• Technology Fitness Landscape for Design Innovation:

    A Deep Neural Embedding approach based on patent data

    Authors: Shuo Jiang and Jianxi Luo

    [Priprint]

     

    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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