Guiding Data-Driven Design Ideation by Knowledge Distance

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.

References in corpus (6)

• TechNet: Technology Semantic Network Based on Patent Data
• Heterogeneous domain adaptation: An unsupervised approach
• Enhancing user creativity: Semantic measures for idea generation
• Mapping Technology Space by Normalizing Patent Networks
• Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
• The Superior Knowledge Proximity Measure for Patent Mapping

Cited by in corpus (12)

• Stopwords in Technical Language Processing
• Data-Driven Innovation: What Is It
• Data-Driven Design-by-Analogy: State of the Art and Future Directions
• Deep Learning for Technical Document Classification
• Design Knowledge Representation with Technology Semantic Network
• Design Representation as Semantic Networks
• Patent Data for Engineering Design: A Critical Review and Future Directions
• Semantic Networks for Engineering Design: A Survey
• WikiLink: an encyclopedia-based semantic network for design innovation
• Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
• A Novel Idea Generation Tool using a Structured Conversational AI (CAI) System
• Engineering Knowledge Graph from Patent Database