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Related Experiment Video

Updated: Jan 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Mapping the technological evolution of generative AI: a patent network analysis.

Navid Mohammadi1, Jalil Heidary Dahooie2, Amir Ali Bengari3

  • 1College of Management, University of Tehran, Tehran, Iran.

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|December 26, 2025
PubMed
Summary

Generative Artificial Intelligence (GAI) patent analysis reveals a shift post-2016 from modular innovations to integrated frameworks. Emerging trends focus on AI frameworks, medical imaging, and personalized systems, highlighting societal impacts.

Keywords:
Generative artificial intelligenceInnovation clustersNetwork modelingPatent analysisTechnology roadmapping

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Innovation Studies

Background:

  • Generative Artificial Intelligence (GAI) is rapidly evolving, necessitating a clear understanding of its innovation landscape.
  • Mapping technological evolution is crucial for stakeholders to navigate GAI advancements.

Purpose of the Study:

  • To systematically map the technological evolution and innovation landscape of Generative Artificial Intelligence (GAI) using patent network analysis.
  • To identify key technological domains, trace conceptual shifts, and highlight emerging trends in GAI.

Main Methods:

  • Large-scale analysis of over 20,000 patents from Lens.org.
  • Application of text mining, network modeling, and community detection techniques.
  • Analysis across three distinct time periods: pre-2016, 2016-2020, and 2021-2025.

Main Results:

  • A significant transition occurred post-2016, moving from modular, domain-specific innovations to integrated generative frameworks and multi-modal capabilities.
  • The post-2016 era shows rapid patent growth, increased conceptual diversity, and convergence of subfields.
  • Emergent clusters include generative AI frameworks, advanced medical imaging, personalized interactive systems, media authentication, and privacy-preserving AI.

Conclusions:

  • The study provides a roadmap for R&D leaders, policymakers, and investors to navigate the dynamic GAI landscape.
  • Growing attention to content authenticity and user personalization reflects the interplay between technological maturity and societal concerns.
  • Future research should explore multilingual and semantic-rich analyses for a more comprehensive understanding.