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DETECTING BAD DESIGN AND BIAS FROM PATENTS

Published online by Cambridge University Press:  27 July 2021

Nicola Melluso*
Affiliation:
University of Pisa B4DS Lab - Business Engineering for Data Science
Sara Pardelli
Affiliation:
Erre Quadro s.r.l.
Gualtiero Fantoni
Affiliation:
University of Pisa B4DS Lab - Business Engineering for Data Science Erre Quadro s.r.l.
Filippo Chiarello
Affiliation:
University of Pisa B4DS Lab - Business Engineering for Data Science Erre Quadro s.r.l.
Andrea Bonaccorsi
Affiliation:
University of Pisa B4DS Lab - Business Engineering for Data Science Erre Quadro s.r.l.
*
Melluso, Nicola, University of Pisa, Department of Civil and Industrial Engineering, Italy, nicolamelluso@gmail.com

Abstract

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The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.

In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

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), 2021. Published by Cambridge University Press

References

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