Augmented intelligence for state-of-the-art patent search

Hafner, Ana, Damij, Nadja and Modic, Dolores (2022) Augmented intelligence for state-of-the-art patent search. In: 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE). IEEE, Piscataway, US, pp. 61-66. ISBN 9781665483148, 9781665483131

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Abstract

The volume of patent data is increasing, which is a big challenge to patent examiners as well as to all inventive companies and individuals. In this paper we take the view of individual inventors who believe they invented something new. Artificial intelligence brings a promise to support their prior art search for existing (similar) inventions with machine learning and deep learning algorithms. We discuss the potential of artificial intelligence in prior art searching. We present an experiment, based on a real-life invention, comparing relevant patents we got from Boolean keyword searching with those from the semantic search supported by artificial intelligence. We can confirm that artificial intelligence has great potential in this field. However, presently it is not yet able to make traditional patent search engines obsolete, hence it still fits better with the notions of augmented intelligence or expertise.

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