Overview:

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

• 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

Examples of patentable technical applications include:

1. a) Image / speech processing

2. b) Fault detection – predictive maintenance,

3. c) Medical analysis,

4. d) Self-driving cars,

5. zz) Further examples may be found in the list under EPC Guidlines G‑II, 3.3

Note that processing image data is rather considered as a method with a technical purpose than processing text data. The questions remains to me whether the EPO has an (unjustified) bias in this regards (see Patentability of AI – Part 5: Image data vs. text data – Does the EPO have a (un)justified bias?).

Most AI-based technologies concern applied machine learning. The SW-developers or data scientists use an open-source AI model (e.g. from OpenCV, Huggingface or Github) without making modifications of e.g. the internal network architecture. Happily, also applied AI can be patentable, in case the application to a particular technical field is not yet known or suggested by the prior art.

However, the following two conditions must be fulfilled for proving that the (new) features of claim 1 provide a technical contribution:

1. Claim features must contribute to the Technical Character:

The features mentioned in the claim must serve a technical purpose, meaning they are instrumental in solving a technical problem within a specific technical field. By demonstrating a clear technical benefit or advancement in the description, the feature should be taken into account when assessing inventive step of the claimed method.

Interestingly, the features do not need to have a technical character on their own: They only need to provide a possible technical purpose in the invention (which should be pointed out with care in the description).

Think of an AI-based technology which changes the colors in images. Does such a feature have a technical character?  Not necessarily. But now image the technology has the purpose of generating a dataset for training a self-driving care to drive in the night. In view of this technical purpose, the claim feature is to be taken into account when assessing inventive step of the claimed method.

Hence, also a specific training dataset can contribute to the technical character of the invention if the dataset supports achieving that technical purpose (also cf. EPC Guidelines G-II, 3.3.1).

2. Functional Limitation to Technical Purpose:

When drafting claims for applied AI inventions, they shall be functionally limited to the technical purpose they serve. This means that the scope of the claim should be precisely defined in terms of how the AI technology addresses a particular technical problem or achieves a specific technical outcome. However, I would recommend to provide a rather broad and generalized definition in originally filed claim 1 and to provide further functional limitations in dependent claims or the description. In this way, the scope of protection remains broad.

Note that merely defining the nature of the data input to an AI model does not necessarily imply that the claimed method contributes to the technical character of the invention (T 2035/11, T 1029/06, T 1161/04).

However, if steps of an AI model are used to derive or predict the physical state of an existing real object from measurements of physical properties, as in the case of indirect measurements, those steps make a technical contribution regardless of what use is made of the results (cf. EPC Guidelines G-II, 3.3.3). In other words, in case the AI model is used to process/enhance any measurement data of a real object (e.g. data captured by a camera or a sensor), there is no need to specify any further technical purpose of the output of AI model.

This assessment principally makes sense to me, since the AI model may be considered as a (post-processing) unit of a sensor system (also cf. example of WO2020053611 below)). However, in this case care should be taken to suitably formulate the output of the AI model: The claimed output should still at least implicitly relate to the initially measured object. For example, in case the input to the AI model is an image of the object, and the output is any kind of heat map of the object or a set of coefficients related to the object, the AI model makes a technical contribution to invention. In contrast, in case the output is e.g. a completely different image, (e.g. produced by a generative AI model) it would be necessary to specify a technical purpose of the generated image.

Is there also an advantage of dimension 2 over dimension 1? Yes!

The claimed AI model may be a “Black Box”: The claimed invention is thus not limited to a particular AI model what not only broadens the scope of protections but also makes a proof of infringement much easier. Likewise, the “black box” definition of the AI model allows to leave it open in the claim, whether and how the AI model has been trained, see example below.

AI, particularly neural networks, is often described as a “black box” due to its complex internal workings, where the relationships between input and output data are not readily interpretable by humans. This is in particular true, in case the software developers have utilized an open-source model which is

a) not new (and thus not patentable on its own), and

b) the developers are not aware of exact internal function of the model.

Despite these challenges, the specific input-output data configuration of the AI system can still be defined and claimed in a patent application. By specifying the input and output data characteristics relevant to the technical application field, the patent claim provides clarity on how the AI technology interacts with its environment to achieve the desired technical objectives.

Have a look to the following example for dimension 2:

Claim 1 of WO2020053611:

1. An electronic device for determining a semantic grid of an environment of a vehicle,
   the electronic device being configured to:
   receive first image data of an optical sensor, the first image data comprising a 2D image of the environment,
   perform a semantic segmentation of the 2D image and project the resulting semantic image into at least one predetermined semantic plane,
   receive an occupancy grid representing an allocentric bird eye’s view of the environment, wherein
   the control device further comprises:
   a neural network configured to determine a semantic grid by fusing the occupancy grid with the at least one predetermined semantic plane.

• Claimed invention concerns Dimension 2: Application of the AI technology to a specific field of technology:

  • Solves a technical problem in a technical field

    • Process 2D image data to obtain a bird’s eye view

  • Functionally limited to the technical purpose

    • “… for determining a semantic grid of an environment of a vehicle”

  • Neural network is defined as “black box”, i.e. merely by its input (occupancy grid and semantic plane) and output (semantic grid). It is not even specified, whether the model is trained, even though the claim implies using trained model in inference time

Preliminary opinion in ISR established by EPO on Dec 5, 2018

• Examiner considers claimed invention as new and inventive due to the specific use of the neural network

Applied AI inventions (dimension 2) may relate to the training method of an AI model

As mentioned, SW-developers or data scientists often use an open-source AI model (e.g. from OpenCV, Huggingface or Github) without making modifications of e.g. the internal network architecture. Instead, they train the model in a new way, e.g. to reduce the computational costs of training or increase the accuracy of the model.

Also such a training method can concern dimension 2, even though during inference (i.e. when exploited) the trained AI model does not mandatorily have any new features (e.g. in the network architecture) in view of the prior art. In particular, the invention may define how the AI model is better adapted to a particular task, i.e. to fulfill a particular technical purpose. For example, this may be achieved by using a particular dataset (see Patentability of AI – Part 6: Can a training dataset be patentable?) or by adapting the steps of the training method.

Also compare the EPC guidelines in this respect: “Where a classification method serves a technical purpose, the steps of generating the training set and training the classifier may also contribute to the technical character of the invention if they support achieving that technical purpose” (EPC Guidelines G-II, 3.3.1). Accordingly, the requirements to the training method are principally the same as to the (trained) AI model belonging to dimension 2: the (new features) of the training method must contribute to achieving the technical purpose.

See also the following example for a training method related to dimension 2:

Claim 1 of WO2020057753:

A method for training a semantic segmentation model performing semantic segmentation of images taken at nighttime, comprising:
a – obtaining (SOI) a first set of labelled images (101) taken at daylight, the labelled images being annotated with predefined semantic segmentation labels,
b – training (S02) a semantic segmentation model using the first set of labelled images,
c – applying (S03) the semantic segmentation model of step b to a second set of unlabeled images (102) taken at twilight of a first predefined degree, where solar illumination is less than at daylight and more than at nighttime, to obtain semantic segmentations (102’) of the images of the second set,
d – labelling (S04) the second set of unlabeled images (102) with the semantic segmentations (102’) of the images of the second set to obtain a second set of labelled images (102”), and
e – training (S05) the semantic segmentation model using the first set of labelled images (101) and the second set of labelled images (102”).

The trained model allows a reliable segmentation of night time images (specific technical purpose) without requiring a large dataset of labelled night time images which is rarely available.

author: Christoph Hewel
email: hewel@paustian.de

(photo: Pelvoux, PACA, France. Glacial pressure for a bottling station. The invention lies in the use of gravity)