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

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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.

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Notes

  1. http://www.nsf.gov/statistics/seind10/c4/c4s5.htm

  2. 11—Agriculture, Food, Textiles; 12—Coating; 13—Gas; 14—Organic Compounds; 15—Resins; 19—Miscellaneous-Chemical; 21—Communications; 22—Computer Hardware&Software; 23—Computer Peripherials; 24—Information Storage; 31—Drugs; 32—Surgery&Med Inst; 33—Biotechnology; 39—Miscellaneous-Drgs&Med; 41—Electrical Devices; 42—Electrical Lighting; 43—Measuring&Testing; 44—Nuclear&X-rays; 45—Power Systems; 46—Semiconductor Devices; 49—Miscellaneous-Electric; 51—Mat.Proc&Handling; 52—Metal Working; 53—Motors&Engines+Parts; 54—Optics; 55—Transportation; 59—Miscellaneous-Mechanical; 61—Agriculture, Husbandry, Food; 62—Amusement Devices; 63—Apparel&Textile; 64—Earth Working&Wells; 65—Furniture, House, Fixtures; 66—Heating; 67—Pipes&Joints, 68—Receptacles, 69—Miscellaneous-Others.

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Acknowledgments

PE thanks the Henry Luce Foundation and the Toyota Research Institue for their support. KJS acknowledges the generous support of The Filomen D’Agostino and Max E. Greenberg Research Fund. Thanks for Fülöp Bazsó, Mihály Bányai, Judit Szente, Balázs Ujfalussy for discussions.

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Authors and Affiliations

  1. Center for Complex Systems Studies, Kalamazoo College, 1200 Academy Street, Kalamazoo, MI, 49006, USA

    Péter Érdi, Kinga Makovi, Jan Tobochnik & László Zalányi

  2. Complex Systems and Computational Neuroscience Group, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary

    Péter Érdi, Kinga Makovi, Zoltán Somogyvári, Péter Volf & László Zalányi

  3. Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary

    Péter Volf

  4. Department of Sociology, Columbia University, New York, NY, USA

    Kinga Makovi

  5. New York University School of Law, New York, NY, USA

    Katherine Strandburg

  6. Network and Subscriber Data Management, Nokia Siemens Network, Budapest, Hungary

    Péter Volf

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  1. Péter Érdi
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Correspondence to Péter Érdi.

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PE thanks the Henry Luce Foundation and the Toyota Research Institue for their support. KJS acknowledges the generous support of The Filomen D’Agostino and Max E. Greenberg Research Fund.

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Érdi, P., Makovi, K., Somogyvári, Z. et al. Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics 95, 225–242 (2013). https://doi.org/10.1007/s11192-012-0796-4

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