Bayh–Dole and Alternative University Technology Transfer Regimes

  • Martin Kenney
  • Donald Patton
Part of the Innovation, Technology, and Knowledge Management book series (ITKM)


One of the primary motivations in passing the Bayh–Dole Act (BD) of 1980 was the belief that government-owned patents were insufficiently utilized. To remedy this shortcoming, Congress designed the BD Act so that federal contractors, including universities, could claim title to inventions made with federal funds. BD also standardized the procedures for vesting the control of federally-funded research inventions in contractors. The U.S. university invention ownership model has been heralded as the global best practice by many observers; more recently, though, some have begun to question this assessment. While BD was supported at the time of its passage as a means to facilitate the transfer of federally funded inventions, it has in fact turned out to be a profound technology policy decision. With BD came a new university invention commercialization model which university administrators believed would be source of income.

The BD model is not the only model for organizing technology diffusion and commercialization. Robert Litan et al., among a number of recommendations, suggested the first model we discuss, which vests invention ownership in the inventor. A second approach argues that the diffusion of university inventions would be improved by weakening property rights in these inventions. One way of doing this is to place university inventions in the public domain. A less radical variant proposed by Richard Nelson limits universities to offering non-exclusive licenses for inventions. In the remainder of this chapter the BD university ownership model is examined, and each of these alternatives is discussed, though we concentrate on the inventor ownership model because it has been less discussed in scholarly literature.


Intellectual Property Technology Transfer Technology Diffusion Technology License Academic Patent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Martin Kenney
    • 1
  • Donald Patton
    • 1
  1. 1.Department of Human and Community DevelopmentUniversity of CaliforniaDavisUSA

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