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2025 , Vol. 69 >Issue 19: 108 - 123

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.19.010

RESEARCH PAPERS

Patent Data Anomaly Detection and Disruptive Technology Prediction: A Case Study on Drone Technology

  • Xiao Junchao ,
  • Wu Linhui ,
  • Zhong Fuli ,
  • Zhang Jinling
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  • 1 Guangdong University of Technology Library, Guangzhou 510006;
    2 School of Computer Science, Guangdong University of Technology, Guangzhou 510006;
    3 School of Systems Science and Engineering, Sun Yat-Sen University, Guangzhou 510275;
    4 School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004
Xiao Junchao, Librarian, PhD, E-mail: chao8003@sina.com; Wu Linhui, Undergraduate; Zhong Fuli, Postdoc; Zhang Jinling, Librarian, Master’s Degree.

Received date: 2024-10-21

  Revised date: 2025-03-12

  Online published: 2025-09-12

Supported by

This work is supported by the youth project of National Natural Science Foundation of China, titled “Service-Oriented Space Information Network Resilience Assessment and Swarm Intelligence Optimization Approach for Resilience Enhancement” (Grant No. 62102466).

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Abstract

[Purpose/Significance] Disruptive technologies can lead the development of technology and industry and enable enterprises to make great progress. Early prediction and deployment of disruptive technologies are crucial for enterprises to innovate and strengthen their market position. This paper takes technology application and disruptive technology identification as the research objects and proposes a disruptive technology prediction method. [Method/Process] First, based on the title, abstract and international patent classification (IPC) code of each patent, a noun co-occurrence graph and an IPC co-occurrence graph were constructed. Secondly, the graph anomaly detection algorithm was used to detect abnormal nouns and IPC numbers in the noun co-occurrence graph and IPC co-occurrence graph, and the detection results were combined with the abnormal patents detected in the isolation forest to construct an abnormal patent dataset. Then, through in-depth analysis, the core features of disruptive patents that appeared in the form of abnormal patents were extracted, and an effective measurement indicator system was constructed using them. Finally, the correlation between patent indicators and disruptive patent labels was fitted by a machine learning model, and potential disruptive technologies were predicted from a large number of abnormal technology patents, IPCs, and nouns. The proposed method was verified by taking “drone technology” as an example. [Result/Conclusion] The empirical study on drone technology shows that the method obtains reasonable prediction results and provides a new perspective for disruptive technology prediction and patent value measurement analysis.

Key words： disruptive technology prediction; graph anomaly detection; noun co-occurrence graph; IPC co-occurrence graph

Cite this article

Xiao Junchao , Wu Linhui , Zhong Fuli , Zhang Jinling . Patent Data Anomaly Detection and Disruptive Technology Prediction: A Case Study on Drone Technology[J]. Library and Information Service, 2025 , 69(19) : 108 -123 . DOI: 10.13266/j.issn.0252-3116.2025.19.010

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