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数据分析与知识发现  2023, Vol. 7 Issue (10): 1-14     https://doi.org/10.11925/infotech.2096-3467.2022.0927
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于引文网络的高颠覆性专利知识扩散特征研究*
潘一如,毛进,李纲()
武汉大学信息管理学院 武汉 430072
武汉大学信息资源研究中心 武汉 430072
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
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摘要 

【目的】探索颠覆性专利的知识扩散规律,丰富颠覆性专利研究。【方法】利用颠覆性指数从USPTO数据库中识别高颠覆性专利,分别从引文量和共引耦合数匹配控制组专利,从引文分布和引文网络特征两方面分析高颠覆性专利的知识扩散特征,并构建回归模型揭示核心特征。【结果】高颠覆性专利存在授权后1~3年达到引文起飞点,3~5年速度达到巅峰,第6年起速度下降的规律。高颠覆性专利与控制组专利在知识扩散强度、知识扩散效率、局部和全局知识扩散能力等方面具有显著差异。首次引用年引用数、首次高峰间隔年和首次高峰年引用数指标,以及低引文代的平均路径长度、平均聚类系数和连通性指标有助于识别高颠覆性专利。【局限】 颠覆性指数会随时间发生波动,本研究按时间区间选择高颠覆性专利,其颠覆性指数值尚不稳定。【结论】研究从专利被引角度揭示颠覆性技术的知识扩散特征,研究发现能够为颠覆性技术识别提供理论支持。

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潘一如
毛进
李纲
关键词 颠覆性技术专利知识扩散引文网络引文分布    
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 wordsDisruptive Technology    Patent    Knowledge Diffusion    Citation Network    Citation Distribution
收稿日期: 2022-09-01      出版日期: 2023-03-28
ZTFLH:  G350  
基金资助:*国家自然科学基金创新研究群体项目(71921002);国家自然科学基金面上项目(72174154)
通讯作者: 李纲,ORCID:0000-0001-5573-6400,E-mail: imiswhu@aliyun.com。   
引用本文:   
潘一如, 毛进, 李纲. 基于引文网络的高颠覆性专利知识扩散特征研究*[J]. 数据分析与知识发现, 2023, 7(10): 1-14.
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.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0927      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I10/1
Fig.1  研究框架
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
Table 1  控制变量及说明
Fig.2  高颠覆性专利的引文分布指标表现
Fig.3  引文宽度、加权累积引文数和平均路径长度等指标表现
Fig.4  平均聚类系数、网络密度和网络连通性等指标表现
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***
Table2  二元逻辑回归结果
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