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| Evaluation of Patents' Cumulative Impact Based on all Generations of Citations: A Case Study of a Nobel Prize Winner's Patents |
| Kang Xudong1, Deng Lele2, Wang Yukai2, Yang Zhongkai2 |
1.R&D Institute of Technology, Dalian University of Technology, Dalian 116024
2.Institute of Science of Science and S&T Management, Dalian University of Technology, Dalian 116024 |
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Abstract Direct patent citations are currently used to measure the impact of patents. However, the practice of evaluating patents’ impacts based on multiple generations of citations is gradually emerging. It is not sufficient to evaluate patents using direct patent citations, and it is thus important to take indirect patent citations into consideration. In this study, we examine the patents of Sydney Brunner, who won the Nobel Prize in Physiology and Medicine in the USPTO. Based on the cumulative impact method, we develop a cumulative impact index to determine the deep research impact diffusion and citation networks of patents. The results show that the cumulative impact index is more effective than direct patent citations in evaluating patent impact and can reveal “hidden” high-impact patents in large-scale patent networks. Patent citations and the number of cited patents of each generation are approximately normally distributed. “Key patents” in the citation networks contribute significantly to the impact of the target patent. The higher the number of direct patent citations, the greater the potential for patents to be indirectly cited. Patents with less generations of citations and more direct patent citations are potentially high-impact patents.
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Received: 15 April 2020
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1 朱大明. 参考文献的主要作用与学术论文的创新性评审[J]. 编辑学报, 2004, 16(2): 91-92.
2 郗建红, 彭爱东. 专利被引频次与专利分类跨领域相关性研究——以中国在美国授权专利为例[J]. 情报杂志, 2016, 35(4): 92-97.
3 段异兵, 孔妍. 高影响力中国海外发明专利的引文分析[J]. 科学学研究, 2009, 27(5): 678-681,710.
4 Albert M B, Avery D, Narin F, et al. Direct validation of citation counts as indicators of industrially important patents[J]. Research Policy, 1991, 20(3): 251-259.
5 肖国华, 王春, 姜禾, 等. 专利分析评价指标体系的设计与构建[J]. 图书情报工作, 2008, 52(3): 96-99.
6 della Briotta Parolo P, Kujala R, Kaski K, et al. Going beneath the shoulders of giants: tracking the cumulative knowledge spreading in a comprehensive citation network[OL]. (2019-08-29) [2020-03-20]. https://arxiv.org/abs/1908.11089.
7 高继平, 高翠玲. 技术縻母视角下的技术进化过程研究[J]. 科学学研究, 2015, 33(7): 967-974.
8 杨中楷, 刘佳. 基于专利引文网络的技术轨道识别研究——以太阳能光伏电池板领域为例[J]. 科学学研究, 2011, 29(9): 1311-1317.
9 刘玉仙, 武夷山. 关于引文本质的思考[J]. 科学学研究, 2015, 33(12): 1779-1786,1882.
10 李睿, 周维, 王雪. 引文生态视角下标准必要专利的引文特征研究[J]. 情报学报, 2018, 37(9): 882-889.
11 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.
12 von Wartburg I, Teichert T, Rost K. Inventive progress measured by multi-stage patent citation analysis[J]. Research Policy, 2005, 34(10): 1591-1607.
13 Atallah G, Rodríguez G. Indirect patent citations[J]. Scientometrics, 2006, 67(3): 437-465.
14 胡小君. 基于科技引用网络结构算法的科学计量新方法研究[D]. 杭州: 浙江大学, 2012.
15 冯岭, 彭智勇, 刘斌, 等. 一种基于潜在引用网络的专利价值评估方法[J]. 计算机研究与发展, 2015, 52(3): 649-660.
16 傅强, 孙杨. 基于专利时效性和PageRank算法的企业技术影响力评价[J]. 系统管理学报, 2018, 27(2): 352-358.
17 王舒, 吴江宁. 基于企业引用网络的技术影响力评价研究[J]. 科学学研究, 2011, 29(3): 396-402.
18 杨中楷, 梁永霞, 刘则渊. USPTO中我国专利引用状况的统计分析[J]. 图书情报工作, 2009, 53(2): 42-46.
19 Hall B H, Jaffe A B, Trajtenberg M. The NBER patent citations data file: lessons, insights and methodological tools[R/OL]. CEPR Discussion Paper No. 3094, (2002-01-08) [2020-03-25]. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=296162.
20 覃佳慧, 何耶奇, 叶鹰. 科学论文和技术专利的引用时滞及循环周期研究——以富勒烯为例[J]. 情报理论与实践, 2018, 41(7): 23-25.
21 杨中楷, 梁永霞, 刘倩楠. 专利引用过程中的知识活动探析[J]. 科研管理, 2010, 31(2): 171-177.
22 王亮, 张庆普, 于光, 等. 基于引文网络的知识扩散速度测度研究[J]. 情报学报, 2014, 33(1): 33-44.
23 柴玥, 冷泉, 高慧艳. 科学松鼠会微博科普的可视化分析[J]. 科普研究, 2014, 9(6): 34-42. |
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