Artificial intelligence (AI) has rapidly evolved from a theoretical concept into a driving force behind modern innovation. As AI systems become more sophisticated, industries ranging from healthcare to finance find themselves navigating a new frontier: the legal landscape of patent law. Central to this discussion is a fundamental question: Can an AI model—or its underlying architecture—be patented? This inquiry not only probes the boundaries of intellectual property rights but also invites us to examine the philosophy of invention itself.
Understanding Patents in the Context of AI
To appreciate the complexities of patenting AI, it is essential to first clarify what a patent is. A patent is a legal right granted for an invention, which allows the patent holder to exclude others from making, using, or selling the invention for a specified period, typically twenty years. The invention must be novel, non-obvious, and useful. Traditionally, patents have protected tangible inventions—machines, chemical compounds, and manufacturing processes. The emergence of software patents in the late 20th century was itself contentious, with many jurisdictions grappling to define the boundaries of what constitutes a patentable subject matter.
“The patent system was not designed with learning machines in mind. It was built for a world in which human inventors, not algorithms, were the primary engine of innovation.” — Professor Ryan Abbott, University of Surrey
In this context, the question becomes: Where do AI models fit? Are they simply the next step in software innovation, or do they represent a new category altogether?
The Anatomy of an AI Model
AI models, particularly those based on deep learning, are defined by their architecture and training data. The architecture describes the structure—such as the arrangement of layers in a neural network, activation functions, and learning algorithms—while the trained model’s weights represent the learned parameters. Both aspects are crucial to the model’s functionality.
When considering patent protection, the following components come under scrutiny:
- AI Architecture: The design of the neural network or machine learning algorithm.
- Training Methods: The specific procedures and innovations in how the model learns.
- Application or Use: The way in which the AI is applied to solve a particular problem.
Each of these elements raises different legal questions about patentability.
Patent Eligibility: Legal Frameworks and Precedents
Patent law varies by jurisdiction, but certain principles are broadly applicable. In the United States, the Supreme Court’s decision in Alice Corp. v. CLS Bank International (2014) significantly impacted software patents. The Court held that abstract ideas, even when implemented on a computer, are not patentable unless they involve an “inventive concept” that transforms the idea into patent-eligible subject matter.
This presents a challenge for AI models. Courts and patent offices must decide whether an AI architecture is an abstract idea (akin to a mathematical algorithm) or a patentable invention. The United States Patent and Trademark Office (USPTO) has issued guidelines stating that while mathematical concepts are not patentable, applications of those concepts to a particular practical problem may be.
“Merely implementing an algorithm on a generic computer does not make it patentable; there must be something more.” — USPTO Examination Guidelines
Can an AI Model Be Patented?
The answer is nuanced. AI models themselves, as abstract mathematical algorithms, are generally not patentable. However, if the architecture is tied to a specific application, or if there is a novel and non-obvious method for training or using the model, patent protection may be available.
For instance, a new method for training a neural network that dramatically increases efficiency or accuracy could be considered inventive. Similarly, an AI system designed for a particular industrial process—if it solves a technical problem in a novel way—could qualify for a patent.
It is important to differentiate between:
- The AI Algorithm or Architecture: Usually viewed as an abstract idea.
- Practical Implementation: The application of the algorithm to a real-world problem.
- Trained Model Parameters: The specific weights resulting from training, which are generally not patentable as they are viewed as data, not invention.
Case Studies: Patents Granted and Denied
Several notable examples illustrate how patent offices have approached AI-related inventions:
- IBM’s Machine Learning Patents: IBM has secured patents for methods of training machine learning models to perform specific tasks, such as fraud detection or image recognition. These patents typically describe a technical problem and a novel solution, emphasizing the application of the AI model.
- Google’s Neural Network Innovations: Google has patented specific architectures, such as innovations in convolutional neural networks (CNNs) for image processing, where the architecture provided significant advantages over prior methods.
- Rejected Applications: Attempts to patent generalized AI architectures or “trained” models without a specific technical application have largely been rejected. Patent offices argue that these are akin to mathematical formulas—useful, but not patentable in their abstract form.
International Perspectives and Challenges
Different jurisdictions interpret the patentability of AI inventions in distinct ways. The European Patent Office (EPO) follows a similar approach to the USPTO, requiring a “technical effect” that goes beyond the mere implementation of an algorithm. Japan and China have also refined their guidelines in recent years, emphasizing the need for a concrete technical contribution.
“The mere use of AI in an invention does not confer patentability; the invention must solve a technical problem in a novel way.” — European Patent Office Guidelines
However, inconsistency between jurisdictions can create challenges for AI innovators seeking global protection. What is patentable in the United States may not be patentable in Europe, and vice versa. This uncertainty can influence strategic decisions about where to file patents and how to structure claims.
Protecting AI Beyond Patents: Trade Secrets and Copyright
Given the hurdles in patenting AI models, many developers turn to alternative forms of protection. Trade secrets—confidential business information that provides a competitive edge—are a popular choice. Companies like OpenAI and Google closely guard the details of their most advanced models, relying on secrecy rather than public disclosure.
Copyright offers limited protection, covering the source code that implements an AI model but not the underlying ideas, architectures, or trained weights. This leaves gaps in protection, particularly for innovations that are not easily kept secret but do not satisfy patent criteria.
Ownership and Inventorship: Who Holds the Patent?
Another profound question emerges as AI systems themselves become more autonomous: Can an AI be an inventor? In recent years, several patent applications have listed an AI—such as the machine “DABUS”—as the inventor. Patent offices in the US, UK, and Europe have thus far rejected these applications, maintaining that only natural persons can be inventors under current law.
“AI can assist in the inventive process, but the law requires a human inventor.” — US Court of Appeals for the Federal Circuit
This stance reflects deeper philosophical and legal questions about creativity, agency, and accountability. As AI systems play a greater role in generating novel solutions, pressure may grow to revisit these definitions.
Ethical and Practical Implications
Patents are meant to incentivize innovation by granting inventors exclusive rights in exchange for public disclosure. The challenge in AI is to strike a balance between rewarding genuine breakthroughs and preventing the monopolization of foundational ideas. Overly broad patents could stifle competition and impede research, while insufficient protection might discourage investment in advanced AI development.
There is also the risk of patent “thickets”—dense webs of overlapping patents that make it difficult for new entrants to innovate without infringing on existing rights. In fast-moving fields like AI, the risk is especially acute, as incremental advances are commonplace and the boundaries between inventions are often ambiguous.
The Path Forward: Innovation and Legal Reform
Legal scholars and policymakers are actively debating how patent law should adapt to the realities of AI. Some advocate for new categories of protection tailored to machine learning and neural networks. Others suggest refining existing frameworks to better distinguish between abstract algorithms and genuine technical contributions.
Possible reforms include:
- Clarifying the standard for technical effect in AI inventions.
- Developing international harmonization of patent standards for AI-related inventions.
- Exploring supplementary protection for trained model parameters or innovative architectures.
- Encouraging open innovation and collaborative frameworks to balance proprietary interests with public benefit.
“As AI becomes more integral to invention, the law must evolve to ensure it continues to foster—not hinder—creative progress.” — Professor Daniel J. Gervais, Vanderbilt University Law School
The Ongoing Dialogue Between Technology and Law
Ultimately, the question of whether an AI model or architecture can be patented is not just a technical or legal issue; it is a reflection of how society values and protects creativity. As AI systems push the boundaries of what is possible, they invite us to reconsider the meaning of invention, the purpose of patents, and the future of innovation itself.
The intersection of AI and patent law will remain a dynamic and evolving space, shaped by new discoveries, legal precedents, and the creativity of both human and machine inventors. For researchers, developers, and policymakers alike, the journey is just beginning.
