Scientometrics

, Volume 95, Issue 1, pp 225–242 | Cite as

Prediction of emerging technologies based on analysis of the US patent citation network

  • Péter Érdi
  • Kinga Makovi
  • Zoltán Somogyvári
  • Katherine Strandburg
  • Jan Tobochnik
  • Péter Volf
  • László Zalányi
Article

Abstract

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.

Keywords

Patent citation Network Co-citation clustering Technological evolution 

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2012

Authors and Affiliations

  • Péter Érdi
    • 1
    • 2
  • Kinga Makovi
    • 1
    • 2
    • 4
  • Zoltán Somogyvári
    • 2
  • Katherine Strandburg
    • 5
  • Jan Tobochnik
    • 1
  • Péter Volf
    • 2
    • 3
    • 6
  • László Zalányi
    • 1
    • 2
  1. 1.Center for Complex Systems StudiesKalamazoo CollegeKalamazooUSA
  2. 2.Complex Systems and Computational Neuroscience GroupWigner Research Centre for Physics, Hungarian Academy of SciencesBudapestHungary
  3. 3.Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
  4. 4.Department of SociologyColumbia UniversityNew YorkUSA
  5. 5.New York University School of LawNew YorkUSA
  6. 6.Network and Subscriber Data ManagementNokia Siemens NetworkBudapestHungary

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