Intellectual Property Law
New Matter VOLUME 50, EDITION 3, FALL 2025
Content
- 2025 New Matter Author Submission Guidelines
- Contents
- Contract Ambiguity Leads to Mistrial In $122m Biotech Royalty Dispute: Lessons from Genentech v. Biogen
- Copyright Roundup
- CRISPR-Cas9 Appeal
- Inside This Issue
- INTELLECTUAL PROPERTY SECTION Executive Committee 2025-2026
- INTELLECTUAL PROPERTY SECTION Interest Group Representatives 2025-2026
- IP and Art: An International Perspective
- Letter from the Chair
- Letter from the Editor-in-Chief
- MCLE Self-Study Article
- Ninth Circuit Report
- Online Cle For Participatory Credit
- Quarterly International IP Law Update
- The California Lawyers Association Intellectual Property Alumni
- The European Patent Corner
- The Licensing Corner
- Trade Secrets: An Interview with Chris Buntel of Tangibly
- TTAB Decisions and Developments
- Federal Circuit Report
Federal Circuit Report
PHILIP EKLEM
Reichman Jorgensen Lehman & Feldberg LLP
THIS ARTICLE DISCUSSES THE FEDERAL CIRCUIT’S recent opinion in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) ("Opinion"), which is the first Federal Circuit case to consider whether a patent claiming an application of machine learning, an aspect of artificial intelligence, is eligible under 35 U.S.C. § 101.
Artificial intelligence ("AI") is taking the world by storm. Although it is still considered to be an emerging technology, companies have been working ferociously to outpace each other in bringing new AI-based products and tools to market and open-source outlets, resulting in a rapid integration of AI into our everyday lives. For example, AI is now being used to enhance the practice of medicine, enable autonomous driving, perform financial analyses, provide customer service, generate personalized advertising, enable new types of human interaction with consumer electronics, and much more. AI is also increasingly being used to fully automate or augment many analytical and physical tasks previously performed by humans (including writing computer code).
Machine learning ("ML") is generally considered to be a subset of AI. Merriam-Webster defines it as follows: "a computational method that is a subfield of artificial intelligence and that enables a computer to learn to perform tasks by analyzing a large dataset without being explicitly programmed."1 The goal is to create a computer model that "thinks" like a human in that it learns from experience and applies its knowledge to future tasks (i.e., is "intelligent"). ML can be implemented in various ways, but it generally involves the steps of collecting training data; building a model to perform a function (such as describing, predicting, or suggesting/recommending something); iteratively training the model (using the training data) to analyze and perform its function on other data received as inputs in the future; and updating the model based on those future data inputs.