This article is the third in a five-part series. Each of these articles relates to the state of machine-learning patentability in the United States during 2019. Each of these articles describe one case in which the PTAB reversed an Examiner’s Section 101 rejection of a machine-learning-based patent application’s claims. The first article of this series described the USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), which was issued on January 7, 2019. The 2019 PEG changed the analysis provided by Examiners in rejecting patents under Section 101 of the patent laws, and by the PTAB in reviewing appeals from theses Examiner rejections. The second article of this series includes methods for overcoming rejections based on the “mental processes” category of abstract ideas, on an application for a “probabilistic programming compiler” that performs the seemingly 101-vulnerable function of “generat[ing] data-parallel inference code.” This article describes another case where the PTAB applied the 2019 PEG to a machine-learning-based patent and concluded that the Examiner was wrong.
Continue Reading Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 3