Modeling patent clarity

Jonathan H. Ashtor

Research Policy, 2022, vol. 51, issue 2

Abstract: This study uses machine learning techniques to model patent claim clarity and analyze how clarity relates to important patent policy objectives. Specifically, machine learning models are trained on a dataset of over 600,000 U.S. patent applications that were (or were not) rejected for indefiniteness, a proxy for claim clarity, using features based on the linguistic attributes of each application. The model is then applied to over 2 million issued patents and their corresponding applications, deriving estimates of the clarity of each patent's claim set at application and issuance.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:respol:v:51:y:2022:i:2:s0048733321002109

DOI: 10.1016/j.respol.2021.104415

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