Mechanizing Alice: Automating the Subject Matter Eligibility Test of Alice v. CLS Bank

Abstract

This Article describes a project to mechanize the subject matter eligibility test of Alice v. CLS Bank. The Alice test asks a human to determine whether or not a patent claim is directed to patent-eligible subject matter. The core research question addressed by this Article is whether it is possible to automate the Alice test. Is it possible to build a machine that takes a patent claim as input and outputs an indication that the claim passes or fails the Alice test? We show that it is possible to implement just such a machine, by casting the Alice test as a classification problem that is amenable to machine learning.

This Article describes the design, development, and applications of a machine classifier that classifies patent claims according to the Alice test. We employ supervised learning to train our classifier with examples of eligible and ineligible claims obtained from patent applications examined by the U.S. Patent Office. In an example application, the classifier is used as part of a patent claim evaluation system that provides a user with feedback regarding the subject matter eligibility of an input patent claim. Finally, we use the classifier to quantitatively estimate the impact of Alice on the universe of issued patents.

Keywords: Patent Subject Matter Eligibility, 35 USC 101, Alice, Mayo, Machine Learning, Machine Classification, Statistical Analysis, Data Driven Patent Analysis, Big Data