What is artificial intelligence (AI)? The term artificial intelligence (AI) describes computer-implemented approaches to emulate human decision-making structures to enable computers and machines to process and solve problems largely independently. An essential tool for being able to arrive at independent solutions is the ability of an AI system to learn. This ability is referred to as machine learning. In this process, the AI system learns because of examples to be able to generalize given patterns after the learning phase is complete.

Potential obstacles to obtaining patent protection in Europe for an improvement in a general method for machine learning have been highlighted by a recent decision (T0702/20) from the EPO Board of Appeal. The decision relates to an application for a novel neural network apparatus having "loose coupling", based on an error code check matrix, between nodes of the neural network resulting in an initial configuration of the neural network that was argued to speed up training and operation of the apparatus while maintaining discrimination performance. The differences of the claimed invention over the prior art had been acknowledged during prosecution, but the Examining Division had rejected the Application on the basis that the distinguishing features "do not serve a technical purpose, and they are not related to a specific technical implementation either. They merely pertain to the initial, fixed structural definition of an abstract mathematical neural network-like model". During the Appeal, the Applicant provided several arguments as to why the claimed system did indeed serve a technical purpose which were not found persuasive by the Board. In response, the Board noted that a neural network can, in principle (if difficult in practice), be analysed to replace the inputs to each neuron by mathematical functions implemented by the nodes of the previous layer, and ultimately to obtain a mathematical description that describes the output of the neural network as a function of the input.

The data associated with machine learning can be extremely valuable, but, Kimberley Bayliss of Haseltine Lake Kempner writes in this co-edited article, before it can be monetized, there are some major issues to be resolved. One of the things I hear over and over again from inventors is that data is the most valuable asset in machine learning (ML). After all, an ML model is only as good as the quality and quantity of data on which it is trained. If data is really that valuable, the burning question is whether it can be successfully protected and monetized. Just as employees must be aware when they access a trade secret, and the responsibilities that come with it, employees must also be aware of their responsibilities when accessing and using company data.