Navigating the AI Patent Landscape: A 2025 Update on USPTO Guidance and Best Practices
Introduction
Patenting AI innovations requires navigating a complex and evolving legal framework. The USPTO's latest guidance marks a significant shift in how AI patent applications are evaluated. This post analyzes critical developments in AI patentability standards and provides strategic approaches for securing protection in this rapidly changing domain.
The Evolution of Software and AI Patentability
Patent eligibility for software and AI has undergone significant transformation over the past several decades. The journey began with early cases such as Gottschalk v. Benson (1972) and Diamond v. Diehr (1981), which established initial boundaries between abstract mathematical algorithms and patentable applications of those algorithms. The landscape shifted dramatically with the Federal Circuit's State Street decision in 1998, which temporarily opened the floodgates for software and business method patents.
The modern era of software patent eligibility was defined by the Supreme Court's decisions in Bilski v. Kappos (2010), Mayo v. Prometheus (2012), and most significantly, Alice Corp. v. CLS Bank (2014). The Alice decision established the current two-step framework for determining patent eligibility: first examining whether claims are directed to a patent-ineligible concept (abstract idea, law of nature, or natural phenomenon), and then determining whether the claims contain an "inventive concept" that transforms them into patent-eligible applications.
Since Alice, the Federal Circuit has issued numerous decisions attempting to clarify the boundaries of software patentability, with cases such as Enfish, McRO, and DDR Holdings providing pathways to eligibility, while others such as Electric Power Group and Ultramercial reinforced limitations. This evolving case law has created significant challenges for patent practitioners working with software and AI technologies, leading to calls for greater clarity from the USPTO.
USPTO's Approach to AI Patentability
Understanding the USPTO's approach to AI patentability requires recognizing the fundamental tension in this area: AI innovations typically involve mathematical algorithms and computational techniques that might be considered abstract ideas, yet they also produce concrete technological effects that can transform industries.
The USPTO handles this tension through its implementation of the Alice/Mayo framework, as codified in the Manual of Patent Examining Procedure (MPEP) sections 2103-2106. This framework applies a structured analysis to determine whether claims fall within one of the four statutory categories of patent-eligible subject matter (process, machine, manufacture, or composition of matter) and whether they avoid or sufficiently transform judicial exceptions (abstract ideas, laws of nature, and natural phenomena).
For AI inventions, the analysis often hinges on the two-pronged inquiry at Step 2A:
1. **Prong One**: Does the claim recite an abstract idea (such as mathematical concepts, certain methods of organizing human activity, or mental processes)?
2. **Prong Two**: If so, does the claim integrate that abstract idea into a practical application by applying, relying on, or using it in a manner that imposes a meaningful limit on the abstract idea?
AI patent applications frequently face challenges at both prongs. Under Prong One, the mathematical operations at the heart of many AI systems (for instance, neural network calculations or statistical analyses) may be classified as abstract mathematical concepts. Under Prong Two, claims that merely apply AI to a problem without technical specificity may fail to demonstrate a sufficient practical application.
The USPTO has been gradually refining its guidance on these issues through periodic updates and example sets. Prior to the 2024 guidance, patent practitioners relied on general software eligibility principles and examples that were not specifically designed for AI technologies. This created uncertainty regarding which aspects of AI systems would be considered abstract ideas versus patentable technical implementations.
Recent USPTO Guidance on AI Patent Eligibility
In July 2024, the USPTO issued a significant update to its patent subject matter eligibility guidance addressing AI inventions. This update, titled "2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence," was developed in response to Executive Order 14110 on the "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence."
The guidance document represents the latest evolution in the USPTO's ongoing efforts to clarify the complex intersection of AI technology and patent law. It builds upon the framework established in the 2019 Revised Patent Subject Matter Eligibility Guidance, which introduced the two-pronged approach to determining whether claims are directed to judicial exceptions under the Alice/Mayo test.
This update does not change the fundamental legal framework for patent eligibility under 35 U.S.C. § 101, which continues to recognize four categories of patentable subject matter (processes, machines, manufactures, and compositions of matter) while excluding abstract ideas, laws of nature, and natural phenomena. Instead, it provides more specific instructions on how to apply this framework to AI-related inventions, an area that has presented significant challenges for patent examiners and applicants alike.
A key feature of the guidance is the introduction of three new hypothetical examples (numbered 47-49 in the USPTO's ongoing series of subject matter eligibility examples) that illustrate how the patent eligibility analysis should be applied to different types of AI technologies. These examples build upon the USPTO's existing body of subject matter eligibility examples but are specifically tailored to address the unique challenges presented by AI inventions.
Particularly valuable are these three new hypothetical examples, which provide detailed analyses of how AI inventions are evaluated under the eligibility framework:
Example 47 focuses on anomaly detection using artificial neural networks, particularly in network security applications. The USPTO presents contrasting claims to illustrate the eligibility boundary. The eligible claim describes an application-specific integrated circuit (ASIC) for an artificial neural network with a detailed hardware architecture, including an array of neurons with registers, processing elements, and inputs interconnected by synaptic circuits containing memory for synaptic weights. This example demonstrates that hardware implementations of neural networks with specific technical improvements can qualify as patent-eligible subject matter, while claims merely reciting abstract neural network concepts without technical improvements are deemed ineligible.
Example 48 examines AI-based methods for analyzing speech signals and separating desired speech from background noise. The USPTO provides multiple claims showcasing the spectrum of eligibility in this domain. The ineligible claim merely processes a mixed speech signal using mathematical techniques, including converting the signal into a spectrogram and using a deep neural network to determine embedding vectors through abstract formulas. In contrast, eligible claims incorporate concrete technical solutions to signal processing problems and demonstrate measurable improvements to speech separation technology. This example emphasizes that speech processing AI inventions must go beyond abstract mathematical concepts to achieve patent eligibility.
Example 49 addresses personalized medical treatment using AI, illustrating how AI applications in healthcare must include specific treatment protocols to qualify for eligibility. The ineligible claim merely collects patient data and generates treatment recommendations without specifying concrete treatment steps. By comparison, the eligible claim includes administering particular medications in specific doses based on AI analysis of patient-specific factors. This example provides critical guidance for healthcare and biotech companies developing AI diagnostic and treatment tools, highlighting the need to connect AI analysis directly to concrete medical interventions rather than stopping at the recommendation stage.
Patent Eligibility and AI System Fundamentals
When navigating patent eligibility for AI innovations, it is important to understand how the USPTO's guidance relates to fundamental AI concepts. The recent guidance acknowledges the multilayered nature of AI systems, which typically combine algorithms, data processing techniques, and specialized hardware implementations to solve particular problems.
AI systems generally involve training and inference phases, each presenting distinct patentability considerations. The examples in the USPTO guidance reflect these different aspects of AI functionality. Example 47 focuses on hardware implementations for neural networks, recognizing that specific technical improvements to computing architecture can qualify as patent-eligible subject matter. Example 48 addresses signal processing applications, showing how AI systems that transform raw data (in this case, audio signals) into useful outputs must demonstrate technical improvement beyond mathematical processing to achieve eligibility. Example 49 illustrates the application layer of AI in healthcare contexts, highlighting that connecting AI analysis to concrete real-world actions (such as specific medical treatments) is critical for eligibility.
These examples suggest that patent applicants should focus on the technical implementation details and concrete applications of their AI systems rather than abstract algorithmic concepts. The guidance recognizes that while the core mathematical operations of AI might fall under judicial exceptions, their implementation in specific technical contexts can transform them into patent-eligible subject matter when properly claimed.
Practical Implications for AI Patent Applicants
The new guidance reveals several important strategic considerations:
1. Demonstrating Technical Improvements
Whereas certain applications of AI may not be eligible for patent protection, the guidance emphasizes that claims demonstrating specific technical improvements or practical applications of AI technology can meet the eligibility requirements under 35 U.S.C. § 101.
The guidance integrates recent Federal Circuit decisions to offer up-to-date legal standards particularly relevant for AI inventions, promoting consistency and clarity in the application of patent eligibility criteria.
2. Avoiding Abstract Idea Rejections
The new examples suggest that examiners will apply increased scrutiny to AI-related claims. Patent practitioners should include elements regarding how the AI-related features operate and also include explanations in the specification of how the AI-related features improve the functioning of a computer or another technology or technical field.
Non-technical AI-related claims that simply recite an "AI model" with functional language or equivalent non-operative language will likely result in a Section 101 rejection from the USPTO.
3. AI-Assisted Inventions and Inventorship
The guidance clarifies that the method of invention creation, including the use of AI, is not a consideration in the subject matter eligibility analysis. However, current statutes do not recognize contributions by AI systems for inventorship purposes.
Patent protection may still be sought for AI-assisted inventions wherein one or more persons made a significant contribution to the claimed invention. This emphasizes the continued importance of human involvement in the inventive process.
Strategic Approaches to Patenting AI Innovations
Based on the latest guidance, the following strategies are recommended for maximizing the chances of obtaining valuable AI patents:
1. Focus on Technical Solutions to Technical Problems
The USPTO continues to be open to issuing patents on AI inventions, including the use of AI. However, there must be a technical solution to a technical problem. This means applications should:
- Clearly articulate the specific technical problem being solved
- Explain how the AI implementation addresses this problem in a non-obvious way
- Highlight technical improvements over conventional approaches
2. Detailed Disclosure of AI Implementation
The 2024 Updated Guidance emphasizes that the Federal Circuit has relied on specifications that explain how claimed rules enable the automation of specific tasks that previously could not be automated, accentuating the importance of describing technical improvements in the specification.
Therefore, patent applications should:
- Provide detailed technical descriptions of AI architectures
- Include specific training methodologies and data preprocessing techniques
- Explain how the AI implementation achieves its technical advantages
3. Claim Drafting Strategies
For medical AI technologies, it is essential to specify particular treatments or methods to transform abstract data analysis into a patent-eligible invention. However, caution should be exercised regarding potential complexities of divided infringement and the degree of enforceability.
More broadly, effective claim strategies include:
- Focusing on specific hardware implementations where possible
- Including technical steps that go beyond merely applying AI to a problem
- Drafting claims that clearly fall within the statutory categories (processes, machines, manufactures, compositions of matter)
Conclusion: The Future of AI Patent Protection
The USPTO's 2024 guidance represents an important milestone in the ongoing evolution of AI patent law, but it is certainly not the final word. As AI technology continues to advance and case law develops, we can expect further refinements to the eligibility framework.
Several key trends are likely to shape the landscape for AI patents in the coming years:
First, the distinction between abstract mathematical concepts and patentable technical implementations will continue to be refined through both USPTO guidance and Federal Circuit decisions. The examples provided in the 2024 guidance offer helpful signposts, but many gray areas remain, particularly for cutting-edge AI technologies.
Second, industry-specific considerations will likely become more prominent. The different approaches illustrated in Examples 47-49 suggest that the USPTO recognizes the varying technical contexts in which AI operates, from hardware implementations to medical applications. This may lead to more specialized guidance for particular domains.
Third, the global landscape for AI patents will continue to influence US practices. Patent offices worldwide are grappling with similar issues, and international harmonization efforts may shape future USPTO approaches.
For innovators in the AI space, the key takeaway is clear: patent protection remains available for AI innovations, but success requires careful attention to claiming strategies that emphasize technical improvements, concrete applications, and specific implementations rather than abstract concepts. By focusing on these elements and staying informed about evolving USPTO guidance, companies can build robust patent portfolios that protect their AI innovations for years to come.