Mapping technology space by normalizing patent networks

Technology is a complex system with technologies relating to each other in a space that can be mapped as a network. The technology network’s structure can reveal properties of technologies and of human behavior, if it can be mapped accurately. Technology networks have been made from patent data using several measures of proximity. These measures, however, are influenced by factors of the patenting system that do not reflect technologies or their proximity. We introduce a method to precisely normalize out multiple impinging factors in patent data and extract the true signal of technological proximity by comparing the empirical proximity measures with what they would be in random situations that remove the impinging factors. With this method, we created technology networks, using data from 3.9 million patents. After normalization, different measures of proximity became more correlated with each other, approaching a single dimension of technological proximity. The normalized technology networks were sparse, with few pairs of technology domains being significantly related. The normalized network corresponded with human behavior: We analyzed the patenting histories of 2.8 million inventors and found they were more likely to invent in two different technology domains if the pair was closely related in the technology network. We also analyzed the patents of 250,000 firms and found that, in contrast with inventors, firms’ inventive activities were only modestly associated with the technology network; firms’ portfolios combined pairs of technology domains about twice as often as inventors. These results suggest that controlling for impinging factors provides meaningful measures of technological proximity for patent-based mapping of the technology space, and that this map can be used to aid in technology innovation planning and management.

[1]  Stuart A. Kauffman,et al.  Optimal search on a technology landscape , 2000 .

[2]  Bart Verspagen,et al.  Does it matter where patent citations come from? Inventor versus examiner citations in European patents , 2005 .

[3]  Alan L. Porter,et al.  Patent overlay mapping: Visualizing technological distance , 2012, J. Assoc. Inf. Sci. Technol..

[4]  R. D. Hunter,et al.  Is the patent system broken? (If it isn't broken, don't fix it) , 2002, IEEE 2002 International Symposium on Technology and Society (ISTAS'02). Social Implications of Information and Communication Technology. Proceedings (Cat. No.02CH37293).

[5]  Henry G. Small,et al.  Co-citation in the scientific literature: A new measure of the relationship between two documents , 1973, J. Am. Soc. Inf. Sci..

[6]  L. Fleming Breakthroughs and the ¿Long Tail¿ of Innovation , 2007 .

[7]  Ismael Rafols,et al.  Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC) , 2012, Scientometrics.

[8]  A. Barabasi,et al.  High-Quality Binary Protein Interaction Map of the Yeast Interactome Network , 2008, Science.

[9]  Yili Hong,et al.  On computing the distribution function for the Poisson binomial distribution , 2013, Comput. Stat. Data Anal..

[10]  W. Brian Arthur,et al.  The Nature of Technology: What it Is and How it Evolves , 2009 .

[11]  Bait Verspagen,et al.  Estimating international technology spillovers using technology flow matrices , 1997 .

[12]  Rosemarie H. Ziedonis,et al.  The patent paradox revisited: an empirical study of patenting in the U , 2001 .

[13]  Katy Börner,et al.  Mapping knowledge domains , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[14]  van Eck Nees Jan,et al.  How to normalize cooccurrence data An analysis of some well-known similarity measures , 2009 .

[15]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Loet Leydesdorff,et al.  Innovation systems as patent networks: The Netherlands, India and nanotech , 2011, 1108.0381.

[17]  A. Jaffe Technological Opportunity and Spillovers of R&D: Evidence from Firms&Apos; Patents, Profits and Market Value , 1986 .

[18]  O. Sorenson,et al.  Science as a Map in Technological Search , 2000 .

[19]  Jianxi Luo,et al.  Measuring technological distance for patent mapping , 2015, J. Assoc. Inf. Sci. Technol..

[20]  M. Gittelman,et al.  Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations , 2006, The Review of Economics and Statistics.

[21]  M Tumminello,et al.  A tool for filtering information in complex systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Adam B. Jaffe,et al.  Characterizing the “technological position” of firms, with application to quantifying technological opportunity and research spillovers☆ , 1989 .

[23]  Pier Paolo Saviotti,et al.  Competition, variety and technological evolution: A replicator dynamics model , 1995 .

[24]  Loet Leydesdorff,et al.  Patent classifications as indicators of intellectual organization , 2008, J. Assoc. Inf. Sci. Technol..

[25]  Ismael Rafols,et al.  Global maps of science based on the new Web-of-Science categories , 2012, Scientometrics.

[26]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Sergi Valverde,et al.  Topology and evolution of technology innovation networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  K. Börner,et al.  Mapping topics and topic bursts in PNAS , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Deborah M Gordon,et al.  Community disassembly by an invasive species , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  魏屹东,et al.  Scientometrics , 2018, Encyclopedia of Big Data.

[31]  Ron Boschma,et al.  Evolutionary Economic Geography , 2018 .

[32]  Nees Jan van Eck,et al.  How to normalize cooccurrence data? An analysis of some well-known similarity measures , 2009, J. Assoc. Inf. Sci. Technol..

[33]  Werner Ulrich,et al.  Disentangling community patterns of nestedness and species co‐occurrence , 2007 .

[34]  Bart van Looy,et al.  Technological Diversification, Coherence and Performance of Firms , 2007 .

[35]  Penny O’Connor,et al.  American Society for Information Science and Technology, Annual Conference , 2002 .

[36]  Jonathan Cagan,et al.  On the benefits and pitfalls of analogies for innovative design : Ideation performance based on analogical distance, commonness, and modality of examples , 2011 .

[37]  B. Verspagen,et al.  The size distribution of innovations revisited: an application of extreme value statistics to citation and value measures of patent significance , 2007 .

[38]  Mark A. Schankerman,et al.  Identifying Technology Spillovers and Product Market Rivalry , 2005 .

[39]  Bart Verspagen,et al.  Self-organization of R&D search in complex technology spaces , 2007 .

[40]  Rosemarie H. Ziedonis,et al.  Reprinted Article The patent paradox revisited: an empirical study of patenting in the U.S. semiconductor industry, 1979–1995 , 2009 .

[41]  Werner Ebeling,et al.  A stochastic model of technological evolution , 1992 .

[42]  Christian D. Schunn,et al.  Do the best design ideas (really) come from conceptually distant sources of inspiration , 2015 .

[43]  J. Lobo,et al.  “ If It Isn ’ t Broken , Don ’ t Fix It ” : Extremal Search on a Technology Landscape , 2002 .

[44]  E. C. Engelsman,et al.  A patent-based cartography of technology , 1994 .

[45]  Jong-Chan Kim,et al.  Technology convergence: What developmental stage are we in? , 2015, Scientometrics.

[46]  Frank Neffke,et al.  Revealed Relatedness: Mapping Industry Space , 2008 .

[47]  L. Stone,et al.  The checkerboard score and species distributions , 1990, Oecologia.

[48]  Julio Saez-Rodriguez,et al.  Fast randomization of large genomic datasets while preserving alteration counts , 2014, Bioinform..

[49]  Giulio Bottazzi,et al.  Measuring Industry Relatedness and Corporate Coherence , 2010 .

[50]  Axel Marx,et al.  Ecological Modernization, Environmental Policy and Employment. Can Environmental Protection and Employment be Reconciled? , 2000 .

[51]  F. Malerba,et al.  Knowledge-relatedness in firm technological diversification , 2003 .

[52]  Bart Verspagen,et al.  A Percolation Model of Innovation in Complex Technology Spaces , 2002 .

[53]  Bart Verspagen,et al.  Measuring intersectoral technology spillovers estimates from the European and US patent office databases , 1997 .

[54]  A. Reinstaller Koen Frenken: Innovation, evolution and complexity theory , 2007 .

[55]  Jun S. Liu,et al.  STATISTICAL APPLICATIONS OF THE POISSON-BINOMIAL AND CONDITIONAL BERNOULLI DISTRIBUTIONS , 1997 .

[56]  D. Simonton Creativity as Blind Variation and Selective Retention : Is the Creative Process Darwinian ? , 2022 .

[57]  Vetle I. Torvik,et al.  Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010) , 2014 .

[58]  Trademark Office,et al.  Manual of patent examining procedure , 2004 .

[59]  W. Myers,et al.  Atypical Combinations and Scientific Impact , 2013 .

[60]  L. Leydesdorff,et al.  Evolutionary Economics and Chaos Theory: New Directions in Technology Studies , 1994 .

[61]  Yeonbae Kim,et al.  Measuring relatedness between technological fields , 2010, Scientometrics.

[62]  M. Fernandez,et al.  Closed-Form Expression for the Poisson-Binomial Probability Density Function , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[63]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[64]  Yves Gingras,et al.  A new approach for detecting scientific specialties from raw cocitation networks , 2009, J. Assoc. Inf. Sci. Technol..

[65]  S. Winter,et al.  Understanding corporate coherence: Theory and evidence , 1994 .

[66]  Vincent A. Traag,et al.  Faster unfolding of communities: speeding up the Louvain algorithm , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[67]  K. Frenken A fitness landscape approach to technological complexity, modularity, and vertical disintegration , 2006 .

[68]  David J. Bryce,et al.  A General Interindustry Relatedness Index , 2009, Manag. Sci..