As artificial intelligence (AI) and machine learning technologies advance at breathtaking speed, innovators face a new challenge: how to secure meaningful intellectual property (IP) protection in a shifting legal landscape. Patents play a critical role in defending against imitation, attracting investment, and laying the groundwork for commercialization. Yet, when it comes to AI inventions, the rules are evolving, and often more complex than they first appear.

We spoke with Mohammad Ahmadi Bidakhvidi and Bart Jan Niestadt, patent attorneys at V.O. Patents & Trademarks, about what inventors and companies should keep in mind when navigating the patent process for AI-driven technologies.

What makes an AI invention patentable?

Bidakhvidi: “The key is technical character. For an AI invention to qualify for patent protection, it must solve a technical problem using technical means. Merely applying a machine learning model does not suffice. The invention must deliver a tangible, technical improvement in a non-obvious manner.

This distinction is crucial. A software tool that uses AI solely for data sorting or administrative processing by European standards is unlikely to qualify as technical. However, if such an AI system contributes to a technical process, say, improving the efficiency of a manufacturing line, enhancing energy optimization in data centers, or increasing the accuracy of medical diagnostics, then it stands on much stronger ground for patentability.

How are patent offices in different regions handling AI inventions?

Driven by a significant increase in the number of patent applications for AI technology, around the world, national and regional patent offices are adapting their practices to address this fast-moving field.

Niestadt: “Harmonization remains limited, but some common standards can be seen. In Europe, AI is treated as a mathematical method and excluded from patentability unless it solves a technical problem by technical means. The U.S., applying the Alice/Mayo-framework, sets a similar but somewhat lower bar by requiring a claimed practical application. For example, speech-to-text processing or natural language translation may qualify in the U.S. but not likely in Europe. China is more permissive, provided the invention improves computer performance or addresses a technical problem. Japan is the most lenient, not demanding a technical application and showing a high granting rate for AI.”

Another key difference lies in disclosure requirements. The EPO is increasingly strict, requiring detailed explanations of how the AI is technically integrated into its application. Simply naming an AI technique is not enough, applications must disclose a training sequence, characteristics of training data, how the model interacts with input data, and how it produces a concrete technical effect. The U.S. Patent and Trademark Office (USPTO) takes a more flexible view, though does require sufficient disclosure as well as a problem-solution type explanation of how the AI solves the technical problem.

What kinds of AI inventions are most likely to be granted patents?

Bidakhvidi: “Applied AI inventions, those that operate in real-world, technical contexts, have a better chance of success. For instance, AI used in autonomous vehicles, 3D printing systems, medical imaging or industrial energy optimization is more likely to be patentable than AI applied to abstract tasks like general data classification or administrative workflows which usually fail to meet the technical threshold. However, to assess whether a patent is achievable it is also important to consider your market and where your international focus is.”

Fields widely recognized as technical include e.g. telecommunications, encryption, and embedded systems. The critical factor is whether the invention makes a measurable, technology-driven contribution.

What does “technical effect” mean in the context of AI patents?

The concept of technical effect is central to AI patentability. It refers to a demonstrable practical technical improvement to a process, device, or system. To qualify, the technical effect must be clear, specific, reproducible, and measurably beneficial from a technological standpoint.

Examples of acceptable technical effects include:

  • AI improving battery efficiency in electric vehicles by optimizing charging cycles.
  • AI reducing energy consumption in industrial settings through real-time process adjustments.
  • AI increasing accuracy in medical imaging by detecting subtle patterns missed by the human eye
  • AI to identify and single out farm animals in heat based on physiological signals from ear tags.

Patent applications should describe in concrete terms how the AI system operates in the real world and what specific technological improvement it delivers.

Can you patent innovations that improve AI itself, like new algorithms?

Niestadt: “These so-called core AI inventions, new training methods, feedback mechanisms, or machine learning model architectures, face greater hurdles. They often are dismissed as mathematical methods or abstract ideas, particularly under European law.”

In both Europe and the U.S., such inventions may still succeed if they can be tied directly to a technical improvement, such as optimizing the system for a particular hardware environment. The critical point is to show how the algorithm delivers a real-world, technical benefit.

What details must an AI patent application include to be accepted?

Bidakhvidi: “Disclosure is key. A successful patent application must provide enough information for a skilled practitioner to reproduce the invention. That means ideally describing the model architecture, the training method, the data characteristics, and the role of inputs and outputs in the system.”

Because many machine learning models function as “black boxes,” applicants must bridge the gap with concrete explanations. How a model is trained determines how it will respond, which is therefore important to explain. While the actual training data need not be disclosed, its nature and purpose should be made explicit. Without such detail, applications risk rejection for lack of reproducibility or insufficient technical contribution. The application should thus explain how and why the improvement occurs—ideally in a measurable, technical way.

Why should companies prioritize patenting their AI inventions now?

Niestadt: “Patents don’t just provide exclusivity, they create assets. In AI, where competition is fierce, a robust patent portfolio can boost investor confidence, open licensing opportunities, and support strategic partnerships. Keep in mind, certainly around computer implemented inventions, that implementing a solid contractual framework around your patents can enhance these effects.”

For startups in particular, patents can be decisive in shaping valuation and acquisition opportunities.

Conclusion

Patenting AI technology is no longer a matter of claiming an algorithm. It requires a careful demonstration of technical contribution, clear disclosure, and strategic positioning. For inventors and companies, the challenge is two-fold: to innovate in AI, and to express that innovation in legal terms that qualify as truly technical. Navigating this terrain is complex and highly-specialized, but with expert guidance, it is possible to transform cutting edge AI solutions into protected, commercially valuable assets. In this process, consulting with experienced patent attorneys is not just helpful, it’s essential.

For tailored advice for AI inventions, the attorneys at V.O. Patents & Trademarks are recognized experts in AI patent law.

 

Written by: Bart Jan Niestadt and Mohammad Bidakhvidi.