The European Patent Office (EPO) held on 30 May 2018 a conference on Patenting Artificial Intelligence (AI).
Koen Lievens, Director Operations at the EPO, presented as keynote speaker the topic “How does the EPO deal with the challenges of AI in patents”.
The key concepts are the following:
Computer-Implemented Inventions would also apply to the inventions relating to AI, therefore to examine AI inventions the EPO two-hurdle approach should be used.
Are AI and Machine Learning (ML) just mathematical methods?
According to Articles 52 (2) and (3) EPC, mathematical methods as such are considered non-inventions and therefore not patentable.
Algorithms used for the purpose of, among other things, classification, clustering, regression and dimensionality reduction would be mathematical method as such and therefore not patentable subject-matter.
Algorithms applied, among other things, to data of technical nature, parameters of technical nature and trainable based on training data would not be considered mathematical method as such and therefore would not be excluded from patentability according to Articles 52 (2) and (3) EPC.
To overcome the second hurdle the mathematical method (steps) should contribute to the technical character of the invention.
There are two dimensions to contribute to the technical character of the invention:
1)The AI and ML method (steps) is adapted to a specific technical implementation, 2)The AI and ML method (steps) is applied to a field of technology.
In the case 1), the AI algorithm should be specifically adapted to a specific technical implementation, and, furthermore, the AI design should be motivated by technical considerations of the internal functioning of the computer.
Usually, generic technical implementation, mere programming or algorithm being merely more efficient than in prior art will not be sufficient to contribute technical character.
In the case 2), the AI and ML method (steps) should serve a technical purpose by means of a technical application, i.e., to solve a technical problem in a technical field. AI technical application fields would be image processing, speech processing, fault detection/predictive maintenance, medical analysis, self-driving cars, etc.
A generic statement like “controlling a technical system” would not be sufficient to contribute technical character.