to-do.md

1. fix llama3.3 - litellm.exceptions.RateLimitError
2. local PatBase?
3. fix inconsistent results
4. alt apprach, but seems very ineff - sentiment analysis
5. patent acceptance prediction
6. predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.
7. chatwPatent
8. Given the claims, summary, and background art, generate an abstract.

dataset--

{ "application_number": "...", "publication_number": "...", "title": "...", "decision": "...", "date_produced": "...", "date_published": "...", "main_cpc_label": "...", "cpc_labels": ["...", "...", "..."], "main_ipcr_label": "...", "ipcr_labels": ["...", "...", "..."], "patent_number": "...", "filing_date": "...", "patent_issue_date": "...", "abandon_date": "...", "uspc_class": "...", "uspc_subclass": "...", "examiner_id": "...", "examiner_name_last": "...", "examiner_name_first": "...", "examiner_name_middle": "...", "inventor_list": [ { "inventor_name_last": "...", "inventor_name_first": "...", "inventor_city": "...", "inventor_state": "...", "inventor_country": "..." } ], "abstract": "...", "claims": "...", "background": "...", "summary": "...", "full_description": "..." }

utilize the above fields (specifically - application and publication numbers, title, decision status, filing and publication dates, primary and secondary classification codes, inventor(s), examiner, attorney, abstract, claims, background, summary, and full description of the proposed invention)