• Technology Fitness Landscape for Design Innovation:

    A Deep Neural Embedding approach based on patent data

    Authors: Shuo Jiang and Jianxi Luo



    Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of 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 curated by Singh et al (2021) covering 95% of the patent database. 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 users to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.

  • Technology embedding space with improvement rates of all domains

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