Detecting Bad Design and Bias From Patents

Abstract

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.

Towards the extraction of semantic relations in design with natural language processing

“…However, it is noteworthy that in their paper may not encompass all essential design entities. Some entities, such as "misuse," "affordance," and "wirk elements" are marginally extracted with NLP, despite the existence of preliminary works attempting to recognize these entities in textual data (Chiarello et al, 2019;Melluso et al, 2021). Another difficulty to be considered is related to certain entities can be considered atomic (a single irreducible unit) and others not.…”