In a paper published in Nature Biotechnology, three Stanford University-affiliated co-authors, including Professor Lisa Larrimore Ouellette, tackle an issue of growing importance at the intersection of law and technology: How the use of artificial intelligence to codify and communicate knowledge may influence the usefulness of the technical literature for subsequent researchers and innovators. In particular, they studied how the growing use of AI in the patent-drafting process is exacerbating existing challenges around patent law’s disclosure requirement.

Patent law requires that inventors not only describe their inventions but also do so with enough clarity that someone skilled in the relevant field can replicate the work. This disclo­sure requirement helps disseminate knowledge about new inventions and is an important part of what society gets in return for granting inventors exclusive rights to their inven­tions. Disclosure also ensures that a patent’s scope is limited to what the inventor contributed, according to the authors of “How Will AI Affect Patent Disclosures?” However, enforcing the disclosure standards has historically been inconsistent, they write. Patent examiners, working under tight time constraints and with sometimes-limited training, often prioritize evaluating novelty and obviousness over assessing disclosure quality.

“AI will amplify errors in both improperly granting poorly disclosed patents and improperly denying patents on the basis of earlier AI-generated references that do not actually disclose those inventions,” write co-authors Lisa Ouellette, Deane F. Johnson Professor of Law; Victoria Fang, JD ’24, a former United States Patent and Trademark Office patent examiner who collaborated on the study while a student at Stanford Law; and Nicholas T. Ouellette, a Stanford University professor of civil and environmental engineering. SL