Artificial intelligence doesn\u2019t alter the law regarding method infringement. If a company performs every element of a patented method, the origin of the instruction isn\u2019t a defense. <\/p>\n
What AI does change is the visibility of risk. The critical decision often occurs before patent counsel is involved: a channel that existing intellectual property processes weren\u2019t designed to catch.<\/p>\n
A recent compelling argument<\/a> said the most consequential AI and IP risks begin before any output is generated, and urged companies building large language models on scientific and technical literature to conduct freedom-to-operate analysis on what those models are trained to replicate.<\/p>\n They are right\u2014from inside the IP function of a company whose scientists aren\u2019t building models but are using them constantly, I want to add the part of the picture that sits at the other end of the prompt.<\/p>\n Where the risk enters: Consider this scenario: The R&D team prepares a product user manual and needs a method for a customer-facing protocol. An employee uses an large language model to obtain a method, which is then included in the manual. Standard legal review occurs, but R&D deems the method routine, so no invention disclosure is filed, and IP legal isn\u2019t involved. <\/p>\n The product ships with the user manual. Six months later, a patentee sends a notice letter. From that point on, the exposure decomposes cleanly. Under Section 271(a), the company\u2019s pre-notice practice of the method is strict liability.Knowledge of the patent is irrelevant.<\/p>\n Under Section 271(b), the post-notice period is where inducement crystallizes. Every customer following the manual is a direct infringer, and the company\u2019s continued distribution of the manual, or its failure to issue prompt corrective communication, is the conduct that grounds the claim.<\/p>\n Why the patent trigger gets missed: The problem isn\u2019t that R&D is refusing to involve IP legal. It\u2019s that an LLM removes the conditions under which that involvement would normally be requested.<\/p>\n Traditional research workflows carry provenance. A journal article has authors, citations, and a trail that elicits questions. A patent search returns assignee names and numbered claims. R&D doesn\u2019t need to be IP-trained to encounter these signals, because the medium delivers them automatically.<\/p>\n An LLM response carries none of this. It arrives without authorship, without citations, and without patent context, reading like a knowledgeable colleague answering a question rather than a document with a trail worth scrutinizing. The researcher\u2019s instinct to flag isn\u2019t overridden\u2014it\u2019s never activated. The signal was never there to be found.<\/p>\n Downstream AI Use<\/p>\n With those signals stripped away, four controls can close the gap:<\/p>\n Define the term. AI-use policies should distinguish executable technical instructions from summaries and background. \u201cLLM-derived method\u201d needs to be a recognizable category. A synthesis route, assay protocol, formulation, manufacturing process, diagnostic workflow, or customer-facing method isn\u2019t a literature summary.<\/p>\n Build a fast escalation path. A brief form or designated IP contact is enough. A process that feels like a full legal stop will be routed around.<\/p>\n Preserve provenance. When an LLM-derived method enters an experimental program or deliverable, the project record should reflect it. \u201cWhen did the company know?\u201d Shouldn\u2019t depend on email reconstruction. Provenance also matters for inventorship, trade secret hygiene, and later defensive narratives.<\/p>\n Review the vendor agreement. Confidentiality, data-use restrictions, and copyright indemnity don\u2019t protect a company that practices a patented process suggested by a tool. Ask about patent indemnity directly, and let its absence inform procurement, policy, and training.<\/p>\n None of these controls require treating LLMs as inherently risky. Research organizations should use them because they\u2019re valuable for summarization, ideation, drafting, and technical orientation. However, the legal significance of LLM output depends on how the company uses it. Searching isn\u2019t the same as practicing, and brainstorming isn\u2019t the same as instructing customers. Once a method is included in a protocol, it becomes company conduct.<\/p>\n The legally significant act isn\u2019t the model generating text, but the company\u2019s decision to practice the method, scale it, or instruct customers to use it.<\/p>\n Model builders should consider what their systems are trained to do. Model users should ask employees how they use the answers they receive. Both ends of the prompt create risk, but the user side may be the one that faces litigation first.<\/p>\n This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.<\/p>\n Author Information<\/p>\n Gregory Kline<\/a> is senior director, transactions, and IP counsel at Thermo Fisher Scientific.<\/p>\n Interested in writing? Review our author guidelines, <\/a>and submit pitches to Insights@bloombergindustry.com.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence doesn\u2019t alter the law regarding method infringement. 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