of claim 1 further comprising: identifying a healthy system configuration associated with each of the one or more healthy system patterns; comparing a system configuration associated with each of the plurality of customer systems with the identified healthy system configurations, the comparing resulting in a selected one of the healthy system configurations and a corresponding selected healthy system pattern; and wherein the comparing of the usage data received from each of the plurality of customer systems is compared to the selected healthy system pattern of the healthy system configuration found to be similar to a customer system configuration corresponding to one of the plurality of customer systems. 3. The method of claim 2 further comprising: comparing one or more configuration settings in the customer system configuration to corresponding configuration settings in the selected healthy system configuration, wherein the comparing of configuration settings results in one or more configuration setting changes included in the generated recommendations. 4. The method of claim 3 further comprising: comparing customer system health data included in the usage data received from each of the plurality of customer systems to one or more thresholds; and identifying a selected set of one or more healthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are healthy, wherein the healthy configurations and healthy system patterns correspond to the identified healthy systems. 5. The method of claim 4 further comprising: identifying a selected set of one or more unhealthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are unhealthy, wherein the customer configurations correspond to the unhealthy systems. 6. (canceled) 7. The method of claim 1 wherein the generation of the unique identifier is performed by the selected customer system by executing a hash function against the usage data, wherein the unique identifier is retained by the customer system before reception of the usage data. 8. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; a network adapter that connects the information handling system to a computer network; and a set of instructions stored in the memory and executed by at least one of the processors to provide anonymous crowd sourced software tuning, wherein the set of instructions perform actions of: anonymously receiving usage data from a plurality of customer systems, wherein the usage data pertains to a software product and includes at least one unique identifier generated by a selected one of the plurality of customer systems; analyzing the received usage data, wherein the analysis identifies one or more healthy system patterns; comparing the usage data received from each of the plurality of customer systems to at least one of the healthy system patterns; generating a plurality of sets of one or more recommendations based on the comparison, wherein each set of recommendations corresponds to one of the plurality of customer systems; assigning the unique identifier to a selected one set of the one or more recommendations that correspond to the selected customer system; and providing the selected set of the one or more recommendations to the selected customer system, wherein the selected customer system is adapted to identify the selected set of the one or more recommendations based upon the unique identifier. 9. The information handling system of claim 8 wherein the actions further comprise: identifying a healthy system configuration associated with each of the one or more healthy system patterns; comparing a system configuration associated with each of the plurality of customer systems with the identified healthy system configurations, the comparing resulting in a selected one of the healthy system configurations and a corresponding selected healthy system pattern; and wherein the comparing of the usage data received from each of the plurality of customer systems is compared to the selected healthy system pattern of the healthy system configuration found to be similar to a customer system configuration corresponding to one of the plurality of customer systems. 10. The information handling system of claim 9 wherein the actions further comprise: comparing one or more configuration settings in the customer system configuration to corresponding configuration settings in the selected healthy system configuration, wherein the comparing of configuration settings results in one or more configuration setting changes included in the generated recommendations. 11. The information handling system of claim 10 wherein the actions further comprise: comparing customer system health data included in the usage data received from each of the plurality of customer systems to one or more thresholds; and identifying a selected set of one or more healthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are healthy, wherein the healthy configurations and healthy system patterns correspond to the identified healthy systems. 12. The information handling system of claim 11 wherein the actions further comprise: identifying a selected set of one or more unhealthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are unhealthy, wherein the customer configurations correspond to the unhealthy systems. 13. (canceled) 14. The information handling system of claim 8 wherein the generation of the unique identifier is performed by the selected customer system by executing a hash function against the usage data, wherein the unique identifier is retained by the customer system before reception of the usage data. 15. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to provide anonymous crowd sourced software tuning by performing actions comprising: anonymously receiving usage data from a plurality of customer systems, wherein the usage data pertains to a software product and includes at least one unique identifier generated by a selected one of the plurality of customer systems; analyzing the received usage data, wherein the analysis identifies one or more healthy system patterns; comparing the usage data received from each of the plurality of customer systems to at least one of the healthy system patterns; generating a plurality of sets of one or more recommendations based on the comparison, wherein each set of recommendations corresponds to one of the plurality of customer systems; assigning the unique identifier to a selected one set of the one or more recommendations that correspond to the selected customer system; and providing the selected set of the one or more recommendations to the selected customer system, wherein the selected customer system is adapted to identify the selected set of the one or more recommendations based upon the unique identifier. 16. The computer program product of claim 15 wherein the actions further comprise: identifying a healthy system configuration associated with each of the one or more healthy system patterns; comparing a system configuration associated with each of the plurality of customer systems with the identified healthy system configurations, the comparing resulting in a selected one of the healthy system configurations and a corresponding selected healthy system pattern; and wherein the comparing of the usage data received from each of the plurality of customer systems is compared to the selected healthy system pattern of the healthy system configuration found to be similar to a customer system configuration corresponding to one of the plurality of customer systems. 17. The computer program product of claim 16 wherein the actions further comprise: comparing one or more configuration settings in the customer system configuration to corresponding configuration settings in the selected healthy system configuration, wherein the comparing of configuration settings results in one or more configuration setting changes included in the generated recommendations. 18. The computer program product of claim 17 wherein the actions further comprise: comparing customer system health data included in the usage data received from each of the plurality of customer systems to one or more thresholds; and identifying a selected set of one or more healthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are healthy, wherein the healthy configurations and healthy system patterns correspond to the identified healthy systems; and identifying a selected set of one or more unhealthy systems in response to the comparison of the customer system health data to the thresholds revealing that at least one of the plurality of customer systems are unhealthy, wherein the customer configurations correspond to the unhealthy systems. 19. (canceled) 20. The computer program product of claim 15 wherein the generation of the unique identifier is performed by the selected customer system by executing a hash function against the usage data, wherein the unique identifier is retained by the customer system before reception of the usage data.
REJECTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: An approach is provided for providing anonymous crowd sourced software tuning. The approach operates by anonymously receiving usage data from a number of software customer systems. The usage data that is received pertains to a software product. The received usage data is analyzed to identify healthy system patterns. The usage data received from each customer system is compared to at least one of the healthy system patterns. In one embodiment, the usage data from a customer system is compared to healthy system patterns from systems with similar configurations as the customer system. Sets of recommendations are generated based on the comparison with each set of recommendations corresponds to one of the software customers. The generated recommendations are provided to the respective software customers.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An approach is provided for providing anonymous crowd sourced software tuning. The approach operates by anonymously receiving usage data from a number of software customer systems. The usage data that is received pertains to a software product. The received usage data is analyzed to identify healthy system patterns. The usage data received from each customer system is compared to at least one of the healthy system patterns. In one embodiment, the usage data from a customer system is compared to healthy system patterns from systems with similar configurations as the customer system. Sets of recommendations are generated based on the comparison with each set of recommendations corresponds to one of the software customers. The generated recommendations are provided to the respective software customers.
A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values.		Please help me write a proper abstract based on the patent claims. CLAIM: 1-11. (canceled) 12. A machine learning system having a plurality of agents, the system comprising: a model manager for providing a model to the plurality of agents, the model specifying attributes and attribute value data types for an event in which the plurality of agents act; an agent input sub-system for receiving agent-provided inputs from the plurality of agents during an instance of the event, the agent-provided inputs include estimated attribute values that are consistent with the attribute value data types; a ground-truth-based expertise weighting determiner having a processor for determining expertise weights for at least some the plurality of agents in response to at least one ground-truth which is learned from the estimated attribute values; and an adaptive attribute value mixer for determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values. 13. The system of claim 12, wherein the model further specifies a respective range of admissible values for at least some of the attributes. 14. The system of claim 12, wherein the agent-provided inputs further include confidence values for the estimated attribute values. 15. The system of claim 12, wherein the respective adaptive mixtures are determined on an attribute-by-attribute basis responsive to the expertise weights and the confidence values. 16. The system of claim 12, wherein the ground-truth-based expertise weighting determiner learns the at least one ground-truth responsive to labeled examples of ground truths, each of the labeled examples corresponding to a particular attribute value for a particular one of the attributes. 17. The system of claim 16, wherein the labeled examples comprise at least one of actual subject labeled examples and surrogate subject labeled examples. 18. The system of claim 16, wherein the ground-truth learning sub-system learns the at least one ground-truth using an N-1 substitution method, wherein a ground truth label for an estimated attribute value for a respective one of the attributes provided by a respective one of the plurality of agents is compared to ground truth labels for the estimated attribute values for the respective one of the attributes provided by remaining ones of the plurality of agents to assess an ability of the respective one of the plurality of agents at estimating a value for the respective one of the attributes. 19. The system of claim 12, wherein a shared model manager agent from among the plurality of agents provides surrogate estimated attribute values for respective ones of the plurality of agents that fail to provide a respective estimated attribute value for one or more of the attributes using correlation coefficients for pairs of the attributes and confidence levels for the correlation coefficients. 20. The system of claim 12, wherein a shared model manager agent from among the plurality of agents learns prior probabilities for pairs of the attributes and generates, responsive to the prior probabilities, surrogate assessments and surrogate confidence levels for respective ones of the plurality of agents that fail to produce certain attribute values but produce other attribute values for which there are well established prior probabilities.
ACCEPTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A system and method are provided for shared machine learning. The method includes providing a model to a plurality of agents included in a machine learning system. The model specifies attributes and attribute value data types for an event in which the agents act. The method further includes receiving agent-provided inputs during an instance of the event. The agent-provided inputs include estimated attribute values that are consistent with the attribute value data types. The method also includes determining expertise weights for at least some agents in response to at least one ground-truth which is learned from the estimated attribute values. The method additionally includes determining an estimate value for one or more of the attributes using respective adaptive mixtures of the estimated attribute values.
A mechanism is provided for optimization of mixed-criticality systems. A plurality of strategies is received that are in a fixed order of criticality. For each strategy in the plurality of strategies, a multivariate objective function and a multivariate constraint in a multivariate decision variable is obtained. A number of strategies of the plurality of strategies that are feasible in combination are maximized. A solution that is feasible for the number of strategies that are feasible in combination is generated such that the objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for optimization of mixed-criticality systems, the method comprising: receiving, by a processor in the data processing system, a plurality of strategies, wherein the plurality of strategies are in a fixed order of criticality; for each strategy in the plurality of strategies, obtaining, by the processor, a multivariate objective function and a multivariate constraint in a multivariate decision variable; maximizing, by the processor, a number of strategies of the plurality of strategies that are feasible in combination; and generating, by the processor, a solution that is feasible for the number of strategies that are feasible in combination, such that the objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized. 2. The method of claim 1, wherein maximizing the number of strategies of the plurality of strategies that are feasible in combination solves: P(ξ):maxsminxƒ(s)([x(1) . . . x(s)],ξ,U(s))s.t.x(1)∈X(ξ,U(1)),[x(1)x(2)]∈X(ξ,U(2)); [x(1) . . . x(s)]∈X(ξ,U(s)); 1≦s≦S where P(86 ) denotes the problem solved, ξ is the multivariate random variable, x=[x(1) . . . x(s)] is the decision variable, ƒ(s) is the objective function of the least-critical strategy supported, and X(s) is the feasible region for the strategy s. 3. The method of claim 1, wherein the plurality of strategies are analyzed with respect to a decreasing level of criticality. 4. The method of claim 1, wherein the multivariate constraint is at east one of a multivariate equality constraint or a multivariate inequality constraint. 5. The method of claim 4, wherein the feasible region for the strategy s (X(s)) is defined by the inequalities F(s)(x, ξ, U(s))≦0 and equalities G(s)(x, ξ, U(s))=0, which make use of the uncertainty parameters U(s) for the strategy s. 6. The method of claim 1, wherein for each strategy in the plurality of strategies, the multivariate objective and the multivariate constraint allow for uncertainty therein. 7. The method of claim 1, wherein the uncertainty sets Z(s) given by ξ, U(s) are at least one of interval-based, polyhedral, ellipsoidal, spectrahedral, or a combination thereof and wherein the selected uncertainty sets solves: P′(ξ):maxsminxmax(ξ(s)∈Z(s)(ξ,U(s))ƒ(s)([x(1). . .x(s)],ξ,ζ(s))s.t.x(1)∈X(ξ,ζ(1)),[x(1)x2]∈X(ξ,ζ(2)); [x(1) . . . x(s)]∈X(ξ, ζ(s)); 1≦s≦S. 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a plurality of strategies, wherein the plurality of strategies are in a fixed order of criticality; for each strategy in the plurality of strategies, obtain a multivariate objective function and a multivariate constraint in a multivariate decision variable; maximize a number of strategies of the plurality of strategies that are feasible in combination; and generate a solution that is feasible for the number of strategies that are feasible in combination, such that the objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized. 9. The computer program product of claim 8, wherein maximizing the number of strategies of the plurality of strategies that are feasible in combination solves: P(ξ):maxsminxƒ(s)([x(1) . . . x(s)],ξ,U(s))s.t.x(1)∈X(ξ,U(1)),[x(1)x(2)]∈X(ξ,U(2)); [x(1) . . . x(s)]∈X(ξ,U(s)); 1≦s≦S where P(ξ) denotes the problem solved, ξ is the multivariate random variable, x=[x(1) . . . x(s)] is the decision variable, ƒ(s) is the objective function of the least-critical strategy supported, and X(s) is the feasible region for the strategy s. 10. The computer program product of claim 8, wherein the plurality of strategies are analyzed with respect to a decreasing level of criticality. 11. The computer program product of claim 8, wherein the multivariate constraint is at least one of a multivariate equality constraint or a multivariate inequality constraint. 12. The computer program product of claim 11, wherein the feasible region for the strategy s (X(s)) is defined by the inequalities F(s)(x, ξ, U(s))≦0 and equalities G(s)(x, ξ, U(s))=0, which make use of the uncertainty parameters U(s) for the strategy s. 13. The computer program product of claim 8, wherein for each strategy in the plurality of strategies, the multivariate objective and the multivariate constraint allow for uncertainty therein. 14. The computer program product of claim 8, wherein the uncertainty sets Z(s) given by ξ, U(s) are at least one of interval-based, polyhedral, ellipsoidal, spectrahedral, or a combination thereof and wherein the selected uncertainty sets solves: P′(ξ):maxs, minxmax(ζ(s)∈Z(s)(ξ,U(s))ƒ(s)([x(1) . . . x(s)],ξ,ζ(s))s.t.x(1)∈X(ξ,ζ(1)),[x(1)x(2)]∈X(ξ,ζ(2)); [x(1) . . . x(s)]∈X(ξ,ζ(s)); 1≦s≦S. 15. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive a plurality of strategies, wherein the plurality of strategies are in a fixed order of criticality; for each strategy in the plurality of strategies, obtain a multivariate objective function and a multivariate constraint in a multivariate decision variable; maximize a number of strategies of the plurality of strategies that are feasible in combination; and generate a solution that is feasible for the number of strategies that are feasible in combination, such that the Objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized. 16. The apparatus of claim 15, wherein maximizing the number of strategies of the plurality of strategies that are feasible in combination solves: P(ξ):maxsminxƒ(s)([x(1) . . . x(s)],ξ,U(s))s.t.x(1)∈X(ξ,U(1)),[x(1)x(2)]∈X(ξ,U(2)); [x(1) . . . x(s)]∈X(ξ,U(s)); 1≦s≦S where P(ξ) denotes the problem solved, ξ is the multivariate random variable, x=[x(1) . . . x(s)] is the decision variable, ƒ(s) is the objective function of the least-critical strategy supported, and X(s) is the feasible region for the strategy s. 17. The apparatus of claim 15, wherein the plurality of strategies are analyzed with respect to a decreasing level of criticality. 18. The apparatus of claim 15, wherein the multivariate constraint is at least one of a multivariate equality constraint or a multivariate inequality constraint. 19. The apparatus of claim 18, wherein the feasible region for the strategy s (X(s)) is defined by the inequalities F(s)(x, ξ, U(s))≦0 and equalities G(s)(x, ξ,U(s))=0, which make use of the uncertainty parameters U(s) for the strategy s. 20. The apparatus of claim 15, wherein for each strategy in the plurality of strategies, the multivariate objective and the multivariate constraint allow for uncertainty therein.
ACCEPTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: A mechanism is provided for optimization of mixed-criticality systems. A plurality of strategies is received that are in a fixed order of criticality. For each strategy in the plurality of strategies, a multivariate objective function and a multivariate constraint in a multivariate decision variable is obtained. A number of strategies of the plurality of strategies that are feasible in combination are maximized. A solution that is feasible for the number of strategies that are feasible in combination is generated such that the objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A mechanism is provided for optimization of mixed-criticality systems. A plurality of strategies is received that are in a fixed order of criticality. For each strategy in the plurality of strategies, a multivariate objective function and a multivariate constraint in a multivariate decision variable is obtained. A number of strategies of the plurality of strategies that are feasible in combination are maximized. A solution that is feasible for the number of strategies that are feasible in combination is generated such that the objective of a least-critical strategy that is feasible in combination with the other strategies in the number of strategies is optimized.
A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more computer processors; one or more computer memories; one or more modules incorporated into the one or more computer memories, the one or more modules configuring the one or more computer processors to perform operations, the operations comprising: extracting a training time series corresponding to a process from an initial time series corresponding to the process; modifying outlier data points in the training time series based on predetermined acceptability criteria; training a plurality of prediction methods using the training time series; receiving an actual data point corresponding to the initial time series; using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point; and receiving an additional actual data point corresponding to the initial time series and extracting an additional training time series from the initial time series based on the additional actual data point. 2. The system of claim 1, wherein the calculation of whether each of the set of predicted data points is statistically different from the actual data point includes a determination that the Mahalanobis distance between the prediction error and the fitted multivariate normal joint probability distribution of each of the set of predicted data points is within a specified range. 3. The system of claim 1, wherein the additional actual data point corresponds to the initial time series and the operations further comprise extracting an additional training time series having the length offset by an additional index prior to a last data point of the initial time series the additional index reflecting a relative position of the actual data point to the additional actual data point. 4. The system of claim 1, further comprising selecting the combination of each of the plurality of prediction methods to minimize a number of false anomaly detections. 5. The system of claim 1, further comprising representing the determination of whether the actual data point is anomalous in a graphical user interface, the representing including providing a strength of the determination. 6. The system of claim 5, wherein the strength of the determination is based on a number of the plurality of prediction methods that indicate an anomaly with respect to the data point. 7. The system of claim 1, wherein the training time series represents a window of the initial time series that is recent in relation to the actual data point. 8. A method comprising: extracting a training time series corresponding to a process from an initial time series corresponding to the process; modifying outlier data points in the training time series based on predetermined acceptability criteria; training a plurality of prediction methods using the training time series; receiving an actual data point corresponding to the initial time series; using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point; and receiving an additional actual data point corresponding to the initial time series and extracting an additional training time series from the initial time series based on the additional actual data point. 9. The method of claim 8, wherein the calculation of whether each of the set of predicted data points is statistically different from the actual data point includes a determination that the Mahalanobis distance between the prediction error and the fitted multivariate normal joint probability distribution of each of the set of predicted data points is within a specified range. 10. The method of claim 8, wherein additional actual data point corresponds to the initial time series and the method further comprises extracting an additional training time series having the length offset by an additional index prior to a last data point of the initial time series the additional index reflecting a relative position of the actual data point to the additional actual data point. 11. The method of claim 8, further comprising selecting the combination of each of the plurality of prediction methods to minimize a number of false anomaly detections. 12. The method of claim 8, further comprising representing the determination of whether the actual data point is anomalous in a graphical user interface, the representing including providing a strength of the determination. 13. The method of claim 12, wherein the strength of the determination is based on a number of the plurality of prediction methods that indicate an anomaly with respect to the data point. 14. The method of claim 8, wherein the training time series represents a window of the initial time series that is recent in relation to the actual data point. 15. A non-transitory machine readable medium comprising a set of instructions that, when executed by a processor, causes the processor to perform operations, the operations comprising: extracting a training time series corresponding to a process from an initial time series corresponding to the process; modifying outlier data points in the training time series based on predetermined acceptability criteria; training a plurality of prediction methods using the training time series; receiving an actual data point corresponding to the initial time series; using the plurality of prediction methods to determine a set of predicted data points corresponding to the actual data point of the initial time series; determining whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point; and receiving an additional actual data point corresponding to the initial time series and extracting an additional training time series from the initial time series based on the additional actual data point. 16. The non-transitory machine readable medium of claim 15, wherein the calculation of whether each of the set of predicted data points is statistically different from the actual data point includes a determination that the Mahalanobis distance between the prediction error and the fitted multivariate normal joint probability distribution of each of the set of predicted data points is within a specified range. 17. The non-transitory machine readable medium of claim 15, wherein the additional actual data point corresponds to the initial time series and the operations further comprise extracting an additional training time series having the length offset by an additional index prior to a last data point of the initial time series the additional index reflecting a relative position of the actual data point to the additional actual data point. 18. The non-transitory machine readable medium of claim 15, the operations further comprising selecting the combination of each of the plurality of prediction methods to minimize a number of false anomaly detections. 19. The non-transitory machine readable medium of claim 15, the operations further comprising representing the determination of whether the actual data point is anomalous in a graphical user interface, the representing including providing a strength of the determination. 20. The non-transitory machine readable medium of claim 19, wherein the strength of the determination is based on a number of the plurality of prediction methods that indicate an anomaly with respect to the data point.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method of detecting anomalies in a time series is disclosed. A training time series corresponding to a process is extracted from an initial time series corresponding to the process, the training time series including a subset of the initial time series. Outlier data points in the training time series are modified based on predetermined acceptability criteria. A plurality of prediction methods are trained using the training time series. An actual data point corresponding to the initial time series is received. The plurality of prediction methods are used to determine a set of predicted data points corresponding to the actual data point. It is determined whether the actual data point is anomalous based on a calculation of whether each of the set of predicted data points is statistically different from the actual data point.
Methods and apparatus for processing data using sequential dependencies are disclosed herein. An example method includes modifying a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each successive pair of values is within a threshold. A satisfiability metric is determined for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: modifying, via a processor, a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each pair of successive values is within a threshold; and determining, via the processor, a satisfiability metric for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence. 2. The method of claim 1, wherein the sequence represents investment data. 3. The method of claim 1, wherein the sequence represents traffic data. 4. The method of claim 1, wherein the sequence represents weather data. 5. The method of claim 1, wherein the satisfiability metric represents a ratio between the number of modifications and the size of the sequence. 6. The method of claim 1, wherein the sequence is a first sequence, the modified sequence is a first modified sequence, the satisfiability metric is a first satisfiability metric, and further comprising: modifying a second number of values in a second sequence of the data set to generate a second modified sequence; determining a second satisfiability metric for the second modified sequence based on a relationship between a number of modifications to values in the second sequence and a size of the second sequence; and selecting one of the first or second sequences based on a comparison of the first satisfiability metric and the second satisfiability metric. 7. The method of claim 6, wherein the first sequence and the second sequence are subsets of the data set. 8. The method of claim 6, further comprising summarizing the selection of the first sequence or second sequence in a table. 9. The method of claim 6, wherein selecting one of the first sequence or the second sequence comprises determining which of the first satisfiability metric or the second satisfiability metric corresponds to a lesser number of modifications in proportion to the respective size of the first sequence and the second sequence. 10. A machine readable memory comprising instructions which, when executed, cause a machine to perform operations comprising: modifying a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each successive pair of values satisfies a threshold; and determine a satisfiability metric for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence. 11. The memory of claim 10, wherein determining the satisfiability metric comprises determining a ratio between the number of modifications and the size of the sequence. 12. The memory of claim 10, wherein the sequence is a first sequence, the modified sequence is a first modified sequence, the satisfiability metric is a first satisfiability metric, and further comprising instructions which, when executed, cause the machine to perform operations comprising: modifying a second number of values in a second sequence of the data set to generate a second modified sequence; determining a second satisfiability metric for the second modified sequence based on a relationship between a number of modifications to values in the second sequence and a size of the second sequence; and selecting one of the first or second sequences based on a comparison of the first satisfiability metric and the second satisfiability metric. 13. The memory of claim 12, wherein the first sequence and the second sequence are subsets of the data set. 14. The memory of claim 12, further comprising summarizing the selection of the first sequence or second sequence in a table. 15. The memory of claim 12, wherein selecting one of the first sequence or the second sequence comprises determining which of the first satisfiability metric or the second satisfiability metric corresponds to a lesser number of modifications in proportion to the respective size of the first sequence and the second sequence. 16. An apparatus comprising: a memory comprising machine readable instructions; and a processor which, when executing the instructions, performs operations comprising: modifying a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each successive pair of values meets a threshold; and determining a satisfiability metric for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence. 17. The apparatus of claim 16, wherein determining the satisfiability metric comprises determining a ratio between the number of modifications and the size of the sequence. 18. The apparatus of claim 16, wherein the sequence is a first sequence, the modified sequence is a first modified sequence, the satisfiability metric is a first satisfiability metric, and the operations further comprise: modifying a second number of values in a second sequence of the data set to generate a second modified sequence; determining a second satisfiability metric for the second modified sequence based on a relationship between a number of modifications to values in the second sequence and a size of the second sequence; and selecting one of the first or second sequences based on a comparison of the first satisfiability metric and the second satisfiability metric. 19. The apparatus of claim 18, wherein the first sequence and the second sequence are subsets of the data set. 20. The apparatus of claim 18, wherein the operations further comprise determining which of the first satisfiability metric or the second satisfiability metric corresponds to a lesser number of modifications in proportion to the respective size of the first sequence and the second sequence.
REJECTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: Methods and apparatus for processing data using sequential dependencies are disclosed herein. An example method includes modifying a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each successive pair of values is within a threshold. A satisfiability metric is determined for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence.
G06N700		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Methods and apparatus for processing data using sequential dependencies are disclosed herein. An example method includes modifying a first number of values in a sequence of a data set to generate a modified sequence such that each difference between each successive pair of values is within a threshold. A satisfiability metric is determined for the modified sequence based on a relationship between a number of modifications to the values in the sequence and a size of the sequence.
A method for expanding an answer key to verify a question and answer system is provided in the illustrative embodiments. A definition is constructed of an extended answer type. The extended answer type represents an answer type of an unrepresented answer. The unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key. The extended answer type is created in the answer key according to the definition. The extended answer type is populated such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key. The populated extended answer type in the extended answer key is used to verify that a generated answer from the Q and A system is correct.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for expanding an answer key to verify a question and answer (Q and A) system, the method comprising: constructing a definition of an extended answer type, wherein the extended answer type represents an answer type of an unrepresented answer, wherein the unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key; creating, using a processor and a memory, the extended answer type in the answer key according to the definition; populating the extended answer type such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key; and using the populated extended answer type in the extended answer key to verify that a generated answer from the Q and A system is correct. 2. The method of claim 1, wherein the generated answer is incorrect according to an existing answer type in the answer key. 3. The method of claim 1, further comprising: forming the definition by modifying an abstract definition; constructing a second definition of a second extended answer type, wherein the second extended answer type represents an answer type of a second unrepresented answer. 4. The method of claim 3, wherein the abstract definition is used as a placeholder in the extended answer key for a third extended answer type. 5. The method of claim 1, wherein the unrepresented answer becomes the additional valid answer because a computation using the populated extended answer type results in the additional valid response. 6. The method of claim 1, wherein the unrepresented answer becomes the additional valid answer because a logic described in a metadata of the extended answer key computes to make the unrepresented answer the additional valid response. 7. The method of claim 1, wherein the unrepresented answer becomes the additional valid answer by being a member of a range of values specified in the populated extended answer type. 8. The method of claim 1, wherein the unrepresented answer becomes the additional valid answer by being a member of a range of values, wherein the range of values is computed using logic in a metadata of the extended answer key. 9. The method of claim 1, further comprising: configuring the extended answer type with a changeable condition, wherein a value of the additional valid answer changes when the condition changes. 10. The method of claim 1, wherein the extended answer key in an Extensible Markup Language (XML) document, wherein the definition is an XML structure. 11. The method of claim 1, wherein the question comprise a sentence in natural language, wherein the Q and A system is configured to respond to the natural language question. 12. The method of claim 1, wherein the question comprise a sentence in natural language, wherein the Q and A system is configured to respond to the natural language question using another extended answer type to represent answers one of (i) based on a cultural reference, (ii) in a particular language, and (iii) from a set of synonyms.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A method for expanding an answer key to verify a question and answer system is provided in the illustrative embodiments. A definition is constructed of an extended answer type. The extended answer type represents an answer type of an unrepresented answer. The unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key. The extended answer type is created in the answer key according to the definition. The extended answer type is populated such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key. The populated extended answer type in the extended answer key is used to verify that a generated answer from the Q and A system is correct.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method for expanding an answer key to verify a question and answer system is provided in the illustrative embodiments. A definition is constructed of an extended answer type. The extended answer type represents an answer type of an unrepresented answer. The unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key. The extended answer type is created in the answer key according to the definition. The extended answer type is populated such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key. The populated extended answer type in the extended answer key is used to verify that a generated answer from the Q and A system is correct.
A method, system and a computer program product are provided for classifying elements in a ground truth training set by iteratively assigning machine-annotated training set elements to clusters which are analyzed to identify a prioritized cluster containing one or more elements which are frequently misclassified and display machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of classifying elements in a ground truth training set, the method comprising: performing, by the information handling system, comprising a processor and a memory, annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning, by the information handling system, elements from the machine-annotated training set to one or more clusters; analyzing, by the information handling system, the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying, by the information handling system, machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 2. The method of claim 1, where the annotator comprises a dictionary annotator, rule-based annotator, or a machine learning annotator. 3. The method of claim 1, where assigning elements from the machine-annotated training set to one or more clusters comprises: generating a vector representation for each element from the machine-annotated training set; and grouping the vector representations for the elements from the machine-annotated training set elements into one or more clusters. 4. The method of claim 1, where analyzing the one or more clusters comprises identifying a group of elements from a confusion matrix that are commonly confused with one another. 5. The method of claim 4, where analyzing the one or more clusters comprises: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 6. The method of claim 5, where analyzing the one or more clusters comprises detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 7. The method of claim 1, further comprising displaying a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified. 8. The method of claim 7, where each reclassification recommendation is paired with a corresponding element which is frequently misclassified based on information derived from a confusion matrix. 9. The method of claim 1, further comprising verifying or correcting classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 10. The method of claim 1, where each element is an entity/relationship element. 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on an information handling system, causes the system to classify elements in a ground truth training set by: performing annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning elements from the machine-annotated training set to one or more clusters; analyzing the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 12. The computer program product of claim 10, wherein the computer readable program, when executed on the system, causes the system to assign elements from the machine-annotated training set to one or more clusters by: generating a vector representation for each element from the machine-annotated training set; and grouping the vector representations for the elements from the machine-annotated training set elements into one or more clusters. 13. The computer program product of claim 10, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by identifying a group of elements from a confusion matrix that are commonly confused with one another. 14. The computer program product of claim 13, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 15. The computer program product of claim 14, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 16. The computer program product of claim 14, wherein the computer readable program, when executed on the system, causes the system to display a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified, where each reclassification recommendation is paired with a corresponding element Which is frequently misclassified based on information derived from a confusion matrix. 17. The computer program product of claim 10, further comprising computer readable program, when executed on the system, causes the system to verify or correct classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 18. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; and a set of instructions stored in the memory and executed by at least one of the processors to classify elements in a ground truth training set, wherein the set of instructions are executable to perform actions of: performing, by the system, annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning, by the system, elements from the machine-annotated training set to one or more clusters; analyzing, by the system, the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying, by the system, machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 19. The information handling system of claim 18, where analyzing the one or more clusters comprises identifying a group of elements from a confusion matrix that are commonly confused with one another. 20. The information handling system of claim 19, where analyzing the one or more clusters comprises: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 21. The information handling system of claim 20, where analyzing the one or more clusters comprises detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 22. The information handling system of claim 18, further comprising displaying a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified, where each reclassification recommendation is paired with a corresponding element which is frequently misclassified based on information derived from a confusion matrix. 23. The information handling system of claim 18, further comprising verifying or correcting all classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 24. The information handling system of claim 18, further comprising verifying or correcting classifications for all machine-annotated training set elements in a cluster one at a time based on verification or correction feedback from the human subject matter expert.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system and a computer program product are provided for classifying elements in a ground truth training set by iteratively assigning machine-annotated training set elements to clusters which are analyzed to identify a prioritized cluster containing one or more elements which are frequently misclassified and display machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system and a computer program product are provided for classifying elements in a ground truth training set by iteratively assigning machine-annotated training set elements to clusters which are analyzed to identify a prioritized cluster containing one or more elements which are frequently misclassified and display machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set.
Machines, systems and methods for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; and classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents. 2. The method of claim 1, wherein the proposed rule is not added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable diminishes accuracy of overall classification for the plurality of documents. 3. The method of claim 1, wherein a scoring mechanism is utilized when a proposed rule is added to the plurality of rules such that a favorable score is assigned to the proposed rule, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable enhances accuracy of overall classification for the plurality of documents. 4. The method of claim 3, wherein a first score is assigned to a first proposed rule, wherein the first score is more favorable than a second score assigned to a second proposed rule, in response to determining that the first rule enhances the accuracy of the overall classification for the plurality of documents more than the second rule. 5. The method of claim 1, wherein a scoring mechanism is utilized when a proposed rule is added to the plurality of rules such that an unfavorable score is assigned to the proposed rule, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable diminishes accuracy of overall classification for the plurality of documents. 6. The method of claim 5, wherein a first score is assigned to a first proposed rule, wherein the first score is less favorable than a second score assigned to a second proposed rule, in response to determining that the first rule diminishes the accuracy of the overall classification for the plurality of documents more than the second rule. 7. The method of claim 1, wherein a scoring mechanism is utilized when a proposed rule is added to the plurality of rules such that a neutral score is assigned to the proposed rule, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable neither enhances nor diminishes accuracy of overall classification for the plurality of documents. 8. The method of claim 1, wherein a scoring mechanism is utilized when a proposed rule is added to the plurality of rules such that: a favorable score is assigned to the proposed rule, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable enhances accuracy of overall classification for the plurality of documents, and an unfavorable score is assigned to the proposed rule, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable diminishes accuracy of overall classification for the plurality of documents, wherein the score assigned to the proposed rule after the proposed rule is added to the plurality of rules is re-evaluated periodically to determine whether a more favorable or less favorable score is to be assigned to the rule based on analysis of data obtained from applying the rule to one or more of the plurality of documents after a period of time has elapsed since the rule was added. 9. The method of claim 1, wherein a data structure is implemented to include N indicators associated with a rule that is applicable to N corresponding documents, wherein a respective indicator provides information about whether application of the rule to a corresponding document has enhanced or diminished the classification for the corresponding document. 10. The method of claim 9, wherein the score associated with the rule is improved, in response to determining that the application of the rule to a portion of the N documents has enhanced the classification of said portion of the N documents.
REJECTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: Machines, systems and methods for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents.
G06N308		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Machines, systems and methods for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents.
Remote computing resource service providers allow customers to execute virtual computer systems in a virtual environment on hardware provided by the computing resource service provider. The hardware may be distributed between various geographic locations connected by a network. The distributed environment may increase latency of various operations of the virtual computer systems executed by the customer. To reduce latency of various operations predictive modeling is used to predict the occurrence of various operations and initiate the operations before they may occur, thereby reducing the amount of latency perceived by the customer.		Please help me write a proper abstract based on the patent claims. CLAIM: 1-25. (canceled) 26. A computer-implemented method, comprising: collecting input data associated with a plurality of instances; generating, based at least in part on the input data, a predictive model usable to resume an instance, the predictive model including a classifier; generating a serialization schedule for the instance based at least in part on the predictive model; and causing a set of operations to be performed to launch the instance based at least in part on the serialization schedule. 27. The computer-implemented method of claim 26, wherein generating the predictive model is based at least in part on a role of the particular virtual machine instance. 28. The computer-implemented method of claim 27, wherein the role corresponds to a service executed by a customer of a computing resource service provider; and wherein the input data indicates a first set of intervals of time during which the service is active and a second set of intervals of time during which the service is idle. 29. The computer-implemented method of claim 26, wherein generating the predictive model is based at least in part on a plurality of predictive models. 30. The computer-implemented method of claim 26, wherein the computer-implemented method further comprises seeding a second predictive model based at least in part on the predictive model, the second predictive model associated with a second customer distinct from a customer associated with the predictive model. 31. The computer-implemented method of claim 26, wherein the serialization schedule further comprises an indication of a start time for at least one operation of the set of operations such that the instance is available to a customer prior to a predicted start time, the predicted start time determined based at least in part on the predictive model. 32. A system, comprising: one or more processors; and memory that stores computer-executable instructions that, if executed, cause the one or more processors to: generate a predictive model associated with a first instance, the predictive model generated based at least in part on the input data associated with a plurality of other instances, the predictive model usable to determine a start time of an event for making available the first instance; and cause the first instance to be instantiated by at least initiating one or more operations to make the first instance available prior to the start time. 33. The system of claim 32, wherein the input data further comprises information indicating operations of the plurality of other instances initiated at least in part by requests from users. 34. The system of claim 32, wherein the one or more operations includes an operation of loading a portion of a virtual machine image associated with the first instance into memory of a server computer system. 35. The system of claim 34, wherein the start time is determined such that the operation of loading a portion of a virtual machine image is completed prior to the event for making available the first instance. 36. The system of claim 32, wherein the input data further comprises information indicating price information associated with a market of instances. 37. The system of claim 32, wherein the input data further comprises information indicating a first interval of time during which operations were executed by the plurality of other instances and a second interval of time during which the plurality of other instances were idle. 38. The system of claim 32, wherein the memory further includes computer-executable instructions that, if executed, cause the one or more processors to: generate a schedule based at least in part on the predictive model, the schedule including the start time; and cause at least one instance of the plurality of other instances to be instantiated based at least in part on the schedule. 39. A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least: obtain input data associated with execution of a plurality of virtual machine instances; generate a predictive model that indicates a probability of receiving a request to instantiate a particular virtual machine instance of the plurality of virtual machine instances by at least: analyzing the input data to generate one or more classifiers of the input data; and generating the predictive model based at least in part on the one or more classifiers; and cause one or more operations involved in instantiating the particular virtual machine instance to occur in accordance with the predictive model. 40. The non-transitory computer-readable storage medium of claim 39, wherein the instructions that cause the computer system to obtain the input data further include instructions that cause the computer system to obtain usage data for the plurality of virtual machine instances. 41. The non-transitory computer-readable storage medium of claim 39, wherein the instructions that cause the computer system to obtain the input data further include instructions that cause the computer system to obtain information indicating a plurality of commands transmitted to a computing resource service provider to perform operations associated with the plurality of virtual machine instances. 42. The non-transitory computer-readable storage medium of claim 39, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to generate one or more additional predictive models based at least in part on obtaining additional input data associated with the plurality of virtual machine instances. 43. The non-transitory computer-readable storage medium of claim 42, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to generate a set of schedules for instantiating virtual machine instances based at least in part on the predictive model and the one or more additional predictive models. 44. The non-transitory computer-readable storage medium of claim 43, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to correlate the set of schedules to determine a start time for causing the one or more operations to occur. 45. The non-transitory computer-readable storage medium of claim 39, wherein the instructions that cause the computer system to obtain the input data further include instructions that cause the computer system to obtain information indicating idle interval of the plurality of virtual machine instances.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Remote computing resource service providers allow customers to execute virtual computer systems in a virtual environment on hardware provided by the computing resource service provider. The hardware may be distributed between various geographic locations connected by a network. The distributed environment may increase latency of various operations of the virtual computer systems executed by the customer. To reduce latency of various operations predictive modeling is used to predict the occurrence of various operations and initiate the operations before they may occur, thereby reducing the amount of latency perceived by the customer.
G06N5048		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Remote computing resource service providers allow customers to execute virtual computer systems in a virtual environment on hardware provided by the computing resource service provider. The hardware may be distributed between various geographic locations connected by a network. The distributed environment may increase latency of various operations of the virtual computer systems executed by the customer. To reduce latency of various operations predictive modeling is used to predict the occurrence of various operations and initiate the operations before they may occur, thereby reducing the amount of latency perceived by the customer.
A decision-assistance system provides a tool for decision makers to receive assistance in making a decision. Through a web page, a decision maker selects a group of one or more advisers and inputs information describing a decision to be made. The decision maker solicits advice from the group of advisers, and the advisers provide advice to assist the decision maker. Other entities can be granted access to streams of information corresponding to particular themes of the decisions to be made. Business entities, for example, can use the streams of information to connect with decision makers facing decisions relevant to the goods and services provided by the business entity.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to receive assistance in making a decision, comprising: operating a personal computing device by a decision maker; accessing a social network via the personal computing device, said accessing including passing personal information through an interactive interface to a computing server communicatively coupled to the personal computing device; receiving displayable web page information from the computing server, the displayable information forming at least a portion of the interactive interface; passing first input information to the computing server via the interactive interface, the first input information identifying a group of one or more advisers; passing second input information to the computing server via the interactive interface, the second input information describing a decision to be made by the decision maker; passing third input information to the computing server via the interactive interface, the third input information soliciting advice from the group of one or more advisers, the advice including decision assistance regarding the decision to be made; and receiving first output information from the computing server via the interactive interface, the first output information including the advice. 2. The method of claim 1 wherein the interactive interface includes one or more of communications through an Internet web site, communications through electronic mail, and communications associated with a short message service (SMS). 3. The method of claim 1, comprising: assigning at least one grade to the advice. 4. The method of claim 3 wherein the at least one grade is derived according to a selected norm-referenced system, a selected criterion-referenced system, or a selected peer-evaluation referenced system. 5. The method of claim 1, comprising: receiving advice from a plurality of advisers; and assigning a grade to the advice from the plurality of advisers, the assigning including assigning one grade to each separate instance of advice or the assigning including assigning a single grade to all of the advice received from the plurality of advisers. 6. The method of claim 1 wherein the second input information describing the decision to be made includes a plurality of choices for the group of one or more advisers to consider. 7. A method to receive decision-assistance information, comprising: operating a personal computing device by a representative of a business entity; accessing a social network via the personal computing device, said accessing including passing system-wide unique account information through an interactive interface to a computing server communicatively coupled to the personal computing device; receiving displayable web-page information from the computing server, the displayable information forming at least a portion of the interactive interface; passing first input information to the computing server via the interactive interface, the first input information identifying at least one decision theme, the decision theme including a set of words; and receiving first output information from the computing server via the interactive interface, the first output information including an alert corresponding to the decision theme, the alert indicating a decision maker has solicited advice regarding a decision to be made and the decision to be made is associated with the decision theme. 8. The method of claim 7 wherein the set of words includes one or more of stems of words, synonyms, antonyms, and related words as defined in a dictionary. 9. The method of claim 8 wherein the set of words is weighed by a probability indicating how likely a decision within the decision theme is to include a particular word. 10. The method of claim 7 wherein the at least one decision theme corresponds to an entry in a classification database of categories of decisions to be made. 11. The method of claim 7 wherein the first output information is a stream of alerts and the representative of the business entity has exchanged one or more credits for access to the stream of alerts, the access associated with at least one of a number of alerts, a geographic region, a group sharing a common demographic parameter, and a time frame. 12. The method of claim 11 wherein the one or more credits are received based on at least one of money paid by the representative of the business entity, a quantity of advice communicated into the computing server and attributed to the business entity, and a quality of advice communicated into the computing server and attributed to the business entity. 13. A decision-assistance server, comprising: a processor module; one or more memory storage devices; a storage interface module coupled to the one or more memory storage devices; an input/output interface module to pass information to and from the decision-assistance server, the information passed to and from the decision-assistance server including: first input information from a decision maker identifying a group of one or more advisers, second input information describing in human language a decision to be made by the decision maker, and first output advice information provided by the group of one or more advisers; a natural-language-detection module to detect analyzable word objects within the second input information and to generate decision-to-be-made information; at least one theme-operations module to determine at least one theme present amongst the second input information; and a decision-processing module to coordinate communication of the decision-to-be-made information to the group of one or more advisers and to coordinate communication of the first output advice to the decision maker. 14. The decision-assistance server of claim 13, comprising: an account-processing module, the account-processing module arranged to service a plurality of registered user accounts, the account-processing module arranged to associate at least some of the plurality of registered user accounts with individuals, respectively, and others of the plurality of registered-user accounts with businesses, respectively. 15. The decision-assistance server of claim 14 wherein the account processing module is arranged to manage more than 100,000 registered-user accounts. 16. The decision-assistance server of claim 15 wherein the decision-processing module is arranged to manage decision-to-be-made information associated with more than 100,000 active decisions to be made. 17. The decision-assistance server of claim 16 wherein the at least one theme-operations module is arranged to automatically determine theme information associated with each managed decision to be made. 18. The decision-assistance server of claim 17 wherein the decision-processing module is arranged to: manage a plurality of subscriptions, each of the plurality of subscriptions identifying at least one theme; determine when a theme is identified in an active decision to be made; and direct communication of at least some of the second input information and at least some of the first output information to an account of a business subscribed to at least one theme. 19. The decision-assistance server of claim 16, comprising: Timing logic arranged monitor how long each active decision to be made has been active. 20. The decision-assistance server of claim 16, comprising: a database arranged store decision-to-be-made information associated with the more than 100,000 active decisions to be made.
REJECTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: A decision-assistance system provides a tool for decision makers to receive assistance in making a decision. Through a web page, a decision maker selects a group of one or more advisers and inputs information describing a decision to be made. The decision maker solicits advice from the group of advisers, and the advisers provide advice to assist the decision maker. Other entities can be granted access to streams of information corresponding to particular themes of the decisions to be made. Business entities, for example, can use the streams of information to connect with decision makers facing decisions relevant to the goods and services provided by the business entity.
G06N5045		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A decision-assistance system provides a tool for decision makers to receive assistance in making a decision. Through a web page, a decision maker selects a group of one or more advisers and inputs information describing a decision to be made. The decision maker solicits advice from the group of advisers, and the advisers provide advice to assist the decision maker. Other entities can be granted access to streams of information corresponding to particular themes of the decisions to be made. Business entities, for example, can use the streams of information to connect with decision makers facing decisions relevant to the goods and services provided by the business entity.
Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parsing the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiating an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently executing each object to generate one or more expected outcomes associated with the corresponding time series data set; and providing the one or more expected outcomes associated with each of the corresponding time series data sets for display. 2. The computer-implemented method of claim 1 further comprising converting a set of raw data to the set of time series data. 3. The computer-implemented method of claim 1, wherein the request to perform a predictive analysis comprises a predict command that includes an indication of each of the time series data sets. 4. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set and a first forecasting algorithm to utilize to perform the predictive analysis based on the first time series data set, and an indication of a second time series data set and a second forecasting algorithm to utilize to perform the predictive analysis based on the second time series data set. 5. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set, an indication of a second time series data set, and an indication of a forecasting algorithm to utilize to perform the predictive analysis in association with the first time series data set and the second time series data set. 6. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set and first corresponding parameters to utilize to perform the predictive analysis in association with the first time series data set, and an indication of a second time series data set and second corresponding parameters to utilize to perform the predictive analysis in association with the second time series data set. 7. The computer-implemented method of claim 1, wherein parsing the request further identifies a forecasting algorithm to utilize for each of the time series data sets. 8. The computer-implemented method of claim 1, wherein parsing the request identifies a first forecasting algorithm associated with a first time series data set and a second forecasting algorithm associated with a second time series data set. 9. The computer-implemented method of claim 1, wherein initiating the object for each time series data set comprises initiating a first object for executing the predictive analysis for a first time series data set and initiating a second object for executing the predictive analysis for a second time series data set. 10. The computer-implemented method of claim 1, wherein the generated one or more expected outcomes associated with the corresponding time series data sets are aggregated and provided for concurrent display. 11. The computer-implemented method of claim 1 further comprising determining that the request to perform a predictive analysis comprises a request to perform concurrent predictive analysis for the multiple time series data sets. 12. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set based on a designated forecasting algorithm specified in the received request to perform the predictive analysis. 13. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: accessing the corresponding time series data set; and applying a forecasting algorithm designated for the corresponding time series data set to generate the one or more expected outcomes. 14. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: accessing the corresponding time series data set; determining that the corresponding time series data set has at least one missing data value; generating a predicted missing value for each of the at least one missing data values; and using the time series data set and the predicted missing values for each of the at least one missing data values to determine periodicity associated with the corresponding time series data set. 15. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: determining that the corresponding time series data set has at least one missing data value; generating a predicted missing value for each of the at least one missing data values; using the corresponding time series data set and the predicted missing values for each of the at least one missing data values to determine periodicity associated with the corresponding time series data set; and using the periodicity to generate a forecasting model used to generate the one or more expected outcomes associated with the corresponding time series data set. 16. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented to a user. 17. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented as a graphical visualization in connection with the corresponding time series data sets. 18. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented in a tabular format. 19. One or more computer-readable storage media having instructions stored thereon, wherein the instructions, when executed by a computing device, cause the computing device to: receive a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parse the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiate an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently execute each object to generate one or more expected outcomes associated with the corresponding time series data set; and provide the one or more expected outcomes associated with each of the corresponding time series data sets for display. 20. A computing device comprising: one or more processors; and a memory coupled with the one or more processors, the memory having instructions stored thereon, wherein the instructions, when executed by the one or more processors, cause the computing device to: receive a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parse the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiate an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently execute each object to generate one or more expected outcomes associated with the corresponding time series data set; and provide the one or more expected outcomes associated with each of the corresponding time series data sets for display.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.
Aspects of the disclosure provide a method for configuring a Quantum Annealing (QA) device. Then QA device has a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits. The method includes mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device, and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. The method further includes mapping a node of the neural network to a chain of qubits. In an embodiment, a coupling between qubits in the chain is configured to be a ferromagnetic coupling in order to map the node of the neural network to the chain of qubits.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for configuring a Quantum Annealing (QA) device, the QA device having a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits, the method comprising: mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device; and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. 2. The method of claim 1, further comprising: mapping a node of the neural network to a chain of qubits. 3. The method of claim 2, wherein mapping the node of the neural network to the chain of qubits includes: configuring a coupling between qubits in the chain to be a ferromagnetic coupling. 4. The method of claim 1, wherein the neural network is a deep learning neural network. 5. The method of claim 1, further comprising: configuring a coupler associated with a faulty qubit in the QA device with a zero weight; and setting a connection associated with a node in the neural network that is mapped to the faulty qubit with a zero weight. 6. The method of claim 2, further comprising: discarding quantum samples that include states of qubits in a chain of qubits that disagree with each other when a sample average is computed. 7. The method of claim 2, further comprising: using a state value of majority qubits that agree with each other in a chain of qubits including a faulty qubit as a state value of the chain of qubits in a quantum sample when a percentage of qubits in each chain of qubits that agree is greater than a voting threshold parameter in the quantum sample. 8. The method of claim 1, further comprising: applying a gauge transformation to qubits of the QA device. 9. The method of claim 8, wherein the gauge transformation is a basket weave gauge transformation. 10. The method of claim 8, wherein applying a gauge transformation to qubits of the QA device includes: generating quantum samples from qubits in the QA device with multiple different gauge transformation arrangements; and averaging the quantum samples to calculate a model expectation. 11. The method of claim 10, wherein the multiple different gauge transformation arrangements includes one of: an identity transformation where no qubits are inverted; a basket weave gauge transformation where a first half of qubits in the QA device are inverted and a second half of qubits are not inverted; a complement of the above basket weave gauge transformation where the second half of the qubits in the QA device are inverted and the first half of the qubits are not inverted; and a negative of the identity transformation where all qubits are inverted. 12. The method of claim 1, further including: calibrating a scale factor βeff for generating quantum samples from a quantum annealing process. 13. The method of claim 12, wherein calibrating the scale factor βeff includes: constructing a restricted Boltzmann machine (RBM) of a particular size; choosing a particular value for the scale factor βeff; performing the quantum annealing process to generate the quantum samples using a quotient of an energy functional of the RBM being divided by the scale factor βeff as a final Hamiltonian; repeating choosing a particular value, performing a quantum annealing process for multiple times; and determining a value of the scale factor βeff that leads to the smallest difference between model expectations of the RBM based on the quantum samples and model expectations of the RBM based on the energy functional of the RBM. 14. The method of claim 13, wherein calibrating the scale factor βeff further includes: calculating model expectations of the RBM based on the quantum samples; calculating model expectations of the RBM based on the energy functional of the RBM; and comparing model expectations of the RBM based on the quantum samples with model expectations of the RBM based on the energy functional of the RBM. 15. A method for training a neural network using a quantum annealing (QA) device including qubits configured with biases and couplers configured with weights, where an original restricted Boltzmann machine (RBM) of one layer of the neural network is mapped onto the QA device that is configured to act as a quantum RBM, the method comprising: generating quantum samples at the QA device; calculating an update to biases and weights for the original RBM and the quantum RBM with a classical computer based on the quantum samples; and using the update to biases and weights to perform a next iteration of training the neural network. 16. The method of claim 15, further comprising: initializing the biases and the weights of the original RBM and the quantum RBM to random values. 17. The method of claim 15, wherein generating quantum samples at the QA device includes: using a quotient of an energy functional of the RBM being divided by the scale factor βeff as a final Hamiltonian for a quantum annealing process at the QA device; running the quantum annealing process for multiple times to generate multiple quantum samples. 18. The method of claim 15, wherein calculating the update to biases and weights for the original RBM and the quantum RBM includes: averaging multiple quantum samples to calculate a model expectation that is consequently used for calculating updates to the biases and weights. 19. The method of claim 15, wherein using the update to biases and weights to perform the next iteration of training the neural network includes: configuring the biases and the weights of the original RBM and the quantum RBM with values of the update to biases and weights for the next iteration of training the neural network; and repeating the steps of generating quantum samples, calculating an update to biases and weights, and using the update to biases and weights to perform the next iteration.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Aspects of the disclosure provide a method for configuring a Quantum Annealing (QA) device. Then QA device has a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits. The method includes mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device, and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. The method further includes mapping a node of the neural network to a chain of qubits. In an embodiment, a coupling between qubits in the chain is configured to be a ferromagnetic coupling in order to map the node of the neural network to the chain of qubits.
G06N308		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Aspects of the disclosure provide a method for configuring a Quantum Annealing (QA) device. Then QA device has a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits. The method includes mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device, and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. The method further includes mapping a node of the neural network to a chain of qubits. In an embodiment, a coupling between qubits in the chain is configured to be a ferromagnetic coupling in order to map the node of the neural network to the chain of qubits.
Technologies for distributed machine learning include a mobile compute device to identify an input dataset including a plurality of dataset elements for machine learning and select a subset of the dataset elements. The mobile compute device transmits the subset to a cloud server for machine learning and receives, from the cloud server, a set of learned parameters for local data classification in response to transmitting the subset to the cloud server. The learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A mobile compute device for distributed machine learning, the mobile compute device comprising: a data management module to (i) identify an input dataset including a plurality of dataset elements for machine learning and (ii) select a subset of the dataset elements; and a communication module (i) transmit the subset to a cloud server for machine learning and (ii) receive, from the cloud server, a set of learned parameters for local data classification in response to transmittal of the subset to the cloud server, wherein the learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements. 2. The mobile compute device of claim 1, wherein to identify the input dataset comprises to identify a set of images for classification. 3. The mobile compute device of claim 1, wherein the learned parameters include one or more transformations of the features extracted by the cloud server. 4. The mobile compute device of claim 1, further comprising a classification module to perform local classification of dataset elements based on the learned parameters. 5. The mobile compute device of claim 4, wherein each of the dataset elements comprises an image; and wherein to perform the local classification comprises to recognize a particular object in one or more images based on the learned parameters. 6. The mobile compute device of claim 1, wherein to receive the set of learned parameters comprises to receive a set of learned parameters for local data classification in response to transmittal of the subset to the cloud server in real-time. 7. The mobile compute device of claim 1, wherein the communication module is to periodically update the set of learned parameters based on a selection of a new subset of the dataset elements, transmittal of the new subset to the cloud server, and receipt of an updated set of learned parameters from the cloud server. 8. The mobile compute device of claim 1, wherein to select the subset of the dataset elements comprises to select a random sample of the dataset elements. 9. One or more machine-readable storage media comprising a plurality of instructions stored thereon that, in response to execution by a mobile compute device, cause the mobile compute device to: identify an input dataset including a plurality of dataset elements for machine learning; select a subset of the dataset elements; transmit the subset to a cloud server for machine learning; and receive, from the cloud server, a set of learned parameters for local data classification in response to transmittal of the subset to the cloud server, wherein the learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements. 10. The one or more machine-readable storage media of claim 9, wherein to identify the input dataset comprises to identify a set of images for classification. 11. The one or more machine-readable storage media of claim 9, wherein the learned parameters include one or more transformations of the features extracted by the cloud server. 12. The one or more machine-readable storage media of claim 9, wherein the plurality of instructions further cause the mobile compute device to perform local classification of dataset elements based on the learned parameters. 13. The one or more machine-readable storage media of claim 12, wherein each of the dataset elements comprises an image; and wherein to perform the local classification comprises to recognize a particular object in one or more images based on the learned parameters. 14. The one or more machine-readable storage media of claim 9, wherein the plurality of instructions further cause the mobile compute device to periodically update the set of learned parameters based on a selection of a new subset of the dataset elements, transmittal of the new subset to the cloud server, and receipt of an updated set of learned parameters from the cloud server. 15. The one or more machine-readable storage media of claim 9, wherein to select the subset of the dataset elements comprises to select a random sample of the dataset elements. 16. A cloud server for distributed machine learning, the cloud server comprising: a communication module to receive a dataset from a mobile compute device; a feature determination module to extract one or more features from the received dataset; and a feature expansion module to generate an expanded feature set based on the one or more extracted features; wherein the communication module is further to transmit the expanded feature set to the mobile compute device as learned parameters for data classification. 17. The cloud server of claim 16, wherein to generate the expanded feature set comprises to: identify one or more transformations to apply to the extracted features; and apply the one or more identified transformations to each of the extracted features to generate one or more additional features for each of the extracted features. 18. The cloud server of claim 17, wherein the dataset comprises a set of images; and wherein the one or more transformations comprise at least one of a rotational transformation or a perspective transformation. 19. The cloud server of claim 17, wherein the dataset comprises a set of images; and wherein the one or more transformations comprise a transformation associated with an illumination of a corresponding image. 20. The cloud server of claim 17, wherein to identify one or more transformations comprises to: identify a type of transformation to apply to the extracted features; and discretize a space of the type transformations to identify a finite number of transformations of the type of transformations to apply. 21. One or more machine-readable storage media comprising a plurality of instructions stored thereon that, in response to execution by a cloud server, cause the cloud server to: receive a dataset from the mobile compute device; extract one or more features from the received dataset; generate an expanded feature set based on the one or more extracted features; and transmit the expanded feature set to the mobile compute device as learned parameters for data classification. 22. The one or more machine-readable storage media of claim 21, wherein to generate the expanded feature set comprises to: identify one or more transformations to apply to the extracted features; and apply the one or more identified transformations to each of the extracted features to generate one or more additional features for each of the extracted features. 23. The one or more machine-readable storage media of claim 22, wherein the dataset comprises a set of images; and wherein the one or more transformations comprise at least one of a rotational transformation or a perspective transformation. 24. The one or more machine-readable storage media of claim 22, wherein to identify one or more transformations comprises to: identify a type of transformation to apply to the extracted features; and discretize a space of the type transformations to identify a finite number of transformations of the type of transformations to apply. 25. The one or more machine-readable storage media of claim 21, wherein the dataset received from the mobile compute device consists of a random subset of data elements extracted by the mobile compute device from a data superset.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Technologies for distributed machine learning include a mobile compute device to identify an input dataset including a plurality of dataset elements for machine learning and select a subset of the dataset elements. The mobile compute device transmits the subset to a cloud server for machine learning and receives, from the cloud server, a set of learned parameters for local data classification in response to transmitting the subset to the cloud server. The learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements.
G06N3088		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Technologies for distributed machine learning include a mobile compute device to identify an input dataset including a plurality of dataset elements for machine learning and select a subset of the dataset elements. The mobile compute device transmits the subset to a cloud server for machine learning and receives, from the cloud server, a set of learned parameters for local data classification in response to transmitting the subset to the cloud server. The learned parameters are based on an expansion of features extracted by the cloud server from the subset of the dataset elements.
A computer-implemented training system is described herein for generating at least one model component based on labeled training data. The training system produces the labels in the training data by leveraging textual information expressed in already-evaluated documents. In another implementation, the training system generates a first model component and a second model component. In one implementation, in an application phase, a computer-implemented model-application system applies the first model component to identify an initial set of related items that are related to an input item (such as a query). The model-application system then applies the second model component to select a subset of related items from among the initial set of related items.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for generating and applying at least one model component, comprising: in a training system that includes one or more computing devices: providing at least one seed item; identifying, for each seed item, a set of candidate items; using a computer-implemented label-generating component to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information, the label being generated, using the label-generating component, by: identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item; determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information; and generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information; using a computer-implemented feature-generating component to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information; using a computer-implemented model-generating component to generate and store a model component based on the label information and the feature information; and in a model-application system that includes one or more computing devices: receiving an input item; applying the model component to generate a set of zero, one, or more related items that are determined, by the model component, to be related to the input item; generating an output result based at least on the set of related items; and providing the output result to an end user, the model-application system leveraging use of the model component to facilitate efficient generation of the output result. 2. The method of claim 1, wherein said identifying of the set of candidate items, as applied with respect to the particular seed item, comprises identifying one or more items that have a nexus to the particular seed item, as assessed based on one or more data sources. 3. The method of claim 1, wherein each document, in the set of documents, is associated with a collection of text items, and wherein the collection of text items encompasses text items within the document as well as text items that are determined to relate to the document. 4. The method of claim 1, wherein said generating of the label for the particular candidate item comprises: generating a retrieved gain measure, corresponding to an aggregation of evaluation measures associated with a subset of documents, among the set of documents, that match the particular candidate item; generating a total gain available measure, corresponding to an aggregation of evaluation measures associated with all of the documents in the set of documents; generating a documents-retrieved measure, which corresponds to a number of documents, among the set of documents, that match the particular candidate item; and generating the label based on the retrieved gain measure, the total gain available measure, and the documents-retrieved measure. 5. The method of claim 4, wherein the label is generated by multiplying the total gain available measure by the documents-retrieved measure, to form a product, and dividing the retrieved gain measure by the product. 6. The method of claim 4, wherein at least one of the retrieved gain measure, the total gain available measure, and/or the documents-retrieved measure is modified by an exponential balancing parameter. 7. The method of claim 1, wherein said generating of the set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value that assesses a text-based similarity between the particular seed item and the particular candidate item. 8. The method of claim 1, wherein said generating of the set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by applying a language model component to determine a probability of an occurrence of the particular candidate item within a language. 9. The method of claim 1, wherein said generating of the particular set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by applying a translation model component to determine a probability that the particular seed item is transformable into the particular candidate item, or vice versa. 10. The method of claim 1, wherein said generating of the particular set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by determining characteristics of prior user behavior pertaining to the particular seed item and/or the particular candidate item. 11. The method of claim 1, wherein the model component that is generated corresponds to a first model component, and wherein the method further comprises: using the training system to generate a second model component; using the model-application system to apply the first model component to generate an initial set of related items that are related to the input item; and using the model-application system to apply the second model component to select a subset of related items from among the initial set of related items. 12. The method of claim 11, wherein the said training system generates the second model component by: using the first model component to generate a plurality of new individual candidate items; generating a plurality of group candidate items, each of which reflects a particular combination of one or more new individual candidate items; using another computer-implemented label-generating component to generate new label information for the group candidate items; using another computer-implemented feature-generating component to generate new feature information for the group candidate items; and using another computer-implemented model-generating component to generate the second model component based on the new label information and the new feature information. 13. The method of claim 1, wherein each of the set of candidate items corresponds to a group candidate item that includes a combination of individual candidate items, selected from among a set of possible combinations, the individual candidate items being generated using any type of candidate-generating component. 14. The method of claim 13, wherein said using of the feature-generating component to generate feature information comprises, for each particular group candidate item: determining a set of feature values for each individual candidate item that is associated with the particular group candidate item, to overall provide a collection of feature sets that is associated with the particular group candidate item; and determining at least one feature value that provides group-based information that summarizes the collection of feature sets. 15. The method of claim 1, wherein: the model-application system implements a search service, the input item corresponds to an input query, and the set of related items corresponds to a set of linguistic items that are determined to be related to the input query. 16. A computer readable storage medium for storing computer readable instructions, the computer readable instructions implementing a training system when executed by one or more processing devices, the computer readable instructions comprising: logic configured to identify, for each of a set of seed items, a set of candidate items; logic configured to generate a label, for each pairing of a particular seed item and a particular candidate item, based on: evaluation measures which measure an extent to which documents in a set of documents have been assessed as being relevant to the particular seed item; and retrieval information which reflects an extent to which the particular candidate item is found in the set of documents; logic configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, said logic configured to generate a label collectively providing label information, when applied to all pairings of seed items and candidate items, said logic configured to generate a set of feature values collectively providing feature information, when applied to all pairings of seed items and candidate items; and logic configured to generate a model component based on the label information and the feature information, the model component, when applied by a model-application system, identifying, zero, one, or more related items with respect to an input item, each particular candidate item corresponding to a particular individual candidate item that includes a single linguistic item, or a particular group candidate item that includes a combination of individual candidate items. 17. The computer readable storage medium of claim 16, wherein said logic configured to generate the label for the particular candidate item comprises: logic configured to generate a retrieved gain measure, corresponding to an aggregation of evaluation measures associated with a subset of documents, among the set of documents, that match the particular candidate item; and logic configured to generate a total gain available measure, corresponding to an aggregation of evaluation measures associated with all of the documents in the set of documents; logic configured to generate a documents-retrieved measure, which corresponds to a number of documents, among the set of documents, that match the particular candidate item; and logic configured to generate the label based at least on the retrieved gain measure, the total gain available measure, and the documents-retrieved measure. 18. One or more computing devices for implementing at least a training system, comprising: a candidate-generating component configured to generate a set of candidate items for each seed item, for a plurality of seed items; a label-generating component configured to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information, said label being generated, using the label-generating component, by: identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item; determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information; and generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information; a feature-generating component configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information; and a model-training component configured to generate and store a model component based on the label information and the feature information. 19. The one or more computing devices of claims 18, further comprising a model-application system, implemented by the one or more computing devices, and comprising: a user interface component configured to receive an input item from an end user; an item-expansion component configured to apply the model component to generate a set of zero, one, or more related items that are determined, by the model component, to be related to the input item; and a processing component configured to generate an output result based on the set of related items, the user interface component further being configured to provide the output result to the end user. 20. The one or more computing devices of claim 19, wherein: the model component that is generated by the training system corresponds to a first model component, the training system is further configured to generate a second model component, the item-expansion component, of the model-application system, is further configured to: apply the first model component to generate an initial set of related items that are related to the input item; and apply the second model component to select a subset of related items from among the initial set of related items.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A computer-implemented training system is described herein for generating at least one model component based on labeled training data. The training system produces the labels in the training data by leveraging textual information expressed in already-evaluated documents. In another implementation, the training system generates a first model component and a second model component. In one implementation, in an application phase, a computer-implemented model-application system applies the first model component to identify an initial set of related items that are related to an input item (such as a query). The model-application system then applies the second model component to select a subset of related items from among the initial set of related items.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A computer-implemented training system is described herein for generating at least one model component based on labeled training data. The training system produces the labels in the training data by leveraging textual information expressed in already-evaluated documents. In another implementation, the training system generates a first model component and a second model component. In one implementation, in an application phase, a computer-implemented model-application system applies the first model component to identify an initial set of related items that are related to an input item (such as a query). The model-application system then applies the second model component to select a subset of related items from among the initial set of related items.
An information processing device for processing trade data for asset management and trading is disclosed. The information processing device includes the one or more processors configured to determine price of a commodity indicated by the trade data. The price being valid in an event the one or more processors receive a user input indicating authorization of a user to revoke a deal of the commodity within predetermined time duration. The one or more processors are configured to determine the price by using a neural network, the neural network being trained by the one or more processors based on one or more data sets corresponding to the commodity.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing device configured to process trade data for asset management and trading, said information processing device comprising: one or more processors configured to determine price of a commodity indicated by said trade data, said price being valid in an event the one or more processors receive a user input indicating authorization of a user to revoke a deal of said commodity within a predetermined time duration, wherein said one or more processors are configured to determine said price by using a neural network, said neural network being trained by said one or more processors based on one or more data sets corresponding to said commodity. 2. The information processing device as claimed in claim 1, wherein said one or more processors are configured to train said neural network by performing one or more steps of data filtering, data validation, data sampling, and/or data sorting on said one or more data sets, wherein said one or more data sets correspond to spot prices of said commodity in an event said neural network is trained by said one or more processors. 3. The information processing device as claimed in claim 2, wherein said one or more data sets correspond to one or more of historical actual prices, historical commercial prices, periodic prices, and/or aperiodic prices, of said commodity. 4. The information processing device as claimed in claim 1, wherein said one or more processors are further configured to predict volatility related to said price of said commodity by using said neural network. 5. The information processing device as claimed in claim 4, wherein said one or more processors are further configured to retrain said neural network to adjust said determined price of said commodity to keep said asset management and said trading profitable. 6. The information processing device as claimed in claim 4, wherein said one or more processors are configured to retrain said neural network based on a feedback mechanism which comprises adjusting said price depending on performance estimation in real time. 7. The information processing device as claimed in claim 6, wherein said performance estimation in real time comprises comparison between stored historical volatility data and said predicted volatility, wherein said stored historical volatility and said volatility are predicted for same time intervals. 8. The information processing device as claimed in claim 6, wherein retraining of said neural network further comprises validating said price by estimating volume pricing for different strikes for said commodity. 9. The information processing device as claimed in claim 1, wherein said one or more processors are further configured to estimate at the money (ATM) volatility for a plurality of time intervals of same duration. 10. The information processing device as claimed in claim 9, wherein said one or more processors are further configured to generate a message indicating avoidance of trading of said commodity in an event a difference between each of said plurality of time intervals exceeds a predetermined threshold value. 11. A method for processing trade data for asset management and trading, said method comprising: in an information processing device: determining price of a commodity indicated by said trade data, wherein said price being valid in an event the information processing device receives a user input indicating authorization of a user to revoke a deal of said commodity within a predetermined time duration, and wherein said price is determined by using a neural network, said neural network being trained by said information processing device based on one or more data sets corresponding to said commodity. 12. A system for asset management and trading, said system comprising: a client computer comprising one or more processors configured to: receive trade data for said asset management and said trading; and determine price of a commodity indicated by said trade data, wherein said price being valid in an event the one or more processors receive a user input indicating authorization of a user to revoke a deal of said commodity within a predetermined time duration, and wherein said one or more processors are configured to determine said price by using a neural network, said neural network being trained by said one or more processors based on one or more data sets corresponding to said commodity, and a server configured to transmit said trade data to said client computer.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: An information processing device for processing trade data for asset management and trading is disclosed. The information processing device includes the one or more processors configured to determine price of a commodity indicated by the trade data. The price being valid in an event the one or more processors receive a user input indicating authorization of a user to revoke a deal of the commodity within predetermined time duration. The one or more processors are configured to determine the price by using a neural network, the neural network being trained by the one or more processors based on one or more data sets corresponding to the commodity.
G06N308		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An information processing device for processing trade data for asset management and trading is disclosed. The information processing device includes the one or more processors configured to determine price of a commodity indicated by the trade data. The price being valid in an event the one or more processors receive a user input indicating authorization of a user to revoke a deal of the commodity within predetermined time duration. The one or more processors are configured to determine the price by using a neural network, the neural network being trained by the one or more processors based on one or more data sets corresponding to the commodity.
Described is a system for decoding spiking reservoirs even when the spiking reservoir has continuous synaptic plasticity. The system uses a set of training patterns to train a neural network having a spiking reservoir comprised of spiking neurons. A test pattern duration d is estimated for a set of test patterns P, and each test pattern is presented to the spiking reservoir for a duration of d/P seconds. Output spikes from the spiking reservoir are generated via readout neurons. The output spikes are measured and the measurements are used to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P. The firing rate codes are used to decode performance of the neural network by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for decoding output from spiking reservoirs, the system comprising: one or more processors and a non-transitory memory having instructions encoded thereon such that when the instructions are executed, the one or more processors perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 2. The system as set forth in claim 1, wherein the neural network exhibits continuous plasticity. 3. The system as set forth in claim 1, wherein the one or more processors further perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 4. The system as set forth in claim 3, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 5. The system as set forth in claim 4, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 6. The system as set forth in claim 5, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 7. A computer-implemented method for decoding output from spiking reservoirs, comprising: an act of causing one or more processors to execute instructions stored on a non-transitory memory such that upon execution, the one or more processors perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 8. The method as set forth in claim 7, wherein the neural network exhibits continuous plasticity. 9. The method as set forth in claim 7, wherein the one or more processors further perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 10. The method as set forth in claim 9, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 11. The method as set forth in claim 10, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 12. The method as set forth in claim 11, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 13. A computer program product for decoding output from spiking reservoirs, the computer program product comprising: computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of: training a neural network having a spiking reservoir comprised of spiking neurons by using a set of training patterns; presenting each test pattern in a set of test patterns to the spiking reservoir; generating output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P; and decoding performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P. 14. The computer program product as set forth in claim 13, wherein the neural network exhibits continuous plasticity. 15. The computer program product as set forth in claim 13, further comprising instructions for causing the one or more processors to perform operations of: computing, for each test pattern p, firing rates fip of a sink neuron i in the neural network as the total number of output spikes during a duration d; estimating a maximum firing rate fmaxp from the firing rates fip of all sink neurons in the neural network for the test pattern p; and computing a firing rate code for each test pattern p using fmaxp and fip. 16. The computer program product as set forth in claim 15, wherein the DI is a product of a separability measure, ε, and a uniqueness measure, γ, wherein the separability measure is defined as a measure of a degree of separation of firing rate codes for the set of test patterns P, and wherein the uniqueness measure is defined as a number of unique firing rate codes produced by the neural network relative to a maximum possible number of unique firing rate codes. 17. The computer program product as set forth in claim 16, wherein the separability measure is computed according to the following: ɛ = 1 - D intra D inter , where Dintra is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the same test pattern, and Dinter is defined as an average pair-wise distance between firing rate codes computed from all possible unique pairs of firing rate codes generated by the neural network for the set of test patterns P. 18. The computer program product as set forth in claim 17, wherein the uniqueness measure is computed according to the following: γ = #  S P , where #S represents the total number of unique firing rate codes for the set of test patterns P. 19. The system as set forth in claim 1, wherein the set of test patterns P are input patterns from images obtained around a vehicle, and wherein the set of test patterns P are used to assist the vehicle in autonomous driving. 20. A system for decoding output from spiking reservoirs, the system comprising: one or more processors and a non-transitory memory having instructions encoded thereon such that when the instructions are executed, the one or more processors perform operations of: providing an input signal to a neural network, the neural network having a spiking reservoir comprised of spiking neurons trained by: presenting each test pattern in a set of test patterns to the spiking reservoir; generating; output spikes from the spiking reservoir via a set of readout neurons; measuring the output spikes, resulting in a plurality of measurements, and using the plurality of measurements to compute firing rate codes, each firing rate code corresponding to a test pattern in a set of test patterns P; and determining performance of the neural network, using the firing rate codes, by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P; obtaining a readout code from the neural network produced in response to the input signal; and identifying a component of the input signal based on the readout code.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Described is a system for decoding spiking reservoirs even when the spiking reservoir has continuous synaptic plasticity. The system uses a set of training patterns to train a neural network having a spiking reservoir comprised of spiking neurons. A test pattern duration d is estimated for a set of test patterns P, and each test pattern is presented to the spiking reservoir for a duration of d/P seconds. Output spikes from the spiking reservoir are generated via readout neurons. The output spikes are measured and the measurements are used to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P. The firing rate codes are used to decode performance of the neural network by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P.
G06N308		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Described is a system for decoding spiking reservoirs even when the spiking reservoir has continuous synaptic plasticity. The system uses a set of training patterns to train a neural network having a spiking reservoir comprised of spiking neurons. A test pattern duration d is estimated for a set of test patterns P, and each test pattern is presented to the spiking reservoir for a duration of d/P seconds. Output spikes from the spiking reservoir are generated via readout neurons. The output spikes are measured and the measurements are used to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P. The firing rate codes are used to decode performance of the neural network by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P.
A time-series prediction apparatus 10, which is an information processing apparatus that predicts transition of time-series data on a matter, calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters, and predicts transition of the time-series data relevant to the matter based on the calculated relevance level. The time-series prediction apparatus 10 calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. The time-series prediction apparatus 10 builds multiple prediction models for predicting the transition of the time-series data relevant to the prediction target matter based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, and integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A time-series prediction apparatus that predicts transition of time-series data on a matter, comprising: a relevance level calculation part that calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters; and a transition prediction part that predicts transition of the time-series data relevant to the matter based on the relevance level. 2. The time-series prediction apparatus according to claim 1, wherein the relevance level calculation part calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. 3. The time-series prediction apparatus according to claim 1, wherein based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, the transition prediction part builds a plurality of prediction models for predicting the transition of the time-series data relevant to the prediction target matter, and the transition prediction part integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level. 4. The time-series prediction apparatus according to claim 1, wherein the time-series prediction apparatus generates a graph representing temporal transition of the time-series data. 5. The time-series prediction apparatus according to claim 4, wherein the time-series prediction apparatus generates a graph representing temporal transition of the relevance level. 6. The time-series prediction apparatus according to claim 1, wherein the time-series prediction apparatus extracts, from time-series data relevant to the causal relation between the matters, time-series data containing both of terms relevant to the respective matters, and generates information indicating appearance frequency of the terms included in the time-series data extracted. 7. The time-series prediction apparatus according to claim 1, further comprising a time-series data collection part that acquires, over the Internet, the time-series data relevant to each of the plurality of matters including the prediction target matter and the time-series data relevant to the causal relation between the matters. 8. A time-series prediction method executed using an information processing apparatus that predicts transition of time-series data on a matter, the method comprising the steps, performed by the information processing apparatus, of: calculating a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters; and predicting transition of the time-series data relevant to the matter based on the relevance level. 9. The time-series prediction method according to claim 8, further comprising the step, performed by the time-series prediction apparatus, of: calculating the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. 10. The time-series prediction method according to claim 8, further comprising the steps, performed by the time-series prediction apparatus, of: based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, building a plurality of prediction models for predicting the transition of the time-series data relevant to the prediction target matter; and integrating prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level. 11. The time-series prediction method according to claim 8, further comprising the step, performed by the time-series prediction apparatus, of: generating a graph representing temporal transition of the time-series data. 12. The time-series prediction method according to claim 11, further comprising the step, performed by the time-series prediction apparatus, of: generating a graph representing temporal transition of the relevance level. 13. The time-series prediction method according to claim 8, further comprising the step, performed by the time-series prediction apparatus, of: extracting, from time-series data relevant to the causal relation between the matters, time-series data containing both of terms relevant to the respective matters, and generating information indicating a frequency of appearance of the terms included in the time-series data extracted. 14. The time-series prediction method according to claim 8, further comprising the step, performed by the time-series prediction apparatus, of: acquiring, over the Internet, the time-series data relevant to each of the plurality of matters including the prediction target matter and the time-series data relevant to the causal relation between the matters.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A time-series prediction apparatus 10, which is an information processing apparatus that predicts transition of time-series data on a matter, calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters, and predicts transition of the time-series data relevant to the matter based on the calculated relevance level. The time-series prediction apparatus 10 calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. The time-series prediction apparatus 10 builds multiple prediction models for predicting the transition of the time-series data relevant to the prediction target matter based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, and integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A time-series prediction apparatus 10, which is an information processing apparatus that predicts transition of time-series data on a matter, calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters, and predicts transition of the time-series data relevant to the matter based on the calculated relevance level. The time-series prediction apparatus 10 calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. The time-series prediction apparatus 10 builds multiple prediction models for predicting the transition of the time-series data relevant to the prediction target matter based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, and integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.
A sign of the landslide disaster is easily detected. A model learning unit (120) of an anomaly detection device (100) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration sensor placed on a monitoring target. The anomaly detection unit (140) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration sensor. Then, the anomaly detection unit (140) determines whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing device comprising: a learning unit that learns a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration detector placed on a monitoring target; and an anomaly detection unit that learns a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration detector, and determines whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period. 2. The information processing device according to claim 1, wherein each of the learning unit and the anomaly detection unit learns a relational expression between vibration strengths at different resonance frequencies. 3. The information processing device according to claim 1, wherein the anomaly detection unit extracts the relational expression related to a new frequency which is higher than frequencies related to the relational expressions learned during the learning period, from the relational expressions learned during the new period, as the relational expression related to the new frequency. 4. The information processing device according to claim 1, wherein the anomaly detection unit determines whether or not there is an anomaly in the monitoring target based on the new relational expression in a case that the time series of the frequency characteristics of the vibration strength detected during the new period does not satisfy a relation represented by the relational expression learned during the learning period. 5. The information processing device according to claim 1, wherein the anomaly detection unit determines that there is an anomaly in the monitoring target in a case that number of the new relational expressions is equal to or larger than a predetermined threshold value. 6. The information processing device according to claim 1, wherein the vibration detector detects vibration generated by raindrops falling on the monitoring target. 7. An anomaly detection method comprising: learning a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration detector placed on a monitoring target; and learning a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration detector, and determining whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period. 8. The anomaly detection method according to claim 7, wherein, in the learning a relational expression, a relational expression between vibration strengths at different resonance frequencies is learned. 9. A non-transitory computer readable storage medium recording thereon a program, causing a computer to perform a method comprising: learning a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration detector placed on a monitoring target; and learning a relational expression between vibration strengths at different frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration detector, and determining whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period. 10. The non-transitory computer readable storage medium recording thereon the program according to claim 9, wherein, in the learning a relational expression, a relational expression between vibration strengths at different resonance frequencies is learned.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A sign of the landslide disaster is easily detected. A model learning unit (120) of an anomaly detection device (100) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration sensor placed on a monitoring target. The anomaly detection unit (140) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration sensor. Then, the anomaly detection unit (140) determines whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period.
G06N99005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A sign of the landslide disaster is easily detected. A model learning unit (120) of an anomaly detection device (100) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a learning period by a vibration sensor placed on a monitoring target. The anomaly detection unit (140) learns a relational expression between vibration strengths at frequencies based on a time series of frequency characteristics of a vibration strength detected during a new period by the vibration sensor. Then, the anomaly detection unit (140) determines whether or not there is an anomaly in the monitoring target based on a relational expression related to a new frequency, which is different from the relational expression learned during the learning period.
Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer readable medium comprising program code executable by a processor for causing the processor to: receive a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filter the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum time duration usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determine that a time series of the subset of time series does not include a time period with inactivity; determine that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determine that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generate a data set that includes the time series; and generate a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 2. The non-transitory computer readable medium of claim 1, wherein the preselected forecasting process comprises: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 3. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to determine that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 4. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to generate the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 5. The non-transitory computer readable medium of claim 4, further comprising program code executable by the processor for causing the processor to: determine the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 6. The non-transitory computer readable medium of claim 1, wherein the time series is a first time series, and further comprising program code executable by the processor for causing the processor to: determine that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determine that the second time series comprises the time period with the inactivity; or determine that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determine that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flag the second time series as incompatible with the preselected forecasting process. 7. The non-transitory computer readable medium of claim 6, further comprising program code executable by the processor for causing the processor to: select another forecasting process for use with the second time series; and use the other forecasting process to generate another forecast from the second time series. 8. The non-transitory computer readable medium of claim 1, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and further comprising program code executable by the processor for causing the processor to: prior to determining the time series exhibits the repetitive characteristic, determine that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregate the time series with another time series to generate an aggregate time series; and use the aggregate time series as the time series. 9. The non-transitory computer readable medium of claim 8, wherein the first preset time duration is one year and the second preset time duration is two years. 10. The non-transitory computer readable medium of claim 1, wherein the non-transitory computer readable medium comprises two or more computer readable media distributed among two or more worker nodes in a communications grid computing system, the two or more worker nodes being separate computing devices that are remote from one another. 11. A method comprising: receiving a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filtering the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum time duration usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determining that a time series of the subset of time series does not include a time period with inactivity; determining that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determining that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generating a data set that includes the time series; and generating a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 12. The method of claim 11, wherein the preselected forecasting process comprises: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 13. The method of claim 11, further comprising determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 14. The method of claim 11, further comprising generating the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 15. The method of claim 14, further comprising determining the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 16. The method of claim 11, wherein the time series is a first time series, and further comprising: determining that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determining that the second time series comprises the time period with the inactivity; or determining that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determining that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flagging the second time series as incompatible with the preselected forecasting process. 17. The method of claim 16, further comprising: selecting another forecasting process for use with the second time series; and using the other forecasting process to generate another forecast from the second time series. 18. The method of claim 11, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and further comprising prior to determining the time series exhibits the repetitive characteristic, determining that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregating the time series with another time series to generate an aggregate time series; and using the aggregate time series as the time series. 19. The method of claim 18, wherein the first preset time duration is one year and the second preset time duration is two years. 20. The method of claim 11, wherein: generating the data set comprises a first worker node of a communications grid computing system receiving information from a second worker node of the communications grid computing system, generating the data set based on the information, and transmitting the data set to a third worker node of the communications grid computing system; and generating the predictive forecast comprises the third worker node of the communications grid computing system receiving the data set and generating the predictive forecast based on the data set. 21. A system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to: receive a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filter the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum length usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determine that a time series of the subset of time series does not include a time period with inactivity; determine that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determine that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generate a data set that includes the time series; and generate a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 22. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to generate the predictive forecast by: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 23. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 24. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to generate the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 25. The system of claim 24, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 26. The system of claim 21, wherein the time series is a first time series, and wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determine that the second time series comprises the time period with the inactivity; or determine that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determine that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flag the second time series as incompatible with the preselected forecasting process. 27. The system of claim 26, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: select another forecasting process for use with the second time series; and use the other forecasting process to generate another forecast from the second time series. 28. The system of claim 21, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: prior to determining the time series exhibits the repetitive characteristic, determine that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregate the time series with another time series to generate an aggregate time series; and use the aggregate time series as the time series. 29. The system of claim 28, wherein the first preset time duration is one year and the second preset time duration is two years. 30. The system of claim 21, further comprising a plurality worker nodes in a communications grid computing system, wherein: a first worker node of the plurality of worker nodes is configured to generate the data set and transmit the data set to a second worker node of the plurality of worker nodes; and the second worker node of the plurality of worker nodes is configured to receive the data set and generate the predictive forecast based on the data set.
REJECTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.
An electronic device described herein includes a sensing unit having at least one sensor to acquire sensing data. An associated computing device extracts sensor specific features from the sensing data, and generates a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features. The motion activity vector, voice activity vector, and spatial environment vector are processed to determine a base level context of the electronic device relative to its surroundings, with the base level context having aspects each based on the motion activity vector, voice activity vector, and spatial environment vector. Meta level context of the electronic device relative to its surroundings is determined as a function of the base level context, with the meta level context being at least one inference made from at least two aspects of the plurality of aspects of the base level context.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic device, comprising: a sensing unit comprising at least one sensor and being configured to acquire sensing data; and a computing device configured to: extract sensor specific features from the sensing data; generate a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features; process the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on at least one of the motion activity vector, voice activity vector, and spatial environment vector; wherein one aspect of the plurality of aspects of the base level context is a mode of locomotion of a user carrying the electronic device, and another aspect of the plurality of aspects of the base level context is a nature of biologically generated sounds within audible distance of the user or a nature of physical space around the user; and determine meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context. 2. The electronic device of claim 1, wherein each aspect of the base level context based on the motion activity vector is mutually exclusive of one another; wherein each aspect of the base level context based on the voice activity vector is mutually exclusive of one another; and wherein each aspect of the base level context based on the spatial environment vector is mutually exclusive of one another. 3. The electronic device of claim 1, wherein the mode of locomotion of the user carrying the electronic device is based upon the motion activity vector, the nature of biologically generated sounds within audible distance of the user is based on the voice activity vector, and the nature of physical space around the user is based upon the spatial environment vector. 4. The electronic device of claim 1, wherein the computing device is further configured to facilitate performance of at least one contextual function of the electronic device as a function of the meta level context of the electronic device. 5. The electronic device of claim 1, wherein the determined mode of locomotion of the user comprises one of the user being stationary, walking, going up stairs, going down stairs, jogging, cycling, climbing, using a wheelchair, and riding in or on a vehicle; wherein the determined nature of the biologically generated sounds comprises one of a telephone conversation engaged in by the user, a multiple party conversation engaged in by the user, the user speaking, another party speaking, background conversation occurring around the user, and an animal making sounds; and wherein the determined nature of the physical space around the user comprises an office environment, a home environment, a shopping mall environment, a street environment, a stadium environment, a restaurant environment, a bar environment, a beach environment, a nature environment, a temperature of the physical space, a barometric pressure of the physical space, and a humidity of the physical space. 6. The electronic device of claim 1, wherein the computing device is configured to process the motion activity vector, voice activity vector, and spatial environment vector by: generating a motion activity posteriorgram as a function of the motion activity vector, the motion activity posteriorgram representing a probability of each element of the motion activity vector as a function of time; generating a voice activity posteriorgram as a function of the voice activity vector, the voice activity posteriorgram representing a probability of each element of the voice activity vector as a function of time; and generating a spatial environment posteriorgram as a function of the spatial environment vector, the spatial environment posteriorgram representing a probability of each element of the spatial environment vector as a function of time. 7. The electronic device of claim 6, wherein a sum of each probability of the motion activity posteriorgram at any given time equals one; wherein a sum of each probability of the voice activity posteriorgram at any given time equals one; and wherein a sum of each probability of the spatial environment posteriorgram at any given time equals one. 8. The electronic device of claim 1, wherein the sensing unit consists essentially of one sensor. 9. The electronic device of claim 1, wherein the sensing unit comprises a plurality of sensors; and wherein the motion activity vector, voice activity vector, and spatial environment vector are generated as a function of a fusion of the sensor specific features. 10. The electronic device of claim 9, wherein the plurality of sensors comprise at least two of an accelerometer, pressure sensor, microphone, gyroscope, magnetometer, GPS unit, and barometer. 11. The electronic device of claim 1, further comprising a printed circuit board (PCB) having at least one conductive trace thereon; further comprising a system on chip (SoC) mounted on the PCB and electrically coupled to the at least one conductive trace; and wherein the computing device comprises a sensor chip mounted on the PCB in a spaced apart relation with the SoC and electrically coupled to the at least one conductive trace such that the sensor chip and SoC are electrically coupled; and wherein the sensor chip comprises an micro-electromechanical system (MEMS) sensing unit, and a control circuit configured to perform the extracting, generating, processing, and determining. 12. An electronic device, comprising: a computing device configured to: extract sensor specific features from sensing data; generate a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features; process the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on at least one of the motion activity vector, voice activity vector, and spatial environment vector; wherein at least one aspect of the plurality of aspects of the base level context is one of: a mode of locomotion of the user carrying the electronic device, a nature of biologically generated sounds within audible distance of the user, or a nature of physical space around the user; and determine meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context. 13. The electronic device of claim 12, wherein each aspect of the base level context based on the motion activity vector is mutually exclusive of one another; wherein each aspect of the base level context based on the voice activity vector is mutually exclusive of one another; and wherein each aspect of the base level context based on the spatial environment vector is mutually exclusive of one another. 14. The electronic device of claim 12, wherein the computing device is configured to process the motion activity vector, voice activity vector, and spatial environment vector by: generating a motion activity posteriorgram as a function of the motion activity vector, the motion activity posteriorgram representing a probability of each element of the motion activity vector as a function of time; generating a voice activity posteriorgram as a function of the voice activity vector, the voice activity posteriorgram representing a probability of each element of the voice activity vector as a function of time; and generating a spatial environment posteriorgram as a function of the spatial environment vector, the spatial environment posteriorgram representing a probability of each element of the spatial environment vector as a function of time. 15. The electronic device of claim 14, wherein a sum of each probability of the motion activity posteriorgram at any given time equals one; wherein a sum of each probability of the voice activity posteriorgram at any given time equals one; and wherein a sum of each probability of the spatial environment posteriorgram at any given time equals one. 16. An electronic device, comprising: a printed circuit board (PCB) having at least one conductive trace thereon; a system on chip (SoC) mounted on the PCB and electrically coupled to the at least one conductive trace; and a sensor chip mounted on the PCB in a spaced apart relation with the SoC and electrically coupled to the at least one conductive trace such that the sensor chip and SoC are electrically coupled, and configured to acquire sensing data; wherein the sensor chip comprises: a micro-electromechanical system (MEMS) sensing unit; an embedded processing node configured to: preprocess the sensing data, extract sensor specific features from the sensing data, generate a motion activity posteriorgram, a voice activity posteriorgram, and a spatial environment posteriorgram as a function of the sensor specific features, process the motion activity posteriorgram, voice activity posteriorgram, and spatial environment posteriorgram so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects, wherein a first aspect of the plurality of aspects of the base level context is determined based upon the motion activity posteriorgram, a second aspect of the plurality of aspects of the base level context is determined based upon the voice activity posteriorgram, and a third aspect of the plurality of aspects of the base level context is determined based upon the spatial environment posteriorgram, and determine meta level context of the electronic device relative to its surroundings as a function of the base level context and at least one known pattern, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context. 17. The electronic device of claim 16, further comprising at least one additional sensor external to the MEMS sensing unit; wherein the SoC is configured to acquire additional data from the at least one additional sensor; wherein the embedded processing node is further configured to receive the additional data from the SoC and to also extract the sensor specific features from the additional data. 18. The electronic device of claim 16, wherein the embedded processing node is configured to generate the motion activity posteriorgram, voice activity posteriorgram, and spatial environment posteriorgram to represent a probability of each element of a motion activity vector, a voice activity vector, and a spatial environment vector as a function of time, respectively. 19. The electronic device of claim 16, wherein a sum of each probability of the motion activity posteriorgram at any given time equals one; wherein a sum of each probability of the voice activity posteriorgram at any given time equals one; and wherein a sum of each probability of the spatial environment posteriorgram at any given time equals one. 20. The electronic device of claim 16, wherein the sensor chip consists essentially of one MEMS sensing unit. 21. The electronic device of claim 16, wherein the sensor chip comprises a plurality of MEMS sensing units; and wherein the motion activity posteriorgram, voice activity posteriorgram, and spatial environment posteriorgram are generated as a function of a fusion of the sensor specific features. 22. A method of operating an electronic device, the method comprising: acquiring sensing data from a sensing unit; extracting sensor specific features from the sensing data, using a computing device; generating a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features, using the computing device; processing the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects based on the motion activity vector, voice activity vector, and spatial environment vector, using the computing device; wherein one aspect of the plurality of aspects of the base level context is a mode of locomotion of a user carrying the electronic device, and another aspect of the plurality of aspects of the base level context is a nature of biologically generated sounds within audible distance of the user or a nature of physical space around the user; and determining meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context, using the computing device. 23. The method of claim 22, wherein processing the motion activity vector, voice activity vector, and spatial environment vector comprises: generating a motion activity posteriorgram as a function of the motion activity vector, the motion activity posteriorgram representing a probability of each element of the motion activity vector as a function of time; generating a voice activity posteriorgram as a function of the voice activity vector, the voice activity posteriorgram representing a probability of each element of the voice activity vector as a function of time; and generating a spatial environment posteriorgram as a function of the spatial environment vector, the spatial environment posteriorgram representing a probability of each element of the spatial environment vector as a function of time.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: An electronic device described herein includes a sensing unit having at least one sensor to acquire sensing data. An associated computing device extracts sensor specific features from the sensing data, and generates a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features. The motion activity vector, voice activity vector, and spatial environment vector are processed to determine a base level context of the electronic device relative to its surroundings, with the base level context having aspects each based on the motion activity vector, voice activity vector, and spatial environment vector. Meta level context of the electronic device relative to its surroundings is determined as a function of the base level context, with the meta level context being at least one inference made from at least two aspects of the plurality of aspects of the base level context.
G06N7005		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An electronic device described herein includes a sensing unit having at least one sensor to acquire sensing data. An associated computing device extracts sensor specific features from the sensing data, and generates a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features. The motion activity vector, voice activity vector, and spatial environment vector are processed to determine a base level context of the electronic device relative to its surroundings, with the base level context having aspects each based on the motion activity vector, voice activity vector, and spatial environment vector. Meta level context of the electronic device relative to its surroundings is determined as a function of the base level context, with the meta level context being at least one inference made from at least two aspects of the plurality of aspects of the base level context.
A method of training a neural network with back propagation includes generating error events representing a gradient of a cost function for the neural network. The error events may be generated based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal. The method further includes updating the weights of the neural network based on the error events.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a neural network with back propagation, comprising: generating error events representing a gradient of a cost function for the neural network based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal; and updating the weights of the neural network based on the error events. 2. The method of claim 1, in which the weights of the neural network are updated based on a single error event. 3. The method of claim 1, in which the input events comprise signed spikes. 4. The method of claim 1, in which the input events includes only positive spikes. 5. The method of claim 1, further comprising: receiving an input vector; and generating the input events corresponding to the input vector. 6. The method of claim 1, further comprising generating output events via the forward pass through the neural network, the output events generated at timings based on an occurrence of a predefined event. 7. The method of claim 1, in which the error events are generated based on a computed error and a mean squared error cost. 8. An apparatus for training a neural network with back propagation, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured: to generate error events representing a gradient of a cost function for the neural network based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal; and to update the weights of the neural network based on the error events. 9. The apparatus of claim 8, in which the at least one processor is further configured to update the weights of the neural network based on a single error event. 10. The apparatus of claim 8, in which the input events comprise signed spikes. 11. The apparatus of claim 8, in which the input events includes only positive spikes. 12. The apparatus of claim 8, in which the at least one processor is further configured: to receive an input vector; and to generate the input events corresponding to the input vector. 13. The apparatus of claim 8, in which the at least one processor is further configured to process the input events via the forward pass through the neural network to generate output events at timings based on an occurrence of a predefined event. 14. The apparatus of claim 8, in which the at least one processor is further configured to generate the error events based on a computed error and a mean squared error cost. 15. An apparatus for training a neural network with back propagation, comprising: means for generating error events representing a gradient of a cost function for the neural network based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal; and means for updating the weights of the neural network based on the error events. 16. The apparatus of claim 15, in which the weights of the neural network are updated based on a single error event. 17. The apparatus of claim 15, in which the input events comprise signed spikes. 18. The apparatus of claim 15, in which the input events includes only positive spikes. 19. The apparatus of claim 15, further comprising: means for receiving an input vector; and means for generating the input events corresponding to the input vector. 20. The apparatus of claim 15, further comprising means for generating output events via the forward pass through the neural network at timings based on an occurrence of a predefined event. 21. The apparatus of claim 15, in which the error events are generated based on a computed error and a mean squared error cost. 22. A non-transitory computer-readable medium having encoded thereon program code for training a neural network with back propagation, the program code being executed by a processor and comprising: program code to generate error events representing a gradient of a cost function for the neural network based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal; and program code to update the weights of the neural network based on the error events. 23. The non-transitory computer-readable medium of claim 22, further comprising program code to update the weights of the neural network based on a single error event. 24. The non-transitory computer-readable medium of claim 22, in which the input events comprise signed spikes. 25. The non-transitory computer-readable medium of claim 22, in which the input events includes only positive spikes. 26. The non-transitory computer-readable medium of claim 22, further comprising: program code to receive an input vector; and program code to generate the input events corresponding to the input vector. 27. The non-transitory computer-readable medium of claim 22, in which the forward pass through the neural network generates an output event at timings based on an occurrence of a predefined event. 28. The non-transitory computer-readable medium of claim 22, in which the error events are generated based on a computed error and a mean squared error cost.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A method of training a neural network with back propagation includes generating error events representing a gradient of a cost function for the neural network. The error events may be generated based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal. The method further includes updating the weights of the neural network based on the error events.
G06N3084		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method of training a neural network with back propagation includes generating error events representing a gradient of a cost function for the neural network. The error events may be generated based on a forward pass through the neural network resulting from input events, weights of the neural network and events from a target signal. The method further includes updating the weights of the neural network based on the error events.
Technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method performed by a computing system, comprising: receiving, at a server, a request for a recommendation for a service from a first user, the request received as a first message from a mobile computing device associated with the first user via a messaging service provided by a wireless telecommunications network of the mobile computing device; analyzing, at the server, the first message to extract a first set of parameters to be used for performing a search for the service, the analyzing including: determining, by the server, whether the first set of parameters satisfy a criterion for performing the search, and responsive to a determination that the first set of parameters do not satisfy the criterion, exchanging a set of messages between the server and the mobile computing device to obtain the first set of parameters from the first user until the analyzing determines that the first set of parameters satisfy the criterion, the exchanging including: receiving an input from a second user associated with the server regarding at least one of the first set of parameters to be obtained from the user, and sending, by the server, at least one of the set of messages to the mobile computing device eliciting a response from the first user for the at least one of the first set of parameters; performing, at the server, the search using the set of parameters to retrieve a plurality of recommendations for the service; generating, by the server, a second message including the plurality of recommendations; and sending, by the server, the second message to the mobile computing device via the messaging service. 2. The method of claim 1 further comprising: receiving, at the server, a response from the first user regarding a selection of a first recommendation from the plurality of the recommendations via a third message from the mobile computing device; and storing, by the server, at a storage system associated with the server, an indication that the first user accepted the first recommendation as part of user habit data. 3. The method of claim 2 further comprising: analyzing, by the server, the third message from the first user to determine if the third message includes a reason for the selection of the first recommendation; and responsive to a determination that the third message includes the reason for the selection of the first recommendation, storing the reason as part of the user habit data. 4. The method of claim 2 further comprising: analyzing, by the server, the third message from the first user to determine if the third message includes a reason for rejecting any of the plurality of recommendations; and responsive to a determination that the third message includes the reason for rejecting, storing the reason as part of the user habit data. 5. The method of claim 2, wherein performing the search using the set of parameters further includes: analyzing, by the server, the user habit data to determine one or more of the set of parameters to generate the plurality of recommendations that is customized to the first user. 6. The method of claim 2 further comprising: receiving, by the server, a request from the first user to perform a task associated with a service associated with the first recommendation, the request received via a fourth message from the mobile computing device; and performing, by the server, the task using an application programming interface (API) associated with the service associated with the first recommendation. 7. The method of claim 1 further comprising: receiving, at the server, a response from the first user regarding a rejection of a first recommendation from the plurality of the recommendations via a third message from the mobile computing device; and storing, by the server, at a storage system associated with the server, an indication that the first user rejected the first recommendation as part of user habit data. 8. The method of claim 7 further comprising: analyzing, by the server, the third message from the first user to determine a reason for the rejection; and performing, by the server, a revised search using one or more parameters determined based on the reason for rejection to retrieve a second plurality of recommendations, the second plurality of recommendations excluding a first set of recommendations that match the reason for rejection and including a second set of recommendations that overcome the reason for rejection. 9. The method of claim 1, wherein performing the search to retrieve the plurality of recommendations for the service further includes: determining, at the server, a second set of parameters to customize the plurality of recommendations to the first user, the second set of parameters determined based on at least one of a context of the request, user habits data, or user profile data, and performing, by the server, a second search based on the second set of parameters to generate a second plurality of recommendations that is customized to the first user. 10. The method of claim 9, wherein determining the second set of parameters includes receiving, at the server, one or more of the second set of parameters as input from the second user. 11. The method of claim 10, wherein the one or more of the second set of parameters received from the second user are determined by the second user based on user habits data generated by the server. 12. The method of claim 10, wherein the one or more of the second set of parameters received from the second user are determined by the second user based on the context of the request for the recommendation from the first user, the one or more parameters relating to the first user and/or an aspect of the service in the plurality of recommendations. 13. The method of claim 9, wherein the user habits data is generated by the server based on information regarding one or more recommendations accepted and/or rejected in the past by the first user and reasons for accepting and/or rejecting the one or more recommendations. 14. The method of claim 1, wherein the analyzing the first message to extract the first set of parameters includes analyzing the first message using artificial intelligence technique. 15. The method of claim 1, wherein the messaging service provided by the wireless telecommunications network of the mobile computing device is a short messaging service, and wherein the first message is a text message. 16. The method of claim 1, wherein exchanging the set of messages between the server and the mobile computing device includes: sending, by the server, a first message of the set of messages to the first user requesting the first user to provide one or more parameters of the first set of parameters; receiving a response from the first user for the first message as a second message of the set of messages, the response including the one or more parameters; and sending, by the server, a third message of the set of messages forming a sequence, wherein a next message of the sequence sent to the first user is based on a response received from the user for a previous message of the sequence. 17. The method of claim 1, wherein performing the search includes: performing, by the server, the search in at a plurality of computing devices accessible via a communication network. 18. A computer-readable storage medium storing computer-readable instructions, comprising: instructions for receiving, at a server, a first set of messages from a mobile computing device associated with a first user, wherein at least one of the first set of messages contains a request for a recommendation for a service; instructions for sending, by the server, a second set of messages to the mobile computing device to elicit information from the first user that is to be used by the server in generating the recommendation, wherein at least some of the second set of messages are sent in response to at least some of the first set of messages received from the first user, wherein at least some of the first set of messages are responses to the at least some of the second set of messages, and includes information requested by the at least some the second set of messages; instructions for analyzing, at the server, the first set of messages using an artificial intelligence technique to determine a first set of parameters for generating the recommendation, the analyzing including: extracting at least some of the first set of parameters provided by the first user in the first set of messages, deriving at least some of the first set of parameters based on information provided in the first set of messages, and deriving at least some of the first set of parameters based on user habits data of the first user; instructions for performing, at the server, the search using the first set of parameters to retrieve a first recommendation for the service; and instructions for sending, by the server, the first recommendation to the first user as a first message to the mobile computing device. 19. The computer-readable storage medium of claim 18 further comprising: instructions for receiving a second message from the mobile computing device including a response from the first user regarding a rejection of the first recommendation; instructions for analyzing the second message to determine a reason for rejection; instructions for generating a “not-preferred” parameter based on the reason for rejection that is used to identify a set of recommendations that match the reason for rejection; instructions for performing a revised search using the “not-preferred” parameter to retrieve a plurality of recommendations, the plurality of recommendations excluding the set of recommendations that match the “not-preferred” parameter; and instructions for sending one of the plurality of recommendations to the first user as a third message to the mobile computing device. 20. The computer-readable storage medium of claim 19 further comprising: instructions for storing the rejection of the first recommendation and the reason for rejection as part of user habits data of the first user in a storage system associated with the server. 21. The computer-readable storage medium of claim 18, wherein the instructions for receiving the first set of messages from the mobile computing device includes instructions for receiving the first set of messages from the mobile computing device via a messaging service provided by a wireless telecommunications network of the mobile computing device. 22. The computer-readable storage medium of claim 21, wherein the instructions for receiving the first set of messages via the messaging service includes receiving the first set of messages via a short messaging service, and wherein the first set of messages are text messages. 23. The computer-readable storage medium of claim 18, wherein the instructions for receiving the first set of messages from the mobile computing device includes instructions for receiving the first set of messages from the mobile computing device via a social networking application executing at the mobile computing device. 24. The computer-readable storage medium of claim 18, wherein the instructions for deriving at least some of the first set of parameters based on information provided in the first set of messages includes: instructions for receiving an input from a second user associated with the server regarding a context of the request, and instructions for deriving the at least some of the first set of parameters based on the context. 25. A system, comprising: a first module to receive, at a server, a first set of messages from a mobile computing device associated with a first user, wherein at least one of the first set of messages contains a request for a recommendation for a service; a second module to send, by the server, a second set of messages to the mobile computing device to elicit information from the first user that is to be used by the server in generating the recommendation, wherein at least some of the first set of messages are responses to the at least some of the second set of messages, and includes information requested by the at least some the second set of messages; a third module to determine, at the server, a context of the request using an artificial intelligence technique, the context including explicit parameters and implicit parameters, which are used for performing a search to generate the recommendation, the third module further configured to determine the context by: extracting the explicit parameters from the first set of messages, the explicit parameters provided by the first user in the first set of messages, deriving at least some of the implicit parameters based on the information provided in the first set of messages and/or information received by a second user associated with the server, and deriving at least some of the implicit parameters based on user habits data of the first user; and a fourth module to perform, at the server, a search based on the context to retrieve a first recommendation for the service, wherein the second module is further configured to send the first recommendation to the first user as a first message to the mobile computing device. 26. The system of claim 25, wherein the second module is further configured to receive a second message from the mobile computing device including a rejection of the first recommendation, and wherein the fourth module is further configured to perform a revised search for retrieving a plurality of recommendations, the plurality of recommendations excluding a set of recommendations that match with a reason for the rejection. 27. The system of claim 26 further comprising: a fifth module to store the rejection of the first recommendation and the reason for rejection as part of user habits data of the first user in a storage system associated with the server. 28. The system of claim 25, wherein the first module is configured to receive the first set of messages from the mobile computing device via a messaging service provided by a wireless telecommunications network of the mobile computing device. 29. The system of claim 28, wherein first module is configured to receive the first set of messages via a short messaging service, and wherein the first set of messages are text messages.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: Technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.
A computer-implemented machine-learning method and system for searching for resources by predicting an intention and pushing resources directly to users based on the predicted intention. The method includes receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; and generating a sorted list of resources matching the weighted list of expressions. The system includes computer instructions for an intention inference engine and an intelligent ranking engine.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system comprising a processor and a non-transitory computer-readable memory, the memory encoded with computer-executable instructions for: an intention inference engine which: receives, as an input, a description of a context; generates, as output, a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; an intelligent ranking engine which: receives, as input, the weighted list of expressions generated by the intention inference engine; and generates, as output, a sorted list of resources matching the weighted list of expressions. 2. The computer system of claim 1 in which the intention inference engine generates the set of weighted expressions by: post-processing one or more historical signals; extracting at least one similarity patterns from the historical signals by solving sub-classification problems in which only a subset of the problem dimensions is used for the solution; defining a resulting set of expressions for each pattern. 3. The computer system of claim 1 further comprising a smart resource browser in which the smart resource browser displays the sorted list of resources matching the weighted list of expressions and receives a navigation action from the user, the navigation action comprising a mute action or an explore action. 4. The computer system of claim 3 in which, in response to receiving a mute action, the smart resource browser removes a resource indicated in the mute action from the sorted list of resources matching the weighted list of expressions and downscores components of the resource indicated in the mute action within the description of the context. 5. The computer system of claim 3 in which, in response to receiving an explore action, the smart resource browser starts a new search for the resource indicated in the explore action and upscores components of the resource indicated in the explore action within the description of the context. 6. The computer system of claim 1 further comprising a preemptive file retriever in which the preemptive file retriever, automatically or by manual user triggering, collects and processes contextual information, provides a description of a context using the contextual information, to the intention inference engine, and displays a sorted list of resources from intelligent ranking engine based on the description of the context provided by the preemptive file retriever. 7. The computer system of claim 1 in which the intelligent ranking engine in generates a predictive daily digest, the predictive daily digest including resources relevant to a particular day. 8. The computer system of claim 1 further comprising an evolving intention-driven resource view in which resources are organized by inferring the intention of recurring actions that the user is doing and by automatically grouping resources that are relevant or that fulfill the intention. 9. A non-transitory computer-readable medium encoded with computer-executable instructions for: an intention inference engine which: receives, as an input, a description of a context; generates, as output, a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; an intelligent ranking engine which: receives, as input, the weighted list of expressions generated by the intention inference engine; and generates, as output, a sorted list of resources matching the weighted list of expressions. 10. The non-transitory computer-readable medium of claim 9 in which the intention inference engine generates the set of weighted expressions by: post-processing one or more historical signals; extracting at least one similarity patterns from the historical signals by solving sub-classification problems in which only a subset of the problem dimensions is used for the solution; defining a resulting set of expressions for each pattern. 11. The non-transitory computer-readable medium of claim 9 further comprising a smart resource browser in which the smart resource browser displays the sorted list of resources matching the weighted list of expressions and receives a navigation action from the user, the navigation action comprising a mute action or an explore action. 12. The non-transitory computer-readable medium of claim 11 in which, in response to receiving a mute action, the smart resource browser removes a resource indicated in the mute action from the sorted list of resources matching the weighted list of expressions and downscores components of the resource indicated in the mute action within the description of the context. 13. The non-transitory computer-readable medium of claim 11 in which, in response to receiving an explore action, the smart resource browser starts a new search for the resource indicated in the explore action and upscores components of the resource indicated in the explore action within the description of the context. 14. The non-transitory computer-readable medium of claim 9 further comprising a preemptive file retriever in which the preemptive file retriever, automatically or by manual user triggering, collects and processes contextual information, provides a description of a context using the contextual information, to the intention inference engine, and displays a sorted list of resources from intelligent ranking engine based on the description of the context provided by the preemptive file retriever. 15. The non-transitory computer-readable medium of claim 9 in which the intelligent ranking engine in generates a predictive daily digest, the predictive daily digest including resources relevant to a particular day. 16. The non-transitory computer-readable medium of claim 9 further comprising an evolving intention-driven resource view in which resources are organized by inferring the intention of recurring actions that the user is doing and by automatically grouping resources that are relevant or that fulfill the intention. 17. A computer-implemented machine-learning method comprising: receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; generating a sorted list of resources matching the weighted list of expressions. 18. The method of claim 17 in which the set of weighted expressions is generated by: post-processing one or more historical signals; extracting at least one similarity patterns from the historical signals by solving sub-classification problems in which only a subset of the problem dimensions is used for the solution; defining a resulting set of expressions for each pattern. 19. The method of claim 17 further comprising displays the sorted list of resources matching the weighted list of expressions and receiving a navigation action from the user, the navigation action comprising a mute action or an explore action. 20. The method of claim 19 in which, in response to receiving a mute action, removing a resource indicated in the mute action from the sorted list of resources matching the weighted list of expressions and downscoring components of the resource indicated in the mute action within the description of the context. 21. The method of claim 19 in which, in response to receiving an explore action, starting a new search for the resource indicated in the explore action and upscoring components of the resource indicated in the explore action within the description of the context. 22. The method of claim 17 further comprising, automatically or by manual user triggering, collecting and processing contextual information and displaying a sorted list of resources based on the description of the context. 23. The method of claim 17 further comprising generating a predictive daily digest, the predictive daily digest including resources relevant to a particular day. 24. The method of claim 17 further comprising inferring the intention of recurring actions that the user is doing and automatically grouping resources that are relevant or that fulfill the intention. 25. (canceled) 26. (canceled) 27. (canceled)
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: A computer-implemented machine-learning method and system for searching for resources by predicting an intention and pushing resources directly to users based on the predicted intention. The method includes receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; and generating a sorted list of resources matching the weighted list of expressions. The system includes computer instructions for an intention inference engine and an intelligent ranking engine.
G06N504		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A computer-implemented machine-learning method and system for searching for resources by predicting an intention and pushing resources directly to users based on the predicted intention. The method includes receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; and generating a sorted list of resources matching the weighted list of expressions. The system includes computer instructions for an intention inference engine and an intelligent ranking engine.
The one or more embodiments disclosed herein provide a method for automatically assembling multiple compounds into a single edible custom composition, in which each compound is individually customized to proportions formulated from a profile of an individual or group. The single custom mixture can contain a plurality of compounds including foods or flavors, nutritional additives, herbals, biologics, or pharmacologically active substances. Using the method and a related algorithm, the formulation of a custom mixture is suggested.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a computing device configured to: receive health-related information from an individual; receive taste-related preference data from a individual; based on the health-related information, determine a suggested formulation for a supplement for the individual; based on the taste-related preference data, determine a suggested filler medium; receive individual alterations to each of the suggested formulation and suggested filler medium.
PENDING		Please predict whether this patent is acceptable.PATENT ABSTRACT: The one or more embodiments disclosed herein provide a method for automatically assembling multiple compounds into a single edible custom composition, in which each compound is individually customized to proportions formulated from a profile of an individual or group. The single custom mixture can contain a plurality of compounds including foods or flavors, nutritional additives, herbals, biologics, or pharmacologically active substances. Using the method and a related algorithm, the formulation of a custom mixture is suggested.
G06N502		Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The one or more embodiments disclosed herein provide a method for automatically assembling multiple compounds into a single edible custom composition, in which each compound is individually customized to proportions formulated from a profile of an individual or group. The single custom mixture can contain a plurality of compounds including foods or flavors, nutritional additives, herbals, biologics, or pharmacologically active substances. Using the method and a related algorithm, the formulation of a custom mixture is suggested.
Performance information and configuration information is received for the plurality of computer systems. The computer systems are grouped into a plurality of clusters based at least in part on the performance information, where the plurality of clusters includes a first cluster and a second cluster. A system configuration associated with the first cluster is automatically identified from the configuration information and is automatically sent to the second cluster.		Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving, for a plurality of computer systems, performance information and configuration information; grouping the plurality of computer systems into a plurality of clusters based at least in part on the performance information, wherein the plurality of clusters includes a first cluster and a second cluster; automatically identifying a system configuration associated with the first cluster from the configuration information; and automatically sending the system configuration associated with the first cluster to the second cluster. 2. The method as recited in claim 1, wherein the system configuration includes network topology. 3. The method as recited in claim 1, wherein the performance information includes one or more of the following: an amount of processing capacity utilized or an amount of storage capacity utilized. 4. The method as recited in claim 3, wherein: a lower amount of processing capacity utilized is associated with a healthier cluster; a lower amount of storage capacity utilized is associated with a healthier cluster; and the first cluster is a healthiest cluster in the plurality of clusters. 5. The method as recited in claim 4 further comprising generating a priority list associated with an order in which defect resolution is performed on one or more clusters, other than the first cluster, in the plurality of clusters. 6. The method as recited in claim 5, wherein generating the priority list is based at least in part on health, such that an unhealthier cluster has a higher priority in the priority list than a healthier cluster. 7. The method as recited in claim 5, wherein generating the priority list is based at least in part on a number of computers systems, such that a cluster with more computer systems has a higher priority in the priority list than a cluster with fewer computer systems. 8. A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for: receiving, for a plurality of computer systems, performance information and configuration information; grouping the plurality of computer systems into a plurality of clusters based at least in part on the performance information, wherein the plurality of clusters includes a first cluster and a second cluster; automatically identifying a system configuration associated with the first cluster from the configuration information; and automatically sending the system configuration associated with the first cluster to the second cluster. 9. The computer program product as recited in claim 8, wherein the system configuration includes network topology. 10. The computer program product as recited in claim 8, wherein the performance information includes one or more of the following: an amount of processing capacity utilized or an amount of storage capacity utilized. 11. The computer program product as recited in claim 10, wherein: a lower amount of processing capacity utilized is associated with a healthier cluster; a lower amount of storage capacity utilized is associated with a healthier cluster; and the first cluster is a healthiest cluster in the plurality of clusters. 12. The computer program product as recited in claim 11 further comprising computer instructions for generating a priority list associated with an order in which defect resolution is performed on one or more clusters, other than the first cluster, in the plurality of clusters. 13. The computer program product as recited in claim 12, wherein generating the priority list is based at least in part on health, such that an unhealthier cluster has a higher priority in the priority list than a healthier cluster. 14. The computer program product as recited in claim 12, wherein generating the priority list is based at least in part on a number of computers systems, such that a cluster with more computer systems has a higher priority in the priority list than a cluster with fewer computer systems. 15. A system, comprising: a plurality of computer systems; and a central repository configured to: receive, for the plurality of computer systems, performance information and configuration information; group the plurality of computer systems into a plurality of clusters based at least in part on the performance information, wherein the plurality of clusters includes a first cluster and a second cluster; automatically identify a system configuration associated with the first cluster from the configuration information; and automatically send the system configuration associated with the first cluster to the second cluster. 16. The system as recited in claim 15, wherein the system configuration includes network topology. 17. The system as recited in claim 15, wherein the performance information includes one or more of the following: an amount of processing capacity utilized or an amount of storage capacity utilized. 18. The system as recited in claim 17, wherein: a lower amount of processing capacity utilized is associated with a healthier cluster; a lower amount of storage capacity utilized is associated with a healthier cluster; and the first cluster is a healthiest cluster in the plurality of clusters. 19. The system as recited in claim 18, wherein the central repository is further configured to generate a priority list associated with an order in which defect resolution is performed on one or more clusters, other than the first cluster, in the plurality of clusters. 20. The system as recited in claim 19, wherein generating the priority list is based at least in part on health, such that an unhealthier cluster has a higher priority in the priority list than a healthier cluster. 21. The system as recited in claim 19, wherein generating the priority list is based at least in part on a number of computers systems, such that a cluster with more computer systems has a higher priority in the priority list than a cluster with fewer computer systems.
ACCEPTED		Please predict whether this patent is acceptable.PATENT ABSTRACT: Performance information and configuration information is received for the plurality of computer systems. The computer systems are grouped into a plurality of clusters based at least in part on the performance information, where the plurality of clusters includes a first cluster and a second cluster. A system configuration associated with the first cluster is automatically identified from the configuration information and is automatically sent to the second cluster.