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특허정보를 활용한 기술융합 및 기술인문융합 분석과 예측 방법론 연구 : Forecasting and identifying technology convergence trends based on patent analysis

김지은 2016년

논문상세정보
인용/피인용
' 특허정보를 활용한 기술융합 및 기술인문융합 분석과 예측 방법론 연구 : Forecasting and identifying technology convergence trends based on patent analysis' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Neural network analysis
  • Patent analysis
  • Technology convergence
  • Technology-humanities convergence
  • designstructurematrix
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
85 0

0.0%

' 특허정보를 활용한 기술융합 및 기술인문융합 분석과 예측 방법론 연구 : Forecasting and identifying technology convergence trends based on patent analysis' 의 참고문헌

  • Y. You, B. Kim, and E. Jeong, “An exploratory study on the development path of converging technologies using patent analysis: the case of nano biosensors,” Asian Journal of Technology Innovation, Vol. 22(1), pp. 100–113, 2014.
  • Y. Tseng, Y. Wang, D. Juang, and C. Lin, “Text mining for patent map analysis,” in proceedings of International Association for Computer Information Systems Pacific, Taipei, pp. 1109–1116, 2005.
  • Y. Lai and H. Che, “Modeling patent legal value by extension neural network,” Expert Systems with Applications, Vol. 36(7), pp. 10520–10528, 2009.
  • Y. Kwon, D. Jeong, “Technology relevance analysis between wind power energy-fuel cell-green car using network analysis, IPC map,” Collnet Journal of Scientometrics and Information, Vol. 8(1), pp. 109–121, 2014.
  • Y. Ju, Y. Sohn, “Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea,” Technological Forecasting & Social Change, Vol. 94, pp.44–64, 2015.
  • Y. Geum, C. Kim, S. Lee, and M. Kim, “Technological convergence of IT and BT: Evidence from patent analysis,” ETRI Journal, Vol. 34(3), pp. 439–449, 2012.
  • Y. Cho, and M. Kim, “Entropy and gravity concepts as new methodological indexes to investigate technological convergence: Patent network-based approach,” PLOS ONE, Vol. 9(6), pp. 1–17 2014.
  • Y. Chen, Z. Yang, F. Shu, Z. Hu, M. Meyer, and S. Bhattacharya, “A patent based evaluation of technological innovation capability in eight economic regions in PR China,” World Patent Information, Vol. 31(2), pp.104–110, 2012.
  • X. Zang and Y. Niu, “The Forecast Model of Patents Granted in Colleges Based on Genetic Neural Network,” in proceeding of Electrical and Control Engineering (ICECE), International Conference, Yichang, China, pp. 5090–5093, 2011.
  • W. Xing, X. Ye, and L. Kui, “Measuring convergence of China’s ICT industry: An input–output analysis,” Telecommunications Policy, Vol. 35 (4), pp. 301–313, 2011.
  • T. Teichert and M.A. Mittermayer, “Text mining for technology monitoring,” in proceeding of IEEE International Conference on, Engineering Management(IEMC), Bern, Switzerland, 2002.
  • T. Heo, Y. Yoo, and Y. Kim, “A study on market expansion strategy via two-stage customer presegmentation based on customer innovativeness and value orientation,” Journal of Korea Technology Innovation Society, Vol. 10(1), pp. 73–97, 2007.
  • S.H. Ha, W. Liu H. Cho, and S.H, Kim, “Technological advances in the fuel cell vehicle: Patent portfolio management,” Technological Forecasting & Social Change, Vol. 100, pp. 277–289, 2015.
  • S. Zhang, C. Yuan, K. Chang, and Y. Kenb, “Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network,” Journal of Informatics, Vol. 6(4), pp. 485– 495, 2012.
  • S. Yeon, S. Park, and S. Kim, “A dynamic diffusion model for managing customer's expectation and satisfaction,” Technological Forecasting & Social Change, Vol. 73, pp. 648–665, 2006.
  • S. Lee, B. Yoon, amd Y. Park, “An approach to discovering new technology opportunities: keyword-based patent map approach,” Technovation, Vol. 29(6/7), pp. 481–497, 2009.
  • S. Kuznets, Inventive activity: problems of definition and measurement, Princeton: Princet on University Press for the National Bureau of Economic Research, 1962.
  • S. Gauch and K. Blin, “Technological convergence and the absorptive capacity of standardization,” Technological Forecasting & Social Change, Vol. 91, pp. 236–249, 2015.
  • S. D. Eppinger and T.R. Browning, Design structure matrix method and applications, Cambridge: The MIT press, 2012.
  • S. Austin, A. Baldwin, and B. Li & P. Waskett, “Analytical design planning technique (ADePT): a dependency structure matrix tool to schedule the building design process,” Construction Management and Economics, Vol. 18, pp. 173–182, 2000.
  • R.S Gutierrez, A.O. Solis and S. Mukhopadhyay, “Lumpy demand forecasting using neural networks,” International Journal of Production Economics, Vol. 111(2), pp. 409–420, 2008.
  • R. Sharda, “Neural networks for the MS/OR analyst: an application bibliography,” Interfaces, Vol. 24(2), pp. 116-130, 1994.
  • R. Henderson, A.B. Jaffe, and M. Trajtenberg, “Universities as a source of commercial technology: a detailed analysis of university patenting 1965–1988,” The Review of Eonomics and Statistics, Vol. 80(1), pp. 119–127, 1998.
  • R. Gordon, The meanings and implications of convergence, in: K. Kawamoto (Ed.), Digital Journalism: emerging media and the changing horizons of journalism, USA: Rowman & Littlefield: Lanham (MD), pp. 57–73, 2003.
  • P. rdi, K. Makovi, Z. Somogyv ri, K. Strandburg, J. Tobochnik, P. Volf, and L. Zal nyi, “Prediction of emerging technologies based on analysis of the US patent citation network,” Scientometrics, Vol. 95, pp. 225–242, 2013.
  • National Science and Technology Commission, Plans of national convergence technology development (2009～2013), 2008.
  • NSF and DOC, Converging Technologies for Improving Human Performance-Nanotechnology, Biotechnology, Information Technology and Cognitive Science, Dordrecht, 2002. Available: http://www.wtec.org/ConvergingTechnologies/Report/NBIC_report.pdf.
  • N.T. Thongpapanl, “The changing landscape of technology and innovation management: an updated ranking of journals in the field,’ Technovation, vol. 32 (5), pp. 257–271, 2012.
  • N. Sangal, E. Jordan, V. Sinha, and D. Jackson, “Using Dependency Models to Manage Complex Software Architecture,” in proceeding of OOPSLA '05 the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, New York, pp. 167–176, 2005.
  • N. Resenberg, “Technological change in the machine tool industry, 1840~1910,” The Journal of Economic History, Vol. 23(4), pp. 414–443, 1963.
  • N. Ko, J. Yoon, and W. Seo, “Analyzing interdisciplinarity of technology fusion using knowledge flows of patents,” Expert Systems with Applications, Vol. 41(4), pp. 1955–1963, 2014..
  • M. Trajtenberg, “A Penny for Your Quotes: Patent Citations and the Value of Innovations,” The RAND Journal of Econimics, Vol. 21(1), pp. 172–187, 1990.
  • M. MacGarvie, “The determinants of international knowledge diffusion as measured by patent citations,” Ecology Letters, Vol. 87 (1), pp. 121–126, 2005.
  • M. Kim and C. Kim, “On a patent analysis method for technological convergence,” Procedia- Social and Behavioral Sciences, Vol. 40, pp. 657–663, 2012.
  • M. Karvonen and T. K ssi, “Patent citations as a tool for analyzing the early stages of convergence,” Technological Forecasting & Social Change, Vol. 80, pp. 1094–1107, 2013.
  • M. Hirschey, V.J. Richardson, and S. Scholz, “Value relevance of nonfinancial information: the case of patent data,” Review of Quantitative Finance and Accounting, Vol. 17 (3), pp. 223–235, 2001.
  • M. Danilovic and T.R. Browning, “Managing complex product development projects with design structure matrices and domain mapping matrices,” International Journal of Project Management, Vol. 25, pp. 300–314, 2007.
  • M. Bengisu and R. Nekhili, “Forecasting emerging technologies with aid of science and technology database,” Technological Forecasting & Social Change, Vol. 73(7), pp. 835–844, 2006.
  • L.Y. Hong and J.S. Lee, “Analysis of the influence of texture and color o building exterior materials on sensibility,” Journal of Korean Society of Color Studies, Vol. 27(2), pp. 51–60, 2013.
  • L. Fleming, C.K. III, A.I. Juda, “Small worlds and regional innovation,” Informs, Vol. 18(6), pp. 938–954, 2007.
  • Korean Institute for Industrial Economic and Trade, “Problems of technology convergence in Korea and policy implications,” e-KiET, p. 446, 2009.
  • Korea Institute of Design promotion, “Read the design process_case studies of international design consulting company”, Report on the design strategy 2013/2014, 2014.
  • K.C. Jeong, “Implementation Strategies for Future Ubiquitous Cities Based on u-City Service Model,” Entrue Journal of Information Technology, Vol. 6(1), pp. 81–100, 2007.
  • K. Kim, and J.Y. Kim, “A Typology of Industry Convergences Based on Sources for Convergence Industries and Analysis of Critical Success Factors,” Journal of the Korean Institute of Industrial Engineers, Vol. 39(3), pp. 204–211, 2013.
  • K. Elgazzar, M. Aboelfotoh, P. Martin, and H.S. Hassanein, “Ubiquitous Health Monitoring Using Mobile Web Services,” Procedia Computer Science, Vol. 10, pp. 332-339, 2012.
  • J.S. Gans, and S. Stern, “The product market and the market for ideas: commercialization strategies for technology entrepreneurs,” Research Policy, Vol. 32 (3), pp. 333–350, 2003.
  • J.P. Martino, Technological Forecasting for Decision Making, NY: McGraw-Hill, Inc., 1993.
  • J.H. Hyun and H.J. Kim, “Reorganization plan for industrial technology innovation structure by technology convergence,” Korea Evaluation Institute of Industrial Technology, Policy issue 08- 06, 2008.
  • J.C. Choi. (2012). “Consilience: Philosophy for proper convergence.” The Science Times. [online].Available:http://www.sciencetimes.co.kr/?news=%ED%86%B5%EC%84%AD%EC%9D %80-%EC%A0%9C%EB%8C%80%EB%A1%9C-%EB%90%9C-%EC%9C%B5%ED%95%A9% EC%9D%84-%EC%9C%84%ED%95%9C-%EC%B2%A0%ED%95%99 [Feb. 2, 2012].
  • J. Lind, “Convergence: History of term usage and lessons for firm strategists,” in proceeding of ITS 15th Biennial Conference, Berlin, Germany, 2004.
  • J. Kim, Y. Jang, and S. Lee, “Issues and Efforts for Technology-humanities convergence: Empirical analysis of Korean SMEs,” Journal of the Korean Institute of Industrial Engineers, Vol. 40(5), pp. 451–461, 2014.
  • J. Kim and S. Lee, “Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO,” Technological Forecasting & Social Change, Vol. 92, pp. 332–345, 2015.
  • J. Choi and Y. Hwang, “Patent keyword network analysis for improving technology development efficiency,” Technological Forecasting & Social Change, Vol. 83, pp. 170–182, 2014.
  • H.J. No and Y. Park, “Trajectory patterns of technology fusion: trend analysis and Taxonomical grouping in nanobiotechnology,” Technological Forecasting & Social Change, Vol. 77(1), pp. 63–75, 2010.
  • H.J. No and H, Lim, “Exploration of Nanobiotechnologies Using Patent Data,” The Journal of Intellectual Property, Vol. 4(3), pp. 109–129, 2009.
  • H. Ernst, “Patenting strategies in the German mechanical engineering industry and their relationship to company performance,” Technovation, vol. 15 (4), pp. 225–240, 1995.
  • H. Ernst, “Patent portfolios for strategic R&D planning,” Journal of Engineering and Technology Management, Vol. 15(4), pp. 279–308, 1998.
  • F. Rizzi., E. Annunziata, G. Liberati, and M. Frey, “Technological trajectories in the automotive industry: are hydrogen technologies still possibility?,” Journal of Cleaner Production, Vol. 66, pp. 328-336, 2014.
  • F. Ma and X. Wang, “Analysis of patent intelligence based on data mining,” Journal of Information Science, Vol. 26(11), pp. 1672–1675, 2008.
  • F. Kodama, “Technology fusion and the new R&D,” Harvard Business Review, July-August, pp. 70–78, 1992.
  • F. Hacklin, C. Marxt, and F. Fahrni, “Coevolutionary cycles of convergence: an extrapolation from the ICT industry,” Technological Forecasting & Social Change, Vol. 76(6), pp. 723–736, 2009.
  • E.S. Hong and H.Y. Shin, “A study on ethical consumption behaviors of college students: Classification and Analysis according to the ethical consumption Behaviors,” Korean Journal of Human Ecology, Vol. 20(4), pp. 801–817, 2013.
  • E. Kim, E. Y. Cho, and W. Kim, “Dynamic patterns of technological convergence in printed electronics technologies: patent citation network,” Scientometrics, Vol. 98(2), pp. 975–998, 2014.
  • E. Duguet and M. MacGarvie, “How well do patent citations measure flows of technology? Evidence from French innovation surveys,” Economics of Innovation and New Technology, Vol. 14(5), pp. 375–393, 2005.
  • D.H. Jeong and Y. Kwon, “Analysis on convergence in green technology field using patent information,” Applied Mechanics and Materials, Vol. 548 – 549, pp. 1981–1993, 2014.
  • D. Lee and C. Yoo, “Predicting a promising fusion technology in geoscience and mineral resources engineering using Korean patent data,” Geosystem. Engineering. Vol. 17(1), pp. 34– 42, 2014.
  • D. Black and P. Ciccolo, Machine learning for patent classification. Available: http://cs229.stanford.edu/proj2005/BlackCiccolo-MachineLearningForPatentClassification.pdf (last accessed on January 2016).
  • D. Archibugi and M. Planta, “Measuring technological change through patents and innovation surveys,” Technovation, Vol. 16 (9), pp. 451–468 1996.
  • D, Hwang, Y. Kim, and B. Lee, “A Study on the Classification for Technology Convergence according to Characteristics,” Journal of Korea Technology Innovation Society, Vol. 11(4), pp. 592–612, 2008.
  • C.V. Trappery, H. Wu, F. Taghaboni-Dutta, and A.J.C. Trappery, “Using patent data for technology forecasting: China RFID patent analysis,” Advanced Engineering Informatics, Vol. 25(1), pp. 53–64, 2013.
  • C.J. Fall, A. Torcsvari, P. Fievet, and G. Karetka, “Automated categorization of German language patent documents,” Expert Systems with Applications, Vol. 26(2), pp. 269–277, 2014.
  • C.A. Mitrea, C.K.M. Lee, and Z. Wu, “A comparison between neural networks and traditional forecasting methods: a case study,” International Journal of Engineering Business Management, Vol. 1(2), pp.19–24, 2009.
  • C. Wu and H. Leu, “Examining the trends of technological development in hydrogen energy using patent co-word map analysis,” International Journal of Hydrogen Energy, Vol. 39(33), pp. 19262–19269, 2014.
  • C. Lee, Y. Cho, H. Seol, and Y. Park, “A stochastic patent citation analysis approach to assessing future technological impacts,” Technological Forecasting & Social Change, Vol. 79(1), pp. 16– 29, 2012.
  • C. Curran and J. Leker, “Patent indicators for monitoring convergence – examples from NFF and ICT,” Technological Forecasting & Social Change, Vol. 78, pp. 256–273, 2011.
  • B.P. Abraham, and S.D. Moitra, “Innovation assessment through patent analysis,” Technovation, vol. 21 (4), pp. 245–252, 2001.
  • B. Verspagen, “Mapping technological trajectories as patent citation networks: a study on the history of fuel cell research,” Advances in Complex Systems, Vol. 10(1), pp. 93–115, 2007.
  • B. Kim and Y. Kim, “A study on emotional interface design based on each smart-phone application category,” Korean Design Knowledge Society, Vol. 20, pp. 181–192, 2011.
  • B. Gomes-Casseres, J. Hagedoorn, A.B. Jaffe, “Do alliances promote knowledge flows?,” Journal of Financial Econimics, Vol. 80(1), pp. 5–33, 2006.
  • A.J.C. Trappey and C.V. Trappey, T.Chiang and Y. Huang, “Ontology-Based Neural Network for Patent Knowledge Management in Design Collaboration,” International Journal of Production Research, Vol. 51 (7), pp. 1992–2005, 2013.
  • A.B. Jaffe, M. Trajtenberg, and M.S. Fogarty, “Knowledge spillovers and patent citations: evidence from a survey of inventors,” American Economic Review, Vol. 90(2), pp. 215–218, 2000.
  • A. Goto, and K. Motohashi, “Construction of a Japanese patent database and a first look at Japanese patenting activities,” Research Policy, Vol. 36 (9), pp. 1431–1442, 2007.