Browse > Article
http://dx.doi.org/10.5391/JKIIS.2017.27.2.179

Technology Forecasting using Bayesian Discrete Model  

Jun, Sunghae (Department of Statistics, Cheongju University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.27, no.2, 2017 , pp. 179-186 More about this Journal
Abstract
Technology forecasting is predict future trend and state of technology by analyzing the results so far of developing technology. In general, a patent has novel information about the result of developed technology, because the exclusive right of technology included in patent is protected for a time period by patent law. So many studies on the technology forecasting using patent data analysis has been performed. The patent keyword data widely used in patent analysis consist of occurred frequency of the keyword. In most previous researches, the continuous data analyses such as regression or Box-Jenkins Models were applied to the patent keyword data. But, we have to apply the analytical methods of discrete data for patent keyword analysis because the keyword data is discrete. To solve this problem, we propose a patent analysis methodology using Bayesian Poisson discrete model. To verify the performance of our research, we carry out a case study by analyzing the patent documents applied by Apple until now.
Keywords
Patent Analysis; Technology Forecasting; Bayesian Poisson Model; Text Mining; Apple Patents;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Feinerer, I., Hornik, K., Meyer, D., "Text mining infrastructure in R", Journal of Statistical Software, Vol. 25, No. 5, pp. 1-54, 2008.
2 Press, S. J., Subjective and Objective Bayesian Statistics, second edition, Hoboken, NJ, John Wiley & Sons, 2003.
3 Feinerer, I., Hornik, K., Package 'tm' Ver. 0.6, Text Mining Package, CRAN of R project, 2016.
4 R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org, 2016, [Accessed: July 1, 2016].
5 Roper, A. T., Cunningham, S. W., Porter, A. L., Mason, T. W., Rossini F. A. Banks J., Forecasting and Management of Technology, Hoboken, NJ, John Wiley & Sons, 2011.
6 Jun, S., Park, S., Jang, D., Patent Analysis and Technology Forecasting, Kyowoo, 2014.
7 Jun, S., Park, S., Jang, D., "Technology Forecasting using Matrix Map and Patent Clustering", Industrial Management & Data Systems, Vol. 112, Iss. 5, pp. 786-807, 2012.   DOI
8 Keller, J., Gracht, H. A. V. D., "The influence of information and communication technology (ICT) on future foresight processes - Results from a Delphi survey", Technological Forecasting and Social Change, Vol. 85, pp. 81-92, 2014.   DOI
9 Jun, S, "A Big Data Learning for Patent Analysis", Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 5, pp. 406-411, 2013.   DOI
10 Jun, S., Lee, S., Ryu, J., Park, S., "A novel method of IP R&D using patent analysis and expert survey," Queen Mary Journal of Intellectual Property, Vol. 5, No. 4, pp. 474-494, 2015.   DOI
11 Jun, S., "A Big Data Preprocessing using Statistical Text Mining", Journal of Korean Institute of Intelligent Systems, Vol. 25, No. 5, pp. 470-476, 2015.   DOI
12 Kim, J., Jun, S., "Graphical Causal Inference and Copula Regression Model for Apple Keywords by Text Mining", Advanced Engineering Informatics, Vol. 29, Iss. 4, pp. 918-929, 2015.   DOI
13 Mishra, D., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Dubey, R., Wamba, S., "Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature", Industrial Management & Data Systems, Vol. 116, No. 7, pp. 1331-1355, 2016.   DOI
14 Petruzzelli, A. M., Rotolo, D., Albino, V., "Determinants of patent citations in biotechnology: An analysis of patent influence across the industrial and organizational boundaries", Technological Forecasting and Social Change, Vol. 91, pp. 208-221, 2015.   DOI
15 Kim, H., Kim, J., Lee, J., Park, S., Jang, D., "A Novel Methodology for Extracting Core Technology and Patents by IP Mining", Journal of Korean Institute of Intelligent Systems, Vol. 25, No. 4, pp. 392-397, 2015.   DOI
16 Kim, J., Lee, J., Park, S., Jang, D., "Technology Strategy based on Patent analysis", Journal of Korean Institute of Intelligent Systems, Vol. 26, No. 2, pp. 141-146, 2016.   DOI
17 Quinn, K. M., Martin, A. D., Park, J. H., MCMCpack: Markov chain Monte Carlo in R. Journal of Statistical Software, Vol. 42, No. 9, pp. 1-21, 2011.
18 Korb, K. B., Nicholson, A. E., Bayesian artificial intelligence, second edition, London, UK CRC press, 2011.
19 Bottcher S. G., Dethlefsen, C., "Learning Bayesian networks with R", International Workshop on Distributed Statistical Computing (DSC2003) Working Papers, pp. 1-11, 2003.
20 Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., Rubin, D. B., Bayesian Data Analysis, Third Edition, Boca Raton, FL, Chapman & Hall/CRC Press, 2003.
21 Jeffreys, S. H., Theory of Probability. third edition. Clarendon Press, Oxford, 1998.
22 Bernardo, J., Smith, A. F. M., Bayesian Theory, John Wiley & Sons, New York, 1994.
23 Chib, S., "Estimation and Comparison of Multiple Change-Point Models", Journal of Econometrics, Vol. 86, No. 2, pp. 221-241, 1998.   DOI
24 Zou, C., Zhang, Y., Wang, Z., "A control chart based on a change-point model for monitoring linear profiles", IIE transactions, Vol. 38, No. 12, pp. 1093-1103, 2006.   DOI
25 USPTO, The United States Patent and Trademark Office, http://www.uspto.gov, 2016, [Accessed: September 1, 2016].
26 WIPSON, WIPS Corporation, http://www.wipson.com, 2016, [Accessed: September 1, 2016].