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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 10-20    DOI: 10.11925/infotech.2096-3467.2017.03.02
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Recommending Potential R&D Partners Based on Patents
Dongsheng Zhai,Cheng Guo(),Jie Zhang,Jun Xia
School of Economics and Management, Beijing University of Technology, Beijing 100024, China
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[Objective] This study presents a recommendation method based on patents to accurately identify potential R&D partners. [Methods] First, we extracted the functions, scientific impacts and functional effects of the related patents based on the TRIZ theory. Second, we constructed a patent technology tree, which was mapped with key information from the enterprise needs. Finally, we identified and evaluated the potential R&D partners in accordance with the patentee. [Results] We successfully assessed the retrieved R&D partners with the proposed method based on water heater related patents. [Limitations] The accuracy of semantic feature extraction needs to be improved. [Conclusions] The proposed method could find and evaluate the potential R&D partners for enterprises effectively.

Key wordsPatent      Technology Tree      TRIZ      R&D Partners     
Received: 29 August 2016      Published: 20 April 2017

Cite this article:

Dongsheng Zhai,Cheng Guo,Jie Zhang,Jun Xia. Recommending Potential R&D Partners Based on Patents. Data Analysis and Knowledge Discovery, 2017, 1(3): 10-20.

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[1] 包昌火, 谢新洲. 竞争情报与企业竞争力[M]. 北京: 华夏出版社, 2001: 137-145.
[1] (Bao Changhuo, Xie Xinzhou.Competitive Intelligence and Enterprise Competitive Power [M]. Beijing: Huaxia Publishing House, 2001: 137-145.)
[2] 谢炜. 中国专利产出研究[D]. 成都: 电子科技大学, 2005.
[2] (Xie Wei.Research on China’s Patent Output[D]. Cheng Du: University of Electronic Science and Technology of China, 2005.)
[3] Yoon B, Phaal R, Probert D.Morphology Analysis for Technology Roadmapping: Application of Text Mining[J]. R&d Management, 2008, 38(1): 51-68.
[4] Lee S, Kang S, Park E, et al.Applying Technology Roadmaps in Project Selection and Planning[J]. International Journal of Quality & Reliability Management, 2008, 25(1): 39-51.
[5] Cascini G, Zini M.Measuring Patent Similarity by Comparing Inventions Functional Trees [A]//Computer-aided Innovation (CAI)[M]. New York: Springer US, 2008: 31-42.
[6] Fantoni G, Apreda R, Dell’Orletta F, et al. Automatic Extraction of Function-Behaviour-State Information from Patents[J]. Advanced Engineering Informatics, 2013, 27(3): 317-334.
[7] Choi S, Park H, Kang D, et al.An SAO-based Text Mining Approach to Building a Technology Tree for Technology Planning[J]. Expert Systems with Applications An International Journal, 2012, 39(13): 11443-11455.
[8] Russo D, Montecchi T, Liu Y.Functional-based Search for Patent Technology Transfer[C]//Proceedings of the 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Chicago: American Society of Mechanical Engineers, 2012: 529-539.
[9] 王朝霞, 邱清盈, 冯培恩, 等. 机械产品专利技术方案信息抽取方法[J]. 机械工程学报, 2009, 45(10): 198-206.
[9] (Wang Zhaoxia, Qiu Qingying, Feng Peien, et al.Information Extraction Method of Technical Solution from Mechanical Product Patent[J]. Journal of Mechanical Engineering, 2009, 45(10): 198-206.)
[10] Cantner U, Meder A.Technological Proximity and the Choice of Cooperation Partner[J]. Journal of Economic Interaction and Coordination, 2007, 2(1): 45-65.
[11] Lhuillery S, Pfister E.R&D Cooperation and Failures in Innovation Projects: Empirical Evidence from French CIS Data[J]. Research Policy, 2009, 38(1): 45-57.
[12] Chun H, Mun S B.Determinants of R&D Cooperation in Small and Medium-sized Enterprises[J]. Small Business Economics, 2012, 39(2): 419-436.
[13] 王进富, 魏珍, 刘江南, 等. 以企业为主体的产学研战略联盟研发伙伴选择影响因素研究——基于3C理论视角[J]. 预测, 2013, 32(4): 74-80.
[13] (Wang Jinfu, Wei Zhen, Liu Jiangnan, et al.Research on Influencing Factors of the IUR Strategic Alliance’s R&D Partner Selection—Based on the Perspective of 3C Theory[J]. Forecasting. 2014, 32(4): 74-80.)
[14] 纪慧生, 王红卫, 陆强. 基于知识特征的企业研发伙伴选择[J]. 沈阳工业大学学报: 社会科学版, 2011, 4(2): 137-140.
[14] (Ji Huisheng, Wang Hongwei, Lu Qiang.R&D partner Selection of Enterprise Based on Knowledge Characteristics[J]. Journal of Shenyang University of Technology: Social Science Edition, 2011, 4(2): 137-140.)
[15] 袁晓东, 陈静. 专利信息分析在技术创新合作伙伴选择中的应用[J]. 情报杂志, 2011, 30(8): 22-27.
[15] (Yuan Xiaodong, Chen Jing.The Application of Patent Information Analysis Method in the Choice of Cooperative Technological Innovation Partners[J]. Journal of Intelligence, 2011, 30(8): 22-27.)
[16] 宿慧爽, 兰衍霏, 衣兰文. 企业研发合作伙伴选择研究综述: 基于影响因素的视角[J]. 现代管理科学, 2013 (6): 48-50.
[16] (Su Huishuang, Lan Yanfei, Yi Lanwen.Review on Selecting Enterprises R&D Partners: Based on the Perspective of Influence Factors[J]. Modern Management Science, 2013 (6): 48-50.)
[17] Song B, Seol H, Park Y.A Patent Portfolio-based Approach for Assessing Potential R&D Partners: An Application of the Shapley Value[J]. Technological Forecasting and Social Change, 2016, 103: 156-165.
[18] Lee K, Yoon B.A Method for Partner Selection in R&D Collaboration Between Large Companies and SMEs Using Patent Information[C]//Proceedings of the 2013 Technology Management in the IT-Driven Services (PICMET). 2013: 1886-1891.
[19] 翟东升, 夏军, 张杰, 等. 基于专利特征抽取的技术树构建方法研究[J]. 情报学报, 2015, 34(7): 717-724.
[19] (Zhai Dongsheng, Xia Jun, Zhang Jie, et al.Research on Construction of Technology Tree Based on Patent Feature Extraction[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(7): 717-724.)
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