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 the economics of information and institutional-design perspectives, the USPTO should develop a counteracting artificial-intelligence unit in response to artificial-intelligence proliferation.
Tabrez Y. Ebrahim,
Automation and Predictive Analytics in Patent Prosecution: USPTO Implications and Policy,
Ga. St. U. L. Rev.
Available at: https://scholarlycommons.law.cwsl.edu/fs/276