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A method for identifying hot patents and linking them to government-funded scientific research

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  • Patrick Thomas
  • Anthony Breitzman

Abstract

This paper is designed to contribute to the innovation literature by describing a method for identifying important, high-impact technologies. To this end, this paper discusses a patent citation analysis technique designed to identify patents whose impact on recent technology developments is particularly strong. These patents are defined as hot patents. This paper also examines links between hot patents and scientific research funded by different government agencies. Our results indicate that patents that cite scientific papers funded by government agencies are more likely to become hot patents than patents that do not have such a citation link to publicly funded scientific research. Our results also reveal how hot patents can be used to demonstrate the geographical breadth of influence of an individual government agency's funding of science. Copyright , Beech Tree Publishing.

Suggested Citation

  • Patrick Thomas & Anthony Breitzman, 2006. "A method for identifying hot patents and linking them to government-funded scientific research," Research Evaluation, Oxford University Press, vol. 15(2), pages 145-152, August.
    • Handle: RePEc:oup:rseval:v:15:y:2006:i:2:p:145-152
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    File URL: http://hdl.handle.net/10.3152/147154406781775986
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    Cited by:

    1. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    2. Anthony Breitzman & Patrick Thomas, 2015. "Inventor team size as a predictor of the future citation impact of patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 631-647, May.
    3. Acosta, Manuel & Coronado, Daniel & Martínez, M. Ángeles, 2012. "Spatial differences in the quality of university patenting: Do regions matter?," Research Policy, Elsevier, vol. 41(4), pages 692-703.
    4. Hung, Chia-Liang & Chou, Jerome Chih-Lung & Roan, Hung-Wei, 2010. "Evaluating a national science and technology program using the human capital and relational asset perspectives," Evaluation and Program Planning, Elsevier, vol. 33(4), pages 487-497, November.
    5. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.

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