Estimating the Impact of Alice v. CLS Bank Based on a Statistical Analysis of Patent Office Subject Matter Rejections

36 Pages Posted: 12 Feb 2016 Last revised: 24 Feb 2016

Ben Dugan

Independent

Date Written: February 23, 2016

Abstract

This paper presents the results of a statistical analysis of the application of the Alice subject matter test by the U.S. Patent Office. In addition, we present a machine-learning effort to classify patent claims as patent eligible or ineligible. We then describe applications of machine classification of patent claims, including computer-supported invention analysis, claim drafting, and pre-litigation risk analysis. We conclude with an estimate of how many issued patents have been invalidated under Alice, based on machine classification of issued patent claims.

Keywords: patent subject matter eligibility, 35 USC 101, Alice, Mayo, machine learning, machine classification, statistical analysis, data driven patent analysis, big data

Suggested Citation

Dugan, Ben, Estimating the Impact of Alice v. CLS Bank Based on a Statistical Analysis of Patent Office Subject Matter Rejections (February 23, 2016). Available at SSRN: https://ssrn.com/abstract=2730803 or http://dx.doi.org/10.2139/ssrn.2730803

Ben Dugan (Contact Author)

Independent ( email )

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