Background
Recentive Analytics, Inc. filed a lawsuit against Fox Corp. for patent infringement, alleging that Fox Corp.’s use of machine learning models to generate television broadcast schedules and network maps infringed on Recentive’s patents. Fox Corp. moved to dismiss the lawsuit, arguing that the asserted patents were directed to ineligible subject matter under 35 U.S.C. § 101. The District Court and the Federal Circuit ultimately agreed with Fox Corp.’s argument, dismissing the lawsuit. The Court’s Ruling
The Federal Circuit held that the patents in question were ineligible under § 101 because they did not represent a technical innovation in the machine learning field. The court emphasized that:
* The claimed inventions relied on generic, conventional machine learning models and training techniques without providing any improvement to the underlying ML technology itself. * The specification failed to disclose any asserted technological improvements. * Applying generic machine learning techniques to a new data environment without disclosing improvements to the machine learning models is not sufficient for patent eligibility. Key Takeaways
* Artificial intelligence inventions must do more than apply generic machine learning techniques to a known problem space. * To survive § 101 challenges, applicants should consider disclosing and claiming features directed to specific improvements to technology. * The court’s ruling underscores the importance of technical innovation in machine learning patent applications. The Recentive Ruling and Its Implications
The Recentive ruling has significant implications for machine learning patent filings. The court’s decision reinforces the courts’ view that applying generic machine learning techniques to known problems is insufficient for patent eligibility. This ruling highlights the need for applicants to clearly articulate how their claimed invention provides a technical innovation, rather than just a new use case for known machine learning tools. What Does the Court Consider a Technical Innovation? * Demonstrating enhanced accuracy, efficiency, or scalability
* Improvements to machine learning models themselves
* Marginal gains in performance
* Empirical evidence showing improved model performance
The Recentive ruling emphasizes the importance of technical innovation in machine learning patent applications. Applicants should ensure that their patent applications clearly articulate how the claimed invention provides a technical innovation, rather than just a new use case for known machine learning tools. Conclusion
The Recentive Analytics, Inc. v. Fox Corp. decision reinforces the courts’ view that applying generic machine learning techniques to known problems is insufficient for patent eligibility. The ruling highlights the need for applicants to disclose and claim features directed to specific improvements to technology. As the courts continue to apply the Alice framework rigorously, applicants in the machine learning space should ensure their patent applications clearly articulate how the claimed invention provides a technical innovation.
