Disclosing AI Inventions - Part I: Identifying the Unique Disclosure Issues

Our recent post “Tracking AI Prosecution Trends at the U.S. Patent Office” presented USPTO data which suggests that future prosecution of AI inventions may be less focused on patent eligibility under 35 U.S.C. §101 and more focused on the traditional requirements of §§ 102, 103 and 112. This post is the first of a two part series looking into the challenges that AI inventions present to one of these traditional requirements: patent disclosure under 35 U.S.C. §112(a). In this Part I, we identify the unique disclosure issues with AI inventions. In Part II, we provide practice tips for describing and enabling AI inventions.

A fundamental premise of most patent systems is the quid pro quo by which an inventor discloses his or her invention to the public in return for exclusive rights to use such invention for a limited time. Recent advances in artificial intelligence (AI) have sparked debate as to whether current patent disclosure requirements can enrich the public with AI inventions such that the granting of the exclusive right is justified. This debate inevitably centers on the “black box” nature of a particular type of AI: machine learning. Machine learning is the dominant AI technique disclosed in patents.[1] As such, understanding the patent disclosure issues presented by AI inventions requires an understanding of the basics of machine learning.  

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