The U.S. Court of Appeals for the Federal Circuit (CAFC) has issued a decision on Friday that addresses an issue of first impression in the patent eligibility context. The court held that “claims that do no more than apply established methods of machine learning to a new data environment” are not patent eligible. Recentive Analytics, Inc. filed a lawsuit against Fox Corp., Fox Broadcasting Company, LLC, and Fox Sports Productions, LLC for infringement of four U.S. Patent, Nos. 10,911,811; 10,958,957; 11,386,367; and 11,537,960. These patents are directed to solving problems in the entertainment industry and television broadcasting with respect to optimizing the scheduling of live events and “network maps,” which “determine the programs or content displayed by a broadcaster’s channels within certain geographic markets at particular times.”
The district court ultimately granted Fox’s motion to dismiss the suit for failure to state a claim on the ground the patents were ineligible under Section 101. The court said the claims of the patents failed at Alice step one as they were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques,” and at step two the claims failed to show an “inventive concept” as “the machine learning limitations were no more than ‘broad, functionally described, well-known techniques’ and claimed ‘only generic and conventional computing devices.’”
On appeal, the CAFC agreed that “the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.” Recentive argued that its application of machine learning in the patents is not generic because it improved the technology by manipulating the algorithms to function so that “the maps and schedules are automatically customizable and updated with real-time data.” However, the CAFC said Recentive conceded that the patents do not claim a specific method of improving the algorithm. Furthermore, neither the claims nor the specifications describe how any improvement was accomplished via steps or otherwise. The court rejected the argument that the inventive concept of the claims is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.” Instead, the CAFC agreed with the district court that this amounted to nothing more than claiming the abstract idea itself. The CAFC opinion ends with a note that machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. The court explained that its instant opinion held “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.”
In a statement, Pillsbury Winthrop Shaw Pittman LLP, which represented Fox, said that the decision “affirms our longstanding contention that technological improvements created merely by the ordinary use of generic machine language (or AI) are not patent eligible.” The statement also noted that the CAFC’s ruling “has far-reaching implications, as courts and companies grapple with the best way to commercialize and use new AI technologies across a wide range of fields.”
Background
The patents in question were directed to solving problems in the entertainment industry and television broadcasting. The claims of the patents were directed to using machine learning to optimize the scheduling of live events and to creating network maps that determine the programs or content displayed by a broadcaster’s channels within certain geographic markets at particular times. The district court ultimately granted Fox’s motion to dismiss the suit for failure to state a claim on the ground the patents were ineligible under Section 101. The court said the claims of the patents failed at Alice step one as they were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques,” and at step two the claims failed to show an “inventive concept” as “the machine learning limitations were no more than ‘broad, functionally described, well-known techniques’ and claimed ‘only generic and conventional computing devices.’”
- Background
- Claims of the Patents
- The District Court’s Decision
- The CAFC’s Ruling
- The CAFC’s Rejection of Recentive’s Argument
- The CAFC’s Conclusion
The CAFC’s ruling is significant because it establishes a clear precedent on the patent eligibility of claims that use machine learning. The court’s decision will likely have far-reaching implications for the patent office and the courts, as companies begin to explore the use of machine learning in their inventions. In a statement, Pillsbury Winthrop Shaw Pittman LLP, which represented Fox, said that the decision “affirms our longstanding contention that technological improvements created merely by the ordinary use of generic machine language (or AI) are not patent eligible.” The statement also noted that the CAFC’s ruling “has far-reaching implications, as courts and companies grapple with the best way to commercialize and use new AI technologies across a wide range of fields.”
The CAFC’s opinion is also notable for its emphasis on the importance of machine learning as a field. The court noted that machine learning is a “burgeoning and increasingly important field” and may lead to patent-eligible improvements in technology. The CAFC’s instant opinion held “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.”
The CAFC’s Decision
The CAFC held that the patents are not patent eligible because they do not claim a specific method of improving the algorithm. The court rejected the argument that the inventive concept of the claims is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.”
The court noted that the claims of the patents failed at Alice step one as they were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.” The court also noted that the claims failed to show an “inventive concept” as “the machine learning limitations were no more than ‘broad, functionally described, well-known techniques’ and claimed ‘only generic and conventional computing devices.’”
The CAFC’s decision is significant because it establishes a clear precedent on the patent eligibility of claims that use machine learning. Differences Between the District Court’s Decision and the CAFC’s Decision
The CAFC’s decision differs from the district court’s decision in several ways. The CAFC rejected Recentive’s argument that the inventive concept of the claims is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.” The CAFC instead agreed with the district court that this amounted to nothing more than claiming the abstract idea itself. The CAFC also rejected Recentive’s argument that its application of machine learning in the patents is not generic because it improved the technology by manipulating the algorithms to function so that “the maps and schedules are automatically customizable and updated with real-time data.” The CAFC said that Recentive conceded that the patents do not claim a specific method of improving the algorithm. Conclusion
The CAFC’s decision holds that “claims that do no more than apply established methods of machine learning to a new data environment” are not patent eligible. The court’s decision establishes a clear precedent on the patent eligibility of claims that use machine learning. The CAFC’s decision will likely have far-reaching implications for the patent office and the courts, as companies begin to explore the use of machine learning in their inventions.
