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The description of an AI invention in a patent application is crucial. It must be detailed enough to enable a person skilled in the art to replicate the invention. This includes a thorough description of the machine learning model, the method of training, requirements to the data (not the data itself), the architectural framework, any relevant hardware implementations, and the technical problem it solves. Additionally, due to the opaque nature of some AI systems, providing evidence like experimental data or simulations that demonstrate the technical effect may be advantageous.
A “technical effect” refers to a clear and specific improvement made by an AI invention to existing technologies or processes. This effect should be directly attributable to the AI technology and not just a byproduct of general computer operation. Examples include optimizing energy consumption in industrial processes, improving the precision of robotic systems, digital audio, image, or video enhancement, efficient data compression algorithms, etc.
For an AI invention to be eligible for patent protection, it must exhibit a “technical character” by solving a technical problem with a technical solution. This means the invention should use technical means to achieve a measurable improvement over existing technology, and not merely automate an abstract idea or business process.
In Europe: No. In the US, may be. Under European standards, this type of applications is not considered to be of technical character, and therefore typically cannot be patented. In the U.S., it's less clear-cut and falls into a bit of a gray area. American patent guidelines are more flexible. Even though the invention might initially seem like it's just about organizing human activity or mental processes (which are usually not patentable), it could still be eligible for a patent if it adds enough unique technical elements that go beyond these simple processes. For instance, if the task it performs couldn't be done by a human alone because of its complexity. However, trying to get such a patent in the U.S. can be complicated and expensive.
Yes, patent requirements can vary between jurisdictions. For example, the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO) have different criteria for what constitutes a patentable AI invention. Although both jurisdictions require the AI invention to relate to a technical application of AI, there are differences in what is considered technical and what not. Also, the inventive step is differently assessed in both countries. It's important to consult with a specialized patent attorney familiar with the specific requirements of the target jurisdiction(s).
No, the data itself does not have to be disclosed, but it is required to disclose the data requirements and the training method that is needed to train the machine learning model. In many cases, it is also advisable to describe what kind of machine learning model needs to be applied. If this is absent, the model itself may be considered to be insufficiently disclosed, which could result in a refusal of the application.
AI inventions that automate non-technical processes, such as business methods or purely abstract ideas, generally do not qualify for patent protection unless they involve a novel technical implementation that solves a technical problem. The key is whether the AI contributes to the technical character of the solution, not just the automation of non-technical activities.