Guiding Data-Driven Design Ideation by Knowledge Distance

TL;DR

This paper introduces a knowledge-based expert system that leverages a network of technology fields and knowledge distance to enhance data-driven design ideation through more effective retrieval of inspirational stimuli across multiple levels.

Contribution

The paper presents a novel system that organizes technological knowledge by knowledge distance and guides stimuli retrieval, improving creativity support in design ideation.

Findings

Effective retrieval of stimuli across multiple levels and fields.

Successful generation of new design ideas through analogy and combination.

System demonstrates rapid, data-driven, and theoretically grounded ideation process.

Abstract

Data-driven conceptual design methods and tools aim to inspire human ideation for new design concepts by providing external inspirational stimuli. In prior studies, the stimuli have been limited in terms of coverage, granularity, and retrieval guidance. Here, we present a knowledge based expert system that provides design stimuli across the semantic, document and field levels simultaneously from all fields of engineering and technology and that follows creativity theories to guide the retrieval and use of stimuli according to the knowledge distance. The system is centered on the use of a network of all technology fields in the patent classification system, to store and organize the world's cumulative data on the technological knowledge, concepts, and solutions in the total patent database according to statistically estimated knowledge distance between technology fields. In turn, knowledge distance guides the network-based exploration and retrieval of inspirational stimuli for inferences across near and far fields to generate new design ideas by analogy and combination. With two case studies, we showcase the effectiveness of using the system to explore and retrieve multilevel inspirational stimuli and generate new design ideas for both problem solving and open ended innovation. These case studies also demonstrate the computer aided ideation process, which is data-driven, computationally augmented, theoretically grounded, visually inspiring, and rapid.