Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591; http://link.springer.com/article/10.1007/s11192-017-2449-

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

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 (i) identifies actual clusters of patents: i.e. technological branches, and (ii) 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

Patent Overlay Mapping: Visualizing Technological Distance

This paper presents a new global patent map that represents all technological categories and a method to locate patent data of individual organizations and technological fields on the global map. This overlay map technique may support competitive intelligence and policy decision making. The global patent map is based on similarities in citing-to-cited relationships between categories of the International Patent Classification (IPC) of European Patent Office (EPO) patents from 2000 to 2006. This patent data set, extracted from the PATSTAT database, includes 760,000 patent records in 466 IPC-based categories. We compare the global patent maps derived from this categorization to related efforts of other global patent maps. The paper overlays the nanotechnology-related patenting activities of two companies and two different nanotechnology subfields on the global patent map. The exercise shows the potential of patent overlay maps to visualize technological areas and potentially support decision making. Furthermore, this study shows that IPC categories that are similar to one another based on citing-to-cited patterns (and thus close in the global patent map) are not necessarily in the same hierarchical IPC branch, thereby revealing new relationships between technologies that are classified as pertaining to different (and sometimes distant) subject areas in the IPC scheme.We thank Kevin Boyack, Loet Leydesdorff, and Antoine Schoen for open and fruitful discussions about this paper. This research was undertaken largely at Georgia Tech drawing on support from the U.S. National Science Foundation (NSF) through the Center for Nanotechnology in Society (Arizona State University; Award No. 0531194); and NSF Award No. 1064146 ("Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight"). Part of this research was also undertaken in collaboration with the Center for Nanotechnology in Society, University of California Santa Barbara (NSF Awards No. 0938099 and No. 0531184). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.Kay L.; Newman, N.; Youtie, J.; Porter A.L.; Rafols García, I. (2014). Patent Overlay Mapping: Visualizing Technological Distance. Journal of the American Society for Information Science and Technology. 65(12):2432-2443. doi:10.1002/asi.23146S243224436512Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M. A., & Balakireva, L. (2009). Clickstream Data Yields High-Resolution Maps of Science. PLoS ONE, 4(3), e4803. doi:10.1371/journal.pone.0004803Boyack, K. W., Börner, K., & Klavans, R. (2008). Mapping the structure and evolution of chemistry research. Scientometrics, 79(1), 45-60. doi:10.1007/s11192-009-0403-5Boyack, K. W., & Klavans, R. (2008). Measuring science–technology interaction using rare inventor–author names. Journal of Informetrics, 2(3), 173-182. doi:10.1016/j.joi.2008.03.001Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351-374. doi:10.1007/s11192-005-0255-6Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69-87. doi:10.1016/s0048-7333(02)00004-5Chen, C. (2003). Mapping Scientific Frontiers: The Quest for Knowledge Visualization. doi:10.1007/978-1-4471-0051-5Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and «Mode 2» to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109-123. doi:10.1016/s0048-7333(99)00055-4Franz , J.S. 2009 Constructing technological distances from US patent dataHinze , S. Reiss , T. Schmoch , U. 1997 Statistical analysis on the distance between fields of technology http://www.isi.fraunhofer.de/isi-media/docs/isi-publ/1997/isi97b81/technology-fields-diastance.pdf?WSESSIONID=5712ff2ca5ffcf0d9590afc8ef7e1486Janssens, F., Zhang, L., Moor, B. D., & Glänzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing & Management, 45(6), 683-702. doi:10.1016/j.ipm.2009.06.003Kauffman, S., Lobo, J., & Macready, W. G. (2000). Optimal search on a technology landscape. Journal of Economic Behavior & Organization, 43(2), 141-166. doi:10.1016/s0167-2681(00)00114-1Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455-476. doi:10.1002/asi.20991Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348-362. doi:10.1002/asi.20967Moya-Anegón, Sci. G. F. de, Vargas-Quesada, B., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., Munoz-Fernández, F. J., & Herrero-Solana, V. (2007). Visualizing the marrow of science. Journal of the American Society for Information Science and Technology, 58(14), 2167-2179. doi:10.1002/asi.20683Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. J. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129-145. doi:10.1023/b:scie.0000037368.31217.34Porter, A. L., & Youtie, J. (2009). Where does nanotechnology belong in the map of science? Nature Nanotechnology, 4(9), 534-536. doi:10.1038/nnano.2009.207Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823-1835. doi:10.1002/asi.21086Rafols, I., & Meyer, M. (2009). Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287. doi:10.1007/s11192-009-0041-yRafols, I., Porter, A. L., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871-1887. doi:10.1002/asi.21368Rosvall, M., & Bergstrom, C. T. (2010). Mapping Change in Large Networks. PLoS ONE, 5(1), e8694. doi:10.1371/journal.pone.0008694Schoen , A. Villard , L. Laurens , P. Cointet , J. Heimeriks , G. Alkemade , F. 2012 The network structure of technological developments: Technological distance as a walk on the technology mapSmall, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. doi:10.1002/asi.4630240406Van den Besselaar, P., & Leydesdorff, L. (1996). Mapping change in scientific specialties: A scientometric reconstruction of the development of artificial intelligence. Journal of the American Society for Information Science, 47(6), 415-436. doi:10.1002/(sici)1097-4571(199606)47:63.0.co;2-yWaltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378-2392. doi:10.1002/asi.2274

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Department of Management EngineeringFirms participating in printer industries have invested their constrained resources into technology development in order to sustain their competitiveness in the industry. Considering the fast-changing market circumstances, each firm???s own investment decisions on technology portfolio may directly affect their performance. In this study, we analyzed patent data, namely number of forward citations and technological classification data (CPC). Using this data, the technological portfolio of a specific firm can be identified, which can further help our understanding on firms??? R&D investment strategies. Number of studies mainly focused on patent class combinations of individual technology level, but portfolios of patent class at a firm level have been understudied. In this study, we tracked the change of class composition within each firms??? technological patents??? portfolio and attempted to identify practical and theoretical implications to portfolio management. We utilized Entropy Index, Co-occurrence and cosine similarities measurements for each indicating diversification, patent scope and portfolio similarities within each patents??? classification subclasses. Additionally, performance evaluation of each portfolio is conducted using forward citation data. This paper shows that in-depth patent data analysis can allow us to explore deeper insights at various levels, individual technology, products and product lines, and firms sufficing different stories.ope

Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence

I present a framework based on the concepts of diversity and coherence for the analysis of knowledge integration and diffusion. Visualisations that help understand insights gained are also introduced. The key novelty offered by this framework compared to previous approaches is the inclusion of cognitive distance (or proximity) between the categories that characterise the body of knowledge under study. I briefly discuss the different methods to map the cognitive dimension

Innovation as a Nonlinear Process, the Scientometric Perspective, and the Specification of an "Innovation Opportunities Explorer"

The process of innovation follows non-linear patterns across the domains of science, technology, and the economy. Novel bibliometric mapping techniques can be used to investigate and represent distinctive, but complementary perspectives on the innovation process (e.g., "demand" and "supply") as well as the interactions among these perspectives. The perspectives can be represented as "continents" of data related to varying extents over time. For example, the different branches of Medical Subject Headings (MeSH) in the Medline database provide sources of such perspectives (e.g., "Diseases" versus "Drugs and Chemicals"). The multiple-perspective approach enables us to reconstruct facets of the dynamics of innovation, in terms of selection mechanisms shaping localizable trajectories and/or resulting in more globalized regimes. By expanding the data with patents and scholarly publications, we demonstrate the use of this multi-perspective approach in the case of RNA Interference (RNAi). The possibility to develop an "Innovation Opportunities Explorer" is specified.Comment: Technology Analysis and Strategic Management (forthcoming in 2013

The Local Emergence and Global Diffusion of Research Technologies: An Exploration of Patterns of Network Formation

Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nano-crystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI Subject Categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time, while the network evolves into an oligopolistic control structure with small-world characteristics. The transition from disciplinary-oriented ("mode-1") to transfer-oriented ("mode-2") research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC