Michael Stein Article for Law360 Examines Patenting Inventions in Machine Learning

Articles / December 21, 2016

Partner Michael Stein authored an article published in two parts by Law360 on Dec. 21-22, 2016. The article, “Patenting Inventions in Machine Learning: Parts 1 and 2,” analyzes the use of structural claim limitations in patents directed to inventions in the rapidly growing field of machine learning. Stein concludes:

By adopting new approaches for applying the laws of §§ 112(a, b, and f), along with new interpretations of § 101, the courts are finding various ways to invalidate functional claims for indefiniteness, over-breadth (lack of enablement), or as being directed to patent-ineligible subject matter. A solution to this problem is to employ structural claim limitations. I submit that machine code algorithms and data structures are structural elements that can be utilized for this purpose. Such limitations confer a level of constraint to the scope of protection, making the claims non-abstract. This approach is consistent with the USPTO’s Enhanced Patent Quality Initiative, and should result in a body of patent art in the ML field that is easier to search.

Read Part 1 (registration required).

Read Part 2 (registration required).

Or contact Michael Stein at for a copy of the article.

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