At the European Patent Office, for the purposes of examination, applications disclosing and claiming subject matter relating to Artificial Intelligence or Machine Learning are treated in a similar manner to applications disclosing and claiming subject matter relating to other types of mathematical methods or algorithms implemented on a computer (see the EPO’s Guidelines for Examination (GfE) on AI inventions).
The guidance provided in relation to such computational and mathematical methods focusses on analysing whether the method contributes to the technical character of the invention. Only those features of a claim that contribute to the technical character of an invention are taken into account when assessing inventive step of the invention.
However, an additional issue that has started to appear in connection with AI inventions at the EPO – that has traditionally been less of an issue for other types of computational and mathematical method inventions – is that of sufficiency of disclosure in the patent application.
Article 83 EPC requires that a European patent application “shall disclose the invention in a manner sufficiently clear and complete for it to be carried out by a person skilled in the art”.
The GfE expand on what is required for sufficiency of disclosure in this context: “A detailed description of at least one way of carrying out the invention must be given. Since the application is addressed to the person skilled in the art, it is neither necessary nor desirable that details of well-known ancillary features are given, but the description must disclose any feature essential for carrying out the invention in sufficient detail to render it apparent to the skilled person how to put the invention into practice.” (GfE F-III 1)
The GfE further indicate what would be regarded as insufficient disclosure in this regard: “Sufficiency of disclosure cannot be acknowledged if the skilled person has to carry out a research programme based on trial and error to reproduce the results of the invention, with limited chances of success.” (GfE F-III 3)
For more ‘deterministic’ or ‘predictable’ computational and mathematical method inventions, a functional description of each step or feature of the method would typically be considered to satisfy the sufficiency requirement, without needing to describe how the steps are to be programmed in the computer.
However, some recent decisions from the EPO’s Boards of Appeal suggest that this may not be ‘sufficient’ for AI-related inventions, which may be more ‘probabilistic’ or less predictable in nature. In particular, the described functionality of an AI system may be achieved only for certain trained parameters of the AI system, e.g. weighting values at nodes, or when certain training data is used to obtain the system parameters.
As such, for AI inventions it is important to consider: has the skilled person really been provided with enough information in the patent specification to be able to implement the invention to achieve the desired functionality without undue burden? Whereas a relatively high-level, functional description of an algorithm may be sufficient for mathematical or computational methods of a more predictable or deterministic nature, for AI inventions training data or model parameters may need to be disclosed in more detail for the skilled person to be able to reproduce the invention and achieve the described functionality in order for the sufficiency of disclosure requirement to be met. For instance, one or more examples of specific sets of training data or model parameters may be provided.
Sufficiency of disclosure was raised as an issue in each of the two Technical Board of Appeal decisions summarised briefly below, where the application in each case related to AI subject matter.
The application in this case disclosed a method for determining cardiac output from an arterial blood pressure curve measured at a peripheral region. This was described as less invasive than known measurement methods. Weighting values to estimate aortic pressure from peripheral blood pressure were determined using an artificial neural network (ANN).
The patent specification disclosed: the input data for training the ANN should cover a wide range of patients of different ages, genders, constitutional types, health conditions, etc.; a particular, standard training technique; and, a particular, standard ANN architecture.
The EPO Board of Appeal decided that the application did not sufficiently disclose the invention because the application did not disclose which input data were suitable for training the ANN according to the invention, or at least one data set suitable for solving the problem at hand. In particular, the Board of Appeal decided that the skilled person could not rework the ANN and so the application was refused.
It is notable in this case that sufficiency was only first raised as an issue at the appeal stage of proceedings.
It is also of note that no sufficiency (or similar) objection was raised during prosecution of the corresponding application in the US.
The application in this case related to using a meta-learning scheme to train multiple identifiers related to personalised interventions in neurorehabilitation. The EPO Board of Appeal decided that the application did not sufficiently disclose the invention, and noted the following points.
The Board of Appeal said that the application did not disclose any example set of training data and validation data which the meta-learning scheme required as an input. The Board noted that the application did not even disclose the minimum number of patients from which training data should be compiled to be able to give a meaningful prediction and the set of relevant parameters. The Board further noted that the structure of the ANNs used as classifiers, their topology, activation functions, end conditions or learning mechanism were not disclosed.
At the level of abstraction of the application, the Board said that the available disclosure was more like an invitation to undertake a research programme. The Board concluded that, under these circumstances, the skilled person would not be able to reproduce, without undue burden, the application of the disclosed meta-learning scheme to solve the problem of predicting personalised interventions for a patient in processes the substrate of which is neuroplasticity.
It is again of note that no sufficiency (or similar) objection was raised during prosecution of the corresponding application in the US.
In view of the above, some useful tips for increasing the chances of successfully prosecuting an AI-related patent application through to grant at the EPO – taking into account both sufficiency and inventive step considerations – include the following.
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