Turning AI Innovation Into Patent Protection: Key Considerations

As companies lean into AI innovation to revolutionize a wide range of industries, they will encounter opportunities to secure patents on their advances. Machine-learning-based patents are relatively untested before the courts and USPTO. But the U.S. Court of Appeals for the Federal Circuit and the U.S. Patent and Trademark Office have filled the vacuum with some key decisions and guidance.

This article provides a practical roadmap to securing valid, enforceable patents and avoiding prosecution pitfalls in the evolving AI patent landscape.

Clarifying the Boundaries of AI Patent Eligibility

What the Federal Circuit Is Looking For

The most authoritative guidance from the Federal Circuit on AI patentability under § 101 comes from Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). That case involved machine learning used to optimize scheduling and geographical-placement of television shows. The court held that simply applying machine learning to a new field of use does not confer eligibility without also disclosing improvements to the machine-learning models to be applied.

Importantly, the court affirmed that AI-based patents are also subject to the two-step framework from Alice Corp. v. CLS Bank International (2014):

1. Step one: Determine whether the claims are directed to an abstract idea.
2. Step two: Assess whether the claim elements — individually or in combination — add “something more,” amounting to an inventive concept that transforms the idea into a patent-eligible application.

For AI inventions, Step One often fails because courts and examiners view high-level algorithmic processing as abstract. Applicants must thus lean into Step Two and show that the claimed invention introduces a specific, technological improvement to computer or machine functionality itself. Recentive reiterates that the “something more” must be rooted in how the system operates, not just what it achieves or how it performs relative to a human.

In the case, the court found the technology at issue was unpatentable because the patents were “directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.”

Recentive was preceded by Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016). That case involved patents claiming systems and methods for performing real-time performance monitoring of an electric power grid by collecting data from multiple data sources, analyzing the data, and displaying the results.

The court held that claims directed only to collecting, analyzing, and displaying information, even when limited to a particular field, are abstract and unpatentable. While this decision predates the current AI revolution, it has informed numerous AI and software cases since.

What the USPTO Added to the § 101 Landscape

The USPTO’s published guidance underscores that many AI claims will implicate abstract ideas (e.g., mathematical concepts) that may require deeper scrutiny under Step 2 of the Alice/Mayo framework. A central focus is thus whether the claim is, as the guidance states, “integrated into a practical application.”

The guidance instructs examiners to look for features that confine or transform the recited abstract idea into a particular technological environment — e.g., with concrete structure or process steps. The guidance thus tracks the traditional “improvement in computer functionality” route to eligibility. And like Recentive, the guidance warns that claims directed only to abstract outcomes or performance gains without linking to structural or constrained implementation may fail eligibility.

Drafting Strong AI Claims

This direction given by the Federal Circuit and the Patent Office, and the lessons learned from the failings and examples addressed, can guide practitioners in drafting strong claims for their AI inventions.

Build a Compelling Technical Narrative

Every successful AI patent begins with a compelling story: a narrative that ties together the technical problem, the inventive mechanism, and the measurable improvement. This narrative should begin in the specification and flow naturally into the claims.

Well-articulated technical specifications and claims are easier to defend in litigation, more attractive to investors, and more resistant to invalidation during due diligence or post-grant review.

Define a Concrete Technical Problem

A strong patent application identifies a specific technical limitation in the prior art. For example:

• Excessive latency in model training pipelines.
• Inefficiency in feature extraction from heterogeneous data sources.
• Inability of existing models to generalize across sparse datasets.

By explicitly stating the technological problem, applicants frame their invention as a targeted, technical solution rather than an abstract automation of human judgment.

Describe the Technological Solution in Detail

The specification should go beyond treating or using an AI model as a black box through the generalized description of “training a model” or “generating predictions.” Instead, it should disclose how the invention changes the computational landscape — through architecture, process, or resource optimization techniques. For example:

• A novel machine learning model architecture
• An inventive feature extraction technique
• An improved training method that avoids bias or overfitting in certain contexts.

Each of these examples ties the invention to a tangible improvement in computer performance relative to existing computing techniques consistent with Alice Step Two.

In short, highlighting machine-only capabilities — such as parallel optimization or dynamic resource allocation — reinforces eligibility. To reinforce patent eligibility, claims should highlight aspects of the invention that are uniquely machine-performed and challenging to replicate mentally.

Common Prosecution Pitfalls in AI/ML Applications

Several recurring errors continue to derail AI patent applications:

Avoid Concessions of Conventionality

In Recentive, the applicant’s acknowledgment that the machine learning (ML) models were “conventional” undermined the case entirely. For in-house counsel, this illustrates the importance of tight coordination between legal and technical teams during drafting.

Counsel should probe for the non-obvious components (e.g., architectural changes to the AI model, the feature extraction method, or algorithmic optimization technique) that differentiates the invention from a generic application of AI.

Even incremental modifications can establish patent eligibility if framed properly. For example, optimizing how input features are normalized across time-series data can qualify as “improvement in the functioning of a computer,” per the USPTO’s guidance, when described with sufficient technical depth.

Overly Abstract Functional Language

Claims written at a high functional level without linking operations to technical context invite §101 rejections. Using abstract, functional language that recites what the system does rather than how it does it raises this concern. For example, applicants may describe systems as “analyzing data,” “generating predictions,” or “providing recommendations.” But these describe results that could just as easily be achieved by human reasoning — which Alice proscribes.

To overcome this hurdle, applicants must translate conceptual functionality into technical detail. Detailed phrasing anchors the invention in machine-specific functionality. It also aligns with USPTO examiners’ expectations that applicants will tie claimed steps directly to improvements in computer performance or operation.

Outcome-Focused Drafting and Over-Reliance on Performance Metrics

A related error is claiming an invention by its end result rather than its technological mechanism. Examples include highlighting that the applicant’s system produces “more accurate predictions” or “reduces bias in classification.” These are desirable outcomes, but stating a result, without disclosing the technological path to achieving it, is insufficient.

Relatedly, examiners and courts will likely be skeptical of claims that rely on comparative statements like “faster than prior systems” or “achieves improved classification accuracy.” Without a detailed causal explanation, such metrics appear as marketing language, not technical substance.

The Federal Circuit’s reasoning in Electric Power Group is instructive: Claims directed to “collecting, analyzing, and displaying information” were deemed abstract even though they were applied to the field of electric power systems. Unless the specification identifies a new way the computer performs the operation, the claim risks being characterized as abstract data manipulation.

When performance is the point of novelty, the claims should explicitly link algorithmic improvements to quantifiable computer performance benefits. The specification should demonstrate why and how that improvement occurs.

Disconnected Claims and Specification

A lack of narrative continuity between the specification and claims weakens the patent’s technical foundation outline. The specification may contain detailed discussions of model design, data normalization, or feature extraction that the claims fail to reflect. When that happens, the examiner is left with the impression that the invention’s true novelty lies in the data or the functional concept, not in the computer’s operation.

Claims should serve as a natural extension of the technical story told in the specification. Each claim element should find explicit support in the specification’s description of system components or operational steps. This coherence also enhances the patent’s enforceability.

Holistic IP Strategy: Patents Are Not the Only Option

Not every AI advancement is best protected by a patent. Companies should evaluate IP strategy holistically. For some innovations, particularly model architectures or proprietary training data, trade secret protection may be more effective, especially given the rapid pace of AI evolution. Similarly, copyright can protect some training data compilations and generated materials.

Contracts and NDAs also play a critical role in securing proprietary algorithms or datasets during partnerships and licensing negotiations. For example, a company developing a core ML platform may benefit from broad patent protection to deter competitors, while one focused on model fine-tuning or deployment may find confidentiality more valuable.

Conclusion

Courts and the Patent Office demand specificity, substance, and a demonstrable link between algorithmic techniques and technological advancement. For legal practitioners, success depends on technical storytelling as much as legal drafting.