In a key decision, the Federal Circuit holds that patents claiming generic machine learning methods applied to existing problems lack eligibility under Section 101, reinforcing that AI inventions must involve genuine technical improvements to secure patent protection.
Key points
- Federal Circuit affirms Section 101 dismissal of ML patent claims applying generic algorithms to scheduling and mapping use cases.
- Court finds no improvement to core machine learning models or training techniques, deeming them “conventional” and ineligible.
- Decision underscores that automating known human tasks with standard ML methods without technical innovation fails patent eligibility.
Why it matters: This ruling establishes a stricter patentability threshold for AI inventions, emphasizing substantive technical contributions over mere applications of off-the-shelf machine learning techniques.
Q&A
- What is Section 101 in patent law?
- Why are generic machine learning methods ineligible under Section 101?
- How will this decision affect future AI patent filings?
- What qualifies as a technical improvement in ML patents?