(Recentive Analytics, Inc. v. Fox Corp., et al., No. 25-505, U.S. Sup.)
(Petition for a writ of certiorari available. Document #16-251103-030B.)
Recentive Analytics Inc.’s petition for a writ of certiorari was docketed on Oct. 23, having been filed Oct. 21. In the petition, the company maintains an argument the Federal Circuit rejected in June when Recentive petitioned for rehearing: that the panel’s April 18 decision wrongly applied Section 101 of the Patent Act, 35 U.S.C. § 101.
According to Recentive, the Federal Circuit’s opinion conflicted with Alice Corporation v. CLS Bank International, 573 U.S. 208 (2014), and Mayo Collaborative Servs. v. Prometheus Lab’ys, Inc., 566 U.S. 66 (2012), by failing to consider preemption. Further, the company argues that the appeals court panel too broadly applied the two-step abstractness analysis under Alice, thus ignoring the claimed advancement of its patents.
In April, Circuit Judge Timothy B. Dyk wrote, “Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, 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.”
Circuit Judge Sharon Prost and Chief Judge Mitchell S. Goldberg of the U.S. Court for the Eastern District of Pennsylvania, who sat by designation, joined in the opinion.
Machine Learning
Recentive owns U.S. patent Nos. 10,911,811, 10,958,957, 11,386,367 and 11,537,960, which the panel said “purport to solve problems confronting the entertainment industry and television broadcasters: how to optimize the scheduling of live events and how to optimize ‘network maps,’ which determine the programs or content displayed by a broadcaster’s channels within certain geographic markets at particular times.”
The ’367 and ‘960 patents focus on machine learning, and the panel said they state “a method containing: (i) a collecting step (receiving event parameters and target features); (ii) an iterative training step for the machine learning model (identifying relationships within the data); (iii) an output step (generating an optimized schedule); and (iv) an updating step (detecting changes to the data inputs and iteratively generating new, further optimized schedules).”
The panel further said the specification provides that “the machine learning model may be ‘trained using a set of training data,’ which can include ‘historical data from previous live events or series of live events.’”
The remaining patents, meanwhile, deal with the so-called network maps. The panel said the patents describe these maps as having “a method containing: (i) a collecting step (receiving current broadcasting schedules); (ii) an analyzing step (creating a network map); (iii) an updating step (incorporating real-time changes to the data inputs); and (iv) a using step (determining program broadcasts using the optimized network map).” The panel also said the network map patents specify the use of machine learning “to generate optimized network maps.”
Recentive Sues
In November 2022, Recentive sued Fox Corp., Fox Broadcasting Co. LLC and Fox Sports Products LLC (together, Fox) in the U.S. District Court for the District of Delaware, alleging that the broadcaster infringed on all four patents. Fox moved to dismiss the complaint, arguing that the patents were ineligible as per the Alice inquiry.
Judge Gregory Brian Williams granted Fox’s motion in September 2023. Performing the Alice inquiry, the judge agreed with Fox that at step one of the inquiry, the patents were directed at abstract ideas using “‘known generic mathematical techniques.’”
Then, at step two, the judge agreed that the patents did not include a necessary inventive concept to raise an abstract idea to the level of patentability. The judge did not allow Recentive leave to amend the case, holding that any amendment would be fruitless.
Recentive appealed to the Federal Circuit, which heard oral arguments on Feb. 6, 2025.
Generic Computing
The panel performed the Alice inquiry de novo. At the beginning of its discussion, the panel said, “This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.”
The panel said it is “clear that they are directed to ineligible, abstract subject matter. Recentive has repeatedly conceded that it is not claiming machine learning itself.” Additionally, the panel said the patents’ description of the machine learning technology is “conventional,” further providing in a footnote that the patents also “employ only generic computing machines and processors.”
According to the panel, Recentive argued that the description of machine learning is not generic because the company claimed that it “‘worked out how to make the algorithms function dynamically, so the maps and schedules are automatically customizable and updated with real-time data.” But the panel also said the company acknowledged that the patents point to no specific method of improving the outputs.
No Improvement
Pointing to IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022), the panel said Recentive failed to “describe how such an improvement was accomplished. That is, the claims do not delineate steps through which the machine learning technology achieves an improvement.”
The panel said the only thing that made the claims slightly less generic was the fact that the patents specifically describe the use of machine learning in the context of event planning, but, quoting Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015), it said, “We have long recognized that ‘[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.’” Further, citing SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018), it added that the use of existing technology for a new database does not on its own make an invention eligible for a patent.
As a final point on abstractness, the panel said the fact that the patent specifies that machine learning can be used to speed up a task that would be ordinarily be done by a human is not enough for eligibility. “We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity,” the panel said, pointing to Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343 (Fed. Cir. 2014).
Further pointing to Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355 (Fed. Cir. 2023), the panel said speed and efficiency resulting from computer use is not itself enough to establish validity, whether considered at Alice step one or two.
No Inventive Concept
Moving to step two, the panel said, “Recentive claims that the inventive concept in its patents is ‘using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.’”
The panel said that this does no more than continue to claim the abstract idea identified in step one of the inquiry and that Judge Williams correctly held as such.
“In short, we perceive nothing in the claims, whether considered individually or in their ordered combination, that would transform the Machine Learning Training and Network Map patents into something ‘significantly more’ than the abstract idea of generating event schedules and network maps through the application of machine learning,” the panel said, pointing again to SAP Am. and Broadband iTV, Inc. v. Amazon.Com, Inc., 113 F.4th 1359 (Fed. Cir. 2024).
Recentive petitioned for panel rehearing or rehearing en banc, arguing that the panel’s decision “erases” the line between Section 101 eligibility and novelty and obviousness under Section 102 of the Patent Act, 35 U.S.C. § 102, and Section 103, 35 U.S.C. § 103, respectively. The Federal Circuit rejected the petition.
Certiorari Bid
Recentive says in its petition before the high court that the Federal Circuit’s opinion “abandons the preemption touchstone at the heart of this Court’s Section 101 jurisprudence: Recentive’s claims do not risk monopolizing the ‘basic tools’ of AI and treating them as abstract ideas expands the exceptions so far that they swallow the rule.” Recentive says that the decision risks limiting the exploding field of artificial intelligence “by effectively deeming AI-enabled applications ineligible unless they recite improvements to the underlying AI architecture itself.”
According to Recentive, the claims in its patents describe “concrete, technical processes” that the high court has found eligible for protection. Recentive says that its patents do not “monopolize” the abstract concept of machine learning but rather specifically describe “a particularized approach to training, weighting, and dynamically updating models to solve a domain-specific optimization problem characterized by combinatorial complexity and rapidly changing constraints.” Because the Federal Circuit disregarded portions of the claim language, Recentive says, it thus applied Alice too broadly.
Further, Recentive says that a requirement that machine-learning patents must recite improvements to the underlying model “is both doctrinally unsound and practically harmful. It conflates the Alice Step One inquiry with Step Two, renders implementation on generic computers into a disqualifying feature for software, and ignores that many technological advances arise from novel training techniques and dynamic, real-time integration—not hardware modifications.”
Recentive says that because the case presented a matter of first impression for the Federal Circuit, it makes for an ideal vehicle for the high court to consider the questions it presents.
Counsel
Recentive is represented by G. Hopkins Guy III in Palo Alto, Calif., Clarke Stavinoha in Dallas, Lauren J. Dreyer and Jamie R. Lynn in Washington and Lori Ding in Houston, all of Baker Botts LLP.
(Additional documents available: Order denying rehearing. Document #16-250804-013R. Petition for rehearing. Document #16-250804-014B. Federal Circuit’s opinion. Document #16-250505-013Z.)