Measuring Patent Novelty using Natural Language Processing

DS 122: Proceedings of the Design Society: 24th International Conference on Engineering Design (ICED23)

Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Yassine, Ali; Lipizzi, Carlo
Series: ICED
Institution: Stevens Institute of Technology
Section: Design Methods
Page(s): 2605-2614
DOI number:


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 development, Machine learning, Open source design

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