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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

Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.Comment: 20 pages, 7 figure

The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources

This paper examines the emergence of technologies, applications and platforms in the area of information and communication technologies (ITC), using patent data. It detects new technologies/applications/products using patents' abstracts and describes them looking at their degree of "hybridisation", in terms of technological domains and knowledge base, at the role of firms in driving the innovation activity, and at the geographical distribution of the innovation. The results show that in emerging technologies in ITC are more concentrated across technological classes and across firms than non emerging ones, and that this pattern is invariant across major countries. Furthermore, a preliminary analysis on patent citations show that in emerging technologies knowledge sources are more specific in terms of technological classes and more dispersed in terms of cited institutions. Also there is evidence of a role for universities and public research centres as sources of knowledge

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

Scientometric mapping as a strategic intelligence tool for the governance of emerging technologies

How can scientometric mapping function as a tool of ’strategic intelligence’ to aid the governance of emerging technologies? The present paper aims to address this question by focusing on a set of recently developed scientometric techniques, namely overlay mapping. We examine the potential these techniques have to inform, in a timely manner, analysts and decision-makers about relevant dynamics of technical emergence. We investigate the capability of overlay mapping in generating informed perspectives about emergence across three spaces: geographical, social, and cognitive. Our analysis relies on three empirical studies of emerging technologies in the biomedical domain: RNA interference (RNAi), Human Papilloma Virus (HPV) testing technologies for cervical cancer, and Thiopurine Methyltransferase (TPMT) genetic testing. The case-studies are analysed and mapped longitudinally by using publication and patent data. Results show the variety of ’intelligence’ inputs overlay mapping can produce for the governance of emerging technologies. Overlay mapping also confers to the investigation of emergence flexibility and granularity in terms of adaptability to different sources of data and selection of the levels of the analysis, respectively. These features make possible the integration and comparison of results from different contexts and cases, thus providing possibilities for a potentially more ’distributed’ strategic intelligence. The generated perspectives allow triangulation of findings, which is important given the complexity featuring in technical emergence and the limitations associated with the use of single scientometric approaches

Patents as a Measure for Eco-Innovation

This paper examines the usefulness of patent analysis for measuring eco-innovation. The overall conclusion is that patents are a useful means for measuring environmentally motivated innovations, such as pollution control technologies and green energy technologies, and for general purpose technologies with environmental benefits. For these types of innovations it is acceptable to use patent analysis, provided they are carefully screened. Patent analysis may be used for measuring five attributes of eco-innovation: (1) eco-inventive activities in specific technology fields, (2) international technological diffusion, (3) research and technical capabilities of companies, (4) institutional knowledge sources of eco-innovation, and (5) technological spillovers and knowledge flows. Up until now it is mainly used for measuring eco-inventive activity.Eco-innovation, patents