# How Has the 2025-2026 USPTO Guidance Changed AI Patent Eligibility Requirements?

patentreviewpro.com · September 25, 2026

> The December 2025 Dual Memos and the Shift in Section 101 Interpretation The legal framework for artificial intelligence patents underwent a...

## The December 2025 Dual Memos and the Shift in Section 101 Interpretation

The legal framework for artificial intelligence patents underwent a substantial transformation following the USPTO Director’s issuance of dual subject matter eligibility guidance memos on December 5, 2025. These memos were designed to address the growing inconsistency in how patent examiners applied the Alice/Mayo framework to machine learning architectures. Prior to this update, many AI applications were rejected as mere abstract ideas under 35 U.S.C. § 101 because they were viewed as mathematical concepts or methods of organizing human activity. The 2025 guidance clarified that an AI-related claim is not directed to an abstract idea if it demonstrates a practical application that improves the functioning of a computer or another technology. This shift moved the focus away from the underlying math and toward the specific technical solution the AI provides to a known computing problem.

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Under the current 2026 standards, examiners are instructed to look for a 'technical improvement' that is clearly articulated in the specification and reflected in the claim limitations. This means that simply stating an AI model performs a task more accurately is no longer enough to secure a patent. Instead, the applicant must describe how the specific architecture—such as a unique neural network topology or a novel loss function—reduces computational overhead or solves a latency issue. The December 2025 memos effectively created a safer harbor for AI inventions that are integrated into hardware or that optimize the internal operations of a data processing system. This change has led to a notable decrease in Step 2A rejections for well-drafted applications that emphasize the 'how' over the 'what' of the AI’s operation.

However, the guidance also introduced stricter requirements for the level of detail needed in the initial filing. The USPTO now expects a level of disclosure that matches the complexity of the AI model being claimed. If a claim is too broad, encompassing any AI that achieves a certain result, it will likely fail the updated 'practical application' test. The 2025 memos emphasize that the 'integration' into a practical application must be more than a field-of-use limitation or a post-solution activity. This requires a tight coupling between the algorithmic steps and the technical environment in which they operate. Practitioners have had to adjust their drafting strategies to include more structural details of the AI models to meet these heightened expectations.

## The Rise of Rule 132 SMED Evidence in AI Prosecution

One of the most practical developments in 2026 is the clarified use of Rule 132 Subject Matter Eligibility Declarations, commonly referred to as SMED evidence. Following the Reed Smith analysis of USPTO practices, it became clear that examiners are now more receptive to external evidence that proves the non-conventionality of an AI invention. A SMED is a formal declaration submitted during prosecution that provides objective data to support the claim that the invention offers a technical improvement over the prior art. This evidence is often used to overcome a Step 2B rejection, where the examiner argues that the claim elements are 'well-understood, routine, and conventional.' By providing benchmarking data or comparative analysis, applicants can demonstrate that their AI approach is a departure from standard industry practices.

In the current 2026 environment, a successful SMED often includes performance metrics such as reduced memory usage, faster inference times, or higher precision in specific edge cases that traditional algorithms cannot handle. This evidence must be tied directly to the claimed features of the AI model. For example, if a patent claim focuses on a specific pruning technique for a large language model, the SMED should provide data showing that this pruning technique maintains accuracy while reducing the model size by a specific percentage, such as 40% or 50%. This objective evidence helps to move the conversation from subjective interpretations of 'abstractness' to factual discussions about technical utility. It serves as a powerful tool for applicants who may have filed their applications before the 2025 guidance was fully implemented.

Despite its effectiveness, the use of SMED evidence is not without risks. The USPTO has warned that such evidence cannot be used to 'cure' a fundamentally flawed claim that lacks technical character. If the claim itself is directed to a purely business method that happens to use AI, no amount of SMED data will make it eligible under Section 101. Additionally, the data provided in a SMED becomes part of the public record and can be used by competitors to understand the limitations of the patented technology. Therefore, the decision to use Rule 132 evidence must be made with a clear understanding of the long-term strategic consequences for the patent portfolio. Most successful 2026 filings now include these technical benchmarks within the original specification to avoid the need for a SMED later in the process.

## Technical Improvements vs. Mathematical Concepts in 2026

The distinction between a patentable technical improvement and an unpatentable mathematical concept has become the central battleground for AI eligibility in 2026. Following the 'Machine-Learning Case' reported by Bloomberg Law, the courts and the USPTO have tightened the definition of what constitutes a 'mathematical concept.' If a claim is essentially a series of equations that could be performed by a human with a pencil and paper, it is almost certainly ineligible. To avoid this, AI patents must now emphasize the 'machine-centric' nature of the invention. This involves describing the specific data structures, the parallel processing requirements, or the specialized hardware accelerators like TPUs or GPUs that are necessary to execute the AI model effectively.

In 2026, the USPTO frequently rejects claims that describe AI processes in purely functional terms. For instance, a claim that describes 'training a model to predict X' is often viewed as a mental process or a mathematical abstraction. To survive, the claim must specify the training parameters, the nature of the training dataset, or the specific optimization algorithm used during the backpropagation phase. This level of detail ensures that the claim is directed to a specific technological implementation rather than the general idea of using machine learning for a particular purpose. The goal is to show that the invention is an improvement to the computer's ability to process information, not just an improvement in the information itself.

This technical focus has led to a resurgence in 'system' claims over 'method' claims in the AI space. By claiming a system that includes specific hardware components and data paths, applicants can more easily demonstrate a 'practical application' under the December 2025 guidance. However, this approach requires a deep understanding of the underlying hardware-software interface. Applicants who treat AI as a 'black box' are finding it increasingly difficult to secure broad protection. The 2026 standard demands transparency in how the AI operates, which can sometimes conflict with the desire to keep certain aspects of a model as a trade secret. Balancing these two needs is a primary challenge for AI developers in the current legal environment.

## Comparison of AI Patent Eligibility Standards: 2024 vs. 2026

| Feature | 2024 Pre-Guidance Standard | 2026 Post-Guidance Standard |
| --- | --- | --- |
| Primary Rejection Basis | Broad 'Abstract Idea' (Alice Step 2A) | Lack of 'Technical Improvement' (Alice Step 2A/2B) |
| Evidence Requirement | Intrinsic evidence only (Claims/Spec) | High reliance on Rule 132 SMED benchmarking |
| Disclosure Level | Functional 'Black Box' descriptions | Structural/Architectural transparency required |
| Inventorship | Ambiguous for AI-assisted work | Strictly human-centric (Per Feb 2024/2025 rules) |
| Rejection Rate | ~70% initial 101 rejections | ~55% initial 101 rejections for technical AI |
| Focus of Claims | Outcome-oriented (What it does) | Implementation-oriented (How it works) |

## Strategic Drafting: Avoiding the Abstract Idea Trap
Drafting an AI patent application in 2026 requires a departure from the software drafting techniques used a decade ago. The most successful applications now start with a detailed 'Problem-Solution' framework that identifies a specific technical bottleneck in existing AI systems. This could be anything from the high energy consumption of transformer models to the 'hallucination' issues in large language models. By framing the invention as a direct solution to a technical problem, the drafter sets the stage for a favorable Section 101 analysis. The specification should then provide multiple embodiments that show how the AI model is integrated into a larger system, ensuring that the 'practical application' is evident from the outset.

Another critical strategy is the inclusion of 'technical effect' language throughout the application. Instead of saying the AI is 'better,' the drafter should use specific terms like 'reduced computational complexity,' 'enhanced data throughput,' or 'minimized memory footprint.' These terms are more than just buzzwords; they are the specific indicators that examiners look for when determining if an invention is a technical improvement. In 2026, it is also common to include 'fallback' claims that add increasing levels of technical detail. If a broad claim is rejected as an abstract idea, these narrower claims provide a clear path to allowance by focusing on the specific architectural innovations that make the AI model unique.

Furthermore, the 2026 guidance emphasizes the importance of the 'preamble' and the 'environment' of the claim. A claim that is 'computer-implemented' is viewed more favorably than one that is 'a method for.' By anchoring the AI process in a specific computing environment—such as a distributed cloud network or an edge device—the applicant can demonstrate that the invention is not a mere mental process. This approach also helps in defining the 'person having ordinary skill in the art' (PHOSITA), which is essential for both eligibility and obviousness analyses. A well-defined PHOSITA in the field of AI will understand the technical challenges involved, making it easier to argue that the solution is non-obvious and technically grounded.

## The Human Inventorship Requirement and AI-Assisted Inventions

The U.S. Patent and Trademark Office has remained firm on its stance regarding AI authorship and inventorship. Following the codified restrictions in February 2024 and subsequent updates in 2025, only natural persons can be named as inventors on a patent. This has created a complex situation for companies that use AI to generate new chemical compounds, optimize circuit designs, or write code. In 2026, the 'significant contribution' test is the standard used to determine if a human has done enough to be considered an inventor. If a human simply provides a prompt to an AI and the AI generates the entire invention, the resulting work is not patentable. The human must have contributed to the 'conception' of the invention in a substantial way.

To navigate this, companies are now keeping detailed logs of the interaction between human researchers and AI tools. These logs serve as evidence that the human directed the AI, refined its outputs, and made the final technical decisions that led to the invention. For example, in the field of drug discovery, a human might define the specific target protein and the constraints for a molecular search, while the AI suggests potential candidates. The human’s role in selecting and validating the most promising candidate is often sufficient to meet the inventorship requirement. However, if the AI is the one that identifies the novel utility of the compound, the inventorship claim becomes much weaker. This distinction is a major point of contention in 2026 patent litigation.

There is also a growing concern about the 'duty of disclosure' regarding the use of AI in the inventive process. While the USPTO does not currently require applicants to disclose every use of a calculator or a word processor, the use of generative AI to create claim language or technical descriptions is a different matter. In 2026, some practitioners recommend disclosing the use of AI tools to avoid future 'inequitable conduct' charges. This is particularly important if the AI tool was used to perform the very 'technical improvement' that the patent is claiming. If the AI is the true 'inventor' of the improvement, the patent may be found invalid. This human-centric requirement ensures that the patent system continues to incentivize human creativity and investment.

## Statistical Trends and Rejection Rates in 2026

Data from the first half of 2026 shows a stabilizing trend in AI patent rejections. While Section 101 rejections remain the most common hurdle, the rate of these rejections has dropped from a peak of nearly 75% in 2023 to approximately 55% for applications filed after the 2025 guidance. This improvement is largely attributed to better drafting practices and a more consistent application of the law by the USPTO’s specialized AI examining corps. However, the 'quality' of the allowance has also changed. Many of the patents being granted in 2026 are narrower in scope than those granted five years ago. This 'narrowing' is the price applicants pay for eligibility; by including more technical detail to satisfy Section 101, they often limit the range of infringing activities.

Interestingly, the rate of Section 103 (obviousness) rejections has seen a corresponding increase. As more AI-related prior art enters the public domain, it is becoming harder to prove that a new AI model is truly 'non-obvious.' In 2026, examiners frequently combine a general machine learning framework with a specific data set to argue that the invention is a predictable application of known technology. To counter this, applicants are relying more heavily on 'secondary considerations' of non-obviousness, such as commercial success, long-felt but unsolved needs, and the failure of others. The 2025 guidance on Rule 132 SMED evidence has been particularly helpful in this regard, as it allows applicants to provide the data needed to rebut these obviousness challenges.

Geographic trends also show that certain USPTO technology centers are more 'AI-friendly' than others. Tech Center 2100, which handles computer architecture and software, has seen the most significant impact from the 2025 memos. Meanwhile, Tech Center 3600, which handles business methods, continues to have the highest rejection rates for AI applications. This suggests that the 'context' of the AI invention remains a major factor in its success. An AI model used for medical imaging is far more likely to be patented than the same model used for high-frequency trading. This disparity highlights the ongoing tension between the technical and commercial applications of artificial intelligence.

## Cost-Benefit Analysis and Strategic Timing for AI Filings

The cost of securing an AI patent has increased in 2026, primarily due to the need for more detailed specifications and the increased likelihood of needing expert declarations. A typical AI patent application now costs between $15,000 and $25,000 for the initial filing, with an additional $10,000 to $20,000 spent on prosecution and responding to rejections. For many startups, this is a substantial investment that must be weighed against the benefits of patent protection. In some cases, trade secret protection may be a more cost-effective way to protect a proprietary AI model, especially if the model is difficult to reverse-engineer and the 'technical improvement' is hard to claim without disclosing the secret sauce.

Timing is also a critical factor in the 2026 AI patenting strategy. With the rapid pace of AI development, a patent that takes three to four years to issue may be obsolete by the time it is granted. To address this, many companies are using the USPTO’s 'Track One' prioritized examination program. While this adds several thousand dollars to the cost, it can result in a final disposition within 12 months. For AI technologies with a short lifecycle, such as specific generative models or UI-focused AI, this speed is essential. On the other hand, for foundational AI technologies that are expected to have a long shelf life, a slower, more deliberate prosecution strategy may be preferable to ensure the broadest possible protection.

Finally, the international context cannot be ignored. While the U.S. has clarified its AI guidance, other jurisdictions like the European Patent Office (EPO) and the China National Intellectual Property Administration (CNIPA) have their own unique requirements. In 2026, a 'global' AI patent strategy must account for the EPO’s 'two-hurdle' approach and China’s focus on 'technical character.' Often, an application that is successful in the U.S. will need significant modification to succeed in Europe or China. This adds another layer of cost and complexity to the process, making it essential for AI developers to work with counsel who have a deep understanding of the international AI patent environment. The 2025-2026 period has proven that while the path to AI patenting is clearer, it is also more demanding than ever before.

## Quick answers

### What is the most common reason for AI patent rejections in 2026?

The most common reason remains 35 U.S.C. § 101 (Subject Matter Eligibility), specifically the 'abstract idea' exception. However, since the December 2025 guidance, many rejections now focus on a lack of 'technical improvement' or 'practical application' rather than just the mathematical nature of the AI.

### Can an AI be listed as an inventor on a U.S. patent?

No, as of late 2026, the USPTO and U.S. courts maintain that only natural persons can be inventors. AI can be used as a tool in the inventive process, but a human must have made a 'significant contribution' to the conception of the invention to be listed.

### How does Rule 132 SMED evidence help in AI patenting?

Subject Matter Eligibility Declarations (SMED) allow applicants to submit objective data, such as benchmarking results, to prove that their AI invention provides a non-conventional technical improvement. This evidence is highly effective in overcoming Step 2B rejections under the Alice/Mayo framework.

### Is it better to protect an AI model as a trade secret or a patent?

This depends on the model's visibility. If the 'technical improvement' can be discovered through reverse engineering or if you need to prevent others from independently developing the same tech, a patent is better. If the model is a 'black box' that stays on your servers, trade secret protection may be more cost-effective.

### What did the December 5, 2025, USPTO memos change?

They provided dual guidance on AI-assisted inventions and software eligibility. They clarified that AI claims are eligible if they are integrated into a 'practical application' that improves technology, and they established clearer standards for using external evidence to prove eligibility.

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