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Knowledge flows from science to AI technology: Identifying core and brokerage technological roles.

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This study maps knowledge flow from science to technology in artificial intelligence (AI). We analyzed AI patents to reveal key trends and innovation pathways, offering insights for R&D and policy.

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Area of Science:

  • Artificial Intelligence (AI)
  • Innovation Studies
  • Bibliometrics

Background:

  • Artificial intelligence (AI) is rapidly advancing and integrating across industries.
  • The flow of knowledge from scientific research to AI technological development is crucial but understudied.
  • Existing research lacks a systematic analysis of AI's science-to-technology knowledge pathways.

Purpose of the Study:

  • To systematically investigate the science-to-technology knowledge flow underpinning AI's evolution.
  • To propose and apply a semantic framework for exploring AI-related knowledge transfer.
  • To identify key technological trends and structural pathways in AI development.

Main Methods:

  • Developed a two-stage framework: technology classification and semantic topic exploration.
  • Classified AI patents using centrality measures from a CPC co-occurrence network.
  • Applied BERTopic modeling to patent and scientific publication abstracts, using generative AI for topic labeling.

Main Results:

  • Analyzed AI patents filed between 2002 and 2021.
  • Traced key technological trends and identified structural pathways of knowledge flow.
  • Revealed the dynamic interplay between scientific discovery and AI innovation.

Conclusions:

  • The findings provide a structural understanding of AI's technological evolution driven by scientific knowledge.
  • Offers practical implications for corporate research and development (R&D) strategies.
  • Informs innovation policy design for the rapidly evolving AI landscape.