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| Core Patent Identification Method Integrating Attribute Indicators and Citation Relationships |
| Guo Jianming1, Wang Jingyi1, Yuan Run1,2 |
1.Institute of Science and Technology Information, Jiangsu University, Zhenjiang 212013
2.Jiangsu University Library, Zhenjiang 212013 |
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Abstract Based on information theory, this paper proposes a core patent identification method integrating attribute index and citation relationships for balancing individual characteristics and the network integrity of patents. First, it analyzes the necessity and feasibility of patent information analysis based on a comprehensive attribute index and citation relationship from the perspective of information theory and constructs core patent identification models based on comprehensive methods. Second, the patent index system is constructed to calculate the patent attribute value, and PageRank and HITs (hyperlink-induced topic search) algorithms are used to measure the importance, authority, and hub of patents before and after the relationship between comprehensive attribute value and direct citation, co-citation and coupling, and identify core patents. Finally, we attempt to compare the effects of the methods before and after synthesis using the method based on the robustness and vulnerability of complex networks. The empirical results indicate that (1) the identification model combining the two methods increases the amount of information for patent analysis, considers the advantages of patent index analysis and network analysis, and realizes the complementary advantages of both methods. (2) Different citation relationships reflect the difference in patent values; the identification results of the three relationships are concentrated and discrete, and a few core patents simultaneously have high importance, authority, and hub. (3) The evaluation method based on the robustness and vulnerability of complex networks is useful for solving the problem of evaluating results in patent information analysis.
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Received: 26 July 2023
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1 马瑞敏, 尉心渊. 技术领域细分视角下核心专利预测研究[J]. 情报学报, 2017, 36(12): 1279-1289.
2 杨冠灿, 李纲, 刘彤. 基于关系融合的专利网络结构分析研究[M]. 北京: 科学技术文献出版社, 2019: 34-35.
3 肖沪卫, 顾震宇. 专利地图方法与应用[M]. 上海: 上海交通大学出版社, 2011: 148.
4 李治东, 熊焰, 方曦. 基于熵权层次分析法的核心专利识别应用研究[J]. 情报学报, 2016, 35(10): 1101-1109.
5 李姝影, 张鑫, 许轶, 等. 核心专利集筛选及专利技术主题识别影响[J]. 情报学报, 2019, 38(1): 13-20.
6 温迎强, 杨伯军, 万鹏, 等. 基于灰色模糊理论的核心专利识别方法研究[J]. 科技管理研究, 2022, 42(2): 172-183.
7 巩永强, 王超, 王锐, 等. 复杂网络视角下的核心专利识别研究[J]. 情报理论与实践, 2022, 45(10): 103-113.
8 顾立平. 专利排名算法——运用引用次数与引文网络计算美国专利的研究[J]. 现代图书情报技术, 2011(6): 14-19.
9 Nonaka H, Kubo D, Makoto, et al. Correlation analysis between financial data and patent score based on HITs algorithm[C]// Proceedings of the 2014 IEEE International Technology Management Conference. Piscataway: IEEE, 2014: 1-4.
10 陈祥, 冯佳, 穆晓敏, 等. 技术知识扩散视角下核心专利识别方法研究[J]. 情报理论与实践, 2022, 45(10): 132-138.
11 郭剑明, 王婧怡, 周云峰, 等. 基于专利网络节点重要性的核心专利识别方法研究[J]. 情报杂志, 2023, 42(5): 162-168, 191.
12 张欣, 马瑞敏. 基于改进PageRank算法的核心专利发现研究[J]. 图书情报工作, 2018, 62(10): 106-115.
13 亢川博, 王伟, 穆晓敏, 等. 核心专利识别的综合价值模型[J]. 情报科学, 2018, 36(2): 67-70.
14 范月蕾, 毛开云, 于建荣. 核心专利指标效力研究评述[J]. 图书情报工作, 2014, 58(24): 121-125.
15 曾闻, 王曰芬. 专业技术领域核心专利组合识别方法构建及其应用比较[J]. 数据分析与知识发现, 2022, 6(11): 61-71.
16 Sorenson O, Rivkin J W, Fleming L. Complexity, networks and knowledge flow[J]. Research Policy, 2006, 35(7): 994-1017.
17 文庭孝. 专利信息计量学[M]. 北京: 科学出版社, 2017: 169-171.
18 NewmanM E J. 网络科学引论[M]. 郭世泽, 陈哲, 译. 北京: 电子工业出版社, 2014: 114.
19 Bruck P, Réthy I, Szente J, et al. Recognition of emerging technology trends: class-selective study of citations in the U.S. patent citation network[J]. Scientometrics, 2016, 107(3): 1465-1475.
20 Latora V, Marchiori M. A measure of centrality based on network efficiency[J]. New Journal of Physics, 2007, 9(6): 188.
21 Borodin A, Roberts G O, Rosenthal J S, et al. Finding authorities and hubs from link structures on the World Wide Web[C]// Proceedings of the 10th International Conference on World Wide Web. New York: ACM Press, 2001: 415-429.
22 许鑫, 叶丁菱. 多维影响力融合视域下的数据论文评价研究[J]. 情报学报, 2022, 41(3): 275-286.
23 阮逸润, 老松杨, 王竣德, 等. 基于领域相似度的复杂网络节点重要度评估算法[J]. 物理学报, 2017, 66(3): 371-379.
24 Dereich S, M?rters P. Random networks with sublinear preferential attachment: the giant component[J]. The Annals of Probability, 2013, 41(1): 329-384.
25 刘亚东. 是什么卡住了我们的脖子[M]. 北京: 中国工人出版社, 2019: 1-4.
26 张海波, 林妩媚, 廖志杰, 等. 光刻机照明系统专利分析[J]. 电子工业专用设备, 2014, 43(8): 1-7, 19.
27 李纲, 任佳佳, 毛进, 等. 专利权人合作网络的社群结构分析——以燃料电池电动汽车专利为例[J]. 情报学报, 2014, 33(3): 267-276. |
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