May 25, 2020

Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 3

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.

Case 3: Appeal 2018-004925[2] (Decided June 7, 2019)

This case involves the PTAB reversing the Examiner’s Section 101 rejections of claims of the 14/080,578 patent application. The PTAB explained that this application relates to a

customer relationship management (CRM) system using machine learning and statistical models for dynamically generating a customer’s preferences (interests, look and feel, purchase habits) when a computed value corresponding to the customer’s predicted intent exceeds a predetermined threshold confirming that the customer is likely to respond to the survey.

The Examiner argued that the claims are abstract ideas under the “organizing human activity” and “mental processes” categories. The PTAB agreed with the Examiner that the claims recite theses abstract ideas. The PTAB explained that the claims involved assessing transactions between consumers and merchants, which it found to be a fundamental economic principle (one example of a “method of organizing human activity”).

Then, the PTAB proceeded to determine that the claims are not directed to the abstract idea (i.e., the abstract idea is integrated into a practical application). The PTAB discussed the “need,” described in the specification, for the improvements disclosed by the patent. The PTAB then stated that the “computer-related limitations of independent claim 6 . . . capture the improvement discussed in the Specification.” Further, the PTAB stated:

In particular, the claim recitation of a computer’s utilization of a machine learning or statistical model to analyze . . . [and] to tailor a survey . . . integrates the judicial exception into a practical application. While statistical modeling can be performed with pen and paper, we agree with Appellants that [the claimed process] is an improvement over the prior art. As such, . . . the claim . . . is directed to a technological improvement.

The PTAB’s expression that the claim is “an improvement over the prior art” and “directed to a technical improvement” is in line with the first example provided in the 2019 PEG for Step 2A Prong 2: “an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field.” Additionally, this reasoning could also have applied to the “inventive concept” requirement of Step 2B.


This case illustrates:

(1) the method of using customer preferences to predict a customer’s likelihood of responding to a survey was held to not be an abstract idea, in this context, even though the claims “recite” an abstract idea;

(2) the PTAB may find that the claims integrate an abstract idea into a practical application where the “needs” or “improvements” described in the specification are solved or addressed by the claim; and

(3) the PTAB may find that an additional element in the claim that provides an improvement in the art (or technical field) of the invention may satisfy Step 2A Prong 2, and make the claim subject-matter eligible.

The next two articles will build on this background, and will provide additional examples of how the PTAB approaches reversing Examiner 101-rejections of machine-learning patents under the 2019 PEG. Stay tuned for the analysis and lessons of the next case, which includes an explanation of how a number of arguments made by an Applicant might be fit into the 2019-PEG structure, including an argument that the Examiner has “over-generalized the claim in characterizing it.”


[1] 35 U.S.C. § 101.

Copyright © 2020, Sheppard Mullin Richter & Hampton LLP.


About this Author

Conner Hutchisson Associate Silicon Valley Intellectual Property Patent Litigation Patent Prosecution and Counseling Post-Grant Proceedings

Conner Hutchisson is an associate in the Intellectual Property Practice Group in the firm's Silicon Valley office.

Areas of Practice

Conner focuses his practice on patent prosecution, patent litigation, and post-grant proceedings.

Conner has experience with a broad range of technology. He had a concentration in devices and electronics for electrical engineering, and a concentration in structures for civil engineering. In graduate school, he presented on topics including MEMS, piezoelectronics, metamaterials, and PUFs.

Conner has also prepared...

Hector A. Agdeppa Partner Intellectual Property

Hector Agdeppa is a partner in the Intellectual Property Practice Group in the firm's San Diego (Del Mar) office. 

Areas of Practice

Hector specializes in patent application drafting and prosecution in the electrical, mechanical and computer software arts, as well as in the medical device field, and he is well-versed in the management and monetization of patent portfolios. He is also experienced in inter partes review proceedings, tech transfer and licensing, IP due diligence and opinion/freedom to operate assessments, and he provides patent litigation support for a variety of clients, large and small, covering a broad range of industries.

Hector's technical experience encompasses wireless communications and data networking/network management; optical networking; multimedia delivery and video coding (including HEVC and HDR/WCG content); LCD, LED and PDP displays; AR/VR headsets; blockchain technologies; data analytics; artificial intelligence and deep/machine learning; enterprise SaaS; edge security; photonics; automotive technologies (including connected car and autonomous driving); as well as electro-mechanical and mechanical devices for remote analyte monitoring, medicant delivery, incentive spirometry, distributed sensing of patient condition, medical imaging, and gene sequencing.