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MEASURING PATENT NOVELTY USING NATURAL LANGUAGE PROCESSING

Published online by Cambridge University Press:  19 June 2023

Ali Yassine  and
Carlo Lipizzi
Ali Yassine*
Affiliation:
Stevens Institute of Technology
Carlo Lipizzi
Affiliation:
Stevens Institute of Technology
*
Yassine, Ali, Stevens Institute of Technology, United States of America, [email protected]

Abstract

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This paper develops a novelty measure for patents. We devise a text-based novelty measure using natural language processing (NLP) techniques. The proposed method is applied on patents that belong to a common category, which represents a subset of patents under a specific patent class. We then extract the novelty-value profile of those patents and discuss a use case for product design and development (i.e., extracting patent novelty and predicting inventive value).

Keywords

New product developmentMachine learningOpen source design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2605 - 2614
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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