Investigating the Effect of Functionality Level of Analogical Stimulation on Design Outcomes

Design-by-analogy is a growing field of study and practice, due to its power to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. A new method for extracting functional analogies from data sources has been developed to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. This search engine and design-by-analogy facilitation tool uses a functional vector space model to quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. Document parsing algorithms are developed to reduce the patent text to key functions and solved conflicts using Zipf’s law to reduce the words within the patents into the applicable functionally analogous terms, providing a mapping process for functional-based search. The mapping of the patents into the functional analogous words enables the generation of novel ideas that can be customized in various ways, providing potentially relevant sources of design-by-analogy inspiration. This paper presents a controlled experiment designed to evaluate the search engine efficacy during the conceptual ideation process. The two treatments explored are the effect of including analogous patents during ideation, and the effect of functionality level. The tool efficacy during concept generation is shown to increase novelty of generated solutions, while there is no significant change in the quantity of generated solutions.