Intellectual Property Law

New Matter VOLUME 49, EDITION 4, FALL 2024

The Patent Eligibility Eras Tour: AI’s Version

HANNAH POWELL
Haynes Beffel & Wolfeld LLP

ARTIFICIAL INTELLIGENCE (AI) HAS RAPIDLY transformed into a cornerstone of modern innovation, driving advancements across industries including healthcare, finance, and robotics. As AI technologies have evolved, so too has the legal framework governing their patent eligibility under 35 U.S.C. § 101. Over the past decade, the treatment of AI inventions under this statute has undergone significant changes, influenced by landmark court decisions, including Alice Corp. v. CLS Bank (2014),1 and updates to USPTO guidance. The legal landscape for patenting AI exists in a near-constant state of change, from the broad software patentability under State Street through the stringent abstract idea exclusions of Alice, more recent USPTO updates, and the potential impact of the proposed Patent Eligibility Restoration Act (PERA). This entry-level review explores different eras of patenting AI, including analyzing key historical developments, the shifting boundaries of subject matter eligibility, and their broader implications for AI innovation. Ultimately, the patent system’s approach to AI provides a fascinating lens through which to examine the dynamic relationship between technological progress and the law, and how this balance will shape the future of AI innovation.

SUBJECT MATTER ELIGIBILITY AND AI

Subject matter eligibility under § 101 continues to be a recurring area of confusion in the prosecution of AI inventions. The Alice/Mayo framework, as established by the Supreme Court, is a two-part test that examines whether a patent claim is directed to one of the categories of judicial exceptions including abstract ideas, laws of nature, and natural phenomena.2 If a claim is directed to a judicial exception, the claim must further include an inventive concept that transforms it into a patent-eligible application of the judicial exception. This test has been particularly challenging for Al-related inventions, which often involve data processing and algorithms that are frequently categorized as abstract ideas.

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