Machine Learning at the Patent Office: Lessons for Patents and Administrative Law

25 Pages Posted: 31 May 2019

See all articles by Arti K. Rai

Arti K. Rai

Duke University School of Law; Duke Innovation & Entrepreneurship Initiative

Date Written: May 24, 2019


The empirical data indicate that a relatively small increment of additional USPTO investment in prior art search at the initial examination stage could be a cost-effective mechanism for improving accuracy in the patent system. This contribution argues that machine learning provides a promising arena for such investment. Notably, the use of machine learning in patent examination does not raise the same potent concerns about individual rights and discrimination that it raises in other areas of administrative and judicial process. That said, even an apparently easy case like prior art search at the USPTO poses challenges. The most important generalizable challenge relates to explainability. The USPTO has stressed transparency to the general public as necessary for achieving adequate explainability. However, at least in contexts like prior art search, adequate explainability does not require full transparency. Moreover, full transparency would chill provision of private sector expertise and would be susceptible to gaming.

Keywords: machine learning, USPTO, prior art

Suggested Citation

Rai, Arti Kaur, Machine Learning at the Patent Office: Lessons for Patents and Administrative Law (May 24, 2019). Duke Law School Public Law & Legal Theory Series No. 2019-37, Available at SSRN: or

Arti Kaur Rai (Contact Author)

Duke University School of Law ( email )

210 Science Drive
Box 90362
Durham, NC 27708
United States

Duke Innovation & Entrepreneurship Initiative ( email )

215 Morris St., Suite 300
Durham, NC 27701
United States

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