In August 2019 the ‘Artificial Inventor Project’ team led by Ryan Abbott, Professor of Law and Health Sciences at University of Surrey UK, announced that it had filed a number of patent applications naming an artificial intelligence (AI) as inventor. The AI, called ‘DABUS’, was developed by Missouri-based physicist Dr Stephen Thaler. The filings – which garnered significant publicity – were a deliberate provocation, calculated to test patent laws and challenge the conventional notion that only a natural person can be an inventor. Both the EPO and the UKIPO have since rejected the applications for failing to meet requirements that an inventor designated in a patent application be a human being. Even so, various IP organisations, including WIPO, the USPTO, and the EPO, have been actively exploring the implications of machine learning (ML) and AI for patent law and practice, including the question of whether a machine can invent.
So, have we really reached the point at which machines can challenge humans in the realm of creativity and ingenuity? And, if so, why are we hearing about it from a law professor and a lone developer, rather than in peer-reviewed publications by leading AI research teams, or in media releases from well-known mega-corporations that have invested billions in this technology? Furthermore, are we really expected to take seriously claims made by Dr Thaler that his AIs exhibit enhanced creativity as a result of infusing symptoms of ‘mental illness’ into their neural networks?
Whatever we might think of DABUS as a specific example, major national and international IP organisations are responding to broader challenges presented by emerging ML technologies that, inventorship aside, raise genuine questions in relation to subject matter eligibility, obviousness, and sufficiency of disclosure. And since ML technologies can be applied in almost any field of endeavour, from engineering design through to drug discovery, these issues are not confined to inventions in the IT space.
In this webinar, we will cast a critical eye over recent developments, and review practical implications of ML and AI technologies for patent law and practice.
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