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Some Facts of High-Tech Patenting

Author

Listed:
  • Michael Webb
  • Nick Short
  • Nicholas Bloom
  • Josh Lerner

Abstract

Patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large US technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon, and Fujitsu. Chinese patenting in the US is small but growing rapidly, and world-leading for drone technology. Patenting in machine learning has seen exponential growth since 2010, although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large increases in inventor productivity in software and semiconductors in the late 1990s. In most high-tech fields, Japan is the only country outside the US with significant US patenting activity; however, whereas Japan played an important role in the burst of neural network patenting in the 1990s, it has not been involved in the current acceleration. Comparing the periods 1970-89 and 2000-15, patenting in the current period has been primarily by entrant assignees, with the exception of neural networks.

Suggested Citation

  • Michael Webb & Nick Short & Nicholas Bloom & Josh Lerner, 2018. "Some Facts of High-Tech Patenting," NBER Working Papers 24793, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24793
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    Cited by:

    1. Li, Yingbo & Wei, Yigang & Li, Yan & Lei, Zhen & Ceriani, Alessandra, 2022. "Connecting emerging industry and regional innovation system: Linkages, effect and paradigm in China," Technovation, Elsevier, vol. 111(C).
    2. Mario Benassi & Elena Grinza & Francesco Rentocchini, 2020. "The rush for patents in the Fourth Industrial Revolution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(4), pages 559-588, December.
    3. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.
    4. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2020. "Going Revolutionary: The Impact of 4IR Technology Development on Firm Performance," SPRU Working Paper Series 2020-08, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. Minyuan Zhao, 2020. "China’s intellectual property rights policies: A strategic view," Journal of International Business Policy, Palgrave Macmillan, vol. 3(1), pages 73-77, March.
    6. Damioli, Giacomo & Van Roy, Vincent & Vertesy, Daniel & Vivarelli, Marco, 2021. "Will the AI revolution be labour-friendly? Some micro evidence from the supply side," MERIT Working Papers 2021-016, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Damioli, Giacomo & Van Roy, Vincent & Vertesy, Daniel & Vivarelli, Marco, 2021. "May AI Revolution Be Labour-Friendly? Some Micro Evidence from the Supply Side," IZA Discussion Papers 14309, Institute of Labor Economics (IZA).
    8. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    9. Chattergoon, B. & Kerr, W.R., 2022. "Winner takes all? Tech clusters, population centers, and the spatial transformation of U.S. invention," Research Policy, Elsevier, vol. 51(2).
    10. Naude, Wim & Dimitri, Nicola, 2018. "The race for an artificial general intelligence: Implications for public policy," MERIT Working Papers 2018-032, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    11. Jason M. Rathje & Riitta Katila, 2021. "Enabling Technologies and the Role of Private Firms: A Machine Learning Matching Analysis," Strategy Science, INFORMS, vol. 6(1), pages 5-21, March.
    12. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    13. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy & Marco Vivarelli, 2021. "Detecting the labour-friendly nature of AI product innovation," DISCE - Quaderni del Dipartimento di Politica Economica dipe0017, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    14. Alessandro Sterlacchini, 2022. "AI Patenting and Employment: Evidence from the World's Top R&D Investors," Working Papers 462, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    15. Hugo Confraria & Vitor Hugo Ferreira & Manuel Mira Godinho, 2021. "Emerging 21st Century technologies: Is Europe still falling behind?," Working Papers REM 2021/0188, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. Chen, Feiqiong & Liu, Huiqian & Ge, Yuhao, 2021. "How does integration affect industrial innovation through networks in technology-sourcing overseas M&A? A comparison between China and the US," Journal of Business Research, Elsevier, vol. 122(C), pages 281-292.
    17. Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022. "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, vol. 47(2), pages 476-505, April.

    More about this item

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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