35 U.S.C. §101 Archives

Broadcast Alert! Applying Conventional Machine Learning to New Data Isn’t Patent Eligible

by Jodi Benassi | Apr 24, 2025 | Patents

The US Court of Appeals for the Federal Circuit affirmed a district court’s ruling that patents applying established machine learning methods to new data are not patent eligible under 35 U.S.C. §101. Recentive Analytics, Inc. v. Fox Corp. et al., Case No. 23-2437 (Fed. Cir. Apr. 18, 2025) (Dyk, Prost, Goldberg, JJ.)

Recentive sued Fox, alleging infringement of four patents designed to tackle long-standing challenges in the entertainment industry – namely, optimizing the scheduling of live events and refining “network maps,” which determine the content aired on specific channels across various geographic markets at set times. These patents aim to streamline broadcast operations and enhance programming efficiency.

The patents at issue can be divided into two categories: network maps and machine learning training. The machine learning training patents focus on generating optimized event schedules by training machine learning models with parameters such as venue availability, ticket prices, performer fees, and other relevant factors. The network map patents describe methods for dynamically generating network maps that assign live events to television stations across different geographic regions. These methods utilize machine learning to optimize television ratings by mapping events to stations and updating the network map in real time based on changes to the schedule or underlying criteria. The patents’ specifications explain that the methods employ “any suitable machine learning technique” using generic computing machines.

Fox moved to dismiss on the grounds that the patents were subject matter ineligible under § 101. Recentive acknowledged that the concept of preparing network maps had existed for a long time. Recentive also recognized that the patents did not claim the machine learning technique. Nonetheless, Recentive argued that its patents claimed eligible subject matter because they involve using machine learning to generate custom algorithms based on training the machine learning model. Recentive characterized its patents as introducing “the application of machine learning models to the unsophisticated, and equally niche, prior art field of generating network maps for broadcasting live events and live event schedules.”

The district court disagreed and granted Fox’s motion. Applying the Alice framework, at step one, the court determined that the asserted claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.” At step two, the court determined that the machine learning limitations were no more than “broad, functionally described, well-known techniques” that claimed “only generic and conventional computing devices.” The court denied Recentive’s request for leave to amend because it determined that any amendment would be futile. Recentive appealed.

For the Federal Circuit, this case presented 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.

Step One

While Recentive claimed that its machine learning approach was uniquely dynamic and capable of uncovering hidden patterns in real time, the Federal Circuit found these features to be merely standard aspects of how machine learning operates. The Court explained that iterative training and model updates are not [...]

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Rewind: Federal Circuit Grants En Banc Rehearing Over Royalty Damages

by Keval Amin | Oct 3, 2024 | Patents

The en banc US Court of Appeals for the Federal Circuit issued a per curiam order vacating its previous panel decision upholding a district court’s denial of the defendant’s motion for a new trial on damages. In that decision, the Federal Circuit found that the plaintiff’s damages expert adequately demonstrated the economic comparability of prior license agreements to a hypothetical negotiation between the parties. Now, the Court has granted the defendant’s petition for rehearing en banc. EcoFactor, Inc. v. Google LLC, Case No. 23-1101 (Fed. Cir. Sept. 25, 2024) (per curiam) (Moore, C.J.; Lourie, Dyk, Prost, Reyna, Taranto, Chen, Hughes, Stoll, Stark, JJ.) Judge Prost dissented in part in the original panel decision.

EcoFactor sued Google over Nest thermostats allegedly infringing EcoFactor’s HVAC patent. The initial appeal revolved around the validity of the patent, the infringement verdict, and the damages awarded. Google argued that the patent was directed to an abstract idea and therefore was patent ineligible under 35 U.S.C. §101. Google also argued that the district court erred in its rulings on noninfringement and damages. The Federal Circuit majority upheld the district court’s decisions, finding genuine issues of material fact on patent validity, substantial evidence of infringement, and admissible expert testimony supporting the damages award. The Court dismissed Google’s challenge to the expert’s use of license agreements for calculating royalties, as the Court found the methodology reasonable. However, Judge Prost’s dissent in the original panel decision criticized the damages calculation, arguing that the expert’s methodology lacked rigor, particularly for failing to apportion the patented technology’s value from other licensed patents.

The en banc Federal Circuit will now reconsider the practice of using a patent owner’s prior license agreements to determine royalty rates, a method that can become complicated when the scope of licenses varies or when lump sums and royalties are not clearly apportioned.

The en banc order directed the parties to file new briefs limited to the issue of whether “the district court[] adhere[d] to Federal Rule of Evidence 702 and Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993), in its allowance of testimony from EcoFactor’s damages expert assigning a per-unit royalty rate to the three licenses in evidence in this case.”

Virtually Done: Computer Visualization Patents Are Ineligible for Protection

by Courtney Seams | Apr 18, 2024 | Patents

Addressing subject matter eligibility under 35 U.S.C. § 101, the US Court of Appeals for the Federal Circuit upheld the district court’s finding that patents related to computer visualizations of medical scans were patent ineligible. AI Visualize, Inc. v. Nuance Communications, Inc., Case No. 22-2019 (Fed. Cir. Apr. 4, 2024) (Moore, Reyna, Hughes, JJ.)

AI Visualize asserted four related patents, each having a substantially similar specification and the same title, against Nuance Communications. The patents are generally directed at systems and methods for users to virtually view a volume visualization dataset (a three-dimensional collection of data representing the scanned area of an MRI) on a computer without having to transmit or locally store the entirety of the dataset.

Nuance moved to dismiss the case, asserting that the claims were directed to patent-ineligible subject matter and invalid under § 101. The district court applied the two-step Alice inquiry to the claims, which the parties had grouped into three representative claims:

Claims where a web application directs the server to check what frames of a virtual view are stored locally and creates any additional frames necessary to create and display the virtual view of the medical image.
Claims with the further requirement that any previously requested virtual view be given a unique key, which the server checks for (and displays if the key exists) prior to completing the steps of the independent claim.
Claims without the requirement of checking to see if any images are stored locally.

In applying part one of Alice, the district court concluded that the asserted claims were directed to the abstract idea of “retrieving user-requested, remotely stored information” and not, as AI Visualize argued, to improvements in computer functionality. The district court then applied Alice step two and considered each of the three representative claims. The district court concluded that none of the claim limitations transformed the claims into patent-eligible applications of an abstract idea. Ultimately, the district court determined that all asserted claims were patent ineligible under § 101. AI Visualize appealed.

The Federal Circuit also applied the Alice analytical framework. Applying Alice step one, the Court considered whether the focus of the claimed advance was on an improvement in computer technologies, rather than the use of computers, and whether the claim limitations described a claimed advance over the prior art. The Court upheld the district court’s finding under Alice step one (i.e., that all three types of asserted claims were directed to an abstract idea) because the steps of obtaining, manipulating and displaying data, when claimed at a high level of generality, constitute an abstract concept. The Court did not agree with AI Visualize’s arguments that the creation of the virtual views is a technical solution to a technical problem because it requires the creation of “on the fly” virtual views at the client computer. In doing so, the Court refused to import details from the specification into the claims.

Applying Alice step two, the Federal Circuit upheld the district court’s finding that [...]

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