University of Hertfordshire

By the same authors

Framework of mechanical design knowledge representations for avoiding patent infringement

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Engineering Design (ICED17)
Pages81-90
Number of pages10
Volume6
Publication statusPublished - 25 Aug 2017
Event21ST INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED17 - THE UNIVERSITY OF BRITISH COLUMBIA, Vancouver, Canada
Duration: 21 Aug 201725 Aug 2017

Publication series

NameProceedings of the International Conference on Engineering Design
ISSN (Print)2220-4334

Conference

Conference21ST INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED17
CountryCanada
CityVancouver
Period21/08/1725/08/17

Abstract

Nowadays companies strive to stay competitive in the market by introducing innovative products and protecting their Intellectual Property by filing patents. However, with an increasing number of patents granted each year, designers face challenges in producing novel designs and patentable inventions such that early identification of potential patent infringement can help them to steer their design away from future litigation and towards a patentable novel solution. This paper presents a framework for representing mechanical design working principles contained in existing patents by developing the Function Analysis Diagram (FAD) and a domain-specific ontology. The developed FAD, named FAD+, provides design insights including device architecture, design features and the functional interactions amongst them. The ontology formulates patent knowledge representation and conceptualisation, which contributes to comparison of an emerging design to existing patents. Overall, the framework enables designers to obtain in-depth understanding of patents, increase their qualitative IP awareness and help them to identify potential patent infringement during the product development process.

ID: 17587220