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AUTOMATED FUNCTIONAL ANALYSIS OF PATENTS FOR PRODUCING DESIGN INSIGHT

Published online by Cambridge University Press:  27 July 2021

Pingfei Jiang ,
Mark Atherton  and
Salvatore Sorce
Pingfei Jiang*
Affiliation:
Department of Mechanical and Aerospace Engineering, Brunel University London
Mark Atherton
Affiliation:
Department of Mechanical and Aerospace Engineering, Brunel University London
Salvatore Sorce
Affiliation:
Department of Mechanical and Aerospace Engineering, Brunel University London
*
Jiang, Pingfei, Brunel University London, Mechanical and Aerospace Engineering, United Kingdom, pingfei.jiang@brunel.ac.uk

Abstract

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Patent analysis is a popular topic of research. However, designers do not engage with patents in the early design stage, as patents are time-consuming to read and understand due to their intricate structure and the legal terminologies used. Manually produced graphical representations of patent working principles for improving designers’ awareness of prior art have been demonstrated in previous research. In this paper, an automated approach is presented, utilising Natural Language Processing (NLP) techniques to identify the invention working principle from the patent independent claims and produce a visualisation. The outcomes of this automated approach are compared with previous manually produced examples. The results indicate over 40% match between the automatic and manual approach, which is a good basis for further development. The comparison suggests that the automated approach works well for features and relationships that are expressed explicitly and consistently but begin to lose accuracy when applied to complex sentences. The comparison also suggests that the accuracy of the proposed automated approach can be improved by using a trained part-of-speech (POS) tagger, improved parsing grammar and an ontology.

Keywords

Functional modellingEarly design phasesSemantic data processingDesign informaticsVisualisation
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 541 - 550
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), 2021. Published by Cambridge University Press

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