6. Reproducibility
6.1. Repeatability
|
You are viewing the 9th edition (2019) of this publication; for the 10th edition (2022) see here |
In T 281/86 (OJ 1989, 202), it was held that there is no requirement under Art. 83 EPC 1973 according to which a specifically described example of a process must be exactly repeatable. Variations in the constitution of an agent used in a process are immaterial to the sufficiency of the disclosure provided the claimed process reliably leads to the desired products. See also T 292/85 (OJ 1989, 275); T 299/86 date: 1987-09-23 (OJ 1988, 88); T 181/87, T 212/88 (OJ 1992, 28); T 182/89 (OJ 1991, 391) and T 19/90 (OJ 1990, 476).
In G 1/03 (point 2.5 of the Reasons) the Enlarged Board of Appeal indicated that a lack of reproducibility of the claimed invention is relevant under the requirements of sufficiency of disclosure if the technical effect is a technical feature of the claim, since then it is a feature characterising the subject-matter claimed (T 1079/08). A lack of reproducibility of the claimed invention (i.e. a failure of the claimed features to deliver the effect aimed for) is seen to represent, in the case of an effect which is not expressed in a claim but is part of the problem to be solved, "a problem of inventive step". If an effect is expressed in a claim, there is lack of sufficient disclosure (G 1/03, OJ, 2004, 413, and T 939/92, OJ 1996, 309, cited by T 2001/12; and more recently in T 1845/14).
- T 161/18
1. Die vorliegende, auf maschinellem Lernen insbesondere im Zusammenhang mit einem künstlichen neuronalen Netz beruhende Erfindung ist nicht ausreichend offenbart, da das erfindungsgemäße Training des künstlichen neuronalen Netzes mangels Offenbarung nicht ausführbar ist.
2. Da sich im vorliegenden Fall das beanspruchte Verfahren vom Stand der Technik nur durch ein künstliches neuronales Netz unterscheidet, dessen Training nicht im Detail offenbart ist, führt die Verwendung des künstlichen neuronalen Netzes nicht zu einem speziellen technischen Effekt, der erfinderische Tätigkeit begründen könnte.
- Case law 2020
-
In T 161/18, the application used an artificial neural network to transform the blood pressure curve measured on the periphery into the equivalent aortic pressure. As regards how the neural network according to the invention was trained, the application disclosed only that the input data should cover a wide range of patients differing in age, sex, constitution type, state of health, etc. to prevent the network from becoming specialised. However, it did not disclose what input data were suitable for training the network or even a suitable set of data for solving the technical problem in question. As a result, the skilled person could not reproduce the network's training and so could not carry out the invention. The invention, which was based on automated learning, in particular in connection with an artificial neural network, was thus insufficiently disclosed, because the training it involved could not be reproduced owing to a lack of disclosure in this regard. See also chapter I.C.4. "Effect not made credible within the whole scope of claim – neural network".