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Long-run dynamics of the U.S. patent classification system

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  • Francois Lafond
  • Daniel Kim

Abstract

Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin.

Suggested Citation

  • Francois Lafond & Daniel Kim, 2017. "Long-run dynamics of the U.S. patent classification system," Papers 1703.02104, arXiv.org, revised Sep 2018.
  • Handle: RePEc:arx:papers:1703.02104
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    Cited by:

    1. Higham, Kyle & de Rassenfosse, Gaétan & Jaffe, Adam B., 2021. "Patent Quality: Towards a Systematic Framework for Analysis and Measurement," Research Policy, Elsevier, vol. 50(4).
    2. Fredström, Ashkan & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak & Parida, Vinit, 2021. "Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    3. Lorenzo Napolitano & Evangelos Evangelou & Emanuele Pugliese & Paolo Zeppini & Graham Room, 2017. "Technology networks: the autocatalytic origins of innovation," Papers 1708.03511, arXiv.org, revised Apr 2018.
    4. Hötte, Kerstin & Pichler, Anton & Lafond, François, 2021. "The rise of science in low-carbon energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    5. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
    6. Anton Pichler & Franc{c}ois Lafond & J. Doyne Farmer, 2020. "Technological interdependencies predict innovation dynamics," Papers 2003.00580, arXiv.org.

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    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O39 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Other

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