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Technology fitness landscape for design innovation: a deep neural embedding approach based on patent data

Shuo Jiang*, Jianxi Luo

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

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. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Original languageEnglish
Pages (from-to)716-727
JournalJournal of Engineering Design
Volume33
Issue number10
Online published11 Nov 2022
DOIs
Publication statusPublished - 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

Research Keywords

  • deep learning
  • design innovation
  • neural embedding
  • patent
  • technology fitness landscape

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