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Words in Patents: Research Inputs and the Value of Innovativeness in Invention

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  • Mikko Packalen
  • Jay Bhattacharya

Abstract

Intelligently allocating research effort and funds requires deciding whether to build on recent advances or on more established knowledge. When recent advances create superior opportunities for invention, their adoption as research inputs in the invention process promotes technological progress. The gains from pursuing such innovative research paths may, however, be very limited, due to the undeveloped nature of new knowledge, quick obsolescence of fast-improving knowledge, and the vast scope of the existing knowledge base. In this paper, we first develop a new approach to identifying research inputs in invention. Next, we estimate the value of pursuing innovative research paths that are created by the arrival of new research inputs. We identify research inputs based on a natural language analysis of 10 billion word and word sequence patent pairs in 6 million patents granted during 1920-2010. This novel textual analysis empirically reveals which single and general purpose technologies and scientific discoveries have been popular as research inputs in invention. We estimate the value of innovative research by comparing patents that mention these research inputs early against the value of other patents. For this comparison, we develop also a new measure of patent value. The measure distinguishes between citations that reflect the cumulative nature of invention and citations that may merely reflect similarity.

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  • Mikko Packalen & Jay Bhattacharya, 2012. "Words in Patents: Research Inputs and the Value of Innovativeness in Invention," NBER Working Papers 18494, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18494 Note: HC HE PR
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    Cited by:

    1. Marchese, Carla & Marsiglio, Simone & Privileggi, Fabio & Ramello, Giovanni, 2014. "Endogenous Recombinant Growth through Market Production of Knowledge and Intellectual Property Rights," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201413, University of Turin.
    2. Antoine Dechezleprêtre & Ralf Martin & Myra Mohnen, 2014. "Knowledge Spillovers from Clean and Dirty Technologies," CEP Discussion Papers dp1300, Centre for Economic Performance, LSE.
    3. Mikko Packalen & Jay Bhattacharya, 2015. "Cities and Ideas," NBER Working Papers 20921, National Bureau of Economic Research, Inc.
    4. Antoine Dechezlepretre, Ralf Martin, Myra Mohnen, 2017. "Knowledge Spillovers from clean and dirty technologies," GRI Working Papers 135, Grantham Research Institute on Climate Change and the Environment.

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    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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