Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T15:06:33.341Z Has data issue: false hasContentIssue false
  • English
  • Français

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, ayassine@stevens.edu

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Abood, A., and Feltenberger, D. (2018), “Automated patent landscaping”, Artificial Intelligence and Law, Vol. 26, No. 2, pp. 103–125. https://doi.org/10.1007/s10506-018-9222-4CrossRefGoogle Scholar
Adams, S. (2010), “The text, the full text and nothing but the text: Part 1–Standards for creating textual information in patent documents and general search implications”, World Patent Information, Vol. 32, No. 1, pp. 22–29. https://doi.org/10.1016/j.wpi.2009.06.001CrossRefGoogle Scholar
Albert, M. B., Avery, D., Narin, F. and McAllister, P. (1991), “Direct validation of citation counts as indicators of industrially important patents”, Research Policy, Vol. 20, No, 3, pp. 251–259. https://doi.org/10.1016/0048-7333(91)90055-uCrossRefGoogle Scholar
Aristodemou, L., and Tietze, F. (2018), “The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data”, World Patent Information, 55, 37–51. https://doi.org/10.1016/j.wpi.2018.07.002CrossRefGoogle Scholar
Bertoni, A. (2020), “Data-driven design in concept development: systematic review and missed opportunities”. In Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 101–110, Cambridge University Press. https://doi.org/10.1017/dsd.2020.4CrossRefGoogle Scholar
Brossard, M., Erntell, H., and Hepp, D. (2018), “Accelerating product development: The tools you need now”, McKinsey Quarterly.Google Scholar
Di Guardo, M. C., and Harrigan, K. (2016), “Shaping the path to inventive activity: The role of past experience in R&D alliances”, The Journal of Technology Transfer, Vol. 41, No. 2, pp. 250–269. https://doi.org/10.1007/s10961-015-9409-8CrossRefGoogle Scholar
Fischer, T., and Leidinger, J. (2014), “Testing patent value indicators on directly observed patent value—An empirical analysis of Ocean Tomo patent auctions”, Research Policy, Vol. 43, No. 3, pp. 519–529. https://doi.org/10.1016/j.respol.2013.07.013CrossRefGoogle Scholar
Fleming, L. (2001), “Recombinant uncertainty in technological search”, Management Science, Vol. 47, No. 1, pp. 117–132. https://doi.org/10.1287/mnsc.47.1.117.10671CrossRefGoogle Scholar
Harhoff, D., Narin, F., Scherer, F. M., and Vopel, K. (1999), “Citation frequency and the value of patented inventions”, Review of Economics and Statistics, Vol. 81, No. 3, pp. 511–515. https://doi.org/10.1162/003465399558265CrossRefGoogle Scholar
He, Y., and Luo, J. (2017), “The novelty ‘sweet spot’ of invention”, Design Science, Vol. 3, p. e21. https://doi.org/10.1017/dsj.2017.23CrossRefGoogle Scholar
Huang, A. (2008), “Similarity measures for text document clustering”, Proceedings of the Sith New Zealand Computer Science Research Student Conference (NZCSRSC 2008), Christchurch, New Zealand, 4, 9–56.Google Scholar
Kuo, Y.-H., and Kusiak, A. (2018). “From data to big data in production research: The past and future trends”, International Journal of Production Research, pp. 1–26. https://doi.org/10.1080/00207543.2018.1443230CrossRefGoogle Scholar
Le, Q., and Mikolov, T. (2014), “Distributed representations of sentences and documents”, International Conference on Machine Learning, pp. 1188–1196.Google Scholar
Lee, Y.-G., Lee, J.-D., Song, Y.-I., and Lee, S.-J. (2007), “An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST”, Scientometrics, Vol. 70, No. 1, pp. 27–39. https://doi.org/10.1007/s11192-007-0102-zCrossRefGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013), “Efficient estimation of word representations in vector space”, ArXiv Preprint ArXiv:1301.3781.Google Scholar
Park, G., and Park, Y. (2006), “On the measurement of patent stock as knowledge indicators,” Technological Forecasting and Social Change, Vol. 73, No. 7, pp. 793–812. https://doi.org/10.1016/j.techfore.2005.09.006CrossRefGoogle Scholar
Zhong, R. Y., Xu, X., Klotz, E., and Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, Vol. 3, No. 5, pp. 616–630. https://doi.org/10.1016/j.eng.2017.05.015CrossRefGoogle Scholar