The False Logic of "Mere Automation" Rejections in AI Patent Applications

As a U.S. patent attorney specializing in software patents, I've observed a troubling trend: patent examiners increasingly rejecting AI-related patent claims under 35 USC 101 by asserting they "merely automate" a "mental process." This reasoning is particularly perplexing when applied to claims involving sophisticated AI technologies like transformer-based generative large language models with billions of parameters. These rejections reflect a fundamental misunderstanding of AI technology and impose an unjustified double standard on software innovations compared to other fields of technology.

Let's dissect why this reasoning is flawed by examining both the "mere automation" and "mental process" arguments separately and then together.

The Fallacy of "Mere Automation"

When we say a computer performs the same high-level function as a human mind—such as generating text or recognizing speech—this does not mean the computer performs that function in the same way as a human mind. This is particularly true for modern AI systems.

Consider transformer-based language models, which are built on specific architectural principles designed explicitly for computer implementation. These models process information through multi-headed attention mechanisms, layer normalization, and positional encodings—concepts that have no direct analogue in human cognition. The architecture itself was created specifically for machines, not as an imitation of human mental processes.

To illustrate why "mere automation" is a flawed argument, consider these historical examples:

1. The Airplane: The Wright brothers didn't simply "automate" bird flight. They invented a fundamentally different approach to achieving powered flight. While birds use flexible wings and complex muscular systems, airplanes use rigid wings, propulsion systems, and control surfaces that operate on entirely different principles.
2. The Mechanical Cash Register: While it performs calculations that could be done mentally, it uses a completely different mechanism involving mechanical digit wheels, carry mechanisms, and physical storage of totals. The fact that it produces the same end result as mental arithmetic doesn't make it a "mere automation" of mental math.
3. The Digital Camera: Although it captures images like the human eye, it uses fundamentally different principles involving photoelectric sensors, digital signal processing, and electronic storage—nothing like the biological processes of human vision.

Although such groundbreaking inventions may have automated existing functions, they didn't "merely" automate such functions, because the very act of automation involved inventing new structures and functions that resulted in automated systems that differed fundamentally in their operation from the previous systems that they automated.

The "Mental Process" Misconception

The second problematic aspect of these rejections is the assertion that if you strip away the technical implementation details, what remains is a mental process. This reasoning is deeply flawed because it could invalidate countless legitimate mechanical and electrical patents if applied consistently.

Consider this thought experiment by imagining that the following hypothetical thermostat patent claim were written before thermostats based on bimetallic strips, containing two different metals having different thermal expansion properties, were known to the public:

A method performed by a bimetallic strip having two metals with different thermal expansion properties, comprising: setting a target temperature; if an ambient temperature exceeds the target, using the bimetallic strip to turn off a furnace; if the ambient temperature doesn't exceed the target, using the bimetallic strip to turn on the furnace.

Using the same logic applied to AI patents, one could argue this claim "merely automates" the mental process of deciding whether to add heat based on temperature through the use of an "if/then" statement. But this would be a category error. The claim explicitly describes a physical mechanism operating on specific principles that are entirely different from mental processes, despite the use of an "if/then" statement to capture that physical mechanism in a patent claim.

Yet if the standard that is applied today to software and AI patent claims were applied to many method claims for mechanical processes that are considered to be uncontroversially patent-eligible, such claims would have to be deemed patent-ineligible for being directed to "merely automating" a "mental process."

The Double Standard

This flawed reasoning creates an unfair double standard for software and AI innovations. When mechanical inventors create new ways to achieve results that could theoretically be achieved through mental processes, their patents aren't rejected as "mere automation." Yet software inventors face exactly this barrier, even when their inventions involve complex computational architectures that process information in ways fundamentally different from human cognition.

Consider a neural network with billions of parameters processing language. It:

• Operates on mathematical principles distinct from human language processing
• Processes information in parallel across multiple layers and attention heads
• Uses gradient descent and backpropagation for learning—mechanisms utterly foreign to human cognition
• Requires specific computer architectures and optimizations to function

To dismiss such innovations as "mere automation" of mental processes ignores their fundamental technical nature and innovative character.

The Cost to Innovation

These rejections impose significant and unnecessary costs on software innovators:

1. Direct Costs: Companies must spend additional time and resources responding to these rejections, often through multiple office action responses and appeals.
2. Opportunity Costs: The extended prosecution time delays patent protection, potentially reducing the effective patent term.
3. Strategic Costs: Some companies may abandon valuable innovations or seek weaker forms of protection due to these barriers.
4. Innovation Costs: The uncertainty created by these rejections may discourage investment in AI research and development.

A Call for Change

The patent system exists to promote innovation across all fields of technology. The current practice of rejecting AI patents as "mere automation" of mental processes:

• Misunderstands the technical nature of AI implementations
• Applies inconsistent standards across different fields of technology
• Creates unnecessary barriers to protecting legitimate innovations

Courts and patent offices -- which provide the basis for the guidelines that patent examiners must follow -- should recognize that implementing functionality through AI often involves specific technical solutions that are fundamentally different from human mental processes. The fact that an AI system achieves results similar to human mental processes, or that patent claims directed to AI systems may use terms describing functions which alternatively could describe mental processes if those functions were performed by a human mind and not a computer, should not overshadow the technical nature of how the AI system achieves those results.

A Call to Action

While patent examiners shouldn't be faulted for following existing guidelines and precedent, our legislators, courts, and patent offices must modernize their approach to AI innovation. The current framework, developed before the rise of sophisticated AI systems, fails to reflect the technical reality of how AI actually works.

Countries that first adapt their patent systems to appropriately protect AI innovations will gain a significant competitive advantage in the global race for AI leadership. They will attract not only patent filings but also AI researchers, companies, and investment. The stakes are particularly high given the rapid pace and transformative potential of current AI development.

As a U.S.-based practitioner, I am concerned that without prompt action, the U.S. risks losing its historic position as a leader in technological innovation. When the U.S. applies outdated mental process doctrine to reject AI patent applications, it creates an artificial barrier that may drive innovators to seek protection—and ultimately relocate their R&D operations—in jurisdictions with more evolved patent systems.

The solution requires a coordinated effort:

1. Legislators must update patent statutes to explicitly recognize that implementing functionality through AI can constitute patent-eligible subject matter, even when the AI achieves results that could theoretically be achieved through human mental processes
2. Courts must evolve their interpretation of patent eligibility to reflect the technical reality that AI implementations often represent innovative solutions distinct from mental processes
3. Patent offices must revise their examination guidelines to ensure consistent treatment of AI innovations compared to other fields of technology

The window of opportunity to establish thoughtful, innovation-promoting standards for AI patent protection is now. The jurisdictions that act first to create clear, equitable paths to AI patent protection will likely shape not just their own economic futures, but the global landscape of AI innovation for years to come.