Automation & Predictive Analytics in Patent Prosecution: USPTO Implications & Policy

60 Pages Posted: 10 Jun 2019 Last revised: 18 May 2020

See all articles by Tabrez Ebrahim

Tabrez Ebrahim

California Western School of Law

Date Written: June 8, 2019


Artificial-intelligence technological advancements bring automation and predictive analytics into patent prosecution. The information asymmetry between inventors and patent examiners is expanded by artificial intelligence, which transforms the inventor–examiner interaction to machine–human interactions. In response to automated patent drafting, automated office-action responses, “cloems” (computer-generated word permutations) for defensive patenting, and machine-learning guidance (based on constantly updated patent-prosecution big data), the United States Patent and Trademark Office (USPTO) should reevaluate patent-examination policy from economic, fairness, time, and transparency perspectives. By conceptualizing the inventor–examiner relationship as a “patenting market,” economic principles suggest stronger efficiencies if both inventors and the USPTO have better information in an artificial-intelligence-driven market. Based on economics of information and institutional-design perspectives, the USPTO should develop a counteracting artificial-intelligence unit in response to artificial-intelligence proliferation.

Keywords: artificial intelligence, patent prosecution, patent law, USPTO, automation, predictive analytics, big data, machine learning, patent examination, patent examiner, patenting market, economics of information, institutional design, information asymmetry

JEL Classification: O34

Suggested Citation

Ebrahim, Tabrez, Automation & Predictive Analytics in Patent Prosecution: USPTO Implications & Policy (June 8, 2019). Georgia State University Law Review, Vol. 35, 2019, Available at SSRN:

Tabrez Ebrahim (Contact Author)

California Western School of Law ( email )

225 Cedar Street
San Diego, CA 92101
United States
512-961-2581 (Phone)


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