This is an informal case summary prepared for the purposes of facilitating exchange during the 2026 WIPO IP Judges Forum.
Session 4: Software and Digital Technology Patents
Boards of Appeal of the European Patent Office [2022]: Case No. T 0702/20 – Sparsely connected neural network/MITSUBISHI
Date of judgment: November 7, 2022
Issuing authority: Boards of Appeal of the European Patent Office
Level of the issuing authority: Final Instance
Type of procedure: Judicial (Administrative)
Subject matter: Patents (Inventions)
Plaintiff/Appellant: Mitsubishi Electric Corporation
Defendant/Respondent: -
Keywords: Inventive step; Technical character; Artificial neural networks (ANNs)
Basic facts: Case T 702/20 concerns European Patent Application No. 14882049 which claimed a neural network apparatus, a method of classifier learning and a discrimination
method. In order to avoid overfitting in a standard, fully-connected neural network, it proposed and claimed the use of a neural network formed only by “loose couplings”, the remaining connections being defined by a binary check matrix.
Held: In T 702/20 the board held that a neural network defines a class of mathematical functions which, as such, was excluded subject matter. As for other "non-technical" matters, it could therefore only be considered for the assessment of inventive step when used to solve a technical problem, e.g. when trained with specific data for a specific technical task.
Relevant holdings in relation to software and digital technology patents: According to the board, the claim as a whole specified abstract computer-implemented mathematical operations on unspecified data, namely that of defining a class of approximating functions (the network with its structure), solving a (complex) system of (non-linear) equations to obtain the parameters of the functions (the learning of the weights). According to the claim, the neural network had a new structure because the hierarchical neural network was formed by loose couplings between the nodes in accordance with a sparse parity-check matrix of a low-density parity-check code.
The appellant argued that the proposed modification in the neural network structure, in comparison with standard fully-connected networks, would reduce the amount of resources required, in particular storage, and that this should be recognized as a technical effect, following G 1/19. The board noted that, while the storage and computational requirements were indeed reduced in comparison with the fully-connected network, this did not, in and by itself, translate to a technical effect, for the simple reason that the modified network was different and would not learn in the same way. So, it required less storage, but it did not do the same thing. For instance, a one-neuron neural network required the least storage, but it would not be able to learn any complex data relationship. The proposed comparison was therefore deemed incomplete, as it only focused on the computational requirements, and insufficient to establish a technical effect. The claimed invention thus lacked inventive step.
As a further remark, the board stressed that there could be no reasonable doubt that neural networks can provide technical tools useful for automating human tasks or solving technical problems. In most cases, however, this required them to be sufficiently specified, in particular as regards the training data and the technical task addressed. In this particular case, the board could not see, considering the content of the application, for which type of learning tasks the proposed structure may be of benefit, and to what extent.
In the recent decision [2026] UKSC 3 Emotional Perception AI Limited v Comptroller General of Patents, Designs and Trade Marks, as well in the decision [2024] EWCA Civ 825 under appeal, both the Supreme Court of the United Kingdom and the Court of Appeal of England and Wales cited T 702/20 with approval.
Relevant legislation: Article 56 of European Patent Convention