nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2024‒03‒11
three papers chosen by
Giovanni Battista Ramello, Università di Turino

  1. The Government Patent Register: A New Resource for Measuring U.S. Government-Funded Patenting By Daniel P. Gross; Bhaven N. Sampat
  2. Ex-ante Novelty and Invention Quality: A Cross-country Sectoral Empirical Study By Yuan Gao; Emiliya Lazarova
  3. Copyright Policy Options for Generative Artificial Intelligence By Joshua S. Gans

  1. By: Daniel P. Gross; Bhaven N. Sampat
    Abstract: We introduce new historical administrative data identifying U.S. government-funded patents since the early twentieth century. In addition to the funding agency, the data report whether the government has title to the patent (“title” patents) or funded a patent assigned to a private organization (“license” patents). The data include a large number of “license” patents that cannot be linked to government funding from patent text or other sources. Combining the historical data with modern administrative sources, we present a public, consolidated data series measuring U.S. government-funded patents—including funding agencies—through 2020, and we provide code to extend this series in the future. We use the data to document long-run patterns in U.S. government-funded patents and federal patent policy, propose ways in which these data can be used in future research, and discuss limitations of the data.
    JEL: N42 N72 O31 O34 O38
    Date: 2024–02
  2. By: Yuan Gao (School of Economics, University of East Anglia); Emiliya Lazarova (School of Economics, University of East Anglia)
    Abstract: The research on measuring technological innovation quality has evolved with our understanding of the origin of novelty. Patents have been widely used in such studies because they are a form of copyright-protected outcome of inventions deemed to be valuable. The quality of technological innovation can be measured in multiple dimensions. In this paper, we make a methodological contribution to the literature on ex-ante technological novelty and propose two new indices based on a network approach: the Inverse Recombination Intensity Index (IRII) to capture the extent to which an invention is the outcome of a novel combination of pre-existing technological components; and the New Technology Ratio (NTR) to measure the share of new knowledge elements in the invention. Through an in-depth empirical study of patents filed in the Pharmaceuticals and Computer Technology sectors, we show that our proposed indices are correlated with some of the conventional patent quality indicators and go beyond that to reveal previously unnoticed features of the inventions process, of which some are sector-specific. Moreover, through our regression analysis, we demonstrate that IRII and NTR are important predictors of a patents’ potential impact on future inventions, which confirms the ex-ante nature of our indices. In the regression analysis we also include sector-country-specific R&D input variables as controls to test the robustness of our results. Our analysis suggests that the distinct characteristics of each sector affect how the quality of innovation is related to the ex-ante measures of technological novelty. We argue, therefore, that future analysis of the link between ex-ante novelty and ex-post quality of innovation needs to take into consideration the recombinant content of the invention and account for sectoral characteristics.
    Keywords: Innovation quality, Disruptive novelty, Ex-ante novelty, Patent, Network
    Date: 2024–02
  3. By: Joshua S. Gans
    Abstract: New generative artificial intelligence (AI) models, including large language models and image generators, have created new challenges for copyright policy as such models may be trained on data that includes copy-protected content. This paper examines this issue from an economics perspective and analyses how different copyright regimes for generative AI will impact the quality of content generated as well as the quality of AI training. A key factor is whether generative AI models are small (with content providers capable of negotiations with AI providers) or large (where negotiations are prohibitive). For small AI models, it is found that giving original content providers copyright protection leads to superior social welfare outcomes compared to having no copyright protection. For large AI models, this comparison is ambiguous and depends on the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex-post `fair use' type mechanism can lead to higher expected social welfare than traditional copyright regimes.
    JEL: K20 O34
    Date: 2024–02

This nep-ipr issue is ©2024 by Giovanni Battista Ramello. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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