In this thesis, we proposes two approaches for different patent analysis purpose: the hybrid patent classification approach for automatically categorizing patents, and the patent trend change mining approach for detect technological change trends.
We have proposed a novel patent network-based classification method, which uses patent metadata to derive the weights of the relationships between different types of nodes in the patent network, Based on the patent network analysis, the classification result can be improved by considering the neighboring patent nodes and class nodes of a query patent in making class prediction. The main contributions of the proposed method include novel designs on (a) patent network construction based on the proposed relationship metrics between different types of patent nodes; and (b) patent class prediction based on the patent network analysis and the modified kNN classifier. Our experiment results demonstrate that the proposed patent network-based method outperforms the content-based, citation-based and metadata-based methods. Moreover, we combine the patent network-based method with three conventional classification methods to develop a hybrid patent classification approach. Our experiment results demonstrate that the hybrid approach performs better than the patent network-based method. The proposed hybrid patent classification approach can further enhance the classification performance by a hybrid of multiple classifiers. For the hybrid effect, the result shows that the patent network-based method is more important than other methods in enhancing the performance of classification.
The proposed patent trend change mining approach captures changes in patent trends without the need for specialist knowledge and reports changes to business managers by ranking the degrees of change. Competitive intelligence of business is derived by an automatic change mining approach that business managers can modify and develop appropriate strategies according to their findings. The proposed approach mines changes in patent trends by analyzing the metadata in patent documents. We applied the proposed PTCM to Taiwan’s semiconductor industry for the period 2001-2004 to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, the technological competitiveness of companies and the technological competitiveness of companies in a specific technological field. The results obtained by the proposed approach can be used as an important reference for decision makers to make more accurate strategies on research and development.
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There remain several extended researches to do base on this study. The primary part of most patent document is textual content which contains rich information to utilize (e.g., abstracts and claims). Through analyzing the textual part we can surely improve the quality of change detection and provide more comprehensive results. Therefore the next research will be a patent trend change mining approach which utilizes text mining techniques.
An obvious task to put effort on is to find out the best combination of weights for hybrid patent classification, i.e, parameterα, β, γ and δ in Eq. 15. The combination might change depending on target industries, data fields to analysis and terminology distribution, etc.
We have designed a series of experiments to find out the best weight combination for the example in this thesis. And in fact, how to accurately determine the best weight combination for various cases will also be an issue to study.
Another important future work is to develop an effective validation approach for examining the results obtained from patent trend change mining, and for conducting further analysis.
Traditional indices for evaluating data mining results might not be adequate for patent trend change mining. Approaches which evaluate the results more accurately can significantly improve the precision and accuracy of the model. This is also a challenging work for us to do.
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References
Agrawal, R., Srikant, R. (1994). Fast algorithm for mining association rules. Proc Int Conf on Very Large Data Bases, Santiago de Chile. San Francisco, CA: Morgan Kaufmann, 487- 499.
Berio, G., Harzallah, M. (2007). Towards an integrating architecture for competence management. Computers in Industry, 58 (2), 199-209.
Breitzman, A. F., Mogee, M. E. (2002). The many applications of patent analysis. Journal of Information Science,28 (3), 187-205.
Brockhoff, K. K. (1991). Indicators of firm patent activities. In: Technology Management: the New International language, 476-481.
Chang, M. C. (2005). Quantum computation patent mapping- a strategic view for the information technique of tomorrow. International Conference on Service Systems and Service Management, 1177-1181.
Chang, C.-W., Lin, C.-T., and Wang L.-O. (2009). Mining the text information to optimizing the customer relationship management. Expert Systems with Applications, 36 (2), 1433-1443.
Chen, M. C., Chiu, A. L., Chang, H. H. (2005). Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28 (4), 773-781.
Chen, S. Y., Liu, X. (2004). The contribution of data mining to information science. Journal of Information Science, 30 (6), 550-558.
CHI-Research, http://www.chiresearch.com.
Cong, H. and Tong, L. H. (2008). Grouping of TRIZ Inventive Principles to facilitate automatic patent classification. Expert Systems with Applications, 34, 788-795.
Cong, H. and Loh, H. T. (2010). Pattern-Oriented Associative Rule-Based Patent Classification. Expert Systems with Applications, 37(3), 2395-2404.
Dou, H., Leveillé, V., Manullang, S. & Dou, J. J. (2005). Patent analysis for competitive technical intelligence and innovative thinking. Data Science Journal, 4, 209-237.
Dürsteler, J. C. Patent analysis. http://www.infovis.net/ , visited 2007/5.
EPO Web Site, http://www.european-patent-office.org/index.en.php , European Patent Office.
Fall, C. J., Torcsvari, A., Benzineb, K. and Karetka, G. (2003). Automated categorization in the International Patent Classification. In SIGIR Forum, 10-25.
Fall, C. J., Torcsvari, A., Benzineb, K. and Karetka, G. (2004). Automated categorization of German-language Patent Documents. Expert Systems with Applications, 26(2), 269-277.
Guan, J. C. and Gao, X. (2009). Exploring the h-Index at Patent Level. Journal of the American Society for Information Science and Technology, 60(1), 35-40.
Han, J., Kamber, M. (2001). Mining association rules in large databases. Data Mining-Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers.
41
Huang, Z., Chen, H., Yip, A., Ng, G., Guo, F., Chen, Z. K., Roco, M. C. (2003). Longitudinal patent analysis for nanoscale science and engineering: country, institution and technology field. Journal of Nanoparticle Research, 5 (3-4), 333-363.
Huang, S.-H., Ke, H.-R., Yang, W.-P. (2008). Structure clustering for Chinese patent documents. Expert Systems with Applications, 34 (4), 2290-2297.
Ian, H. W., Eibe, F. (2000). Output: Knowledge Representation. Data Mining. San Francisco:
Morgan Kaufmann Publishers.
Kim, J. H. and Choi, K. S. (2007). Patent document categorization based on semantic structural information. Information Processing and management, 43, 1200-1215.
Kim, Y. G., Suh, J. H., Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34 (3), 1804-1812.
Kohonen, T., Kaski, S., Lagus, K., Salojavi, J., Honkela, J., Paatetro, V., et al. (2000). Self organization of a massive document collection. IEEE Transactions on Neural Networks, 11(3), 574-585.
Kuo, R. J., Lin, S. Y., Shih, C.W. (2007). Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications, 33 (3), 794-808.
Lai, K. K., Wu, S. J. (2005). Using the Patent Co-citation Approach to Establish a New Patent Classification System. Information Processing and Management, 41, 313-330.
Larkey, L. S. (1999). A Patent Search and classification system. In Proceedings of the fourth ACM conference on Digital libraries, 179-183.
Li, X., Chen, H. C., Zhang, Z., Li, J. (2007). Automatic Patent Classification using Citation Network Information: An Experimental Study in Nanotechnology. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, 419-427.
Liu, B., Hsu, W., Ma, Y. (2001). Discovering the set of fundamental rule changes. Proc. of the 7th ACM International Conference on Knowledge Discovery and Data Mining. San Francisco, California, 335-340.
Liu, B., Hsu, W. (1996). Post-analysis of learned rules. Proc. of 13th National Conference on Artificial Intelligence, Menlo Park, California, 828-834.
Liu, B., Hsu, W., Han, H. S., Xia, Y. (2000). Mining changes for real-life applications. Proc.
of the 2nd Int Conf on Data Warehousing and Know Discovery, London, 337-346.
Liu, D. R., Shih M. J., Liau C. J. & Lai, C. H. (2009). Mining the change of event trends for decision support in environmental scanning. Expert Systems with Applications, 36 (2), 972-984.
Loh, H. T., He, C. and Shen, L. (2006). Automatic classification of patent documents for TRIZ users. World Patent Information, 28(1), 6-13.
Ngai, E.W.T., Xiu L., and Chau, D.C.K., (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems
42
with Applications, 36 (2), 2592-2602.
O’Hara, K. Alani, H. and Shadbolt, N. (2002). Identifying Communities of Practice:
Analysing Ontologies as Network to Support Community Recognition. In Proceeding Conference International Federation Information Processing, World Computer Congress.
Reitzig, M. (2004). Improving patent valuations for management purposes- validating new indicators by analyzing application rationales. Research Policy , 33 (6-7), 939-957.
Richter, G. and MacFarlane, A. (2005). The Impact of metadata on the accuracy of automated patent classification. World patent Information, 27(1), 13-26.
Salton, G. and Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval.
Information Process Management, 24(4), 323-328.
Shih, M. J. and Liu, D. R. (2010). Patent classification using ontology-based patent network analysis. In Pacific Asia Conference on Information System (PACIS 2010), July 9-12, 2010, Taipei, Taiwan.
Shih, M. J., Liu, D. R., and Hsu, M. L. (2010). Discovering competitive intelligence by mining changes in patent trends. Expert Systems with Applications, 37, 2882-2890.
Song, H. S., Kim, J. K., Kim, S. H. (2001). Mining the change of customer behavior in an internet shopping mall. Expert Systems with Applications, 21 (3), 157-168.
Stembridge, B. (2005). Sorting the wheat from the chaff- the use of patent analysis in evaluating portfolios. http://www.scientific.thomson.com/newsletter.
Stembridge, B., Corish, B. (2004). Patent data mining and effective patent portfolio management. Intellectual Asset Management, Oct./Nov, 30-35.
Su, F. P., Lai, K. K., Sharma, R. R. K. and Kuo, T. H. (2009). Patent priority Network:
Linking Patent Portfolio to Strategic Goals. Journal of the American Society for Information Science and Technology, 60(11), 2353-2361.
Trappey, A.J.C., Hsu, F. C., Trappey, C. V., Lin, C-I. (2006). Development of a patent document classification and search platform using a back-propagation network. Expert Systems with Applications, 31, 755-765.
Tuomo, N., Hermans, R., Kulvik, M. Patent citations indicating present value of the biotechnology business. http://www.etla.fi/.
USPTO Web Site, http://www.uspto.gov/ , United States Patent & Trademark Office.
Van Rijsbergen, C. J. (1979). Information retrieval (2nd ed.). London: Butterworths.
WIPO Web Site, http://www.wipo.int/portal/index.html.en , World Intellectual Property Organization.
Yang, Y. (1994). Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorisation and Retrieval. In: Croft WB, van Rijsbergen CJ, editors. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, 3-6 July. New York: ACM, 13-22.
Yang, Y. Y., Akers, L., Klose, T., Yang, C. B. (2008). Text mining and visualization tools –
43
Impressions of emerging capabilities. World Patent Information, 30 (4), 280-293.
Yen, S. J., Lee, Y. S. (2006). An efficient data mining approach for discovering interesting knowledge from customer transactions. Expert Systems with Applications, 30 (4), 650-657.
Yuan, Y. C., Carboni, I. and Ehrlich K. (2010). The Impact of Awareness and Accessibility on Expertise Retrieval: A Multilevel Network Perspective. Journal of the American Society for Information Science and Technology, 61(4), 700-714.
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Appendix A.
The algorithm of patent network analysis.
Initialize all nodes weights to 1
Create a relationship-array of relationships and weights Set query patent document as the active node
Mark current node as unlocked and add it to a node-array Loop to the maximum number of links to traverse
Search for the current node in node-array If found:
Mark node as locked Set node as the active node
Get all node connected to current node with a relationship in the relationship-array
Loop to number of connected nodes If node not in node-array (new node)
Weight of node=initial weight + current node weight
* weight of connecting relation Mark node as unlocked and add it to node-array If node already in node-array
Weight of node=node weight + current node weight
* weight of connecting relation End loop
If not found then exit End loop
Relevance of node = Weight of node / n
(n= the minimum number of the links traversed to reach the node starting from the query node)
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Appendix B.
IPCs belonging to the semiconductor industry identified by the Taiwan Intellectual Property Office
IPC Description
C23C Coating metallic material; Coating material with metallic material; Surface treatment of metallic material by diffusion into the surface, by chemical
conversion or substitution; Coating by vacuum evaporation, by sputtering, by ion implantation or by chemical vapor deposition
016/00 Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapor deposition (CVD) processes
G01R Measuring electric variables; Measuring magnetic variables 031/02 General constructional details
G03F Photomechanical production of textured or patterned surfaces, e.g., for printing, for processing of semiconductor devices;
007/00 Photomechanical, e.g., photolithographic, production of textured or patterned surfaces, e.g., printed surfaces;
009/00 Registration or positioning of originals, masks, frames, photographic sheets, or textured or patterned surfaces
G05F Systems for regulating electric or magnetic variables 001/10 Regulating voltage or current
G11C Static stores
007/00 Arrangements for writing information into, or reading information from, a digital store
016/04 Using variable threshold transistors, e.g., FAMOS H01L Semiconductor devices; Electronic solid state devices
021/00 Processes or apparatus specially adapted for the manufacture or treatment of semiconductor or solid state devices or parts thereof
023/34 Arrangements for cooling, heating, ventilating or temperature compensation 023/48 Arrangements for conducting electric current to or from the solid state body in
operation, e.g., leads, terminal arrangements 023/495 Lead-frames
023/52 Arrangements for conducting electric current within the device in operation from one component to another
023/58 Structural electrical arrangements for semiconductor devices 023/62 Protection against over-current or overload, e.g., fuses
027/108 Dynamic random access memory structures
029/00 Semiconductor devices specially adapted for rectifying, amplifying, oscillating or switching and having at least one potential-jump barrier or surface barrier;
Capacitors or resistors with at least one potential-jump barrier or surface barrier, e.g. PN-junction depletion layer or carrier concentration layer; details of
semiconductor bodies 029/40 Electrodes
029/76 Unipolar devices 029/788 With floating gate
029/94 Metal-insulator-semiconductors, e.g., MOS
031/062 The potential barriers being only of the metal-insulator-semiconductor type
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031/113 Being of the conductor-insulator- semiconductor type, e.g., metal- insulator-semiconductor field-effect transistor
031/119 Characterized by field-effect operation, e.g., MIS type detectors
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Appendix C.
Normative sections of patent documents.