Recent Federal Circuit Ruling Shapes AI Patentability Standards

Apr 30

On April 18, 2025, the Federal Circuit issued a significant ruling in Recentive Analytics, Inc. v. Fox Corp. that establishes clear boundaries for AI patentability. This decision represents the first time the Federal Circuit has directly addressed whether claims that merely apply established methods of machine learning to new data environments are patent eligible under 35 U.S.C. § 101. The court's unambiguous answer: they are not.

The court's holding is clear: "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101."

The Case and Its Holdings

In Recentive Analytics, the Federal Circuit affirmed a district court's dismissal of patent infringement claims, concluding that patents directed to using generic machine learning techniques to generate “network maps” (which determine content displayed by a broadcaster’s channels within certain geographic markets at particular times) and event schedules for television broadcasts were ineligible under § 101. The court found that the patents were directed to the abstract idea of "using a generic machine learning technique in a particular environment, with no inventive concept."

Judge Dyk, writing for the court, emphasized that the patents employed conventional machine learning technology, as evident from the specifications themselves, which described using "any suitable machine learning technique" including gradient boosted random forests, regression, neural networks, decision trees, support vector machines, and Bayesian networks. The patents failed to disclose any technical improvements to these machine learning approaches.

Key Takeaways for AI Patent Eligibility

The Federal Circuit's decision provides several important guidelines for AI patent eligibility:

Generic Applications Are Not Eligible: Patents that do no more than apply generic machine learning to new data environments, without disclosing improvements to the machine learning models, are ineligible under § 101.
Field of Use Restrictions Don't Create Eligibility: The court rejected the argument that applying machine learning to a new field (event scheduling and network mapping) transformed an abstract idea into patent-eligible subject matter, citing established precedent that "[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment."
Efficiency Improvements Alone Are Insufficient: The court held that methods are not rendered patent-eligible merely because they perform tasks previously done by humans with greater speed and efficiency, consistent with its computer-assisted methods jurisprudence.
Lack of Technical Specificity Is Fatal: The patents failed to "delineate steps through which the machine learning technology achieves an improvement" or "disclose a specific implementation of a solution to a problem in the software arts."

Alignment with USPTO's Examples 47-49 from July 2024 Guidance

The Federal Circuit's decision aligns with the USPTO's July 2024 guidance update on AI patent eligibility. The USPTO's July 2024 guidance update on Patent Subject Matter Eligibility focused specifically on artificial intelligence inventions in response to Executive Order 14110 on the "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence." The guidance maintained the fundamental Alice/Mayo framework for patent eligibility under 35 U.S.C. § 101, but provided clearer instructions on how to apply this framework to AI-related inventions. Most significantly, it introduced three new hypothetical examples (numbered 47-49) that illustrate the application of eligibility analysis to AI technologies. We revisit those specific examples in light of the Recentive Analytics decision.

Example 47: Anomaly Detection using Neural Networks

Example 47 from the USPTO guidance addresses anomaly detection using artificial neural networks, illustrating when hardware implementations can be patent-eligible:

Claim 1 (Eligible):

1,. An application specific integrated circuit (ASIC) for an artificial neural network (ANN), the ASIC comprising:

a plurality of neurons organized in an array, wherein each neuron comprises a register, a microprocessor, and at least one input; and

a plurality of synaptic circuits, each synaptic circuit including a memory for storing a synaptic weight, wherein each neuron is connected to at least one other neuron via one of the plurality of synaptic circuits.

This claim is eligible because it recites specific hardware architecture for implementing the neural network rather than merely applying a generic neural network to a problem. The claim features detailed technical elements (registers, microprocessors, synaptic circuits with memory) that represent a technological implementation.

In contrast, Claim 2 (ineligible) merely describes a generic machine learning process without technical specificity:

2. A method of using an artificial neural network (ANN) comprising:

(a) receiving, at a computer, continuous training data;

(b) discretizing, by the computer, the continuous training data to generate input data;

(c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm;

(d) detecting one or more anomalies in a data set using the trained ANN;

(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and

(f) outputting the anomaly data from the trained ANN.

Example 48: Speech Separation

Example 48 demonstrates that applications involving real-world technological improvements can be eligible:

Claim 1 (Ineligible):

1. A speech separation method comprising:

(a) receiving a mixed speech signal x comprising speech from multiple different sources sn, where n ∈ {1, . . . N};

(b) converting the mixed speech signal x into a spectrogram in a time-frequency domain using a short time Fourier transform and obtaining feature representation X, wherein X corresponds to the spectrogram of the mixed speech signal x and temporal features extracted from the mixed speech signal x; and

(c) using a deep neural network (DNN) to determine embedding vectors V using the formula V = fθ(X), where fθ(X) is a global function of the mixed speech signal x.

This claim merely applies mathematical operations without specific technical improvements. However, Claim 2 (eligible) shows how adding specific technical implementations can transform an abstract idea into eligible subject matter:

2. The speech separation method of claim 1 further comprising:

(d) partitioning the embedding vectors V into clusters corresponding to the different sources sn;

(e) applying binary masks to the clusters to create masked clusters;

(f) synthesizing speech waveforms from the masked clusters, wherein each speech waveform corresponds to a different source sn;

(g) combining the speech waveforms to generate a mixed speech signal x' by stitching together the speech waveforms corresponding to the different sources sn, excluding the speech waveform from a target source ss such that the mixed speech signal x' includes speech waveforms from the different sources sn and excludes the speech waveform from the target source ss; and

(h) transmitting the mixed speech signal x' for storage to a remote location.

Example 49: Fibrosis Treatment

Example 49 illustrates how AI applications in healthcare can include specific treatment protocols to qualify for eligibility:

Claim 1 (Ineligible):

1. A post-surgical fibrosis treatment method comprising:

(a) collecting and genotyping a sample from a glaucoma patient to a provide a genotype dataset;

(b) identifying the glaucoma patient as at high risk of post-implantation inflammation (PI) based on a weighted polygenic risk score that is generated from informative single-nucleotide polymorphisms (SNPs) in the genotype dataset by an ezAI model that uses multiplication to weight corresponding alleles in the dataset by their effect sizes and addition to sum the weighted values to provide the score; and

This claim merely suggests administering a treatment without specificity. In contrast, Claim 2 (eligible) specifies a particular treatment:

2. The method of claim 1, wherein the appropriate treatment is Compound X eye drops.

Practical Implications for AI Patent Applicants

In light of Recentive Analytics and the USPTO examples, patent applicants should focus on:

Technical Improvements: Claims should articulate specific technical improvements to the machine learning models themselves, not just benefits from applying existing models to new data.
Implementation Details: Applications should provide detailed technical descriptions of AI architectures, training methodologies, and how these achieve technical advantages.
Hardware Integration: Where possible, claims should focus on specific hardware implementations or technical steps that go beyond merely applying AI to a problem.
Specific Treatments in Healthcare Applications: For medical AI technologies, specify particular treatments or methods to transform abstract data analysis into a patent-eligible invention.

The Future of AI Patentability

The Federal Circuit recognized that "machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology." The court limited its holding to patents that apply generic machine learning to new data environments without disclosing improvements to the machine learning models.

This decision establishes a clear framework for AI patentability that rewards genuine technological innovation while preventing overly broad patents on the mere application of known machine learning techniques to new fields. Applicants need carefully consider these guidelines when drafting and prosecuting AI-related patent applications to ensure they meet the increasingly well-defined eligibility standards.

James Denaro