According to Art. 83 EPC, an application shall disclose the invention in a manner sufficiently clear and complete for it to be carried out by a skilled person.

  • 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 (T 2574/16)

  • Depending on the claimed AI-related invention this could require disclosure of underlying algorithms and/or corresponding training steps (T 161/18)

In view of the recent decision G 2/21 of the Enlarged Board of Appeal (Evidence standard for inventive step/plausibility) the EPC guidelines also have been updated in 2024 re. the disclosure requirements of AI inventions:

As pointed out in the EPC Guidelines G-II, 3.3.1, the technical effect that a machine learning algorithm achieves may be readily apparent or established by explanations, mathematical proof, experimental data or the like. While mere allegations are not enough, comprehensive proof is not required either. If the technical effect is dependent on particular characteristics of the training dataset used, those characteristics that are required to reproduce the technical effect must be disclosed unless the skilled person can determine them without undue burden using common general knowledge. However, in general, there is no need to disclose the specific training dataset itself.

As further stipulated in the F-III, 3, 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 (T 38/11, Reasons 2.6). This applies to the field of artificial intelligence if the mathematical methods and the training datasets are disclosed in insufficient detail to reproduce the technical effect over the whole range claimed. Such a lack of detail may result in a disclosure that is more like an invitation to a research programme (see also EPC Guidelines G-II, 3.3.1).

What does this mean in concrete terms? To me, these statements in the guidelines appear rather vague and do not seem to not provide helpful guidance. By the way, the latter seems to be true for the decision G2/21 itself (the guidance of said decision is also rather vague).

However, it appears from the Board of Appeal decision in T 0116/18 (i.e. referring Board of Appeal case behind G 2/21) that a technical effect may be solely supported by post-published data, i.e. does not need to be explicitly disclosed in the application as filed. Accordingly, following T 0116/18, it can be sufficient to provide evidence for a technical effect of an AI invention only after filing, e.g. during the examination or opposition proceedings, in order to be considered by the inventive step assessment. In case this rule is confirmed by the future EPC case law, it make life much easier for applicants of AI inventions, regardless of whether they concern dimension 1 (see blog: Patentability of AI – Part 2: Claim directed to specific technical implementation (“Core AI” – Dimension 1)) or dimension 2 (see blog: Patentability of AI – Part 3: Claim directed to technical application field (“Applied AI” – Dimension 2)).

As a best practice, I would generally recommend using all available information of the planned publication, in case the inventors plan to publish their work otherwise, e.g. as a scientific paper or on Github, etc. Typically, the publication is made shortly after filing the application, e.g. to share the work with other scientists or to advertise the related products. Conference papers usually require a more detailed and deeper level of disclosure than what is required for patents in view of Art. 83 EPC. For example, conference papers are often only accepted, in case they disclose in detail the used AI model and its training procedure (including performance numbers of the tests in view of a baseline model). In contrast, the standards to patents are typically lower (cf. EPC Guidelines G-II, 3.3.1 “comprehensive proof is not required”, “no need to disclose the specific training dataset”).

By the way, there exist some powerful tools to convert your paper draft (typically having .tex file format) into a .docx file (i.e. the format of your patent attorney who will draft and file the application). In particular, these tools are able to convert all the equations you have assembled in your paper, a task where MS Word mostly fails according to my experience.

author: Christoph Hewel

(photo: Camargue, France. How much training data is required to make a pink aircraft fly?)

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