Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
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Abstract (arXiv)
Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 31 references that are in the map. First posted on arXiv 21 Oct 2021.
Builds on
4 of 31 references.
References · 4
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2014Distributed Representations of Sentences and Documentscited by 677
- 2021Data-Driven Design-by-Analogy: State of the Art and Future Directionscited by 18
- 2020Technological improvement rate estimates for all technologies: Use of patent data and an extended domain descriptioncited by 3
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Citing papers per year: 2023: 1.
1 paper.