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

Augmented intelligence for state-of-the-art patent search_final.pdf - Accepted Version

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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.

Item Type: Book Section
Uncontrolled Keywords: Patents, Technological innovation, Art, Machine learning algorithms, Semantic search, Engineering management, Keyword search
Subjects: G700 Artificial Intelligence
N100 Business studies
Department: Faculties > Business and Law > Newcastle Business School
Depositing User: John Coen
Date Deposited: 08 Jul 2022 09:49
Last Modified: 08 Jul 2022 10:00

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