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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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.
Alcacer, J., & Gittelman, M. (2006). How do i know what you know? Patent examiners and the generation of patent citations. Review of Economics and Statistics, 88(4), 774–779.
Almeida, P., & Kogut, B. (1997). The exploration of technological diversity and the geographic localization of innovation. Small Business Economics, 9, 21–31.
Berlingerio, M., Bonchi, F., Bringmann, B., Gionis, A. (2009). Mining graph evolution rules. In: W. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor (Eds.), Machine learning and knowledge discovery in databases (pp. 115–130), European Conference on Machine Learning and Knowledge Discovery in Databases. Springer
Blondel, V., Guillaume, J.L., Lambiotte, R., Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, p P10008.
Breitzman, A. (2007). The emerging clusters project. Final Report, 1790 Analytics, http://www.ntis.gov/pdf/Report-EmergingClusters.pdf.
Chang, S., Lai, K., Chang, S. (2009). Exploring technology diffusion and classification of business methods: using the patent citation network. Technological Forecasting and Social Change, 76(1), 107–117.
Chen, C., Ibekwe-SanJuan, F., Hou, J. (2010). The structure and dynamics of co-citation clusters: a multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology, 61, 1386–1409.
Criscuolo, P., & Verspagen, B. (2008). Does it matter where patent citations come from? Inventor versus examiner citations in european patents. Research Policy, 37, 1892–1908.
Csárdi, G., Strandburg, K., Tobochnik, J., Érdi, P. (2009). Chapter 10. the inverse problem of evolving networks – with application to social nets. In: B. Bollobás, R. Kozma, D. Miklós (Eds.) Handbook of Large-Scale Random Networks (pp. 409–443). Berlin: Heidelberg.
Day, G., Schoemaker, P. (2005). Scanning the periphery. Harvard Business Review (pp. 1–12).
Debackere, K., Verbeek, A., Luwel, M., Zimmermann, E. (2002). The multiple uses of technometric indicators. International Journal of Management Reviews, 4, 213–231.
van Dongen, S. (2000). A cluster algorithm for graphs. Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam.
Duguet, E., MacGarvie, M. (2005) How well do patent citations measure flows of technology? Evidence from French innovation surveys. Economics of Innovation and New Technology, 14(5), 375–393.
Érdi, P. (2007). Complexity explained. Berlin: Heidelberg.
Érdi, P. (2010). Scope and limits of predictions by social dynamic models: Crisis, innovation, decision making. Evolutionary and Institutional Economic Review, 7, 21–42.
Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.
Fleming, L. (2004). Science as a map in technological search. Strategic Management Journal, 25, 909–928.
Fleming, L., Sorenson, O. (2001) Technology as a complex adaptive system: evidence from patent data. Research Policy, 30, 1019–1039.
Fleming, L., Juda, A., III, C.K. (2006). Small worlds and regional innovation. Harvard Business School Working Paper Series, No. 04–008, available at http://ssrn.com/abstract=892871.
Fontana, R., Nuvolari, A., Verspagen, M. (2009). Mapping technological trajectories as patent citation networks. an application to data communication standards. Economics of Innovation and New Technology, 18, 311–336.
Garfield, E. (1983). Citation Indexing – Its Theory and Application in Science, Technology and Humanities. ISI Press, Philadelphia.
Garfield, E. (1993) Co-citation analysis of the scientific literature: Henry small on mapping the collective mind of science. Current Contents, 19, 3–13.
Girvan, M., Newman, M. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821–7826.
Hagedoorn, J., Cloodt, M. (2003). Measuring innovative performance: Is there an advantage in using multiple indicators? Research Policy, 32, 1365–1379.
Hall, B., Jaffe, A., Trajtenberg, M. (2001). The NBER patent citation data file: lessons, insights and methodological tools. Working Paper 8498, National Bureau of Economic Research.
Hargadon, A., & Sutton, R. (1997). Technology brokering and innovation in a product development firm. Administrative Science Quarterly, 42, 716–749.
Harhoff, D., Narin, F., Scherer, F., Vopel, K. (1999). Citation frequency and the value of patented inventions. The Review of Economics and Statistics, 81, 511–515.
Henderson, R., Clark, K. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35(1), 9–30.
Huang Z., Chen H., Yip A., Ng G., Guo F., Chen Z.K., Roco M. (2003). Longitudinal patent analysis for nanoscale science and engineering: Country, institution and technology field. Journal of Nanoparticle Research ,5, 333–363.
Huang, Z., Chen, H., Chen, Z.K., Roco, M. (2004). International nanotechnology development in 2003: Country, institution, and technology field analysis based on uspto patent database. Journal of Nanoparticle Research,6, 325–354.
Jaffe, A., & Trajtenberg, M. (2005). Patents, Citations and Innovations: a Window on the Knowledge Economy. MIT Press, Cambridge.
Kajikawa, Y., Takeda, Y. (2008) Structure of research on biomass and bio-fuels: A citation-based approach. Technological Forecasting and Social Change, 75, 1349–1359.
Kajikawa, Y., Usui, O., Hakata, K., Yasunaga, Y., Matsushima, K. (2008). Structure of knowledge in the science and technology roadmaps. Technological Forecasting and Social Change, 75, 1–11.
Kostoff, R., Geisler, E. (2007). The unintended consequences of metrics in technology evaluation. Journal of Infometrics, 1, 103–114.
Kostoff, R., & Schaller, R. (2001). Science and technology roadmaps. IEEE Transactions on Engineering Management, 48, 132–143.
Kostoff, R., Stump, J., Johnson, D., Murday, J., Lau, C., Tolles, W. (2006). The structure and infrastructure of the global nanotechnology literature. Journal of Nanoparticle Research, 8, 301–321.
Lai, K., Wu, S.J. (2005) Using the patent co-citation approach to establish a new patent classification system. Information Processing and Management, 41, 313–330.
Lanjouw, J., Schankerman, M. (2004). Patent quality and research productivity: Measuring innovation with multiple indicators. The Economic Journal, 114(495), 441–465.
Lee, P.C., Su, H.N., Wu, F.S. (2010). Quantitative mapping of patented technology – the case of electrical conducting polymer nanocomposite. Technological Forecasting and Social Change, 77(3):466–478.
Leskovec, J., Kleinberg, J., Faloutsos, C. (2005). Graphs over time: densification laws, shrinking diameters and possible explanations. In: KDD 2005: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 177–187). ACM, New York.
Leydesdorff, L. (2008). Patent classifications as indicators of intellectual organization. Journal of the American Society for Information Science and Technology, 59(10), 1582–1597
McMillanm, G., Narin, F., Deeds, D. (2000). An analysis of the critical role of public science in innovation: the case of biotechnology. Research Policy, 29, 1–8.
Meyer, M. (2000). What is special about patent citations? differences between scientific and patent citations. Scientometrics, 49, 93–123.
Meyer, M. (2001). Patent citation analysis in a novel field of technology: An exploration of nano-science and nano-technology. Scientometrics, 51, 163–183.
Milman, B. (1994). Individual cocitation clusters as nuclei of complete and dynamic infrometric models of scientific and technological areas. Scientometrics, 31, 45–57.
Moed, H. (2005). Citation Analysis in Research Evaluation. Netherlands: Springer.
Mogee, M., & Kolar, R. (1998a). Patent citation analysis of allergan pharmaceutical patents. Expert Opinion on Therapeutic Patents, 8(10), 1323–1346.
Mogee, M., & Kolar, R. (1998b). Patent citation analysis of new chemical entities claimed as pharmaceuticals. Expert Opinion on Therapeutic Patents,8(3), 213–222.
Mogee, M., & Kolar, R. (1999). Patent co-citation analysis of eli lilly & co. patents. Expert Opinion on Therapeutic Patents, 9(3), 291–305.
Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: exploring tissue engineering. Research Policy, 31, 1389–1403.
Narin, F. (1994). Patent bibliometrics. Scientometrics , 30, 147–155.
Newman, M. (2002). Assortative mixing in networks. Physical Review Letters , 89(20), 208701.
Newman, M. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E ,74, 036104
Newman, M., Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E,69, (026113).
OuYang, K., & Weng, C. (2011). A new comprehensive patent analysis approach for new product design in mechanical engineering. Technological Forecasting and Social Change , 78(7), 1183–1199.
Palla G., Barabási A.L., Vicsek T. (2007). Quantifying social group evolution. Nature, 446, 664–667.
Podolny, J., & Stuart, T. (1995) A role-based ecology of technological change. The American Journal of Sociology, 100(5), 1224–1260.
Podolny, J., Stuart, T., Hannan, M. (1996) Networks, knowledge, and niches: Competition in the worldwide semiconductor industry, 1984-1991. The American Journal of Sociology, 102(3), 659–689.
Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10, 191–218.
Pyka, A., & Scharnhost, A. (2009). Innovation Networks. New Approaches in Modelling and Analyzing Heidelberg: Springer-Verlag.
Sampat, B. (2004). Examining patent examination: an analysis of examiner and application generated prior art. Working Paper, Prepared for NBER Summer Institute.
Sampat, B., & Ziedonis, A. (2002). Cite seeing: Patent citations and the economic value of patents. Unpublished manuscript, from the author.
Saviotti, P. (2005). On the co-evolution of technologies and institutions. In: Weber, M., & Hemmelskamp, J. (eds) Towards Environmental Innovations Systems. Heidelberg: Springer.
Saviotti, P., de Looze, M., Maopertuis, M. (2003) Knowledge dynamics and the mergers of firms in the biotechnology based sectors. International Journal of Biotechnology, 5(3–4), 371–401.
Saviotti, P., de Looze, M., Maopertuis, M. (2005) Knowledge dynamics, firm strategy, mergers and acquisitions in the biotechnology based sectors. Economics of Innovation and New Technology, 14(1–2), 103–124.
Schumpeter, J. (1939). Business Cycles. New York: McGraw-Hill.
Shibata, N., Kajikawa, Y., Takeda, Y., Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28, 758–775.
Shibata N., Kajikawa Y., Sakata I. (2010) Extracting the commercialization gap between science and technology – case study of a solar cell. Technological Forecasting and Social Change, 77, 1147–1155.
Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78, 274–282.
Small, H. (1973). Cocitation in scientific literature: New measure of relationship between two documents. Journal of The American Society For Information Science, 24, 265–269.
Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68, 595–610.
Sood A., & Tellis G. (2005) Technological evolution and radical innovation. Journal of Marketing, 69, 152–168.
Sorenson, O., Rivkin, J., Fleming, L. (2006) Complexity, networks and knowledge flow. Research Policy ,35(7), 994–1017.
Sternitzke, C. (2009) Patents and publications as sources of novel and inventive knowledge. Scientometrics, 79, 551–561.
Strandburg, K., Csárdi, G., Tobochnik, J., Érdi, P., Zalányi, L. (2007). Law and the science of networks: An overview and an application to the “patent explosion”. Berkeley Technology Law Journal, 21, 1293.
Strandburg, K., Csárdi, G., Tobochnik, J., Érdi, P., Zalányi, L. (2009). Patent citation networks revisited: signs of a twenty-first century change? North Carolina Law Review, 87, 1657–1698.
Strumsky, D., Lobo, J., Fleming, L. (2005). Metropolitan patenting, inventor agglomeration and social networks: A tale of two effects. SFI Working Paper No. 05-02-004, available at http://www.santafe.edu/media/workingpapers/05-02-004.pdf.
Tijssen, R. (2001). Global and domestic utilization of industrial relevant science: patent citation analysis of science-technology interactions and knowledge flows. Research Policy, 30, 35–54.
Usher, A. (1954). A History of Mechanical InventionCambridge: Dover.
Verbeek, A., Debackere, K., Luwel, M., Zimmermann, E. (2002). Measuring progress and evolution in science and technology– I: The multiple uses of bibliometric indicators. International Journal of Management Reviews, 4(2), 179–211.
Vespignani, A. (2009). Predicting the behavior of techno-social systems. Science, 325(5939), 425–428.
Wallace, M., Gingras, Y., Duhon, R. (2009). A new approach for detecting scientific specialties from raw cocitation networks. Journal of the American Society for Information Science and Technology, 60(2), 240–246.
Ward, J. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244.
Weitzman, M. (1996). Hybridizing growth theory. American Economic Review, 86(2), 207–12.
Weng, C., Chen, W.Y., Hsu, H.Y., Chien, S.H. (2010). To study the technological network by structural equivalence. Journal of High Technology Management Research, 21, 52–63.
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.
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