Automated Functional Analysis of Patents for Producing Design Insight

Abstract

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.