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