Please wait a minute...
Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (10): 1-14    DOI: 10.11925/infotech.2096-3467.2022.0927
Current Issue | Archive | Adv Search |
Knowledge Diffusion Characteristics of Highly Disruptive Patents Based on Citation Network
Pan Yiru,Mao Jin,Li Gang()
School of Information Management, Wuhan University, Wuhan 430072, China
Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
Download: PDF (1966 KB)   HTML ( 28 ) 
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper explores the knowledge diffusion of disruptive patents. [Methods] First, we used the disruption index to identify highly disruptive patents from the USPTO database. Then, we matched the patents of the control group according to the number of citations and co-citation couplings. Third, we analyzed the knowledge diffusion characteristics of highly disruptive patents from citation distribution and citation network characteristics. Finally, we built a regression model to reveal the core features. [Results] The citation take-off point of highly disruptive patents appeared 1 to 3 years after authorization. The increasing speed peaked in 3 to 5 years and decreased from the 6th year. Significant differences exist between highly disruptive and control group patents in knowledge diffusion intensity, efficiency, local and global knowledge diffusion capabilities, etc. First citation-year, first-peak interval-year, and first-peak-year citation metrics, average path length, average clustering coefficient, and connectivity metrics for low-citation generations help us identify highly disruptive patents. [Limitations] The disruption index fluctuates over time. This study selects highly disruptive patents according to the time interval, and its disruption index value is not yet stable. [Conclusions] The study reveals the knowledge diffusion characteristics of disruptive technologies from the perspective of patent citations and provides theoretical support for identifying disruptive technologies.
Key words： Disruptive Technology      Patent      Knowledge Diffusion      Citation Network      Citation Distribution     
Received: 01 September 2022      Published: 28 March 2023
ZTFLH:  G350  
Fund:National Natural Science Foundation of China(71921002);National Natural Science Foundation of China(72174154)
Corresponding Authors: Li Gang，ORCID：0000-0001-5573-6400，E-mail： imiswhu@aliyun.com。   
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Yiru Pan
Jin Mao
Gang Li

Cite this article:

Pan Yiru, Mao Jin, Li Gang. Knowledge Diffusion Characteristics of Highly Disruptive Patents Based on Citation Network. Data Analysis and Knowledge Discovery, 2023, 7(10): 1-14.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0927     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I10/1

Research Framework
角度 变量 说明
环境特征 授权年 专利授权年份
领域 专利所属IPC部
自身特征 领域范围 专利拥有的IPC分类号数量
权利要求 权利要求数量
发明人特征 发明人专利数量 发明人在专利授权年之前拥有的专利数量
发明人数量 专利拥有的发明人数量
Control Variables and Introduction
High Disruptive Patent Citation Distribution Indicators Performance
Indicators Performance （Citation Width， Weighted Cumulated Citations and Average Path Length）
Indicators Performance （Average Clustering Coefficient， Network Density and Network Connectivity）
变量类型 变量 模型1 模型2 模型3 模型4
控制变量 授权年 0.006 -0.025*** -0.035*** -0.089***
领域 0.003 -0.01 0.028*** 0.016*
领域范围 0.004 0.007 0.001 0.001
权利要求 -0.016*** -0.016*** -0.015*** -0.015***
发明人专利数量 -0.001** -0.001*** -0.001*** -0.001***
发明人数量 -0.032*** -0.03** -0.028** -0.025**
引文分布自变量 引用延时年 0.099*** 0.044
首次引用年引用数 0.077*** 0.077***
引文起飞间隔年 -0.024 -0.091***
引文起飞前累积引用数 0.001 0.009***
首次高峰间隔年 -0.025* -0.036**
首次高峰年引用数 0.028*** 0.046***
全局高峰间隔年 -0.07*** -0.015
全局高峰年引用数 -0.006** 0.014***
引文网络特征自变量 第一代引文宽度 0.000 -0.009***
第二代引文宽度 0.000 0.000*
第三代引文宽度 0.000 0.000
第四代引文宽度 0.000 0.000
前二代平均路径长度 -1.375*** -1.549***
前三代平均路径长度 0.488* 0.668**
前四代平均路径长度 -0.238 -0.481***
前一代平均聚类系数 -1.247*** -0.716***
前二代平均聚类系数 -0.349 0.131
前三代平均聚类系数 0.416 0.298
前四代平均聚类系数 -0.519 -0.577
前一代网络密度 0.149 -0.033
前二代网络密度 -0.285 -0.750
前三代网络密度 -3.006* -2.390
前四代网络密度 2.498 2.165
前一代网络连通性 0.864*** 0.842***
前二代网络连通性 1.065*** 0.645***
前三代网络连通性 -0.128 -0.225
前四代网络连通性 -0.507* -0.304
常数项 -0.178** 0.132 1.886*** 2.917***
Binary Logistic Regression Results
[1] Lemley M A, Shapiro C. Probabilistic Patents[J]. Journal of Economic Perspectives, 2005, 19(2): 75-98.
doi: 10.1257/0895330054048650
[2] Nelson R R, Winter S G. An Evolutionary Theory of Economic Change[M]. Harvard University Press, 1985.
[3] Chen C M, Hicks D. Tracing Knowledge Diffusion[J]. Scientometrics, 2004, 59(2): 199-211.
doi: 10.1023/B:SCIE.0000018528.59913.48
[4] Ho M H C, Lin V H, Liu J S. Exploring Knowledge Diffusion Among Nations: A Study of Core Technologies in Fuel Cells[J]. Scientometrics, 2014, 100(1): 149-171.
doi: 10.1007/s11192-014-1265-z
[5] Liu N, Shapira P, Yue X X, et al. Mapping Technological Innovation Dynamics in Artificial Intelligence Domains: Evidence from a Global Patent Analysis[J]. PLoS One, 2021, 16(12): e0262050.
[6] Lee S, Kim W, Lee H, et al. Identifying the Structure of Knowledge Networks in the US Mobile Ecosystems: Patent Citation Analysis[J]. Technology Analysis & Strategic Management, 2016, 28(4): 411-434.
[7] 巴志超, 李纲, 朱世伟. 科研合作网络的知识扩散机理研究[J]. 中国图书馆学报, 2016, 42(5): 68-84
[7] Ba Zhichao, Li Gang, Zhu Shiwei. Knowledge Diffusion Mechanism of Scientific Cooperation Network[J]. Journal of Library Science in China, 2016, 42(5): 68-84.)
[8] 贵淑婷, 彭爱东. 基于专利引文网络的技术扩散速度研究[J]. 情报理论与实践, 2016, 39(5): 40-45.
[8] (Gui Shuting, Peng Aidong. Research on the Speed of Technology Diffusion Based on Patent Citation Network[J]. Information Studies: Theory & Application, 2016, 39(5): 40-45.)
[9] 梁镇涛, 毛进, 操玉杰, 等. 基于知识模因级联网络的领域知识扩散模式分析[J]. 情报理论与实践, 2020, 43(4): 40-46.
[9] (Liang Zhentao, Mao Jin, Cao Yujie, et al. Knowledge Diffusion Pattern Analysis Based on Knowledge Meme Cascade Networks[J]. Information Studies: Theory & Application, 2020, 43(4): 40-46.)
[10] Kneeland M K, Schilling M A, Aharonson B S. Exploring Uncharted Territory: Knowledge Search Processes in the Origination of Outlier Innovation[J]. Organization Science, 2020, 31(3): 535-557.
doi: 10.1287/orsc.2019.1328
[11] Schoenmakers W, Duysters G. The Technological Origins of Radical Inventions[J]. Research Policy, 2010, 39(8): 1051-1059.
doi: 10.1016/j.respol.2010.05.013
[12] Righi C, Simcoe T. Patent Examiner Specialization[J]. Research Policy, 2019, 48(1): 137-148.
doi: 10.1016/j.respol.2018.08.003
[13] 苏敬勤, 刘建华, 王智琦, 等. 颠覆性技术的演化轨迹及早期识别——以智能手机等技术为例[J]. 科研管理, 2016, 37(3): 13-20.
[13] (Su Jingqin, Liu Jianhua, Wang Zhiqi, et al. The Evolution Trajectory and Early Identification of Disruptive Technology by Taking Smartphones and Other Technologies as an Example[J]. Science Research Management, 2016, 37(3): 13-20.)
[14] Funk R J, Owen-Smith J. A Dynamic Network Measure of Technological Change[J]. Management Science, 2017, 63(3): 791-817.
doi: 10.1287/mnsc.2015.2366
[15] Wu L F, Wang D S, Evans J A. Large Teams Develop and Small Teams Disrupt Science and Technology[J]. Nature, 2019, 566(7744): 378-382.
doi: 10.1038/s41586-019-0941-9
[16] Harhoff D, Scherer F M, Vopel K. Citations, Family Size, Opposition and the Value of Patent Rights[J]. Research Policy, 2003, 32(8): 1343-1363.
doi: 10.1016/S0048-7333(02)00124-5
[17] Trajtenberg M, Henderson R, Jaffe A. University Versus Corporate Patents: A Window on the Basicness of Invention[J]. Economics of Innovation and New Technology, 1997, 5(1): 19-50.
doi: 10.1080/10438599700000006
[18] Tijssen R J W. Global and Domestic Utilization of Industrial Relevant Science: Patent Citation Analysis of Science-Technology Interactions and Knowledge Flows[J]. Research Policy, 2001, 30(1): 35-54.
doi: 10.1016/S0048-7333(99)00080-3
[19] Yang G C, Li G, Li C Y, et al. Using the Comprehensive Patent Citation Network (CPC) to Evaluate Patent Value[J]. Scientometrics, 2015, 105(3): 1319-1346.
doi: 10.1007/s11192-015-1763-7
[20] Kim E, Cho Y, Kim W. Dynamic Patterns of Technological Convergence in Printed Electronics Technologies: Patent Citation Network[J]. Scientometrics, 2014, 98(2): 975-998.
doi: 10.1007/s11192-013-1104-7
[21] 吴可凡, 王伟, 张世玉, 等. 技术不连续性视角下颠覆性技术识别方法研究[J]. 情报理论与实践, 2022, 45(10): 125-131.
[21] (Wu Kefan, Wang Wei, Zhang Shiyu, et al. Research on Disruptive Technology Identification Methods from the Perspective of Technology Discontinuities[J]. Information Studies: Theory & Application, 2022, 45(10): 125-131.)
[22] Rogers E M, Singhal A, Quinlan M M. Diffusion of Innovations[M]. Simon and Schuster, 2010.
[23] 王丽, 刘细文. 基于专利数据的技术主题扩散量化研究与实现[J]. 数据分析与知识发现, 2022, 6(6): 1-10.
[23] (Wang Li, Liu Xiwen. Measuring Diffusion of Technology Topics with Patent Data[J]. Data Analysis and Knowledge Discovery, 2022, 6(6): 1-10.)
[24] de Paiva Britto J N, Ribeiro L C, Araújo L T, et al. Patent Citations, Knowledge Flows, and Catching-Up: Evidences of Different National Experiences for the Period 1982-2006[J]. Science and Public Policy, 2021, 47(6): 788-802.
doi: 10.1093/scipol/scaa041
[25] Bornmann L, Tekles A. Disruption Index Depends on Length of Citation Window[J]. El Profesional De La Información. DOI: https://doi.org/10.3145/epi.2019.mar.07.
[26] Bornmann L, Devarakonda S, Tekles A, et al. Disruptive Papers Published in Scientometrics: Meaningful Results by Using an Improved Variant of the Disruption Index Originally Proposed by Wu, Wang, and Evans (2019)[J]. Scientometrics, 2020, 123(2): 1149-1155.
doi: 10.1007/s11192-020-03406-8
[27] Kessler M M. Bibliographic Coupling Between Scientific Papers[J]. American Documentation, 1963, 14(1): 10-25.
doi: 10.1002/asi.v14:1
[28] Tahamtan I, Safipour Afshar A, Ahamdzadeh K. Factors Affecting Number of Citations: A Comprehensive Review of the Literature[J]. Scientometrics, 2016, 107(3): 1195-1225.
doi: 10.1007/s11192-016-1889-2
[29] Min C, Bu Y, Wu D, et al. Identifying Citation Patterns of Scientific Breakthroughs: A Perspective of Dynamic Citation Process[J]. Information Processing & Management, 2021, 58(1): Article No.102428.
[30] Park H W, Kang J. Patterns of Scientific and Technological Knowledge Flows Based on Scientific Papers and Patents[J]. Scientometrics, 2009, 81(3): 811-820.
doi: 10.1007/s11192-008-2224-3
[31] Glänzel W, Rousseau R, Zhang L. A Visual Representation of Relative First-Citation Times[J]. Journal of the American Society for Information Science and Technology, 2012, 63(7): 1420-1425.
doi: 10.1002/asi.v63.7
[32] Ke Q, Ferrara E, Radicchi F, et al. Defining and Identifying Sleeping Beauties in Science[J]. PNAS, 2015, 112(24): 7426-7431.
doi: 10.1073/pnas.1424329112 pmid: 26015563
[33] 李凌英, 闵超, 孙建军. 引文波峰的量化与分布探究[J]. 情报学报, 2019, 38(7): 697-708.
[33] (Li Lingying, Min Chao, Sun Jianjun. On the Quantification and Distribution of Citation Peaks[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7): 697-708.)
[34] 康旭东, 邓乐乐, 王宇开, 等. 基于全代引证的专利累积影响力评价——一个诺奖得主专利的案例研究[J]. 情报学报, 2021, 40(3): 267-277.
[34] (Kang Xudong, Deng Lele, Wang Yukai, et al. Evaluation of Patents’ Cumulative Impact Based on All Generations of Citations: A Case Study of a Nobel Prize Winner’s Patents[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(3): 267-277.)
[35] Min C, Chen Q Y, Yan E J, et al. Citation Cascade and the Evolution of Topic Relevance[J]. Journal of the Association for Information Science and Technology, 2021, 72(1): 110-127.
doi: 10.1002/asi.v72.1
[36] 刘臣, 张庆普, 单伟, 等. 学科知识流动网络的构建与分析[J]. 情报学报, 2009, 28(2): 257-265.
[36] (Liu Chen, Zhang Qingpu, Shan Wei, et al. Construction and Analysis of Disciplinary Knowledge Flow Network[J]. Journal of the China Society for Scientific and Technical Information, 2009, 28(2): 257-265.)
[37] Ávila-Robinson A, Miyazaki K. Dynamics of Scientific Knowledge Bases as Proxies for Discerning Technological Emergence — The Case of MEMS/NEMS Technologies[J]. Technological Forecasting and Social Change, 2013, 80(6): 1071-1084.
doi: 10.1016/j.techfore.2012.07.012
[38] Haupt R, Kloyer M, Lange M. Patent Indicators for the Technology Life Cycle Development[J]. Research Policy, 2007, 36(3): 387-398.
doi: 10.1016/j.respol.2006.12.004
[39] Lerner J. The Importance of Patent Scope: An Empirical Analysis[J]. The RAND Journal of Economics, 1994, 25(2): 319-333.
doi: 10.2307/2555833
[40] Tong X S, Frame J D. Measuring National Technological Performance with Patent Claims Data[J]. Research Policy, 1994, 23(2): 133-141.
doi: 10.1016/0048-7333(94)90050-7
[41] Breitzman A, Thomas P. Inventor Team Size as a Predictor of the Future Citation Impact of Patents[J]. Scientometrics, 2015, 103(2): 631-647.
doi: 10.1007/s11192-015-1550-5
[42] Malhotra A, Zhang H T, Beuse M, et al. How Do New Use Environments Influence a Technology’s Knowledge Trajectory? A Patent Citation Network Analysis of Lithium-Ion Battery Technology[J]. Research Policy, 2021, 50(9): Article No.104318.
[43] Amin M, Mabe M A. Impact Factors: Use and Abuse[J]. Medicina, 2003, 63(4): 347-354.
[44] Harhoff D, Narin F, Scherer F M, et al. Citation Frequency and the Value of Patented Inventions[J]. Review of Economics and Statistics, 1999, 81(3): 511-515.
doi: 10.1162/003465399558265
[45] Christensen C M. The Innovator’s Dilemma: The Revolutionary Book That Will Change the Way You Do Business[M]. Harper Business, 2011.
[46] McConnell S. After the Gold Rush: Creating a True Profession of Software Engineering[M]. Microsoft Press, 1999.
[47] Min C, Bu Y, Sun J J, et al. Is Scientific Novelty Reflected in Citation Patterns?[J]. Proceedings of the Association for Information Science and Technology, 2018, 55(1): 875-876.
doi: 10.1002/pra2.2018.55.issue-1
[48] 杨冠灿, 陈亮, 张静, 等. 专利引用关系形成的解释框架: 一个指数随机图模型视角[J]. 图书情报工作, 2019, 63(5): 100-109.
doi: 10.13266/j.issn.0252-3116.2019.05.012
[48] (Yang Guancan, Chen Liang, Zhang Jing, et al. Framework for Explanations of Patent Citation Formation: An Exponential Random Graph Model Perspective[J]. Library and Information Service, 2019, 63(5): 100-109.)
doi: 10.13266/j.issn.0252-3116.2019.05.012
[49] Trajtenberg M. A Penny for Your Quotes: Patent Citations and the Value of Innovations[J]. The RAND Journal of Economics, 1990, 21(1): 172-187.
doi: 10.2307/2555502
[1] Zhai Dongsheng, Lou Ying, Kan Huimin, He Xijun, Liang Guoqiang, Ma Zifei. Constructing TCM Knowledge Graph with Multi-Source Heterogeneous Data[J]. 数据分析与知识发现, 2023, 7(9): 146-158.
[2] Feng Lijie, Liu Kehui, Wang Jinfeng, Zhang Ke, Zhang Shibin. Identifying Opportunities Based on Knowledge Network and Multidimensional Map of Technology Innovation[J]. 数据分析与知识发现, 2023, 7(8): 62-77.
[3] Zhao Xuefeng, Wu Delin, Wu Weiwei, Sun Zhuoluo, Hu Jinjin, Lian Ying, Shan Jiayu. Identifying High-Quality Technology Patents Based on Deep Learning and Multi-Category Polling Mechanism——Case Study of Patent Applications[J]. 数据分析与知识发现, 2023, 7(8): 30-45.
[4] Wang Shiwei, Chen Chun. Review of Latent Knowledge Discovery Methods Based on Association Between Scientific Papers and Technology Patents[J]. 数据分析与知识发现, 2023, 7(7): 18-31.
[5] Shi Guoliang, Zhou Shu, Wang Yunfeng, Shi Chunjiang, Liu Liang. Generating Patent Text Abstracts Based on Improved Multi-head Attention Mechanism[J]. 数据分析与知识发现, 2023, 7(6): 61-72.
[6] Yu Yan, Wang Li, Zheng Siyu. Patent Keyphrase Extraction Based on Patent Term and Layer Information[J]. 数据分析与知识发现, 2023, 7(6): 99-112.
[7] Deng Na, He Xinyang, Chen Weijie, Chen Xu. MPMFC: A Traditional Chinese Medicine Patent Classification Model Integrating Network Neighborhood Structural Features and Patent Semantic Features[J]. 数据分析与知识发现, 2023, 7(4): 145-158.
[8] Xie Zhen, Ma Jianxia, Hu Wenjing. Mapping and Analyzing Personal Academic Trajectory from Multiple Dimensions[J]. 数据分析与知识发现, 2023, 7(2): 129-140.
[9] Lyu Haihua, Li Jiang. Review of “Obliteration by Incorporation” in Knowledge Diffusion[J]. 数据分析与知识发现, 2022, 6(8): 12-19.
[10] Ou Guiyan, Pang Na, Wu Jiang. Influencing Factors of Patent Examination Cycle: Case Study of Artificial Intelligence in China[J]. 数据分析与知识发现, 2022, 6(8): 20-30.
[11] Zhang Le, Du Yifan, Lü Xueqiang, Dong Zhian. STNLTP: Generating Chinese Patent Abstracts Based on Integrated Strategy[J]. 数据分析与知识发现, 2022, 6(7): 107-117.
[12] Zhang Jinzhu, Wang Qiuyue, Qiu Mengmeng. Review of Studies Identifying Disruptive Technologies[J]. 数据分析与知识发现, 2022, 6(7): 12-31.
[13] Wang Li, Liu Xiwen. Measuring Diffusion of Technology Topics with Patent Data[J]. 数据分析与知识发现, 2022, 6(6): 1-10.
[14] Guan Peng,Wang Yuefen,Fu Zhu,Jin Jialin. Identifying R&D Teams and Innovations with Patent Collaboration Networks[J]. 数据分析与知识发现, 2022, 6(5): 99-111.
[15] Xiao Yuejun, Li Honglian, Zhang Le, Lv Xueqiang, You Xindong. Classifying Chinese Patent Texts with Feature Fusion[J]. 数据分析与知识发现, 2022, 6(4): 49-59.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn