|
|
|
| Formulation of a Predictive Modeling Approach for Emerging Technologies: Based on Patent Dynamic Indicators in the Cancer Drug Area |
| Yang Guancan1, Ding Yue1, Xu Shuo2, Lu Xiaobin1 |
1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.College of Economic and Management, Beijing University of Technology, Beijing 100124 |
|
|
|
|
Abstract The automated identification and prediction of emerging technologies based on massive patent data have become a hot topic of research with the increasing use of big data mining technology. The purpose of this study is to identify which cancer drug patents are most likely to become emerging technologies in the future. This study has two main contributions: first, the predictions are based on an ex-ante perspective instead of a retrospective perspective; second, it provides a complete analytical framework to address the issues of unbalanced data and early-stage forecast requirements for emerging technology predictions in the cancer drug field. Finally, it confirms that a model constructed on the basis of supervised learning methods that include dynamic patent metrics has good predictive power for emerging technologies. The dynamic indicators “forward patent family” and “forward citation” can effectively enhance the model’s performance.
|
|
Received: 09 June 2021
|
|
|
|
1 卢小宾, 杨冠灿, 徐硕, 等. 计量与演化视角下的新兴技术识别研究进展评述[J]. 情报学报, 2020, 39(6): 651-661.
2 Su H N, Lin Y S. How do patent-based measures inform product commercialization? —The case of the United States pharmaceutical industry[J]. Journal of Engineering and Technology Management, 2018, 50: 24-38.
3 Kneller R. The origins of new drugs[J]. Nature Biotechnology, 2005, 23(5): 529-530.
4 Kinch M S, Haynesworth A, Kinch S L, et al. An overview of FDA-approved new molecular entities: 1827-2013[J]. Drug Discovery Today, 2014, 19(8): 1033-1039.
5 Fernandez D S, Huie J T. Balancing US patent and FDA approval processes: strategically optimizing market exclusivity[J]. Drug Discovery Today, 2004, 9(12): 509-512.
6 Alexander J, Chase J, Newman N, et al. Emergence as a conceptual framework for understanding scientific and technological progress[C]// 2012 Proceedings of PICMET'12: Technology Management for Emerging Technologies. IEEE, 2012: 1286-1292.
7 Day G S, Schoemaker P J H. Avoiding the pitfalls of emerging technologies[J]. California Management Review, 2000, 42(2): 8-33.
8 Martin B R. Foresight in science and technology[J]. Technology Analysis & Strategic Management, 1995, 7(2): 139-168.
9 Cozzens S, Gatchair S, Kang J, et al. Emerging technologies: quantitative identification and measurement[J]. Technology Analysis & Strategic Management, 2010, 22(3): 361-376.
10 Rotolo D, Hicks D, Martin B R. What is an emerging technology?[J]. Research Policy, 2015, 44(10): 1827-1843.
11 Jeong G H, Kim S H. A qualitative cross-impact approach to find the key technology[J]. Technological Forecasting and Social Change, 1997, 55(3): 203-214.
12 Lee C Y, Kim J, Kwon O, et al. Stochastic technology life cycle analysis using multiple patent indicators[J]. Technological Forecasting and Social Change, 2016, 106: 53-64.
13 Lee C Y, Cho Y, Seol H, et al. A stochastic patent citation analysis approach to assessing future technological impacts[J]. Technological Forecasting and Social Change, 2012, 79(1): 16-29.
14 Lee C Y, Kwon O, Kim M, et al. Early identification of emerging technologies: a machine learning approach using multiple patent indicators[J]. Technological Forecasting and Social Change, 2018, 127: 291-303.
15 érdi P, Makovi K, Somogyvári Z, et al. Prediction of emerging technologies based on analysis of the US patent citation network[J]. Scientometrics, 2013, 95(1): 225-242.
16 Breitzman A, Thomas P. The emerging clusters model: a tool for identifying emerging technologies across multiple patent systems[J]. Research Policy, 2015, 44(1): 195-205.
17 Arora S K, Porter A L, Youtie J, et al. Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs[J]. Scientometrics, 2013, 95(1): 351-370.
18 Lee C Y, Kang B, Shin J. Novelty-focused patent mapping for technology opportunity analysis[J]. Technological Forecasting and Social Change, 2015, 90: 355-365.
19 Moehrle M G, Passing F. Applying an anchor based patent mapping approach: basic conception and the case of carbon fiber reinforcements[J]. World Patent Information, 2016, 45: 1-9.
20 Yoon J, Kim K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks[J]. Scientometrics, 2011, 88(1): 213-228.
21 Joung J, Kim K. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data[J]. Technological Forecasting and Social Change, 2017, 114: 281-292.
22 Ju Y H, Sohn S Y. Patent-based QFD framework development for identification of emerging technologies and related business models: a case of robot technology in Korea[J]. Technological Forecasting and Social Change, 2015, 94: 44-64.
23 Shin J, Coh B Y, Lee C Y. Robust future-oriented technology portfolios: Black-Litterman approach[J]. R&D Management, 2013, 43(5): 409-419.
24 Jang H J, Woo H G, Lee C Y. Hawkes process-based technology impact analysis[J]. Journal of Informetrics, 2017, 11(2): 511-529.
25 Lee C Y, Kim J, Noh M, et al. Patterns of technology life cycles: stochastic analysis based on patent citations[J]. Technology Analysis & Strategic Management, 2017, 29(1): 53-67.
26 周源, 刘宇飞, 薛澜. 一种基于机器学习的新兴技术识别方法: 以机器人技术为例[J]. 情报学报, 2018, 37(9): 939-955.
27 Wagner S, Wakeman S. What do patent-based measures tell us about product commercialization? Evidence from the pharmaceutical industry[J]. Research Policy, 2016, 45(5): 1091-1102.
28 Kovarik J E. Cancer moonshot: patents for patients[J]. Trends in Cancer, 2018, 4(8): 515-516.
29 Blind K, Cremers K, Mueller E. The influence of strategic patenting on companies’ patent portfolios[J]. Research Policy, 2009, 38(2): 428-436.
30 Squicciarini M, Dernis H, Criscuolo C. Measuring patent quality: indicators of technological and economic value[R]. OECD Science, Technology and IndustryWorking Papers. Paris: OECD Publishing, 2013: No.2013/03.
31 Marco A C. The option value of patent litigation: theory and evidence[J]. Review of Financial Economics, 2005, 14(3/4): 323-351.
32 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.
33 Kelleher J D, Mac Namee B, D’Arcy A. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies[M]. Cambridge: The MIT Press, 2015.
34 Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
35 Ushijima T. Patent rights protection and Japanese foreign direct investment[J]. Research Policy, 2013, 42(3): 738-748.
36 Cowart T W, Lirely R, Avery S. Two methodologies for predicting patent litigation outcomes: logistic regression versus classification trees[J]. American Business Law Journal, 2014, 51(4): 843-877. |
|
|
|
