Enterprise-Led High-Value Patents Call for Patient Capital: A Data-Driven Empirical Analysis
DOI:
https://doi.org/10.54097/ydpepg05Keywords:
High-value patents, innovation network, ITGInsight, enterprise-ledAbstract
High-value patents serve as a key indicator of regional innovation capability. However, existing research lacks systematic exploration of the structural characteristics and dynamic evolution of enterprise-led high-value patents. This study identifies and evaluates such patents from the perspective of regional innovation networks, providing a basis for optimizing innovation resource allocation, promoting cross-regional collaboration, and formulating intellectual property strategies in the Sichuan-Chongqing region. Focusing on enterprises in the Sichuan-Chongqing region and utilizing authoritative patent data, this study employs social network analysis to examine the development paths and network characteristics of enterprise-led high-value patents from multiple dimensions. High-value patent activities show an evolutionary trend from “policy-driven scale expansion” to “market-oriented quality optimization”, with an average growth cycle of 9.17 years. The cooperation network presents a “core-periphery” structure, with Sichuan and Chongqing as the hub, eastern coastal areas as the key region, and preliminary inter-provincial collaboration in western China. The technological layout constructs a collaborative innovation system centered on “computing” and “telecommunications”, evolving toward intelligent integration. The enterprise coupling network is characterized by the coexistence of leading enterprise guidance, industrial focus, and diversified collaboration.
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[1] Liu, P. P., Zhong, Y. H., Liu, J., et al. (2024). Analysis on the current situation and breakthrough suggestions of China’s "bottleneck stuck" enterprises from the perspective of patents. Science and Technology Management Research, 44(16), 162–172.
[2] Wang, S. S., Zhou, H. Y., & Zhao, T. Y. (2024). Configuration paths to high-value patents: evidence from patents winning the China Patent Awards. Scientometrics, 129(5), 2633–2658. https://doi.org/10.1007/s11192-024-04987-2 DOI: https://doi.org/10.1007/s11192-024-04976-7
[3] Ma, T. Q., & Zhao, X. (2018). Exploration on the connotation and restrictive factors of high-value patents. China Invention & Patent, 15(3), 24–28.
[4] Wang, J., Sun, Y. T., & Liu, F. C. (2012). Research on the dominant position of Chinese enterprises in technological innovation. China Soft Science, (9), 146–153.
[5] Wang, K., Chen, Y., Wang, Y. Q., et al. (2024). Identification and diffusion trend prediction of disruptive technologies: Conceptual model and empirical analysis. Journal of the China Society for Scientific and Technical Information, 43(8), 899–913.
[6] Hu, Z., Zhou, X., & Lin, A. (2023). Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches. Journal of Informetrics, 17(2), 101406. https://doi.org/10.1016/j.joi.2023.101406 DOI: https://doi.org/10.1016/j.joi.2023.101406
[7] Zhang, Y. R., & Zhu, J. Y. (2025). Research on influencing factors of high-value patents based on China Patent Gold Award: A fuzzy-set Qualitative Comparative Analysis. Science of Science and Management of S. & T., 46(2), 109–119.
[8] Schettino, F., Sterlacchini, A., & Venturini, F. (2013). Inventive productivity and patent quality: Evidence from Italian inventors. Journal of Policy Modeling, 35(6), 1043–1056. https://doi.org/10.1016/j.jpolmod.2013.08.001 DOI: https://doi.org/10.1016/j.jpolmod.2013.02.008
[9] Fischer, T., & Leidinger, J. (2014). Testing patent value indicators on directly observed patent value—An empirical analysis of Ocean Tomo patent auctions. Research Policy, 43(3), 519–529. https://doi.org/10.1016/j.respol.2013.12.003 DOI: https://doi.org/10.1016/j.respol.2013.07.013
[10] Hu, Z. W., Zhou, X. J., & Ren, P. (2022). Review on evaluation and identification of high-value patents based on grounded theory. Information Science, 40(2), 183–192.
[11] Cui, W. J., Sun, C., Wu, J., et al. (2023). Research on the impact of high-value patents on enterprises' technology standardization capability. Studies in Science of Science, 41(2), 296–306.
[12] Kelly, B., Seru, D. P. A., & Taddy, M. (2018). Measuring technological innovation over the long run. American Economic Review: Insights, 3(3), 303–320. https://doi.org/10.1257/aeri.20170211 DOI: https://doi.org/10.1257/aeri.20190499
[13] Chen, Y., Hu, Z. W., & Zhou, X. J. (2024). A review on international high-value patents: Concept connotation, thematic categories, multi-dimensional value indicators and evaluation & prediction methods. Journal of Modern Information, 44(8), 153–170.
[14] Liu, Q., Yang, Y. M., & Liu, Y. H. (2021). Modeling and empirical research on high-value patent evaluation. Information Studies: Theory & Application, 44(2), 122–127.
[15] Liu, P. P., Lu, D. T., Su, W., et al. (2021). System design of patent competitiveness evaluation and identification of high-value patents. Science and Technology Management Research, 41(7), 110–115.
[16] Wang, S. P., & Han, T. (2020). Research on prediction method of potential high-value patents based on random forest algorithm. Information Science, 38(5), 120–125.
[17] Park, I., Park, G., Yoon, B., et al. (2016). Exploring Promising Technology in ICT Sector Using Patent Network and Promising Index Based on Patent Information. ETRI Journal, 38(2), 405–415. https://doi.org/10.4218/etrij.15.0112 DOI: https://doi.org/10.4218/etrij.16.0115.0613
[18] Guo, J. M., Wang, J. Y., Zhou, Y. F., et al. (2023). Research on core patent identification method based on node importance of patent network. Journal of Intelligence, 42(5), 162–168.
[19] Xu, Z. L., Wang, Z., & Gao, T. Y. (2024). Screening research of high-value patents based on AHP-Delphi method: Evidence from rare earth permanent magnet industry. Forum on Science and Technology in China, (1), 62–71.
[20] Yang, Y., & Chen, S. X. (2025). Research on the structure and evolution of global urban innovation network based on patent big data. Science & Technology and Economy, 38(3), 31–35.
[21] Feng, X. H., Gao, Z. Y., Xu, M. H., et al. (2024). Innovation network structure and organizational model of urban agglomerations based on patented technology transfer. Economic Geography, 44(10), 66–75.
[22] Nie, J. X. (2024). Evolution of Beijing’s innovative spatial structure from a polycentric perspective. Economic Geography, 44(12), 72–81.
[23] Wang, Q. X., & Zhu, W. Q. (2022). Evolution Characteristics of Advanced Nonferrous Metal Industry Patent Cooperation Network in China from the Perspective of Multilayer Network. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/9976542 DOI: https://doi.org/10.1155/2022/1023816
[24] Harhoff, D., Narin, F., Scherer, F. M., et al. (1999). Citation Frequency and the Value of Patented Inventions. The Review of Economics and Statistics, 81(3), 511–515. https://doi.org/10.1162/003465399558260 DOI: https://doi.org/10.1162/003465399558265
[25] Albert, M. B., et al. (1991). Direct validation of citation counts as indicators of industrially important patents. Research Policy, 20(3), 251–259. https://doi.org/10.1016/0048-7333(91)90055-4 DOI: https://doi.org/10.1016/0048-7333(91)90055-U
[26] Bakker, N. J. (2017). The log-linear relation between patent citations and patent value. Scientometrics, 110(2), 1–14. https://doi.org/10.1007/s11192-016-2189-2 DOI: https://doi.org/10.1007/s11192-016-2208-7
[27] Fu, Z. K., Liu, B. X., Yan, C. G., et al. (2023). Research on identification method of potential high-value patents from the perspective of patent lifespan. Journal of Intelligence, 42(5), 154–161.
[28] Choi, J., Jeong, B., Yoon, J., et al. (2020). A novel approach to evaluating the business potential of intellectual properties: A machine learning-based predictive analysis of patent lifetime. Computers & Industrial Engineering, 145, 106544. https://doi.org/10.1016/j.cie.2020.106544 DOI: https://doi.org/10.1016/j.cie.2020.106544
[29] Li, X. D., Gong, M., Rao, M. X., et al. (2026). Research on resilience measurement and spatial correlation network of innovation ecosystem in intelligent manufacturing industry. Science of Science and Management of S.& T., 1–39.
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