Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is a technology which can be used to find relevant patterns in a large data set such as the data generated in the sequencing of genomes and produce smaller subsets. In Health Discovery Corp. v. Intel Corp., the patent owner HDC, in its complaint for infringement, discussed the innovative aspects of the technology:
Support Vector Machine — Recursive Feature Elimination (“SVM-RFE”) is an application of SVM that was invented by Dr. Weston and Dr. Guyon as members of HDC’s science team, to find discriminate relationships within clinical datasets, as well as within gene expression and proteomic datasets created from micro-arrays of tumor versus normal tissues. In general, SVMs identify patterns — for instance, a biomarker/genetic expression signature of a disease. The SVM-RFE utilizes this pattern recognition capability to identify, rank and order the features that contribute most to the desired results, and successively eliminate the features with the lowest rank order, until the optimal feature set is obtained to define the model.
However, Judge Albright, in his December 27, 2021 opinion, stated that the patent claim reciting the pattern recognition method would “merely improve or ‘enhance’ an abstract idea” and satisfy Alice step one, meaning it is directed to judicial exception of abstract idea.
Judge Albright analyzed whether the claim is directed to a “specific means or method that improves [that] relevant technology.” The claim would be found eligible in the Alice step one if it is directed to “improvements to the functioning of a computer or network.” However, looking at representative claim 1 (below) of U.S. Patent No. 7,177,188 (the 188 patent), Judge Albright stated that “the claims here merely produce data with improved quality relative to that produced by conventional mathematical methods.” The “relevant technology” that is improved is an abstract, mathematical method, and the improvement is not tied to the “physical,” which was the distinction over the cases such as McRO where the improvement was “allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters.’”
1. A computer-implemented method for identifying patterns in data,
the method comprising:
(a) inputting into at least one support vector machine of a plurality
of support vector machines a training set having known outcomes,
the at least one support vector machine comprising a decision
function having a plurality of weights, each having a weight value,
wherein the training set comprises features corresponding to the data
and wherein each feature has a corresponding weight;
(b) optimizing the plurality of weights so that classifier error is
(c) computing ranking criteria using the optimized plurality of
(d) eliminating at least one feature corresponding to the smallest
(e) repeating steps (a) through (d) for a plurality of iterations until a
subset of features of pre-determined size remains; and
(f) inputting into the at least one support vector machine a live set
of data wherein the features within the live set are selected according to the subset of features.
The Alice step two did not save the claim, either, as the inventive concept was lacking. Judge Albright cited Stanford II that decided as follows:
That a specific or different combination of mathematical steps yields more accurate [data] than previously achievable under the prior art is not enough to transform the abstract idea in claim 1 into a patent eligible application.
In other words, the claim did not have enough to move “the claims out of the realm of abstract ideas.”
Based on Judge Albright’s analysis in Alice step one, the claim should have been drafted to involve improvement tied to something physical, not improvement in an abstract idea itself or a mathematical method. In the Alice step one analysis, as noted above, Judge Albright ultimately found the facts of the case analogous to those of Stanford II and SAP, and stated “In Stanford II, SAP, and the instant Action, the patents' written description characterizes conventional systems as invoking mathematical analyses that the claimed inventions merely improve.” Judge Albright also found the present facts different than those of McRO and Thales, which were found to have improvements tied to the “physical.” As stated by the Court “McRO's invention was directed to the display of “animated characters on screens for viewing by human eyes…In Thales, the invention used mathematics to improve a “physical tracking system.” 
Perhaps most significant to this particular field of AI is that the Court believes that the field of SVM-RFE itself is a mathematical concept. While this is a District Court level ruling, if this case ends up before the Court of Appeals for the Federal Circuit (CAFC), it should put all AI-based pattern recognition developers on alert.
We will follow this case closely as it continues its way through the courts. Just last week, HDC refiled its infringement lawsuit against Intel since the dismissal in the instant case was without prejudice. HDC had appealed this case to the CAFC on February 4th, but it appears that they have voluntary dismissed the appeal in light of the refiled lawsuit.
 Health Discovery Corp. v. Intel Corp., Case No. 6:20-cv-00666-ADA (W.D. Tex. 2021).
 Opinion (Document 66) at 21.
 McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016).
 Opinion at 9.
 Id. at 21.
 Id. at 14.
 McRO at 1313.
 In re Bd. of Trustees of Leland Stanford Junior Univ., 991 F.3d 1245, 1252 (Fed. Cir. 2021) (Stanford II).
 SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018).
 Opinion at 19
 Thales Visionix Inc. v. United States, 850 F.3d 1343 (Fed. Cir. 2017)
 Opinion at13
 Id. at 21