Design opportunity conception using the total technology space map

Abstract

Traditionally, design opportunities and directions are conceived based on expertise, intuition, or time-consuming user studies and marketing research at the fuzzy front end of the design process. Herein, we propose the use of the total technology space map (TSM) as a visual ideation aid for rapidly conceiving high-level design opportunities. The map is comprised of various technology domains positioned according to knowledge proximity, which is measured based on a large quantity of patent data. It provides a systematic picture of the total technology space to enable stimulated ideation beyond the designer's knowledge. Designers can browse the map and navigate various technologies to conceive new design opportunities that relate different technologies across the space. We demonstrate the process of using TSM as a rapid ideation aid and then analyze its applications in two experiments to show its effectiveness and limitations. Furthermore, we have developed a cloud-based system for computer-aided ideation, that is, InnoGPS, to integrate interactive map browsing for conceiving high-level design opportunities with domain-specific patent retrieval for stimulating concrete technical concepts, and to potentially embed machine-learning and artificial intelligence in the map-aided ideation process.

A Data-Driven Approach for Creative Concept Generation and Evaluation

“…The system provides quantitative and visual guidance to concept combinations by informing the knowledge distances, which indicate the novelty and feasibility of the combinations. The indication is based on prior human experiments (Luo et al, 2018) as well as big data experiments (Alstott et al, 2017). With regards to using idea distances for concept evaluation, other semantic measurement tools, such as SEMILAR and TechNet, as well as several existing computational idea evaluation methods such as the InnoGPS and the machine learning-based concept evaluation method proposed by Camburn et al (2019;2020), which have employed the use of semantic distances, could be used.…”

Semantic Networks for Engineering Design: A Survey

“…Hu et al (2017) developed an Intelligent Creative Conceptual Design System, which retrieves a domain-specific Function-Behaviour-Structure (FBS) knowledge cell library according to WordNet ontology. InnoGPS, developed by Luo et al (2018, is a computer-aided design ideation support tool that provides rapid concept retrieval as inspirational stimuli and real-time evaluation of ideas generated. It uses a technology space map as the knowledge base, which is constructed based on patent data and enables exploration of technical terms in technology domains.…”

“…Triplets classification (Cheong et al, 2017) ConceptNet Knowledge retrieval and reasoning (Yuan and Hsieh, 2015) Concept association and expansion (Han et al, 2018a) Knowledge retrieval and reasoning (Han et al, 2018b) Query expansion; ideation aid (Chen and Krishnamurthy, 2020) Hand-built extracting thematic relations in domain-specific knowledge Knowledge retrieval and reasoning (Georgiev et al, 2017) Total technology space map (TSM) Knowledge retrieval and reasoning (Luo et al, 2018; B-Link Cross-domain knowledge associations (Chen et al, 2019) TechNet Prior art search, design knowledge discovery and representation, idea generation and evaluation (Sarica et al, 2019a;2019b;Han et al, 2020) SAPPhIRE model ontology Knowledge retrieval (Acharya and Chakrabarti, 2020)…”

Design Knowledge Representation With Technology Semantic Network

“…Network metrics have provided a medium to derive useful design-related insights from the structure of the graphs, and various layout methods have provided ways of representing the design-related data in an easily comprehensible way (Lim et al, 2016;. For example, network visualizations have been utilized to represent the whole technology space to support innovation and competitive intelligence (Luo et al, 2017(Luo et al, , 2018Sarica, Yan, et al, 2020), show the relations between components and subsystems to evalute designs (He and Luo, 2017;Pasqual and De Weck, 2012;Sosa et al, 2007) and inform design decisions (Kim and Kim, 2012;Sosa et al, 2007), discover the patterns of design activities (Alstott et al, 2017;Cash et al, 2014;Cash and Štorga, 2015), reveal the structure of design document repositories to guide retrievals (Fu et al, 2013;Luo et al, 2021), and represent mind maps (Camburn, Arlitt, et al, 2020;Camburn, He, et al, 2020) and concept networks (Chen et al, 2019;Chen and Krishnamurthy, 2020;Liu et al, 2020;Sarica et al, 2019Sarica et al, , 2021Shi et al, 2017;Song, Evans, et al, 2020;Souili et al, 2015) for design ideation uses. On the other hand, a few studies explored other visualization methods such as word-clouds (He, Camburn, Liu, et al, 2019; based on design description texts.…”