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This forms part of a wider report on the 2022 updates to EPO Guidelines for Examination.
In 2018, the EPO reviewed around 1000 of its Board of Appeal decisions and distilled some new principles on patentability of mathematical methods, noting that these principles will be applied to many artificial intelligence inventions such as machine learning algorithms. They noted that
“A mathematical method may contribute to the technical character of an invention, i.e. contribute to producing a technical effect that serves a technical purpose, by its application to a field of technology and/or by being adapted to a specific technical implementation.”
The patentability of “specific technical applications” of mathematical methods was clear and helpful – the EPO gave us a list of technical examples – but their explanation that “specific technical implementations” are also patentable was quite brief and lacked examples. We made a recommendation to clarify the intended scope of this principle (drafting some new wording based on helpful wording from the EPO Enlarged Board of Appeal in case G1/19) and to add examples. The EPO has done both.
The clarified wording is shown below, with the new wording underlined:
“Technical implementations
A mathematical method may also contribute to the technical character of the invention independently of any technical application when the claim is directed to a specific technical implementation of the mathematical method and the mathematical method is particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer system or network (T 1358/09, G 1/19). This may happen if the mathematical method is designed to exploit particular technical properties of the technical system on which it is implemented to bring about a technical effect such as efficient use of computer storage capacity or network bandwidth.
The two examples are:
For instance, the adaptation of a polynomial reduction algorithm to exploit wordsize shifts matched to the word size of the computer hardware is based on such technical considerations and can contribute to producing the technical effect of an efficient hardware implementation of said algorithm. Another example is assigning the execution of data-intensive training steps of a machine-learning algorithm to a graphical processing unit (GPU) and preparatory steps to a standard central processing unit (CPU) to take advantage of the parallel architecture of the computing platform. The claim should be directed to the implementation of the steps on the GPU and CPU for this mathematical method to contribute to the technical character.”
This shows that the EPO welcomes recommendations for improvements.
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