Good morning from ClaimWise!

In patent litigation, the concept of the Person of Ordinary Skill in the Art (POSITA) is essential. This hypothetical standard helps assess whether an invention is obvious or if a patent's claims are adequately described and enabled. As AI tools become common in research and development, they challenge the traditional boundaries of “ordinary skill.”

The POSITA

The POSITA represents a practitioner with average skills and knowledge in a specific technical field, as determined by factors such as the complexity of the technology and the typical qualifications of workers in the field. Historically, the POSITA’s capabilities have reflected the tools and techniques available at the time of the invention. However, the integration of AI tools, which can rapidly analyze data and generate insights, is raising the baseline of what a POSITA is presumed to know and accomplish. This evolution forces courts to reconsider how these enhanced capabilities should influence patentability standards.

Obviousness in an AI-Driven World

As we delve deeper into how AI impacts patent law, one critical area to explore is how it shapes the obviousness standard. Understanding the interplay between AI’s capabilities and the expectations of a POSITA is key to navigating the evolving patent landscape.

The obviousness standard under 35 U.S.C. §103 asks whether a POSITA could combine existing knowledge to achieve a claimed invention. Defined in part by the Supreme Court’s decision in KSR v. Teleflex, a POSITA is expected to apply common sense and creativity in solving technical problems, not just follow rigid formulas or predefined paths. This standard emphasizes flexibility and practicality, ensuring the obviousness test evolves alongside technological advancements.

AI’s ability to quickly analyze data, simulate scenarios, and identify solutions could make previously innovative ideas seem obvious. For instance, in drug discovery, AI might predict a compound’s ability to bind to a specific receptor based on existing data. While this process might once have required years of experimentation, it is now feasible with AI. In such cases, courts must determine whether the inventive act lies in the AI’s analysis or in how the inventor uses those results.

Another example can be seen in materials science. Imagine developing a new alloy for aerospace applications. Traditionally, researchers might have tested various combinations of metals through trial and error to optimize for weight, strength, and temperature resistance. Today, AI can simulate thousands of alloy combinations, predicting their properties and narrowing down viable candidates in a fraction of the time. As with drug discovery, the question arises: does the innovation lie in the AI’s computational power or in the inventor’s ability to interpret and apply those results creatively?

These examples underscore how AI tools shift the balance of what constitutes “ordinary” skill. Courts must carefully consider whether the use of AI enhances the POSITA’s capabilities to the point where certain inventions become obvious, while still recognizing the human ingenuity that guides these tools toward meaningful innovation.

Enablement and written Description: AI as a benchmark

Patent law also requires that an invention’s disclosure enables a POSITA to replicate it without undue experimentation. Generative AI could serve as a tool to test whether a disclosure meets this standard.

For example, AI could analyze whether a patent provides enough detail for a POSITA to reproduce the invention. In cases like Amgen v. Sanofi, the Supreme Court highlighted the need for patents to avoid requiring excessive experimentation. AI tools could redefine what qualifies as “undue experimentation” by simulating experiments and testing whether they can be replicated efficiently.

The written description requirement—ensuring the inventor has adequately described their invention—is also impacted. AI could help determine whether a patent sufficiently conveys the scope of the invention to someone skilled in the art.

Challenges and practical considerations

Integrating AI into the POSITA framework raises important questions and challenges for courts, policymakers, and practitioners.

1. Defining AI access: Not all AI tools are equally accessible. While some are publicly available, others are proprietary or experimental. Courts must establish a clear baseline of what AI tools a POSITA can reasonably be expected to use. This requires considering both the cost and availability of these tools, ensuring fairness for inventors with varying levels of resources.
2. Balancing creativity and routine use: AI often generates solutions based on predefined parameters. Determining whether an invention reflects genuine creativity or is simply the product of routine AI use is a nuanced challenge. Courts must carefully assess whether the inventive step lies in how the AI is applied or in the insights gained from its results.
3. Establishing industry norms: Different industries adopt AI at varying rates. For example, AI is more integrated in fields like pharmaceuticals and materials science than in others. Policymakers must account for these disparities, ensuring the POSITA’s assumed skill level reflects current practices in each field without penalizing slower adopters.
4. Ensuring equity: Smaller companies and independent inventors often lack access to advanced AI tools, creating potential inequities. Courts and policymakers should aim to level the playing field by defining a reasonable baseline for AI capabilities while avoiding an over-reliance on cutting-edge tools available only to well-funded organizations.
5. Maintaining documentation: Practitioners need to maintain comprehensive records of how AI tools are used in the inventive process. This documentation is critical for distinguishing between routine outputs and innovative applications. It also provides evidence to rebut unsupported assumptions about the capabilities of AI at the time of invention.

The way forward

The USPTO has begun addressing these issues through public consultations and policy discussions. These efforts are critical to adapting patent law to an AI-driven world.

For practitioners, this evolution offers opportunities to highlight the role of human creativity in leveraging AI. By documenting how AI tools are used in the inventive process, attorneys can differentiate routine applications from genuine innovations, strengthening their arguments for patentability.