|
|
|
| Integrating Science-Technology Knowledge Linkage to Predict Disruptive Patents |
| Liang Zhentao1,2, Mao Jin1,2, Li Gang1,2 |
1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072 |
|
|
|
|
Abstract Identifying and predicting disruptive technologies is critical to the national need for strategic development. This study treats patent families as technology units and calculates their disruption index, technological features, and Science-Technology (S&T) association features based on two large-scale patent (PATSTAT) and bibliographic (MAG) datasets. An approach to predicting potential disruptive patents is proposed. The prediction is considered a supervised binary classification task, which predicts the patent disruptiveness in five years, given the features calculated in the year it was published. Our results show that: (1) disruptive patents are characterized by less prior knowledge, stronger teams, an underestimated commercial value, and a higher long-term impact; (2) S&T linkage is an important feature in predicting disruptive patents; and (3) the LightGBM model achieves the best results in terms of performance and efficiency. However, the prediction of disruptive patents remains difficult. Future studies should consider incorporating semantic features and multiple data sources to improve the performance.
|
|
Received: 28 July 2022
|
|
|
|
1 Bower J L, Christensen C M. Disruptive technologies: catching the wave[J]. Harvard Business Review, 1995, 73(1): 43-53.
2 苏成, 赵志耘, 赵筱媛, 等. 颠覆性技术新阐释: 概念、内涵及特征[J]. 情报学报, 2021, 40(12): 1253-1262.
3 Kostoff R N, Boylan R, Simons G R. Disruptive technology roadmaps[J]. Technological Forecasting and Social Change, 2004, 71(1/2): 141-159.
4 李乾瑞, 郭俊芳, 黄颖, 等. 基于突变-融合视角的颠覆性技术主题演化研究[J]. 科学学研究, 2021, 39(12): 2129-2139.
5 Momeni A, Rost K. Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling[J]. Technological Forecasting and Social Change, 2016, 104: 16-29.
6 赵志耘, 潘云涛, 苏成, 等. 颠覆性技术感知响应系统框架研究[J]. 情报学报, 2021, 40(12): 1245-1252.
7 Capponi G, Martinelli A, Nuvolari A. Breakthrough innovations and where to find them[J]. Research Policy, 2022, 51(1): 104376.
8 Funk R J, Owen-Smith J. A dynamic network measure of technological change[J]. Management Science, 2017, 63(3): 791-817.
9 李乾瑞, 郭俊芳, 黄颖, 等. 基于专利计量的颠覆性技术识别方法研究[J]. 科学学研究, 2021, 39(7): 1166-1175.
10 黄鲁成, 成雨, 吴菲菲, 等. 关于颠覆性技术识别框架的探索[J]. 科学学研究, 2015, 33(5): 654-664.
11 White G R T. Future applications of blockchain in business and management: a Delphi study[J]. Strategic Change, 2017, 26(5): 439-451.
12 李晓龙, 鲁平, 李存斌. 基于Delphi和DEMATEL法影响国网的颠覆性创新技术影响因素综合排序分析[J]. 科技管理研究, 2017, 37(6): 127-133.
13 刘志辉, 张均胜, 林毅, 等. 基于隐性知识的潜在颠覆性技术评估方法研究[J]. 情报学报, 2021, 40(12): 1271-1278.
14 Phaal R, Farrukh C J P, Probert D R. Technology roadmapping—a planning framework for evolution and revolution[J]. Technological Forecasting and Social Change, 2004, 71(1/2): 5-26.
15 Vojak B A, Chambers F A. Roadmapping disruptive technical threats and opportunities in complex, technology-based subsystems: the SAILS methodology[J]. Technological Forecasting and Social Change, 2004, 71(1/2): 121-139.
16 张金柱, 王秋月, 仇蒙蒙. 颠覆性技术识别研究进展综述[J]. 数据分析与知识发现, 2022, 6(7): 12-31.
17 Rafii F, Kampas P J. How to identify your enemies before they destroy you[J]. Harvard Business Review, 2002, 80(11): 115-123, 134.
18 Nagy D, Schuessler J, Dubinsky A. Defining and identifying disruptive innovations[J]. Industrial Marketing Management, 2016, 57: 119-126.
19 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.
20 周洋, 张庆普. 高端颠覆性创新的技术演进轨迹和市场扩散路径[J]. 研究与发展管理, 2017, 29(6): 99-108.
21 王康, 陈悦, 王玉奇, 等. 基于专利引用变化的颠覆性技术识别研究[J]. 情报杂志, 2022, 41(1): 74-80, 169.
22 程如烟, 孙浩林. 主要经济体支持颠覆性技术创新的政策措施研究[J]. 情报学报, 2021, 40(12): 1263-1270.
23 曹晓阳, 魏永静, 李莉, 等. DARPA的颠覆性技术创新及其启示[J]. 中国工程科学, 2018, 20(6): 122-128.
24 石慧, 潘云涛, 苏成. 颠覆性技术及其识别预测方法研究综述[J]. 情报工程, 2019, 5(3): 33-48.
25 Schoenmakers W, Duysters G. The technological origins of radical inventions[J]. Research Policy, 2010, 39(8): 1051-1059.
26 王康, 陈悦. 技术融合视角下基于专利的颠覆性技术识别研究[J]. 情报杂志, 2022, 41(4): 29-36, 134.
27 Narin F, Hamilton K S, Olivastro D. The increasing linkage between U.S. technology and public science[J]. Research Policy, 1997, 26(3): 317-330.
28 Huang M H, Yang H W, Chen D Z. Increasing science and technology linkage in fuel cells: a cross citation analysis of papers and patents[J]. Journal of Informetrics, 2015, 9(2): 237-249.
29 刘自强, 许海云, 罗瑞, 等. 基于主题关联分析的科技互动模式识别方法研究[J]. 情报学报, 2019, 38(10): 997-1011.
30 Ba Z C, Liang Z T. A novel approach to measuring science-technology linkage: from the perspective of knowledge network coupling[J]. Journal of Informetrics, 2021, 15(3): 101167.
31 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.
32 Ahmadpoor M, Jones B F. The dual frontier: patented inventions and prior scientific advance[J]. Science, 2017, 357(6351): 583-587.
33 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.
34 Bornmann L, Tekles A. Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts[J]. Journal of Informetrics, 2021, 15(3): 101159.
35 Bornmann L, Devarakonda S, Tekles A, et al. Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers[J]. Quantitative Science Studies, 2020, 1(3): 1242-1259.
36 刘小慧, 廖宇, 朱曼曼. 颠覆性指数用于科研评价初探[J]. 情报理论与实践, 2021, 44(12): 34-40.
37 罗素平, 寇翠翠, 金金, 等. 基于离群专利的颠覆性技术预测——以中药专利为例[J]. 情报理论与实践, 2019, 42(7): 165-170.
38 孔德婧, 董放, 陈子婧, 等. 离群专利视角下的新兴技术预测——基于BERT模型和深度神经网络[J]. 图书情报工作, 2021, 65(17): 131-141.
39 Marx M, Fuegi A. Reliance on science: worldwide front-page patent citations to scientific articles[J]. Strategic Management Journal, 2020, 41(9): 1572-1594.
40 Bessen J. The value of U.S. patents by owner and patent characteristics[J]. Research Policy, 2008, 37(5): 932-945.
41 Kayal A A, Waters R C. An empirical evaluation of the technology cycle time indicator as a measure of the pace of technological progress in superconductor technology[J]. IEEE Transactions on Engineering Management, 1999, 46(2): 127-131.
42 Marco A C, Sarnoff J D, DeGrazia C A W. Patent claims and patent scope[J]. Research Policy, 2019, 48(9): 103790.
43 张静, 杨冠灿, 刘会景. 全球专利统计数据库(PATSTAT)研究述评[J]. 数字图书馆论坛, 2015(12): 62-68.
44 Sinha A, Shen Z, Song Y, et al. An overview of Microsoft academic service (mas) and applications[C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 243-246. New York: ACM Press, 2015: 243-246. 责任编辑 魏瑞斌） |
|
|
|
