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This article is the second in a five-part series. Each of thesearticles relates to the state of machine-learning patentability inthe United States during 2019. Each of these articles describe onecase in which the PTAB reversed an Examiner's Section-101rejection of a machine-learning-based patent application'sclaims. The first article of thisseries described the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance (2019PEG), which was issued on January 7, 2019. The 2019 PEG changed theanalysis provided by Examiners in rejecting patents under Section 1011 of thepatent laws, and bythe PTAB in reviewing appeals from theseExaminer rejections. The first article of this series alsoincludes a case that illustrates the effect of reciting AIcomponents in the claims of a patent application. The followingsection of this article describes another case where the PTABapplied the 2019 PEG to a machine-learning-based patent andconcluded that the Examiner was wrong.

Case 2: Appeal 2018-0044592 (Decided June 21,2019)

This case involves the PTAB reversing the Examiner's Section101 rejections of claims of the 14/316,186 patent application. Thisapplication relates to "a probabilistic programming compilerthat generates data-parallel inference code." The Examinercontended that "the claims are directed to the abstract ideaof 'mathematical relationships,' which the Examiner appearsto conclude are [also] mental processes i.e., identifying aparticular inference algorithm and producing inferencecode."

The PTAB quickly dismissed the "mathematical concept"category of abstract ideas. The PTAB stated: "the specificmathematical algorithm or formula is not explicitly recited in theclaims. As such, under the recent [2019 PEG], the claims do notrecite a mathematical concept." This is the same reasoningthat was provided for the PTAB decision in the previous article,once again requiring that a mathematical algorithm be"explicitly recited." As explained before, the 2019 PEGdoes not use the language "explicitly recited," so thePTAB's reasoning is not exactly lined-up with the language ofthe 2019 PEG however, the PTAB's ultimate conclusion isconsistent with the 2019 PEG.

Next, the PTAB addressed and dismissed the "organizinghuman activity" category of abstract ideas just as quickly.Then, the PTAB moved on to the third category of abstract ideas:"mental processes." The PTAB noted the following relevantlanguage from the specification of the patent application:

There are many different inference algorithms, most of which areconceptually complicated and difficult to implement at scale.. . .Probabilistic programming is a way to simplify the application ofmachine learning based on Bayesian inference.. . .Doing inference on probabilistic programs is computationallyintensive and challenging. Most of the algorithms developed toperform inference are conceptually complicated.

The PTAB opined that the method is complicated, based at leastpartially on the specification explicitly stating that the methodis complicated. Then, in determining whether the method of theclaims is able to be performed in the human mind, the PTAB foundthat this language from the specification was sufficient evidenceto prove the truth of the matter it asserted (i.e., that the methodis complicated). The PTAB did not seem to find the self-servingnature of the statements in the specification to be an issue.

The PTAB then stated:

In other words, when read in light of the Specification, theclaimed 'identifying a particular inference algorithm' isdifficult and challenging for non-experts due to theircomputational complexity. . . . Additionally, Appellant'sSpecification explicitly states that 'the compiler thengenerates inference code' not an individual using his/her mindor pen and paper.

First, as explained above, it seems that the PTAB used theassertions of "complexity" made in the specification toconclude that the method is complex and cannot be a mental process.Second, the PTAB seems to have used the fact that the algorithm isnot actually performed in the human mind as evidence that it cannotpractically be performed in the human mind. Footnote 14 of the 2019PEG states:

If a claim, under its broadest reasonable interpretation, coversperformance in the mind but for the recitation of generic computercomponents, then it is still in the mental processes categoryunless the claim cannot practically be performed in the mind.

Accordingly, the fact that the patent application provides thatthe method is performed on a computer, and not performed in a humanmind, should not be the sole reason for determining that it is nota mental process. However, as the PTAB demonstrated in thisopinion, the fact that a method is performed on a computer may beused as corroborative evidence for the argument that the method isnot a mental process.

This case illustrates:

(1) the probabilistic programming compiler that generatesdata-parallel inference code was held to not be an abstract idea,in this context;(2) reciting in the specification that the method is"complicated" did not seem to hurt the argument that themethod is in fact complicated, and is therefore not an abstractidea;(3) reciting that a method is performed on a computer, though notalone sufficient to overcome the "mental processes"category of abstract ideas, may be useful for corroborating otherevidence; and(4) the PTAB might not always use the exact language of the 2019PEG in its reasoning (e.g., the "explicitly recited"requirement), but seems to come to the same overall conclusion asthe 2019 PEG.

The next three articles will build on this background, and willprovide different examples of how the PTAB approaches reversingExaminer 101-rejections of machine-learning patents under the 2019PEG. Stay tuned for the analysis and lessons of the next case,which includes methods for overcoming 101 rejections where the PTABhas found that an abstract idea is "recited,"and focuses on Step 2A Prong 2.

Footnotes

1 35U.S.C. 101.

2 https://e-foia.uspto.gov/Foia/RetrievePdf?system=BPAI&flNm=fd2018004459-06-21-2019-1.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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Machine Learning Patentability In 2019: 5 Cases Analyzed And Lessons Learned Part 2 - Mondaq News Alerts