Note: First published in The Intellectual Property Strategist and Law.com.
This article is Part Three 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.
Part Two can be viewed here.
“‘Machine Learning’ Is Little More Than Just Another, Known, Data Processing Technique”
The PTAB can provide subject matter eligibility guidance on artificial intelligence related inventions even when not asked. Ex parte Kneuper, Appeal 2020-005835 (PTAB April 28, 2021) is a reminder to patent applicants about inherent unpredictability and risk in PTAB appeal, especially in relation to Alice. In Kneuper, the sole issue on appeal before the PTAB was whether the claims were properly rejected during examination under section 103. The independent claim at issue recited:
1. An aircraft flight planning apparatus comprising: a database including
a plurality of forecasting models configured to
generate predictions of a predetermined characteristic on which at least a portion of an aircraft flight plan is based, where the predetermined characteristic includes at least a portion of a weather forecast, and
at least one data matrix of test predictions for the
predetermined characteristic generated by each of the plurality of forecasting models, each of the at least one data matrix of test prediction includes a plurality of test prediction data points; and
an aircraft flight planning controller coupled to the database, the aircraft flight planning controller being configured
to receive analysis forecast data having at least one
analysis data point,
select a forecasting model, from the plurality of
forecasting models, based on a comparison between the at least one analysis data point and the plurality of test prediction data points of a respective forecasting model, and
provide a prediction of the predetermined
characteristic generated with the forecasting model, selected from the plurality of forecasting models, that corresponds to a test prediction data point that is representative of the at least one analysis data point.
Id. at 2. Claim 4 in Kneuper depended from claim 1, and added the following limitation: “wherein each of the plurality of forecasting models are machine learning models.” Thus, claim 4 specifically covered machine learning models that generate predictions of a predetermined characteristic, including a portion of a weather forecast, on which at least a portion of an aircraft flight plan is based.
Prior to discussing the prior art issue on appeal, the PTAB warned:
Before delving into the merits of the art rejection, we would be remiss
if we failed to mention that Appellant’s claims appear to recite little more
than using computer software for data collection, analysis, and display.
Such is generally considered an abstract idea in the form of a mental process
under our Guidelines for analysis under 35 U.S.C. § 101 . . .
Id. at 3. The first three paragraphs of the decision reflect the PTAB’s uninvited, albeit active, skepticism regarding eligibility, a non-issue up to that point. Of note, that skepticism was not supported by any discussion of, for example, an abstract idea, specific limitations, additional limitations, prong one, prong two, an inventive concept, or Example 39. Without regard to the analytical framework that typically supports an Alice decision, or an opportunity for the patent applicant to make its case, the PTAB likely sealed the fate of the claims at issue with this directive to the examiner: “In the event that Appellant continues prosecution after resolution of this appeal, the Examiner may want to evaluate the eligibility of this application under Section 101.” This admonishment as to eligibility was signaled by the PTAB’s later observation in relation to section 103 that “[a]t the end of the day, ‘machine learning’ is little more than just another, known, data processing technique.” The PTAB acknowledged but dismissed the fact that the specification in Kneuper referenced decision trees, random forest algorithms, polynomial fit, and k-nearest neighbors as suitable machine learning models.
Kneuper is not surprising. Experienced practitioners know that the PTAB is not shy raising issues without invitation. While there should be no doubt that such risk also applies to artificial intelligence and machine learning related inventions, the added unpredictability of Alice issues in particular exacerbates risk. In this regard, patent applicants should remember that claim limitations involving artificial intelligence and machine learning may be deemed so deficient in terms of eligibility as to warrant preemptive PTAB refusals.
Patent strategy on artificial intelligence and machine learning inventions should account for recent PTAB decisions. The decisions explored in this three-part article series show that claims reciting predictive capabilities of machine learning models, even when relatively detailed, may not satisfy USPTO guidelines on subject matter eligibility. Drafters accordingly should prepare patent applications to support claims that recite detail about implementation and training of the models. In addition, discussion in the specification about technological difficulties overcome by machine learning claim limitations may strengthen eligibility positions.
Other considerations addressed by the PTAB decisions are also relevant to patent strategy. As the decisions reflect variation regarding PTAB focus on the first prong versus the second prong of Step 2A, patent applicants should seize opportunities to present arguments under both. When machine learning claim limitations regarding implementation are detailed, the first prong and Example 39 more easily support eligibility. Such detailed claim limitations likewise may bolster arguments establishing a technical improvement under the second prong, especially when complemented with strong distinctions over prior art. Further, before appealing even non-Alice issues, patent applicants should be prepared for the PTAB proactively questioning the eligibility of claims relating to artificial intelligence and machine learning.