![]() ![]() These domains typically have an aim of increasing profit. These inventions have a greater chance of claim features being excluded for being “non-technical”. On the other hand, inventions that apply machine learning approaches within a business or “enterprise” domain are likely to be analysed more closely. For example, the following domains are less likely to have features excluded from an inventive step evaluation for being “non-technical”: navigating a robot within a three-dimensional space dynamic adaptive change of a Field Programmable Gate Array audio signal analysis in speech processing and controlling a power supply to a data centre. ![]() These fields will typically either operate on low-level data that represents physical properties or have some form of actuation or change in the physical world. Inventions that apply machine learning approaches to fields in engineering are generally considered more positively by the European Patent Office. This may be seen as the context of the invention as presented in the claims and patent description. For patent attorneys who are drafting new applications, it is recommended to perform a pre-filing search of such publication sources and ensure that the inventors provide a full appraisal of what is public knowledge.Ī second issue is the domain of the invention. Hence, many applicants may face novelty and inventive step objections if the invention involves the application of known techniques to new domains or problems. The following review is based on knowledge of these applications, evaluated in the context of existing Board of Appeal cases.Ī first issue regarding machine learning and artificial intelligence systems is that many of the underlying techniques are public knowledge, given the rapid turn-over of publications and repositories of electronic pre-prints such as arXiv. However, applications in the field are being filed and examined. It is likely we will see many Board of Appeal decisions in this field, but it is unlikely these will filter through the system much before 2020. Claims for new hardware to implement machine learning and artificial intelligence systems, such as new graphical processing unit configurations, would not be classed as computer-implemented inventions and would be considered in the same manner as conventional computer devices.Īs key advances in the field have only been seen since 2010, there are few Board of Appeal cases that explicitly consider these inventions. The innovation in such systems occurs in the design of the algorithms and/or software architectures. Claims that specify machine learning and artificial intelligence systems are almost certainly to be considered “computer-implemented inventions”. “Computer-implemented invention” is the European Patent Office term for a software invention. Obligatory “Terminator” Patent Attorney Stock Image This quickly brings them into the realm of computer-implemented inventions, and the nuances of protection at the European Patent Office. This recent resurgence has meant that more companies wish to protect innovations in this field. Machine learning may be considered a subdiscipline of “artificial intelligence” that deals with algorithms that are trained to perform tasks such as classification based on collections of data. For example, convolutional neural networks now provide state-of-the-art performance in many image recognition tasks and recurrent neural networks have been used to increase the accuracy of many commercial machine translation systems. Much of this resurgence is based on advances in so-called “deep learning”, neural networks with multiple layers of connections. In recent years there has been a resurgence of interest in machine learning and so-called “artificial intelligence” systems. ![]()
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