Algo IP: Intellectual Property In Algorithms, Computer Generated Works And Computer Implemented Inventions

Publication Date12 June 2020
AuthorMr Richard Kemp
SubjectIntellectual Property, Technology, Copyright, Patent, New Technology
Law FirmKemp IT Law

"It's only AI when you don't know what it does, then it's just software and data" remains a useful heuristic to get to grips with AI algorithms. In legal terms, AI is a combination of software and data. An algorithm is a set of rules to solve a problem. The implementation in code of the algorithm is the software that gives instructions to the computer's processor. What distinguishes an AI algorithm from traditional software is first, that the algorithm's rules and software implementation are themselves dynamic and change as the machine learns; and second, the very large datasets ('big data') that the AI algorithm processes. The data is (i) the input training, testing and operational datasets; (ii) that input data as processed by the computer; (iii) the output data from those processing operations; and (iv) insights and data derived from the output data.

This section of looks at IP rights in algorithms as software and inventions made by computer.

AI - a set of technologies

AI is a set of technologies not a single one and is best observed as a number of streams as shown in the Figure below. The main streams are machine learning, natural language processing, expert systems, vision, speech, planning and robotics.

Figure: The main AI streams1


Machine learning is the technique by which computers learn by example or by being set goals and then teach themselves to recognise patterns from the examples or reach the goal without being explicitly programmed to do so. The three main subsets of machine learning are deep, supervised and unsupervised learning. Deep learning has emerged as AI's 'killer app' enabler and uses large training datasets to teach the AI algo software to accurately recognise patterns from images, sounds and other input data. In supervised learning, the AI algorithm is programmed to recognise a sound or image pattern and is then exposed to large datasets of different sounds or images that have been labelled so the algorithm can learn to tell them apart. Labelling is time consuming, expensive and not easily transferable, so in unsupervised learning the data is unlabelled and the system is set a particular goal - to reach a high score in a game for example - and the algorithm is then exposed to large unlabelled datasets that it instructs the computer to process to find a way to reach the goal.

Machine learning techniques when combined with cameras and other sensors are accelerating machine perception - the ability of AI algorithms to recognise, analyse and respond to the data around them and 'see', 'hear', 'listen', 'speak' and 'reason'. Natural language processing has emerged as a primary human user interface for AI. Enabled by accurate voice recognition, NLP algorithms respond to one-way user input requests and interact in two-way conversations. An Expert System emulates human decision-making skills by applying rules (from its 'inference engine') to the facts in the system (its 'knowledge base'). Vision is currently the most prominent form of machine perception, with AI algorithms trained to recognise faces, objects and activity. Machine perception has developed quickly in speech, where the error rate now matches humans'.


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