Patenting Computer Implemented Inventions at the EPO: Part One

Page 1

Patenting Computer Implemented Inventions at the EPO: Part One 31st October 2021 By Stuart Clarkson, Partner and Frances Wilding, Partner --------------------------------------------------------------------------------------------------------------In this first of two articles, we summarise our recommendations for drafting patent applications for inventions concerning software and algorithms. As these are subject matter areas that have gained a reputation for being difficult to successfully prosecute before the European Patent Office (EPO), we aim to provide some helpful tips to increase the chances of these applications granting before the EPO. What kinds of software can be patented at the EPO? EPC Article 52, relating to what constitutes a patentable invention, states that mathematical methods and programs for computers are not inventions to the extent that the claims relate to this subject matter “as such.” Much case law has been and continues to be the focus of what those two words, “as such”, mean but it is established in our case law that if the invention is “technical”, then it does not fall within the exclusion. Therefore, the conclusion is that only “technical” subject matter is patentable. However, this effectively moves the discussion from how the words “as such” are to be understood to what does/does not constitute a “technical” invention. At the EPO, a computer-implemented invention (“CII” invention) is one which involves the use of a computer, computer network or other programmable apparatus, where one or more features are realised wholly or partly by means of a computer program. Artificial Intelligence (“AI”)/Machine Learning is always computer implemented, and the EPO considers AI to be a mathematical method. The most recent March 2021 edition of the Guidelines for Examination in the EPO specifically addressed these kinds of inventions and the EPO’s practice for AI and machine-learning: “Artificial intelligence and machine learning are based on computational models and algorithms for classification, clustering, regression and dimensionality reduction, such as neural networks, genetic algorithms, support vector machines, k-means, kernel regression and discriminant analysis. Such computational models and algorithms are per se of an abstract mathematical nature, irrespective of whether they can be “trained” based on training data. Hence, the guidance provided in G-II, 3.3 generally applies also


Turn static files into dynamic content formats.

Create a flipbook
Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.