Recentive Analytics, Inc. v. Fox Corp.

In case of first impression alleging infringement of patents relating to use of machine learning to optimize scheduling of live events and television programming, Federal Circuit holds that mere application of generic machine learning to new data environments is not eligible for patent protection.

Recentive Analytics Inc. is the owner of U.S. Patent Nos. 10,911,811, 10,958,957, 11,386,367 and 11,537,960. The four patents relate to the application of machine learning—that is, artificial intelligence—either, in the case of the ’811 and ’957 patents, to the scheduling of live events or, in the case of the ’367 and ’960 patents, to the creation of so-called network maps, upon which broadcasters rely in determining what content to show on their channels in certain geographic markets and at particular times. Recentive sued Fox Corp., Fox Broadcasting Company LLC and Fox Sports Productions LLC for infringing its four patents, and Fox responded by moving to dismiss on the basis that none of the four patents were eligible for protection under 35 U.S.C. § 101. 

By way of background, Section 101 provides that anyone who “invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent” for what they have invented or discovered. The Supreme Court has interpreted Section 101 to exclude “[l]aws of nature, natural phenomena, and abstract ideas” from patent eligibility. In order to determine whether or not a claimed method is eligible for patent protection, the Supreme Court has developed a two-step test known as the “Alice test.” That test asks (1) whether a patent’s claims are directed to a patent-ineligible concept, and (2) if they are, whether the claims disclose an “inventive concept” that is “sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.”

Applying the Alice test to the patents at issue, the district court found that, in light of Recentive’s admission that it did not invent or improve upon any particular machine learning technique, the patents were ineligible for protection, because (1) they were based on the “abstract ideas of producing network maps and event schedules … using known generic mathematical techniques,” and (2) the patents did not set forth any “inventive concept” beyond those same abstract ideas. The district court therefore granted Fox’s motion to dismiss, and Recentive appealed to the Federal Circuit.

On appeal, the Federal Circuit found itself faced with the novel question of “whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible.” Addressing the first prong of the Alice test, the Federal Circuit agreed with the lower court that Recentive’s patents were merely directed to abstract ideas, because the patent claims did not, as is required for software-related patents, contain any actual technological improvement. To the contrary, the claims merely applied generic machine learning techniques that Recentive neither invented nor improved upon. Recentive, for its part, argued that its inventions were patent-eligible because they applied machine learning in a novel context and applied it in such a way as to make certain tasks vastly more efficient. However, the Federal Circuit rejected those arguments on the grounds that “the application of existing technology to a novel database does not create patent eligibility” and that “in the context of computer-assisted methods, … claims are not made patent eligible under § 101 simply because they speed up human activity.” As for the second prong of the Alice test, the Federal Circuit again agreed with the district court that nothing in Recentive’s patents transformed the claims “into something ‘significantly more’ than the abstract idea of generating event schedules and network maps through the application of machine learning.” The appellate court therefore found that Recentive’s patents were ineligible for protection. 

Reflecting on the implications of its holding, the Federal Circuit noted that “[m]achine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology” and cautioned that “[t]oday, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” It remains to be seen how the courts and the U.S. Patent and Trademark Office will apply this new decision.

Summary prepared by Tal Dickstein and Edward Delman