Kilpatrick’s Tyler McAllister - a partner who focuses his practice on patent preparation and prosecution, patent clearance and opinions, patent licensing, patent litigation, post-grant review, and related business and intellectual property counseling - recently joined other firm thought leaders to discuss “From Copyright to Patents: Global IP and Legal Issues in GenAI Innovations” at the 21st annual KTIPS (Kilpatrick Townsend Intellectual Property Seminar).

Speakers examined a broad array of topics, beginning with the latest rulings and Copyright Office guidance that are rapidly reshaping copyright’s fair-use doctrine, and the evolving recognition of AI as an inventor, followed by subject matter eligibility for AI-driven inventions and best practices for enablement and disclosure. The session provided a practical overview of how major patent offices in the U.S., China, Japan, and Europe are addressing the most pressing legal issues at the intersection of GenAI and IP.

Tyler  provides these key takeaways from the discussion:

1. The Recentive v. Fox Corp. case considered the patentability of AI-related patents. The Federal Circuit applied Alice and its progeny to define a rule that merely applying generic machine learning techniques to new data environments, without disclosing any improvement to the machine learning models themselves, are patent ineligible under 35 U.S.C. § 101. The Federal Circuit emphasized that efficiency gains or automation alone do not constitute a technological improvement, and that functional claim language lacking details on implementation or technical advancement is insufficient for eligibility.
2. In light of Recentive, USPTO Subject Matter Eligibility (SME) examples 47-49 are now more vulnerable if the claims merely recite the use of generic AI techniques for automation or functional results, without detailing how the AI provides a technical improvement. For instance, claims that rely on field-of-use limitations or generic downstream steps may not survive scrutiny unless the specification and claims specify the technical means and "how" the invention achieves a technological advancement.

3. Effective patent drafting and prosecution strategies for AI inventions focus on detailing how neural networks or predictive models are used and trained, describing practical applications, and emphasizing technical improvements. Specifications should include real-world impact, clear identification of technical problem and technical improvements to computer technology, solutions to computer-centric challenges, or meaningfully controlling or impacting a downstream system or device. Dependent claims that utilize the output of the AI model in a broader system context are recommended to strengthen eligibility and withstand examination.

For more information, please contact:
Tyler McAllister, tmcallister@ktslaw.com.