Arti K. Rai, Duke University School of Law, has published Machine Learning at the Patent Office: Lessons for Patents and Administrative Law as Duke Law School Public Law & Legal Theory Series No. 2019-37. Here is the abstract.
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.
Download the article from SSRN at the link.
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