Note: First published in The Intellectual Property Strategist and Law.com.
This article is Part Two of a Three-Part Article Series
Artificial intelligence is changing industry and society, and metrics at the US Patent and Trademark Office (USPTO) reflect its impact. In a recent publication, the USPTO indicated that from 2002 to 2018 the share of all patent applications relating to artificial intelligence grew from 9% to approximately 16%. See “Inventing AI, Tracing the diffusion of artificial intelligence with U.S. patents,” Office of the Chief Economist, IP Data Highlights (October 2020). For the foreseeable future, patent applications involving artificial intelligence technologies, including machine learning, will increase with the continued proliferation of such technologies. However, subject matter eligibility can be a significant challenge in securing patents on artificial intelligence and machine learning.
This three-part article series explores USPTO handling of Alice issues involving artificial intelligence and machine learning through a sampling of recent Patent Trial and Appeal Board (PTAB) decisions. See Alice Corp. v. CLS Bank Int’l, 134 S. Ct. 2347 (2014). Some decisions dutifully applied USPTO guidelines on subject matter eligibility, including Example 39 thereof, to resolve appeal issues brought to the PTAB. In one case, the PTAB sua sponte offered eligibility guidance even with no Alice appeal issue before it. These decisions inform strategies to optimize patent drafting and prosecution for artificial intelligence and machine learning related inventions.
Part One can be viewed here.
“No More Than Conceptual Advice To Use Machine Learning”
The PTAB also considered claim language involving machine learning to determine subject matter eligibility in Ex parte Costello, Appeal 2021-000658 (PTAB June 7, 2021). The claims involved computerized fraud detection using machine learning and network analysis. The claim at issue in Costello recited the following:
13. A method for computerized fraud detection using machine learning and network analysis, comprising the steps of:  electronically obtaining insurance claims data at a first computer system from a second computer system in electronic communication with the first computer system via a communication network;  executing a network detection module at the first computer system, the network detection module processing the insurance claims data received from the second computer system using at least one machine learning algorithm which automatically identifies network nodes, edges, and relationships based on the processed insurance claims data, the identified network nodes, edges, and relationships indicative of potential insurance fraud; and  generating and displaying at a third computer system in communication with the first computer system via the communication network an interactive visualization user interface to a user of the third computer system, the interactive visualization user interface including an interactive graphical representation of the identified network nodes, edges, and relationships indicative of potential insurance fraud.
Id. at 2-3 (emphasis added).
In contrast to Hussain, the PTAB in Costelllo substantively analyzed the machine learning claim limitations under both prongs of Step 2A. As to the first prong, the PTAB acknowledged yet quickly rejected the patent applicant’s attempted analogy to Example 39. According to the PTAB, Example 39 hinged on “training of a neural network,” features absent in the claims in Costello. The PTAB also observed that, unlike Example 39, the claims at issue did not address “technological difficulties.” The PTAB further remarked that the patent application did not provide technological details about how claimed nodes, edges, and networks were identified. According to the PTAB, the patent application described a solution merely at the level of a “generic black box” to identify and analyze nodes, edges, and networks.
With respect to the second prong, the PTAB again faulted the absence of implementation details. It noted that the steps of the claimed process were expressed solely in terms of results. As to the machine learning claim limitations in particular, the PTAB again minimized them as “no more than conceptual advice to use machine learning” for a stated goal but devoid of technological implementation or application details. Notwithstanding the machine learning claim limitations, the PTAB stated that the claims at issue, when viewed as a whole, recited a concept performable by a generic computer. The PTAB found no improvement to technology or a technical field, despite being persuaded by the patent applicant’s separate appeal arguments under sections 102 and 103 that cited prior art did not disclose the very same machine learning claim limitations. After analysis under the second prong, the PTAB found no inventive concept without specifically addressing the machine learning claim limitations in Step 2B.
Like Hussain, Costello informs that seemingly specific but off-target recitations of machine learning functionality may not save claims. The result under the first prong is not surprising when viewed from the lens of Example 39 with its recitation of training data details and discussion of overcome technological challenges, both missing in Costello. In relation to the second prong, the PTAB’s position that the machine learning claim limitations were performable by a generic computer, a position that otherwise seems debatable, was perhaps grounded on the absence of relevant claim details. The result under the second prong is also interesting given the PTAB’s determination that the machine learning claim limitations were not taught by the cited prior art references to date. A different decision on eligibility would have been likely had the specification in Costello supported more claim details about implementation specifics for the machine learning algorithm.
Part Three Preview
Part Three of this article series will delve further into PTAB decisions relating to artificial intelligence and subject matter eligibility, and conclude with practical considerations to optimize prosecution before the Patent Office.