In this blog post we will give some insights into the patentability of AI (artificial intelligence) inventions in Europe (according to EPO practice). Can AI be patented and under which circumstances?

But first, in order to assess these questions, let’s try to define “AI” (artificial intelligence).

Introduction to AI / Machine Learning / Deep Learning

  • Conventional Programming: “hard coding”

    • Defining explicit instructions in a programming language

  • New approach: AI / Machine Learning / Deep Learning

    • Data driven approach = training a mathematical model with training examples

    • Many arbitrary parameter to be tuned during training (e.g. GPT-3: 175B, GPT-4: 1 Trillion)

    • Mathematical model in the form of a layer with several layers

When we refer nowadays to “AI”, we typically mean a software program that is obtained by machine learning, more in particular deep learning.

Now we all know software programs since at least the early 90s. However, they have been implemented by conventional programming. Conventional Programming involves manually coding explicit instructions and rules to solve problems. Imagine a program for an automatic door: “If the door is closed and the sensor’s light barrier is blocked, open the door”.

In contrast, machine learning utilizes a data-driven approach: It trains a mathematical model having many tunable parameters (e.g. GPT-3: 175B, GPT-4: 1 Trillion) with large set of data samples. In other words, instead of manually defining and coding any rules, the mathematical model learns itself from the data. Imagine you want to define all rules of a proper German Grammar. That’s practically not possible, unless you use a data-driven approach (the reason why machine translations are not useable until DeepL and other companies started to train neural networks on the translation task).

An AI-based software program thus principally consists of a mathematical model with trained parameters. The mathematical model typically has the form of an artificial neural network (ANN) comprising several layers.


Artificial neural network with an input layer, a hidden layer and an output layer

Any layers between the input and output layers are referred to as “hidden layers”. There is nothing mysterious about these hidden layers. But when we have them, the neural network becomes a “DEEP” neural network, so we can also speak of “Deep Learning”, not only “Machine Learning”.

If you wish to learn more about the concepts behind AI, have a look to the AI-Basics parts 1 to 4!

Patentability of AI inventions – same requirements like for other computer-implemented inventions

So, since we now have a better understanding of AI, i.e. machine learning, we can tackle the question of its patentability.

The EPO applies the same criteria to AI inventions as to any other kind of computer-implemented inventions, e.g. of conventional software programs, Hence, even if AI-based software is obtained in a completely different manner than conventional software, they are handled in the same manner in terms of patentability.

Basically, there are two hurdles to take:

1st hurdle (Art. 52(2),(3) EPC)

  • Mathematical methods are excluded from patentability (Art. 52 (2)(a) EPC and EPC Guidelines G-II, 3.3.1)

  • But: No exclusion, if the invention has technical character v(cf. EPC Guidelines F-IV, 3.9)

–> Solution: Claims are directed to computer-implemented methods / systems

2nd hurdle (Art. 54, 56 EPC)

  • Only the features contributing to the technical character are taken into account for assessment of inventive step (Art. 56 EPC)

So what does this mean in practice for AI based inventions? How can we fullfill this condition?

Simply start claim 1 with: “A computer-implemented method…”.

By defining that the method (i.e. its mathematical model) is computer-implemented, a technical character is added to the invention. So the AI-based method cannot be excluded anymore according to Art. 52 (2)(a) EPC. That’s simple, but wat: There is a second hurdle to take!

Second hurdle (Art. 54, 56 EPC)

  • Only those (new) features of claim 1, which contribute to the technical character of the invention, are taken into account for assessment of inventive step (Art. 56 EPC).

Accordingly, in. a first step it has to be determined which features of claim 1 actually differ from the closest prior art, or in other words: Which features are new? Then, these features are assessed in view of their contribution to the technical character of the invention.

For example, in case claim 1 relates to a computer-implemented method for a new heathcare app, but does not contain any new features which contribute to a technical character of the method, the patent application would be refused as being not inventive. The simple reason is that computer-implemented methods per se (i.e. software programs) are well known from the prior art and any potentially new features of the healthcare app are not regarded as providing a technical contribution.

In fact, the reason for rejection of a large majority AI-related patent applications is an alleged lack of inventive step.

It is thus important to understand which features of an AI-based technology can actually contribute to the technical character. In many cases the second hurdle of patentability can be taken, if these features are described in detail and their technical contribution is sufficiently pointed out in the patent application.

So the question is: Which features contribute to the technical character?

There are two “dimensions” of how such technical contribution may be achieved:

Hence, we may either proof an inventive step due to a specific technical implementation of our AI-based technology or due to its application to a specific technical field.

For both cases we must also exceed an inventive step threshold. In other words, the technically contributing feature of claim 1 must be non-obvious (= inventive) in view of the prior art.

Dimension 1 – “Core AI” (simplified term!, see my blog post re. dimension 1): Claim directed to specific technical implementation

  • AI model must be specifically adapted to this technical implementation by:

    • AI design must be motivated by technical consideration of internal functioning of the computer

If you want to learn more about Dimension 1, have a look here!

core AI invention

Dimension 2 (Applied AI): Claim directed to technical application field

  • Claim features contribute to the technical character of the AI invention, when they serve a technical purpose:

    • By technical application, i.e to solve a technical problem in a technical field

    • The claims need to be functionally limited to the technical purpose

    • AI (Neural network) may be defined as “black box” by its specific input and output data

applied AI invention

If you want to learn more about Dimension 2, have a look here!

The scope of protection of AI patents and their enforceability

It appears straight forward to focus on claims defining the underlying AI model of the invention as a black-box, which is merely defined by its input and its out. In case these input and output are detectable, e.g. in form of a camera interface and a particular driving behavior of a vehicle, a patent infringement can be proven without any investigation about the internal function of the competitor’s AI. This makes patents of dimension 2 (applied AI) quite attractive.

However, also patents related to dimension 1 (i.e. core AI) or to particular training methods or training datasets can have an important value: Since many developments in the AI field are published as open source, e.g. on Github, included by the inventors themselves, third parties may tend to use the same technology. In the end, a patented invention may become an industry standard, and thus it can be difficult to develop a workaround.

Likewise, it appears that cloud service providers like AWS, Microsoft (OpenAI) or Google are about to establish de-facto standards in AI by developing and offering foundational LLMs (GPT, Claude, Gemini, etc.). Since these LLMs  can outperform most customized (smaller) models, they have already become kind of standards for many downstream applications. Furthermore, these models are at least partially open-source and users tend to publish which model they use for offering AI-based services. Consequently, it might become increasingly easier to proof a patent infringement in AI, in case the patent covers an essential aspect of the concerned model.

Note that this topic will need to be evolved in future blog articles. Similarly, I expect that key case law will evolve in this field.

author: Christoph Hewel

(photo: St. Tulle, PACA, France. AI patentibility is a vast filed and it is only just beginning to blossom)

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