On March 31, 2020, the USPTO announced that it is permitting applicants, for delays that are based on the ongoing COVID-19 emergency in the United States, to request a 30-day extension of the time allowed to file certain documents and to pay certain fees. We provided an explanation of this announcement when it was published. Since then, the USPTO provided a form to be used for the “statement of delay” required in claiming a COVID-19-related filing extension. Extensions were originally applicable to due dates falling between March 27 and April 30. The USPTO, on April 28, 2020, extended the applicable period for due dates to June 1, 2020.
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Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 5
Introduction
This article is the fifth 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[1] of the patent laws, and by the PTAB in reviewing appeals from these Examiner rejections. 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.
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Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 4
Introduction
This article is the fourth 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[1] of the patent laws, and by the PTAB in reviewing appeals from these Examiner rejections. The previous article of this series described methods for overcoming 101 rejections where the PTAB has found that an abstract idea is “recited.” 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 4
Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 3
Introduction
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[1] 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
Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 2
Introduction
This article is the second 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[1] of the patent laws, and by the PTAB in reviewing appeals from these Examiner rejections. The first article of this series also includes a case that illustrates the effect of reciting AI components in the claims of a patent application. The following section of 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 2
Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 1
Introduction
This article is the first of a five-part series of articles dealing with what patentability of machine learning looks like in 2019. This article begins the series by describing the USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) in the context of the U.S. patent system. Then, this article – and the four following articles – will describe one of five cases in which Examiner’s rejections under Section 101 were reversed by the PTAB under this new 2019 PEG. Each of the five cases discussed deal with machine-learning patents, and may provide some insight into how the 2019 PEG affects the patentability of machine-learning, as well as software more broadly.
Continue Reading Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 1