Sage Journals: Discover world-class research

Abstract

This study develops an integrated analytical framework that connects the early identification of emerging technologies with the design of targeted support policies. Leveraging large AI models and multi-source data—including global patent databases (e.g., WIPO, USPTO, Lens.org), scientific literature corpora, and industry intelligence platforms (e.g., CB Insights, Qichacha)—the research applies advanced techniques such as LDA topic modelling, BERT-based clustering, and co-citation analysis to detect innovation trajectories. Technologies such as AI-driven healthcare, quantum communication, hydrogen energy, and smart educational AI are identified as key domains of convergence.

Temporal mapping and citation networks reveal distinct technology maturity patterns, which are visualised using S-curve and hype cycle models. These insights are triangulated with market data and sentiment analysis, confirming that public enthusiasm often outpaces actual technological readiness. A technology maturity classification categorises innovations into emerging, developing, and mature stages, forming the basis for strategic policy matching.

To validate and prioritise policy instruments, Delphi rounds with domain experts and Analytic Hierarchy Process (AHP) weighting are employed. The resulting policy matrix includes R&D funding, regulatory sandboxes, public procurement incentives, and tax relief, tailored to each stage of technological evolution.

The study concludes that a data-driven, foresight-based approach to policy design significantly enhances responsiveness, precision, and resource efficiency in science and technology governance. This framework offers a replicable model for governments and institutions seeking to proactively support high-potential innovations across sectors.

Keywords

Early identification emerging technologies large AI models targeted support policies patent mining industry trend

Get full access to this article

View all access options for this article.

References

Aristodemou

, & Tietze

(2018). The state-of-the-art on intellectual property analytics (IP analytics): A literature review on the use of patent data. World Patent Information, 55, 1–10. https://doi.org/10.1016/j.wpi.2018.10.002

Bai

, Chen

, Zhou

, & Feng

(2022). A data-driven model for patent technology convergence and industry evolution: Evidence from AI-related patents. Sustainability, 14(23), 13501. https://doi.org/10.3390/su142313501

CB Insights. (2023). Venture trends and innovation signals. Retrieved from https://www.cbinsights.com

CNIPA. (n.d.). China National Intellectual Property Administration. Retrieved from https://www.cnipa.gov.cn/

EPO. (n.d.). European patent office patent search. Retrieved from https://www.epo.org/

Etemad

, Bagherzadeh

, & Shafiee

(2022). Using Delphi method for foresight in innovation ecosystems: A systematic approach. Sustainability, 14(5), 10812. https://doi.org/10.3390/su140510812

Gonzales

(2023). Overcoming the scale-up bottleneck in technology maturity transitions. Technological Forecasting and Social Change, 187, 122190. https://doi.org/10.1016/j.techfore.2022.122190

Guderian

C. C.

, Bican

P. M.

, & Wenzel

(2021). Managing innovation ecosystems through patent analytics: Mapping collaboration networks and technology trends. R&D Management, 51(3), 341–356. https://doi.org/10.1111/radm.12436

Howlett

(2011). Designing public policies: Principles and instruments. Routledge.

10.

IEEE Xplore. (n.d.). Digital library of IEEE publications. Retrieved from https://ieeexplore.ieee.org/

11.

Kim

, Bae

, & Lee

(2019). Early identification of emerging technologies: A bibliometric perspective. Technological Forecasting and Social Change, 146, 834–845. https://doi.org/10.1016/j.techfore.2019.06.001

12.

, Wen

, Jiang

, & Wang

(2024). Assessing the effect of urban digital infrastructure on green innovation: Mechanism identification and spatial–temporal characteristics. Humanities and Social Sciences Communications, 11, Article 320. https://doi.org/10.1057/s41599-024-02787-y

13.

Malerba

(2004). Sectoral systems of innovation: Concepts, issues and analyses of six major sectors in Europe. Cambridge University Press.

14.

Mohammadi

, Ritala

, & Torkkeli

(2024). A lifecycle-based framework for tailoring innovation support policy: Evidence from emerging technologies. Technovation, 132, 102650. https://doi.org/10.1016/j.technovation.2023.102650

15.

Qichacha. (n.d.). Enterprise and investment insight platform. Retrieved from https://www.qcc.com/

16.

Ren

, Ma

, & Luo

(2025). Leveraging large language models for technology foresight and innovation management. Journal of Technology Forecasting and Innovation Strategy. Advance online publication.

17.

Ren

, Xu

, & Zhang

(in press). Designing policy interventions based on innovation maturity: An empirical approach. Research Policy.

18.

Roberts

M. E.

, Stewart

B. M.

, & Airoldi

E. M.

(2016). A model of text for experimentation in the social sciences. Journal of the American Statistical Association, 111(515), 988–1003. https://doi.org/10.1080/01621459.2016.1141684

19.

Rowe

, & Wright

(2001). Expert opinions in forecasting: The role of the Delphi technique. International Journal of Forecasting, 15(4), 353–375. https://doi.org/10.1016/S0169-2070(99)00018-7

20.

Salamzadeh

, Azimi

, & Tajpour

(2022). Bridging market signals and technology readiness: A new paradigm for innovation policy. Sustainability, 14(12), 8456. https://doi.org/10.3390/su14128456

21.

Sharma

, Kalra

, & Ranjan

(2021). Topic modeling-based trend analysis in emerging digital technologies: Applications to blockchain and IoT. Technological Forecasting and Social Change, 166, 120644. https://doi.org/10.1016/j.techfore.2021.120644

22.

Statista. (2023). Technology market insights. Retrieved from https://www.statista.com/

23.

Teng

, & Chen

(2020). A Delphi–AHP-based strategic planning framework for emerging technology policy. Technological Forecasting and Social Change, 161, 120251. https://doi.org/10.1016/j.techfore.2020.120251

24.

The Lens. (n.d.). The Lens patent and research discovery platform. Retrieved from https://www.lens.org/

25.

US Patent and Trademark Office. (n.d.). Patent full-text and image database. Retrieved from https://patft.uspto.gov/

26.

Web of Science. (n.d.). Research analytics platform. Retrieved from https://www.webofscience.com/

27.

WIPO. (n.d.). PATENTSCOPE. Retrieved from https://patentscope.wipo.int/

28.

Zhang

, Yu

, & Xu

(2014). Mapping the knowledge structure of innovation research: A co-citation analysis. Scientometrics, 100(2), 423–440. https://doi.org/10.1007/s11192-014-1302-0

29.

Zhang

, Zhou

, & Wang

(2022). A machine learning approach for detecting convergence in emerging technologies using patent citation networks. Scientometrics, 127(4), 2211–2230. https://doi.org/10.1007/s11192-022-04456-0

30.

Zhou

, Liu

, & Wu

(2020). Technology forecasting using hype cycle and S-curve theory: Evidence from artificial intelligence patents. Journal of Informetrics, 14(3), 101045. https://doi.org/10.1016/j.joi.2020.101045

Emerging Technologies Based on Large AI Models and the Design of Support Policies: Patent Mining and Industry Trends

Abstract

Keywords

Get full access to this article

References