INFORMATION RESEARCH

Identification and Characterization of Bó Lè Patents

  • Hou Jianhua ,
  • Yang Xiucai ,
  • Yao Haiyue
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  • 1 School of Information Management, Sun Yat-sen University, Guangzhou 510006;
    2 School of Business, The Chinese University of Hong Kong, Hong Kong 999077

Received date: 2023-07-06

  Revised date: 2023-10-31

  Online published: 2024-03-28

Supported by

This work is supported by the project of Natural Science Foundation of Guangdong Province titled “Study on the Transfer Laws of Basic Science Research Centers for the Greater Bay Area of Guangdong, Hong Kong and Macao and Their Applications” (Grant No. 2021A1515012291).

Abstract

[Purpose/Significance] This study explores the formation and evolution of high-quality scientific and technological achievements represented by “ Bó Lè”-type patents, in order to promote the transfer and transformation of scientific and technological achievements, and further provide strong support for the innovative development of enterprises.[Method/Process] This study took patent families in artificial intelligence in China and the United States as research objects, constructed the knowledge burst detection model (KBDM) to search for patents that could discover, continue, and inherit high-quality patent knowledge from a large number of patents.[Result/Conclusion] In this study, a total of 29,925 “Bó Lè”-type patents are identified through the KBDM model. Its geographical distribution is characterized by internationalization and cross-discipline. The applicant body is mainly leading enterprises in artificial intelligence, with leading technological experience and the ability to identify potential high-value patents. Bó Lè patents have agglomeration effect, several Bó Lè patents can often discover potential knowledge value together, and actively awaken the entire patent family through synergy. It shows the importance of “Bó Lè” patents in the innovation ecosystem and their role in knowledge dissemination. It also finds a positive relationship between the citations frequency of the patent family and the number of “Bó Lè”-type patents that awakened the patent family. It provides a comprehensive understanding of the characteristics and roles of “Bó Lè”-type patents and contributes to a better application in science and technology innovation and knowledge dissemination.

Cite this article

Hou Jianhua , Yang Xiucai , Yao Haiyue . Identification and Characterization of Bó Lè Patents[J]. Library and Information Service, 2024 , 68(6) : 104 -118 . DOI: 10.13266/j.issn.0252-3116.2024.06.010

References

[1] HOU J H, YANG X C. Patent sleeping beauties: evolutionary trajectories and identification methods[J]. Scientometrics, 2019, 120(1): 187-215.
[2] VAN RAAN A F J. Sleeping beauties gain impact in overdrive mode[J]. Scientometrics, 2021, 126(6): 4311–4332.
[3] 李贺, 袁翠敏, 解梦凡. 专利文献中的睡美人现象分析与研究[J]. 图书情报工作, 2019, 63(6): 64-74. (LI H, YUAN C M, XIE M F. Analysis and study on phenomenon of sleeping beauty in patent literature[J]. Library and information service, 2019, 63(6): 64-74.)
[4] 李凤侠. 基于简单专利族和BCP指数方法探讨专利”睡美人”的识别与分析[J]. 情报探索, 2021(3): 69-77. (LI F X. Identification and analysis on “Sleeping Beauties” patents based on simple patent family and parameter free BCP index[J]. Information research, 2021(3): 69-77.)
[5] 侯剑华, 张雪雯. 基于累积引文的科学睡美人识别方法研究[J]. 情报学报, 2020, 39(9): 949-962. (HOU J H, ZHANG X W. Identification method and empirical research of sleeping beauty in science based on cumulative citation[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(9): 949-962.)
[6] 杜建, 武夷山. 睡美人与王子文献的识别方法研究[J]. 图书情报工作, 2015, 59(19): 84-92. (DU J, WU Y S. Defining and identifying the sleeping beauty and her princes in science[J]. Library and information service, 2015, 59(19): 84-92.)
[7] 唐洁, 曾静静, 曲建升. 科学中的“睡美人”现象研究综述[J]. 情报杂志, 2020, 39(12): 202-206, 209. (TANG J, ZENG J J, QU J S. Review of sleeping beauties in science[J]. Journal of intelligence, 2020, 39(12): 202-206, 209.)
[8] VAN RAAN A F J. Dormitory of physical and engineering sciences: sleeping Beauties may be sleeping innovations[J]. Plos one, 2015, 10(10): e0139786.
[9] 冷雪. 睡美人创新研究在科学、技术及创新政策制定中的应用[J]. 情报杂志, 2019, 38(2): 43-48. (LENG X. The application of sleeping beauty innovation in science, technology and innovation policy-making[J]. Journal of intelligence, 2019, 38(2): 43-48.)
[10] 张克群, 孙泓, 耿筠, 等. 专利被引证数与专利价值关系研究的再验证[J]. 图书馆论坛, 2020, 40(3): 55-62. (ZHANG K Q, SUN H, GENG Y, et al. Re-examination of the relationship between patent forward citation count and patent value[J]. Library tribune, 2020, 40(3): 55-62.)
[11] 马天旗, 赵星.高价值专利内涵及受制因素探究[J]. 中国发明与专利, 2018, 15(3): 24-28. (MA T Q, ZHAO X. Research on the connotation and controlling factors of high value patent[J]. China invention & patent, 2018, 15(3): 24-28.)
[12] 郑金, 王琦, 唐晚成. 企业高价值专利培育路径研究[J]. 科学与管理, 2020, 40(2): 104-109. (ZHENG J, WANG Q, TANG WC. The cultivation of high-value patents[J]. Science and management, 2020, 40(2): 104-109.)
[13] 孙智, 冯桂凤. 高价值专利的产生背景、内涵界定及培育意义[J]. 中国发明与专利, 2020, 17(11): 37-44. (SUN Z, FENG G F. The background, connotation definition and cultivation significance of high-value patent[J]. China invention & patent, 2020, 17(11): 37-44.)
[14] 张克群, 牛悾悾, 夏伟伟. 高被引专利质量的影响因素分析——以LED产业为例[J]. 情报杂志, 2018, 37(2): 81-87. (ZHANG K Q, NIU K K, XIA W W. Analysis of the factors influencing the quality of the highly-cited patents: taking LED industry as an example[J]. Journal of intelligence, 2018, 37(2): 81-87.)
[15] KARKI M M S. Patent citation analysis: a policy analysis tool[J]. World patent information, 1997, 33(4): 269-272.
[16] NARIN F. Patent bibliometrics[J]. Scientometrics, 1994, 30(1): 147-155.
[17] NO H J, PARK Y. Trajectory patterns of technology fusion: trend analysis and taxonomical grouping in nanobiotechnology[J]. Technological forecasting and social change, 2010, 77(1): 63-75.
[18] 李黎明. 专利价值研究的文献综述与未来展望[J]. 情报杂志, 2023, 42(2): 166-174. (LI L M. Patent value: a systematic literature review and guidance for future research[J]. Journal of intelligence, 2023, 42(2): 166-174.)
[19] 杨中楷, 梁永霞, 刘则渊. 美国专利商标局10个高被引专利的计量分析[J]. 科学学与科学技术管理, 2008(11): 35-39. (YANG Z K, LIANG Y X, LIU Z Y. Top ten highly patents in USPTO[J]. Science of science and management of S.& T., 2008(11): 35-39.)
[20] HALL B H, JAFFE A, TRAJTENBERG M. Market value and patent citations: a first look[J]. NBER working papers, 2000, 36(1): 16-38.
[21] HALL B H. Trajtenberg, market value and patent citations[J]. RAND journal economics, 2005, 36(1): 16-38.
[22] 魏太琛, 刘敏榕, 陈振标. 高校专利技术转移转化价值影响因素实证分析——基于11所一流高校专利转移转化数据[J]. 图书情报工作, 2022, 66(9): 103-116. (WEI T S, LIU M R, CHEN Z B. An empirical study on influencing factors of the value of university patent technology transfer and transformation: based on the patent transfer and transformation data from 11 first-class universities[J]. Library and information service, 2022, 66(9): 103-116.)
[23] 吴小康, 铁瑛. 知识产品交易、边界障碍与效率损失评估[J]. 世界经济, 2023, 46(10): 170-195. (WU XK, TIE Y. Knowledge product trading, boundary barriers and efficiency loss assessment[J]. World economy, 2023, 46(10): 170-195.)
[24] 伊惠芳, 吴红. 多级需求分析视域下高校专利转移对象识别研究——以石墨烯为例[J]. 图书情报工作, 2020, 64(12): 18-126. (YIN H F, WU H. A study on university patent transfer object recognition based on multi-level requirements analysis: graphene as a case study[J]. Library and information service, 2020, 64(12): 18-126.)
[25] 李建蓉. 专利文献与信息[M]. 北京: 知识产权出版社, 2002: 16. (LI J R. Patent literature and information[M]. Beijing: Intellectual Property Publishing House, 2002: 16)
[26] 马永涛, 张旭, 傅俊英, 等. 核心专利及其识别方法综述[J]. 情报杂志, 2014, 33(5): 38-43. (MA Y T, ZHANG X, FU J Y, et al. An overview of core patents and their identification methods[J]. Journal of intelligence, 2014, 33(5): 38-43.)
[27] 栾春娟, 王续琨, 刘则渊. 基于《德温特》数据库的核心技术确认方法[J]. 科学学与科学技术管理, 2008(6): 32-34. (LUAN C J, WANG X K, LIU Z Y. Methods of affirming core technology via Derwent innovations index[J]. Science of science and management of S.& T., 2008(6): 32-34.)
[28] 孙涛涛, 唐小利, 李越. 核心专利的识别方法及其实证研究[J]. 图书情报工作, 2012, 56(4): 80-84. (SUN T T, TANG X L, LI Y. Method and application of core patents identification[J]. Library and information service, 2012, 56(4): 80-84.)
[29] OHBA N, NAKAO K. Sleeping beauties in ophthalmology[J]. Scientometrics, 2012, 93(2): 253-264.
[30] RAAN V F J. Sleeping Beauties in science[J]. Scientometrics, 2004, 59(3): 467-472.
[31] DALEN H P V, HENKENS K. Signals in science-on the importance of signaling in gaining attention in science[J]. Scientometrics, 2005, 64(2): 209-233.
[32] BRAUN T, W GLÄNZEL, SCHUBERT A. On sleeping beauties, princes and other tales of citation distributions[J]. Research evaluation, 2010, 19(3): 195-202.
[33] DU J, WU Y S. A bibliometric framework for identifying “princes” who wake up the “sleeping beauty” in challenge-type scientific discoveries[J]. Journal of data and information science, 2016, 1(1): 50-68.
[34] HUANG T C, HSU C, CIOU Z J. Systematic methodology for excavating sleeping beauty publications and their princes from medical and biological engineering studies[J]. Journal of medical & biological engineering, 2015, 35(6): 749-758.
[35] HU X, HU X, ZHANG Y, et al. Hibernators, their awakeners and the roles of subsequent authoritative citers[J]. Malaysian journal of library and information science, 2018, 23(1): 103-113.
[36] AURORA A C T, VIEIRA P C, ABREU A P. Sleeping beauties and their princes in innovation studies[J]. Scientometrics, 2016, 110(2): 541-580.
[37] SONG Y, FANGLING S, ZHU H, et al. To be the prince to wake up sleeping beauty: the rediscovery of the delayed recognition studies[J]. Scientometrics, 2018, 117(10): 9-24.
[38] GLNZEL W, GARFIELD E. The myth of delayed recognition[J]. Scientist, 2004, 18(11): 8.
[39] 王清, 郭永海, 容书超, 等. 中美人工智能发展对比研究及对江苏的启示[J]. 现代管理科学, 2021(2): 13. (WANG Q, GUO Y Q, RONG S C, et al. A comparative study on the development of artificial intelligence between China and the United States and its enlightenment to Jiangsu Province[J]. Modern management science, 2021(2): 13.)
[40] CHEN C, HICKS D. Tracing knowledge diffusion[J]. Scientometrics, 2004, 59(2): 199-211.
[41] DERNIS H, SQUICCIARINI M, DE PINHO R. Detecting the emergence of technologies and the evolution and co-development trajectories in science (DETECTS): a ‘burst' analysis-based approach[J]. The journal of technology transfer, 2016, 41(10): 930-960.
[42] DANISH M, SHARMA R. The value of Indian patents: an empirical analysis using citation lags approach[J]. Economics of innovation and new technology, 2023(4): 1-25.
[43] HOU J, YANG X, SONG H. et al. Will patent family be dormant? Research on the identification and characteristics of sleeping beauty's patent family[J]. Scientometrics, 2023, 128(7): 5361–5387.
[44] 迟国泰, 祝志川, 张玉玲. 基于熵权-G1法的科技评价模型及实证研究[J]. 科学学研究, 2008, 26(6): 1210-1220. (CHI G T, ZHU Z C, ZHANG Y L. The science and technology evaluation model based on entropy and G1 and empirical research of China[J]. Studies in science of science, 2008, 26(6): 1210-1220.)
[45] 杨大飞, 杨武, 田雪姣, 等. 基于专利数据的核心技术识别模型构建及实证研究[J]. 情报杂志, 2021, 40(2): 47-54. (YANG D F, YANG W, TIAN X J, et al. Construction of core technology identification model based on patent data and empirical research[J]. Journal of intelligence, 2021, 40(2): 47-54.)
[46] KLEINBERG J. Bursty and hierarchical structure in streams[J]. Data mining and knowledge discovery, 2003, 7(4): 373-397.
[47] 李江. 基于引文的知识扩散研究评述[J]. 情报资料工作2013(4): 36-40. (LI J. Review of research of citation-based knowledge diffusion[J]. Information and documentation services, 2013(4): 36-40.)
[48] 贵淑婷. 基于专利引文网络的技术扩散速度研究[D]. 南京: 南京农业大学, 2016. (GUI S T. Research on the speed of technology diffusion based on patent citation network[D]. Nanjing: Nanjing Agricultural University, 2016.)
[49] 陶于祥, 吴超楠, 李晶莹, 等. 基于技术生命周期的中美人工智能原始创新能力研究[J]. 中国电子科学研究院学报, 2021, 16(12): 1215-1223. (TAO Y X, WU C N, LI J Y, et al. Research on the original innovation capability of artificial intelligence[J]. Journal of China Academy of Electronics and Information Technology, 2021, 16(12): 1215-1223.)
[50] 清华大学人工智能研究院, 清华-中国工程院知识智能联合研究中心. 人工智能发展报告2020[R]. 北京: 清华大学人工智能研究院, 2020. (Institute of Artificial Intelligence, Tsinghua University, Tsinghua-Chinese Academy of Engineering Joint Research Center for Knowledge Intelligence. Artificial intelligence development report 2020[R]. Beijing: Institute of Artificial Intelligence, Tsinghua University, 2020.)
[51] HUANG J, LI J, CHEN Y, et al. Burst hotspots dynamic detection and tracking on large-scale text stream[J]. IEEE access, 2019, 7: 30913-30924.
[52] 宋维翔. “王子”对“睡美人文献”引用的动机分析——基于邮件访谈调查的实证研究[J]. 现代情报, 2018, 38(5): 32-36. (SONG W X. An analysis of the mobilization of first cite for “sleeping beauty paper”: based on e-mail interview for “prince paper”[J]. Journal of modern information, 2018, 38(5): 32-36.
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