The Federal Circuit upheld a lower court’s ruling that denied Recentive Analytics’ patents on machine learning for scheduling as they were deemed abstract ideas. The court established that merely using generic machine learning without improvements in model application does not meet patent eligibility criteria under § 101. Recentive’s patents aimed to enhance event scheduling and network mapping in broadcasting, transitioning from manual, static methods to dynamic, optimized solutions. However, this decision highlights the challenges of patenting AI-related innovations without substantial technical developments.
The Federal Circuit's decision underscores that merely applying generic machine learning techniques without improvements to the models themselves does not qualify for patent eligibility.
This ruling emphasizes that while machine learning may spur technological advances, generic applications of such technologies are still considered abstract ideas and, therefore, ineligible for patenting.
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