AI and the Word That's Been Missing From Patent Eligibility Case Law

Principal Michael Shepherd authored an article for IPWatchdog exploring the shortcomings of the current body of patent eligibility law in assessing the patentability of artificial intelligence inventions.

Read the full article at IPWatchdog. The article was originally published on June 12, 2024.

The artificial intelligence (AI) revolution poses new problems for deciding patent eligibility, problems for which the current body of U.S. Court of Appeals for the Federal Circuit case law and U.S. Patent and Trademark Office (USPTO) policy is ill-equipped to address. In particular, Alice Step 2, one of the most misunderstood doctrines in all of patent law, has the potential to become even more muddled when considering AI inventions. This is because the case law, as well as examiner practice, have tended to over-emphasize the importance of the conventionality or genericness of computers recited in the claims or described in the specification.

This state of affairs poses two converse problems for addressing the eligibility of AI inventions. First, groundbreaking AI inventions can be executed on generic computers. Thus, such inventions, which should be eligible for patent protection, should be entitled to claims that encompass completely conventional and generic computers. Conversely, inventions that are nothing more than abstract ideas can be, and often are, claimed as being performed by highly specialized machine learning accelerators in an attempt to perform an end run around Alice Step 2. In other words, such ineligible inventions should not be entitled to patent protection simply for reciting specialized processors.

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