Overview:

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

Simplified, dimension 1 refers to core AI inventions. (attention, this term is only partially correct, as explained below).

Examples:

• AI model adapted for parallel processing on several processors

• AI model adapted to hardware properties of the executing computer for efficient use of computer storage capacity or network bandwidth

The AI model must be particularly adapted for a technical implementation in that its design is motivated by technical considerations of the internal functioning of the computer system or network (T 1358/09, G 1/19). This may happen if the mathematical method is designed to exploit particular technical properties of the technical system on which it is implemented to bring about a technical effect such as efficient use of computer storage capacity or network bandwidth

Hence, such features related to a specific technical implementation, which differ from the prior art, must be taken into account, when assessing the inventive step of the AI-based technology. In case these features cannot be rendered obvious by the prior art (=are sufficiently different from what is known), the AI-based technology is patentable.

However, an increased efficiency of the AI model does not contribute to the technical character of the invention, in case the AI model does not go beyond a generic technical implementation (cf. the EPC Guidelines G-II, 3.3). The EPC guidelines thereby note that exceptions are existing and refers to the examples mentioned by the Guidlenes in context of database systems (cf. the EPC Guidelines G-II, 3.6.4). It appears obvious to me that the guidelines and probably also the present caselaw is still incomplete in this context. (Hard-coded) database systems are obviously a completely other technical domain than computer-implemented mathematical models, such as (trained) AI models. Consequently, the requirements to an increased efficiency in both domains also differ substantially.

A simplified test to find out whether an AI invention concerns the core AI and might thus falls under dimension 1 could be to ask the inventor: “Did you change anything within the used AI model (e.g. in the (ANN) network architecture or in a hyper parameter) compared to the prior art?”

Attention, not all core AI inventions concern dimension 1:

• if the core AI – invention adapts the AI model to the hardware (“internal functioning of the computer”), it is patentable according to dimension 1.

• If the core AI – invention adapts the AI model to the tasks to be performed, it is NOT patentable according to dimension 1 but might still concern dimension 2 (consider further requirements of dimension 2!)

Personally, I would be in favor of making both types of AI core inventions patentable in accordance with Dimension 1 (i.e. without additional requirements of dimension 2, such as claiming a specific technical application). Dimension 1 should also cover those cases where the AI design adapts the internal functioning of the computer to the task to be performed, i.e. to the real world. Inventions like LSTM (adding a memory to artificial neurons for compensating the vanishing problem), attention mechanism (focusing on the parts of the input data that are most important for making a prediction) or GAN (training a generative model in competition with a discriminator model to better imitate real images, see Patentability of AI – Part 6: Can a training dataset be patentable?) do not adapt the AI design to the internal functioning of the computer, but in contrary, adapt the computer to better understand the real world. The computer can thus better accomplish any kind of tasks originating from the real world. Isn’t that already technical, independently of any (technical) application of the task, i.e. without adding an explicit technical purpose of the AI invention?

This view rather seems to correspond with the former EPC case law, where such core AI patents have been allowed (cf. e.g. EP 0 554 083 B1 granted in 1999).

See the following example of a core AI invention which however does not concern dimension 1:

Claim 1 of PCT/EP2018/064534 filed on June 1, 2018

1. A method for training a prediction system,
the prediction system comprises a hidden variable model using a hidden random variable for sequence prediction,
the method comprising the steps of:
multiple input of a sequence input (x) into the hidden variable model which outputs in response multiple distinct samples (y) conditioned by the random variable,
use of the best of the multiple samples (y) to train the model, the best sample being the closest to the ground truth.

The claimed invention addresses properties of the used AI-model (let’s ignore for simplicity that claim 1 refers to training the AI model).

Essentially, the EPO Examiner is quite favorable re. the patentability of the invention (cf. preliminary opinion in ISR established by EPO on Feb 5, 2019).

However, he objects claim 1 for an obvious reason. Can you find it? Claim 1 is objected to be a mere mathematical operation without any technical character (= hurdle 1 failed). Happily, this objection can be easily overcome as also stated by the Examiner. As a takeaway, make sure to provide a clear definition in the application that the claimed method is computer implemented (if not already defined in claim 1).

Furthermore, the Examiner requests to specify a technical purpose in claim 1 (in the present case: prediction of a future trajectory of a detected object, e.g. a pedestrian, cf. [0045]). It can be left open to which extent such a technical purpose is to be added to a claim 1 referring to dimension 1, i.e. which already differs from the prior art by the internal functioning of the AI model. Actually, specifying the technical purpose is a requirement in dimension 2 (see the blog article “Patentability of AI – Part 3: Claim directed to technical application field (“Applied AI” – Dimension 2)“).

But wait! Isn’t this example actually falling under dimension 2 (at best)? As we have just learnt, dimension 1 requires an AI design motivated by technical considerations of the internal functioning of the computer. Just having a quick look to par. [0012] of PCT/EP2018/064534, we read the AI model of the invention “leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data”.

The exemplary Core AI invention actually concerns Dimension 2, i.e. an Applied AI invention. Sounds strange but is in line with EPC practice.

Indeed, the AI model of PCT/EP2018/064534 seems to be rather motivated by considerations of properties of real-world data than by the internal functioning of the computer. However, doesn’t the AI model contribute to the technical character of the invention by adapting the internal functioning of the computer to make more accurate predictions of the real world (see my thoughts above)? It will be interesting to see whether the EPO’s practice on patentability of AI inventions will evolve in such a direction in future.

Anyway, we generally recommend explaining in detail any possible technical purpose in the description of the patent application. In the end, when filing a patent application, it cannot be foreseen, which features might differ from the prior art coted in the examination proceedings. Accordingly, it is possible that the used AI design (according to dimension 1) is in fact not new, but at least relates to a new technical application field (i.e. also falls under dimension 2).

Furthermore, we recommended explaining all potentially possible technical purposes of the core AI in the application. In this way also the risk can be reduced that claim 1 is objected as being unduly broad and thus unclear (see EPC Guidelines F-IV, 4.22).

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

A claimed AI model related to dimension 2 must be limited to a technical purpose. In contrast, a claimed AI model related to dimension 1 may also cover non-technical purposes. This can make an important difference, in case the scope of protection shall also cover other technical (and eventually non-technical) applications.

Core AI inventions (of dimension 1) may relate to the training method of an AI model

In many cases, 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. 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 1 in my opinion, 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 model parameters are optimized during training, e.g. by defining the loss function.

What is a loss function?

In simple terms, the loss function is a method of evaluating how well your algorithm is modeling your dataset. It is a mathematical function of the parameters of the machine learning algorithm. Loss functions serve as the basis for model training, guiding algorithms to adjust model parameters in a direction that minimizes the loss and improves predictive accuracy.

In case it can be argued that the loss function of the invention is motivated by technical considerations of the internal functioning of the computer (e.g. makes the training process faster by memory optimization or parallel processing on several GPUs), the invention belongs to dimension 1. Accordingly, it is not required to limit the trained AI model to any specific technical purpose.

author: Christoph Hewel
email: hewel@paustian.de

(photo: Gorges du Verdon, PACA, France. Water flow is motivated by technical considerations of the internal functioning of the world. You may also call it “gravity”.)