Haynes Boone Partner Dina Blikshteyn authored an article for IPWatchdog as a recent court case serves as a reminder that artificial intelligence and machine learning, regardless of their sophistication or widespread use, are not automatically patent eligible.
Read an excerpt below.
On April 18, 2025, the U.S. Court of Appeals for the Federal Circuit affirmed the district court’s dismissal of a patent infringement lawsuit brought by Recentive Analytics against Fox Corporation, holding that the asserted AI and machine learning patents were not patent eligible under 35 U.S.C. § 101. The decision is significant for patent attorneys and applicants in the AI space, particularly those seeking protection for inventions that incorporate machine learning (ML).
Recentive’s four asserted patents involved software for generating event schedules and network maps using machine learning models trained on historical data. Although the applications had successfully overcome Section 101 rejections during examination before the U.S. Patent and Trademark Office (USPTO), the district court, and now the Federal Circuit, held that the claims were directed to abstract ideas under Alice Step One, and lacked the inventive concept necessary to confer eligibility under Alice Step Two.
In Recentive, the Federal Circuit acknowledged the growing significance of AI and machine learning and emphasized that its holding is limited to generic machine learning applications. However, the broader implications of this decision for existing patents and applications pending before the USPTO remain uncertain.
Simply Training Machine Learning Models is Insufficient to overcome Patent Eligibility
A key fact in the case was Recentive’s own concession: the machine learning models employed were conventional. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Similarly, confining the trained machine learning model to a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished.
It is important to note that most machine learning models are inherently trained on large, often complex datasets to generate predictions or classifications. However, this alone is routine and well-understood in the field. Although prior to Recentive one could argue that the trained machine learning model represents a technological improvement, the Recentive decision makes clear that such arguments are insufficient unless the claims specifically describe how the technological improvement is achieved.
To read the full article from IPWatchdog, click here.
