研究生: |
簡英琪 Chien, Ying-Chi |
---|---|
論文名稱: |
以多準則決策與專利探勘方法定義機器視覺之技術地圖 Defining the Technology Roadmap for Machine Vision Technology using MCDM Methods and Patent Mining |
指導教授: |
黃啟祐
Huang, Chi-Yo |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 英文 |
論文頁數: | 143 |
中文關鍵詞: | 文字探勘 、關聯規則探勘 、優勢關係為基礎的粗略集理論 、正規化概念分析 、決策實驗室分析法 、技術路徑圖 |
英文關鍵詞: | Text Mining, Association Rule Mining(ARM), Dominance-based Rough Set Approach (DRSA), Formal Concept Analysis(FCA), Decision Making Trial and Evaluation Laboratory (DEMATEL), Technology Roadmap |
DOI URL: | http://doi.org/10.6345/THE.NTNU.DIE.036.2018.E01 |
論文種類: | 學術論文 |
相關次數: | 點閱:233 下載:0 |
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大數據概念迅速發展,成為跨越資訊技術顯學,在眾多樣態、良莠不齊的巨量資料中,精萃出有潛在價值、商業趨勢的資訊,提供企業改善與經營決策之參考依據,進一步提升市場上競爭力,真正實現大數據的價值。專利是推動科技進步的重要機制,在法律嚴密的保障下,激勵人們發明創新與增進經濟發展,並可作為競爭分析及技術發展基礎。以往技術路徑圖的相關研究通常聚焦於特定技術的結果,或是企業技術發展歷程,較少有研究探討如何分析專利探勘結果產生技術路徑圖藉以預測未來技術趨勢。因此,本研究透過文字探勘技術收集專利關鍵字,並依據專家評估意見,進行關鍵字分組。再透過優勢關係為基礎的粗略集理論推導技術關鍵字與產品關鍵字間之推理關係,再導入正規化概念分析,將技術關鍵字歸納為技術之概念,最後利用決策實驗室分析法訂定關鍵字及概念之影響關係,參考組織現況定義出技術路徑圖。實證研究以機器視覺技術為例,進行專利文字探勘並定義出技術路徑圖,驗證此方法架構的可行性,期許本研究結果可作為產業技術發展之依據。
The concept of big data has developed rapidly, it is important to find out meaningful information from big and different quality data, and then transform these information into visualized chart is an important issue for all companies. Patent is an important mechanism to push scientific and technological progressive, because of its highly exclusive and well protect by the law that will encourage people. Many researches of technology roadmap usually focus on specific technology or on the development of technology in enterprises, seldom on deriving the technology trends in the future. Therefore, this research will collect keywords of patents by using text mining and grouping these keywords by experts’ opinions. Then, deriving the inference relationship among the keywords of technology, product by using DRSA, conducting the technology concept from the keyword by using FCA and deriving the influence relationship between the keyword and concept by using the DEMATEL. Finally defining the technology roadmap with consideration of the current situation of the organization. Empirical studies based on the patent analysis of the Machine Vision defined the technical roadmap which can be used to demonstrate the feasibility of the proposed analytic framework. The analytic framework and results can serve as the industry develops technology in the future.
Aggarwal, C. C., & Zhai, C. (2012). Mining text data: Springer Science & Business Media.
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Paper presented at the Acm sigmod record.
Aleina, S. C., Viola, N., Fusaro, R., & Saccoccia, G. (2017). Approach to technology prioritization in support of moon initiatives in the framework of ESA exploration technology roadmaps. Acta Astronautica, 139, 42-53.
Altuntas, S., Dereli, T., & Kusiak, A. (2015). Analysis of patent documents with weighted association rules. Technological Forecasting and Social Change, 92, 249-262.
Amer, M., & Daim, T. U. (2010). Application of technology roadmaps for renewable energy sector. Technological Forecasting and Social Change, 77(8), 1355-1370.
Andersen, B. (1998). The evolution of technological trajectories 1890–1990. Structural Change and Economic Dynamics, 9(1), 5-34.
Balachandran, B. M., & Prasad, S. (2017). Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence. Procedia Computer Science, 112, 1112-1122.
Berkowitz, L. (1993). Getting the most from your patents. Research-Technology Management, 36(2), 26-31.
Bettis, R. A., & Hitt, M. A. (1995). The new competitive landscape. Strategic management journal, 16(S1), 7-19.
Bhanuse, S. S., Kamble, S. D., & Kakde, S. M. (2016). Text Mining Using Metadata for Generation of Side Information. Procedia Computer Science, 78, 807-814.
Biswas, A., Mohan, S., Panigrahy, J., Tripathy, A., & Mahapatra, R. (2009). Representation of complex concepts for semantic routed network. Distributed Computing and Networking, 127-138.
Biswas, A., Mohan, S., Tripathy, A., Panigrahy, J., & Mahapatra, R. (2009). Semantic key for meaning based searching. Paper presented at the Semantic Computing, 2009. ICSC'09. IEEE International Conference on.
Błaszczyński, J., Greco, S., & Słowiński, R. (2007). Multi-criteria classification–A new scheme for application of dominance-based decision rules. European Journal of Operational Research, 181(3), 1030-1044.
Brüggemann, R., Voig, K., & Steinberg, C. (1997). Application of formal concept analysis to evaluate environmental databases. Chemosphere, 35(3), 479-486.
Cao, G., Luo, P., Wang, L., & Yang, X. (2016). Key Technologies for Sustainable Design Based on Patent Knowledge Mining. Procedia CIRP, 39, 97-102.
Chang, P.-L., Wu, C.-C., & Leu, H.-J. (2010). Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display. Scientometrics, 82(1), 5-19.
Chen, A., & Chen, R. (2007). Design patent map: an innovative measure for corporative design strategies. Engineering Management Journal, 19(3), 14-29.
Chen, F.-H., Hsu, T.-S., & Tzeng, G.-H. (2011). A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP. International Journal of Hospitality Management, 30(4), 908-932.
Chen, Y., Spangler, S., Kreulen, J., Boyer, S., Griffin, T. D., Alba, A., . . . Lelescu, A. (2009). SIMPLE: a strategic information mining platform for licensing and execution. Paper presented at the Data Mining Workshops, 2009. ICDMW'09. IEEE International Conference On.
Chiu, Y.-J., Chen, H.-C., Tzeng, G.-H., & Shyu, J. Z. (2006). Marketing strategy based on customer behaviour for the LCD-TV. International journal of management and decision making, 7(2-3), 143-165.
Collan, M., & Heikkilä, M. (2011). Enhancing patent valuation with the pay-off method.
Comanor, W. S., & Scherer, F. M. (1969). Patent statistics as a measure of technical change. Journal of political economy, 77(3), 392-398.
Dörre, J., Gerstl, P., & Seiffert, R. (1999). Text mining: finding nuggets in mountains of textual data. Paper presented at the Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining.
Doostan, M., & Chowdhury, B. H. (2017). Power distribution system fault cause analysis by using association rule mining. Electric Power Systems Research, 152, 140-147
Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233-242.
Fai, F., & Von Tunzelmann, N. (2001). Industry-specific competencies and converging technological systems: evidence from patents. Structural Change and Economic Dynamics, 12(2), 141-170.
Fan, T.-F., Liu, D.-R., & Tzeng, G.-H. (2007). Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 182(1), 340-355.
Fang, S.-K., Shyng, J.-Y., Lee, W.-S., & Tzeng, G.-H. (2012). Exploring the preference of customers between financial companies and agents based on TCA. Knowledge-Based Systems, 27, 137-151.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining (Vol. 21): AAAI press Menlo Park.
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data: Cambridge university press.
Formica, A. (2008). Concept similarity in formal concept analysis: An information content approach. Knowledge-Based Systems, 21(1), 80-87.
Gabus, A., & Fontela, E. (1972). World problems. An Invitation to Further Thought within the Framework of DEMATEL, BATTELLE Institute, Geneva Research Centre, Geneva.
Galvin, R. (1998). Science roadmaps. Science, 280(5365), 803-804.
Gambardella, A., & Torrisi, S. (1998). Does technological convergence imply convergence in markets? Evidence from the electronics industry. Research Policy, 27(5), 445-463.
Garcia, M. L., & Bray, O. H. (1997). Fundamentals of technology roadmapping: Sandia National Laboratories Albuquerque, NM.
Geum, Y., Lee, H., Lee, Y., & Park, Y. (2015). Development of data-driven technology roadmap considering dependency: An ARM-based technology roadmapping. Technological Forecasting and Social Change, 91, 264-279.
Ghazinoory, S., Ameri, F., & Farnoodi, S. (2013). An application of the text mining approach to select technology centers of excellence. Technological Forecasting and Social Change, 80(5), 918-931.
Greco, S., Matarazzo, B., & Slowinski, R. (1998). A new rough set approach to evaluation of bankruptcy risk. Operational tools in the management of financial risks, 121-136.
Greco, S., Matarazzo, B., & Slowinski, R. (2000). Extension of the rough set approach to multicriteria decision support. INFOR: Information Systems and Operational Research, 38(3), 161-195.
Greco, S., Matarazzo, B., & Slowinski, R. (2002). Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research, 138(2), 247-259.
Greer, K. (2015). Concept trees: building dynamic concepts from semi-structured data using nature-inspired methods Complex System Modelling and Control Through Intelligent Soft Computations (pp. 221-252): Springer.
Han, J., Cai, Y., & Cercone, N. (1992). Knowledge discovery in databases: An attribute-oriented approach. Paper presented at the VLDB.
Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining: MIT press.
Haussler, D. (1987). Bias, version spaces and valiant's learning framework. Paper presented at the Proc. 4th Int. Workshop on Machine Learning.
Holmes, C., & Ferrill, M. (2005). The application of operation and technology roadmapping to aid Singaporean SMEs identify and select emerging technologies. Technological Forecasting and Social Change, 72(3), 349-357.
Hori, S., & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control engineering practice, 7(11), 1413-1419.
Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.
Huang, C.-Y., Tzeng, G.-H., & Ho, W.-R. J. (2011). System on chip design service e-business value maximization through a novel MCDM framework. Expert Systems with Applications, 38(7), 7947-7962
Janssens, F. (2007). Clustering of scientific fields by integrating text mining and bibliometrics.
Jeon, J., Lee, C., & Park, Y. (2011). How to use patent information to search potential technology partners in open innovation.
Jin, B., Teng, H.-F., Shi, Y.-J., & Qu, F.-Z. (2007). Chinese patent mining based on sememe statistics and key-phrase extraction. Advanced Data Mining and Applications, 516-523.
Jin, X., Spangler, S., Chen, Y., Cai, K., Ma, R., Zhang, L., . . . Han, J. (2011). Patent maintenance recommendation with patent information network model. Paper presented at the Data Mining (ICDM), 2011 IEEE 11th International Conference on.
Kao, A., & Poteet, S. R. (2007). Natural language processing and text mining: Springer Science & Business Media.
Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094-1107.
Kaur, E. R. (2015). Big Data- Is a Turnkey Solution. Procedia Computer Science, 62, 326-331
Khramova, E., Meissner, D., & Sagieva, G. (2013). Statistical patent analysis indicators as a means of determining country technological specialisation.
Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128-147.
Lansley, G., & Longley, P. (2016). Deriving age and gender from forenames for consumer analytics. Journal of Retailing and Consumer Services, 30, 271-278.
Lee, C., Jeon, J., & Park, Y. (2011). Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach. Technological Forecasting and Social Change, 78(4), 690-702.
Lee, C., Kang, B., & Shin, J. (2015). Novelty-focused patent mapping for technology opportunity analysis. Technological Forecasting and Social Change, 90, 355-365.
Lee, S., & Park, Y. (2005). Customization of technology roadmaps according to roadmapping purposes: Overall process and detailed modules. Technological Forecasting and Social Change, 72(5), 567-583.
Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
Liou, J. J., Tzeng, G.-H., & Chang, H.-C. (2007). Airline safety measurement using a hybrid model. Journal of air transport management, 13(4), 243-249.
Liu, G., & Peng, C. (2017). Research on Reliability Modeling of CNC System Based on Association Rule Mining. Procedia Manufacturing, 11, 1162-1169.
Liu, M., Shao, M., Zhang, W., & Wu, C. (2007). Reduction method for concept lattices based on rough set theory and its application. Computers & Mathematics with Applications, 53(9), 1390-1410.
lo Storto, C. (2008). Exploring innovation trajectories in high-tech industries through patent analysis: the case of the optical memories industry. Paper presented at the Engineering Management Conference, 2008. IEMC Europe 2008. IEEE International.
Lucas, J. P., Segrera, S., & Moreno, M. N. (2012). Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems. Expert Systems with Applications, 39(1), 1273-1283.
Ma, J., & Porter, A. L. (2015). Analyzing patent topical information to identify technology pathways and potential opportunities. Scientometrics, 102(1), 811-827.
Madani, F., & Weber, C. (2016). The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Patent Information, 46, 32-48
Manufacturing, U. o. C. I. f. (2001). T-plan: the fast start to technology roadmapping: planning your route to success: University of Cambridge.
Mark, B., & Munakata, T. (2002). Computing, artificial intelligence and information technology. European Journal of Operational Research, 136(1), 212-229.
Maron, M. E. (1961). Automatic indexing: an experimental inquiry. Journal of the ACM (JACM), 8(3), 404-417.
McDowall, W. (2012). Technology roadmaps for transition management: The case of hydrogen energy. Technological Forecasting and Social Change, 79(3), 530-542.
Mehta, B. B., & Rao, U. P. (2016). Privacy Preserving Unstructured Big Data Analytics: Issues and Challenges. Procedia Computer Science, 78, 120-124.
Miner, G. (2012). Practical text mining and statistical analysis for non-structured text data applications: Academic Press.
Moehrle, M. G., Walter, L., Geritz, A., & Müller, S. (2005). Patent‐based inventor profiles as a basis for human resource decisions in research and development. R&D Management, 35(5), 513-524.
Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
Najafabadi, M. K., Mahrin, M. N. r., Chuprat, S., & Sarkan, H. M. (2017). Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Computers in Human Behavior, 67, 113-128
Nanba, H., Fujii, A., Iwayama, M., & Hashimoto, T. (2008). The patent mining task in the seventh NTCIR workshop. Paper presented at the Proceedings of the 1st ACM workshop on Patent information retrieval.
Narvekar, M., & Syed, S. F. (2015). An Optimized Algorithm for Association Rule Mining Using FP Tree. Procedia Computer Science, 45, 101-110.
Noh, H., Jo, Y., & Lee, S. (2015). Keyword selection and processing strategy for applying text mining to patent analysis. Expert Systems with Applications, 42(9), 4348-4360.
OuYang, K., & Weng, C. S. (2011). A new comprehensive patent analysis approach for new product design in mechanical engineering. Technological Forecasting and Social Change, 78(7), 1183-1199.
Panigrahy, J. (2011). Generating Tensor Representation from Concept Tree in meaning based search. Texas A & M University.
Patel, P., & Pavitt, K. (1997). The technological competencies of the world's largest firms: complex and path-dependent, but not much variety. Research Policy, 26(2), 141-156.
Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. doi: 10.1007/bf01001956
Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. 1991. Dordrecht & Boston: Kluwer Academic Publishers.
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 88-95.
Phaal, R., Farrukh, C. J., & Probert, D. R. (2004). Technology roadmapping—a planning framework for evolution and revolution. Technological Forecasting and Social Change, 71(1), 5-26.
Phaal, R., & Muller, G. (2009). An architectural framework for roadmapping: Towards visual strategy. Technological Forecasting and Social Change, 76(1), 39-49.
Phadnis, R., & Hirwani, R. (2005). Patent analysis as a tool for research planning: Case study of phytochemicals in tea. Journal of Intellectual Property Rights, 10(3), 221-231.
Poelmans, J., Ignatov, D. I., Kuznetsov, S. O., & Dedene, G. (2013). Formal concept analysis in knowledge processing: A survey on applications. Expert systems with applications, 40(16), 6538-6560.
Priss, U. (2006). Formal concept analysis in information science. Arist, 40(1), 521-543.
Probert, D., & Radnor, M. (2003). Technology roadmapping: Frontier experiences from industry-academia consortia. Research Technology Management, 46(2), 26-26.
Rinne, M. (2004). Technology roadmaps: Infrastructure for innovation. Technological Forecasting and Social Change, 71(1), 67-80.
Risov, M., & Kasravi, K. (2007). Patent mining-discovery of business value from patent repositories. Paper presented at the IEEE Computer Society. 40th Annual Hawii International Conference on System Science. US A, Hawii: IEEE Computer Society Publication.
Sahoo, J., Das, A. K., & Goswami, A. (2015). An efficient approach for mining association rules from high utility itemsets. Expert Systems with Applications, 42(13), 5754-5778
Sawant, V., & Shah, K. (2016). Performance Evaluation of Distributed Association Rule Mining Algorithms. Procedia Computer Science, 79, 127-134.
Shen, K.-Y., Hu, S.-K., & Tzeng, G.-H. (2017). Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model. Information Sciences, 375, 296-313.
Shen, K.-Y., & Tzeng, G.-H. (2015). Combined soft computing model for value stock selection based on fundamental analysis. Applied Soft Computing, 37, 142-155.
Shyng, J.-Y., Shieh, H.-M., & Tzeng, G.-H. (2010). An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios. Knowledge-Based Systems, 23(6), 586-597.
Shyng, J.-Y., Wang, F.-K., Tzeng, G.-H., & Wu, K.-S. (2007). Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32(1), 56-64.
Słowiński, R., Greco, S., & Matarazzo, B. (2014). Rough-set-based decision support Search Methodologies (pp. 557-609): Springer.
Souili, A., Cavallucci, D., & Rousselot, F. (2015). Identifying and Reformulating Knowledge Items to Fit with the Inventive Design Method (IDM) Model for a Semantically-based Patent Mining. Procedia Engineering, 131, 1130-1139.
Soysal, Ö. M. (2015). Association rule mining with mostly associated sequential patterns. Expert Systems with Applications, 42(5), 2582-2592.
Suzuki, J., & Kodama, F. (2004). Technological diversity of persistent innovators in Japan: Two case studies of large Japanese firms. Research Policy, 33(3), 531-549.
Szalkai, B., Grolmusz, V. K., & Grolmusz, V. I. (2017). Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer's database. Archives of Gerontology and Geriatrics, 73, 300-307.
Tamura, M., Nagata, H., & Akazawa, K. (2002). Extraction and systems analysis of factors that prevent safety and security by structural models. Paper presented at the SICE 2002. Proceedings of the 41st SICE Annual Conference.
Tay, F. E., & Shen, L. (2002). Economic and financial prediction using rough sets model. European Journal of Operational Research, 141(3), 641-659.
Tessmer, M., & Driscoll, M. P. (1986). Effects of a diagrammatic display of coordinate concept definitions on concept classification performance. ECTJ, 34(4), 195-205.
Tsai, W.-H., Chou, W.-C., & Hsu, W. (2009). The sustainability balanced scorecard as a framework for selecting socially responsible investment: an effective MCDM model. Journal of the Operational Research Society, 60(10), 1396-1410.
Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216-1247.
Tyagi, S., & Bharadwaj, K. K. (2013). Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining. Swarm and Evolutionary Computation, 13, 1-12.
Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044.
Van Zeebroeck, N. (2011). The puzzle of patent value indicators. Economics of innovation and new technology, 20(1), 33-62.
Wang, C.-H., Chin, Y.-C., & Tzeng, G.-H. (2010). Mining the R&D innovation performance processes for high-tech firms based on rough set theory. Technovation, 30(7), 447-458.
Watts, R. J., Porter, A. L., & Courseault, C. (1999). Functional analysis: Deriving systems knowledge from bibliographic information resources. Information Knowledge Systems Management, 1(1), 45-61.
Wille, R. (2005). Formal concept analysis as mathematical theory of concepts and concept hierarchies. Formal concept analysis, 3626, 1-33.
Winebrake, J. J. (2004). Alternate energy: Assessment and implementation reference book: The Fairmont Press, Inc.
Wormuth, B., & Becker, P. (2004). Introduction to formal concept analysis. Paper presented at the 2nd International Conference of Formal Concept Analysis February.
Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507.
Xianjin, Z., & Minghong, C. (2010). Study on early warning of competitive technical intelligence based on the patent map. Journal of Com-puters, 5(2), 274-281.
Yang, T., Jin, R., Chi, Y., & Zhu, S. (2009). Combining link and content for community detection: a discriminative approach. Paper presented at the Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.
Yang, Y., Tang, J., Keomany, J., Zhao, Y., Li, J., Ding, Y., . . . Wang, L. (2012). Mining competitive relationships by learning across heterogeneous networks. Paper presented at the Proceedings of the 21st ACM international conference on Information and knowledge management.
Yoon, B. (2010). Strategic visualisation tools for managing technological information. Technology Analysis & Strategic Management, 22(3), 377-397.
Yoon, B. U., Yoon, C. B., & Park, Y. T. (2002). On the development and application of a self–organizing feature map–based patent map. R&D Management, 32(4), 291-300.
Zhang, L., Li, L., & Li, T. (2015). Patent mining: A survey. ACM SIGKDD Explorations Newsletter, 16(2), 1-19.