36 Pages Posted: 12 Feb 2016 Last revised: 24 Feb 2016
Date Written: February 23, 2016
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: 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