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?
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Patent Eligibility Under Section 101

Section 101 of the United States Patent Act defines what subject matter can be patented. It covers four statutory categories: process, machine, manufacture, and composition of matter, plus improvements to any of these. However, judicial exceptions for abstract ideas, natural phenomena, and laws of nature limit eligibility. In particular, courts view mere algorithms and mathematical operations as abstract unless they contribute a technical innovation.

The Supreme Court’s Alice Corp. v. CLS Bank International decision established a two-step framework for evaluating patents under Section 101:

  1. Determine if the claim is directed to a judicial exception. Identify whether the claimed invention involves an abstract idea, natural law, or phenomenon.
  2. Search for an inventive concept. Evaluate whether additional elements transform the nature of the claim into a patent-eligible application, beyond the abstract idea itself.

Under this framework, generic implementation of machine learning is often deemed an abstract idea because it performs data analysis using well-known algorithms. Simply selecting a neural network, feeding it data, and invoking standard training does not meet the inventive concept requirement unless there are specific improvements to model architecture, computational efficiency, or integration with hardware accelerators.

Key Considerations for AI Patent Filers

  • Detail technical enhancements: Describe novel algorithms, custom layer designs, or specialized training routines that go beyond off-the-shelf frameworks.
  • Provide empirical evidence: Include performance benchmarks, error-rate comparisons, or resource-usage metrics to illustrate tangible improvements.
  • Integrate system-level innovations: Explain how your invention interacts with hardware, optimizes data flow, or reduces latency in practical deployments.
  • Avoid broad claims: Narrowly define how your method differs from existing techniques, focusing on concrete steps that yield technical benefits.

By aligning patent applications with these guidelines, AI innovators can strengthen their claims against Section 101 challenges and secure protection for genuine technical advances in machine learning.

Federal Circuit Clarifies Limits of Patent Eligibility for Machine Learning Claims | Fish & Richardson