PatLitig.ai (forthcoming AI-related & AI-based work on patent litigation)
- First, do no harm: any discussion of using AI in litigation must begin with a parade of horribles relating to attorney and expert overly-credulous of AI chat output in court filings, without sufficiently checking for so-called “hallucinations” (which are really instances of standard LLM BS-ing not always working)
- Damien Charlotin, AI Hallucination Cases — Database currently with over 380 cases internationally; you can select jurisdiction, “nature of hallucination” (fabricated case law, false quotes, misrepresented case, ), outcome/sanction, monetary penalty, party (attorney vs. expert vs. judge vs. pro se litigant [which are largest group])
- Possible responsibility to check the other side’s work?: Lawyers Dinged for Failing to Detect Opponent’s Fake Citations
- Experts too: Judge Strikes Part of Anthropic (Claude.AI) Expert’s Declaration, Because of Uncaught AI Hallucination in Part of Citation (the AI chatbot front-end to this web site reminds me that there is discussion elsewhere on this site of ways experts might responsibly use AI-generated material in expert reports — TODO: umm, where?)
- Possible use of AI (given uncharacteristically flowery language) in attorney’s apology for using AI — hilarious AboveTheLaw article: Lawyer Cites AI Hallucinations, Responds With Pretentious Meditation On Nature Of Being (though falsely characterizing it as “going all Camus” — a horrible slur against the author of The Stranger and The Plague — when brief says e.g. “Your Honor, in the ancient libraries of Ashurbanipal, scribes carried their stylus as both tool and sacred trust—understanding that every mark upon clay would endure long beyond their mortal span”)
- News re: attorneys & AI hallucinations
- Claim chart drafting using AI tools such as ChatGPT for infringement and invalidity contentions, and drafts of expert reports.
- Mining PTO dataset on patent litigation, as well as PTAB data, and linking to patent claims (to help assess “what is special about claims in litigation?”).
- Comparing litigated vs. non-litigated patents across CPC classifications, and across patent-claim characteristics.
- Finding potential infringement or non-infringement using semantic similarity of claims to, or distance from, product-related text (this will leverage earlier work on “CodeClaim”).
- Writing Python code with ChatGPT to analyze huge datasets.
- Using AI in discovery, including developing local models to avoid confidentiality problems with cloud-based AI models.
- Discoverability of AI training data?:
- Building local offline (non-cloud; for use under court protective order) source-code examination/comparison tools, using e.g. CodeBERT and CodeLlama .
