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Approaches to Automatically Extract Affordances from Patents

Published online by Cambridge University Press:  26 July 2019

Filippo Chiarello*
Affiliation:
Università di Pisa;
Ilenia Cirri
Affiliation:
Università di Pisa;
Nicola Melluso
Affiliation:
Università di Pisa;
Gualtiero Fantoni
Affiliation:
Università di Pisa;
Andrea Bonaccorsi
Affiliation:
Università di Pisa;
Tommaso Pavanello
Affiliation:
Erre Quadro s.r.l.
*
Contact: Chiarello, Filippo, Università di Pisa, Italy, filippochiarello.90@gmail.com

Abstract

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The importance of affordance in Engineering design is well established. Artifacts that are able to activate spontaneous and immediate users’ reactions are considered the outcome of good design practice.

A huge effort has been made by researchers for understanding affordances: yet these efforts have been somewhat elusive. In particular, they have been limited to case studies and experimental studies, usually involving a small subset of affordances. No systematic effort has been carried out to list all known affordance effects. This paper offers preliminary steps for such an ambitious effort.

We propose a set of three different approaches of Natural Language Processing techniques to be used to extract meaningful affordance information from the full text of patents: 1) a simple word search, 2) a lexicon of affordances and 3) a rule-based system.

The results give in-depth measures of how rare affordances in patents are, and a fine grain analysis of the linguistical construction of affordances. Finally, we show an interesting output of our method, that has detected affordances for disabled people, showing the ability of our system to automatically collect design-relevant knowledge.

Type
Article
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) 2019

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