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Modeling patent clarity

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  • Ashtor, Jonathan H.

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.

Suggested Citation

  • Ashtor, Jonathan H., 2022. "Modeling patent clarity," Research Policy, Elsevier, vol. 51(2).
    • Handle: RePEc:eee:respol:v:51:y:2022:i:2:s0048733321002109
    DOI: 10.1016/j.respol.2021.104415
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    Cited by:

    1. Kong, Nancy & Dulleck, Uwe & Jaffe, Adam B. & Sun, Shupeng & Vajjala, Sowmya, 2023. "Linguistic metrics for patent disclosure: Evidence from university versus corporate patents," Research Policy, Elsevier, vol. 52(2).
    2. Scott, Peter & Spadavecchia, Anna, 2023. "Patents, industry control, and the rise of the giant American corporation," Research Policy, Elsevier, vol. 52(1).
    3. Chávez-Bustamante, Felipe & Rojas-Mora, Julio & Wong, Sulan, 2024. "Balancing act: Navigating the tensions between patenting practices and open science in Chilean academic research," Technology in Society, Elsevier, vol. 77(C).

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