A tangible understanding of the latent design knowledge base of a technology domain, i.e., the set of technologies and related design knowledge used to solve the specific problems of a domain, and how it evolves, can guide engineering design efforts in that domain. However, methods for extracting, analyzing and understanding the structure and evolutionary trajectories of a domain’s accumulated design knowledge base are still underdeveloped. This study introduces a network-based methodology for visualizing and analyzing the structure and expansion trajectories of the design knowledge base of a given technology domain. The methodology is centered on overlaying the total technology space, represented as a network of all known technologies based on patent data, with the specific knowledge positions and estimated expansion paths of a specific domain as a subgraph of the total network. We demonstrate the methodology via a case study of hybrid electric vehicles. The methodology may help designers understand the technology evolution trajectories of their domain and suggest next design opportunities or directions.
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For detailed definitions of these IPC classes, please visit the website of the World Intellectual Property Organization: http://www.wipo.int/classifications/ipc/en/.
In Fig. 3c, Honda and Chrysler stand out showing several relatively “long” jumps in their expansion histories, i.e., about 10% of Honda’s and about 5% of Chrysler’s technology entries have a relatedness percentile lower than 0.8. Such long jumps may suggest the intents or abilities of these firms to integrate relatively distant technologies for more novel product or system designs. However, without grounded firm-level data, we are unable to conclude on these interpretations. Interested readers and researchers may utilize more in-depth data from or within these firms to explain the observed behaviors of theirs.
Meanwhile, the coherence of the HEV design knowledge base of the entire domain and individual firms is significantly greater than the average coherence among all 122 technologies in the total technology space (the dotted line at the bottom of Fig. 6b). This finding implies that the design knowledge base of the HEV domain and individual firms comprise a set of highly related technologies.
There are alternative approaches to identify the unexplored technologies in the total technology space that are highly related to an established design space (i.e., a subspace of the total space). For instance, instead of the top 10% unexplored technologies according to their weighted average relatedness to the prior design knowledge base (approach #1), one can also identify the unexplored technologies, each of which is the most related to each of the covered technologies in the current design knowledge base (approach #2). We found approach #1 better predicted the historical technology entries at the times of their respective entries based on our HEV patent data. The hit rates of approach #1 are 49% for the HEV domain’s historical entries, 88% for all assignees’ entries together, and 71–100% for entries of individual assignees. The hit rates of approach #2 are 37% for the HEV domain, 24% for all assignees pooled together, and 0–50% for individual assignees.
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We are grateful for the support from the Singapore Ministry of Education Tier 2 academic research Grant (#MOE2013-T2-2-167) “Theoretical Foundations of Technology Network Modeling for Innovation” and the SUTD-MIT International Design Centre. An early version of this paper was presented at the International Design Conference 2016. The feedback we received during the conference session greatly helped us refine the research and the paper.
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Song, B., Yan, B., Triulzi, G. et al. Overlay technology space map for analyzing design knowledge base of a technology domain: the case of hybrid electric vehicles. Res Eng Design 30, 405–423 (2019). https://doi.org/10.1007/s00163-019-00312-w
- Design theory
- Data-driven design
- Knowledge management
- Technology evolution
- Network analysis and visualization
- Hybrid electric vehicles