Abstract
In knowledge-based economy, technologic capability would be the core assets of enterprises, and technology management is considered one of the most important management activities. The foremost work of technology managers should be exploring technology trends to support technology planning. However, exploring technology trend is no longer concentrated in one disciplines, much innovative research results from integrating technologies developed in different disciplines. An automatic tool to help explore inter-discipline technology should be very useful for technology managers. Recently, some researches which through the patent documents to explore technology trend mostly identify the trend by patent map, described the technology trend each discipline separately, and can’t explore inter-discipline trend to help technology manager broader vision. Moreover, most of discussing technology trend studies, even discovering emerging technology—the most popular topic, adopted the published patent documents written by English and based on USPTO for patent analysis. Seldom of patent analysis research collected patent documents written by Chinese—the most population language. Our research attempts to remedy the shortfall of aforementioned studies, and propose an integrated technique. The research utilizes common themes development methodology to identify multiple discipline and the methods work with patents written in Chinese. Our developed method does not try to predict future development, but try to present current technology development as it is in a succinct way to help technology managers make the judgment. Our system would provide inter-discipline technology analysis which across transmission technology and transmission of digital information technology, and the empirical experiment found twenty three inter-discipline technological subjects. Furthermore, extended experimental scope—added one discipline (image communication technology), our experiment found thirteen inter-discipline technological subjects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aboelela, S.W., Larson, E., et al.: Defining interdisciplinary research: conclusions from a critical review of the literature. Health Service Res. 42(1 pt. 1), 329–346 (2007)
Bengisu, M., Nekhili, R.: Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change 73(7), 835–844 (2006)
Zhai, C.X., Velivelli, A., Yu, B.: A cross-collection mixture model for comparative text mining. ACM 1-58113-888-1/04/0008 (2004)
Daim, T.U., Rueda, G., et al.: Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change 73(8), 981–1012 (2006)
Kim, Y.G., Suh, J.H., et al.: Visualization of patent analysis for emerging technology. Expert Systems with Applications 34(3), 1804–1812 (2008)
Kostoff, R.N.: Science and technology innovation. Technovation 19(10), 593–604 (1999)
Kostoff, R.N., Demarco, R.A.: Extracting information from the literature by text mining. Analytical Chemistry 73(13), 370A-378A (2001)
Lee, H.J., Lee, S., et al.: Technology clustering based on evolutionary patterns: The case of information and communications technologies. Technological Forecasting and Social Change 78(6), 953–967 (2011)
Lee, S., Yoon, B., et al.: Business planning based on technological capabilities: Patent analysis for technology-driven road-mapping. Technological Forecasting and Social Change 76(6), 769–786 (2009)
Lee, S., Yoon, B., et al.: An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation 29(6-7), 481–497 (2009)
Mei, Q., Zhai, C.X.: A Mixture Model for Contextual Text Mining. ACM 1-59593-339-5/06/0008 (2006)
OuYang, K., Weng, C.S.: A new comprehensive patent analysis approach for new product design in mechanical engineering. Technological Forecasting and Social Change 78(7), 1183–1199 (2011)
Park, H., et al.: A Patent intelligence system for strategic technology planning. Expert Systems with Applications (2012)
Wu, Y., Ding, Y., Wang, X., Xu, J.: Topic Detection by Topic Model Induced Distance Using Biased Initiation. In: Kim, T.-h., Adeli, H. (eds.) AST/UCMA/ISA/ACN 2010. LNCS, vol. 6059, pp. 310–323. Springer, Heidelberg (2010)
Morinaga, S., Yamanishi, K.: Tracking Dynamics of Topic Trends Using a Finite Mixture Model. ACM (2004), 1-58113-888-1/04/0008
Shih, M.-J., et al.: Discovering competitive intelligence by mining changes in patent trends. Expert Systems with Applications 37(4), 2882–2890 (2010)
Son, C., Suh, Y., et al.: Development of a GTM-based patent map for identifying patent vacuums. Expert Systems with Applications 39(3), 2489–2500 (2012)
Tseng, Y.H., et al.: Text mining techniques for patent analysis. Information Processing & Management 43(5), 1216–1247 (2007)
Yoon, B., Park, Y.: A text-mining-based patent network: Analytical tool for high-technology trend. The Journal of High Technology Management Research 15(1), 37–50 (2004)
Zhai, C.X., Velivelli, A., Yu, B.: A cross-collection mixture model for comparative text mining. ACM 1-58113-888-1/04/0008 (2004)
R&D, Wikipedia, http://en.wikipedia.org/wiki/Research_and_development
The, EU Survey on R&D Investment Business Trends (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Cheng, M.S., Hsu, P. (2014). Automatically Explore Inter-Discipline Technology from Chinese Patent Documents. In: Chau, M., Chen, H., Wang, G.A., Wang, JH. (eds) Intelligence and Security Informatics. PAISI 2014. Lecture Notes in Computer Science, vol 8440. Springer, Cham. https://doi.org/10.1007/978-3-319-06677-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-06677-6_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06676-9
Online ISBN: 978-3-319-06677-6
eBook Packages: Computer ScienceComputer Science (R0)