Reading functional requirements using machine learning-based language processing

Abstract

Industrial innovation has accumulated big data in the form of past design successes and failures. Designers must painstakingly identify, extract, and structure requirements from texts and drawings of archived documents to understand the past and guide future designs. This is not a trivial task for human designers, despite the digitalization of design data. This paper presents a system of “Design Reading” which takes in textual design data and applies a machine learning-based language processing model to extract a structured hierarchy of functional requirements by recursively decomposing text passages. Design Reading will benefit future design practice by learning from the past.