专题:突破性/颠覆性技术识别

基于技术融合视角的颠覆性专利识别研究

  • 王丹 ,
  • 周潇 ,
  • 赵捧未 ,
  • 樊嘉逸
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  • 西安电子科技大学经济与管理学院 西安 710126
王丹,博士研究生;周潇,副教授,博士,硕士生导师,通信作者,E-mail:belinda1214@126.com;赵捧未,教授,博士,博士生导师;樊嘉逸,硕士研究生。

收稿日期: 2023-10-25

  修回日期: 2024-01-24

  网络出版日期: 2024-07-30

基金资助

本文系国家自然科学基金面上项目“重组创新视角下新兴共性技术识别及突破路径预测研究”(项目编号:72374165)和国家自然科学基金面上项目“生物医学领域潜在颠覆性技术识别方法研究”(项目编号:72074020)研究成果之一。

Research on Disruptive Patent Identification from the Perspective of Technology Convergence

  • Wang Dan ,
  • Zhou Xiao ,
  • Zhao Pengwei ,
  • Fan Jiayi
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  • School of Economics and Management, Xidian University, Xi'an 710126

Received date: 2023-10-25

  Revised date: 2024-01-24

  Online published: 2024-07-30

Supported by

This work is supported by the general program of National Natural Science Foundation of China titled “Research on emerging general-purpose Technology Identification and Breakthrough Pathway Forecast from the Perspective of Technology Re-combination” (Grant No. 72374165) and “Identification of Potentially Disruptive Technologies in Biomedicine” (Grant No. 72074020).

摘要

[目的/意义] 在新一轮科技革命和产业变革的时代背景下,颠覆性技术是一种另辟蹊径、容易造成技术突袭、颠覆传统游戏规则的技术。积极识别和前瞻布局颠覆性技术对实现国家科技创新跨越式发展具有重要战略意义。[方法/过程] 从技术融合视角提出一种颠覆性技术识别方法。首先,基于专利IPC共现关系,并采用时间序列方式动态构建技术领域融合网络,利用结构熵指标识别领域融合网络中具有颠覆性的领域融合对;其次,将颠覆性的领域融合对与专利映射,筛选出候选颠覆性专利;在此基础上,通过深度剖析颠覆性专利的核心特征,筛选有效的测度指标;最后,基于深度学习Tabnet模型构建专利指标与颠覆性专利之间的关联关系,实现从大规模的候选专利中识别颠覆性专利。[结果/结论] 以人工智能领域为例验证方法的可行性和有效性。研究结果共得到1 025条颠覆性专利。其中,颠覆性融合领域(G06K9,G06N3)包含443条颠覆性专利,这些专利主要涉及4大颠覆性方向:智慧医疗、智慧交通、智慧安防和智能制造。研究结果能够为政府、产业界和企业等利益相关者的科技布局、战略制定提供参考。

本文引用格式

王丹 , 周潇 , 赵捧未 , 樊嘉逸 . 基于技术融合视角的颠覆性专利识别研究[J]. 图书情报工作, 2024 , 68(15) : 58 -71 . DOI: 10.13266/j.issn.0252-3116.2024.15.005

Abstract

[Purpose/Significance] In the context of the technological revolution and industrial transformation, disruptive technology is a new way to easily lead to technological breakthroughs and subvert traditional game rules. It is of great strategic significance to actively identify and layout disruptive technologies to realize leapfrog development in national scientific and technological innovation. [Method/Process] It proposed a disruptive technology prediction method from the perspective of technology convergence. Firstly, based on the co-occurrence relationship of patent IPC, it constructed a technology domain fusion network through a time series approach, and identified the disruptive domain convergence pairs in the domain fusion network with structure entropy indicators. Secondly, it mapped convergence disruptive fields to patents to obtain candidate disruptive patents. On this basis, through in-depth analysis of the core characteristics of disruptive patents, it explored effective measurement indicators. Finally, based on the deep learning Tabnet model, it constructed the correlation between patent indicators and disruptive patents to achieve disruptive technology prediction. [Result/Conclusion] Taking the field of artificial intelligence as an example, it verifies the feasibility and effectiveness of the method. The research results obtain a total of 1025 disruptive patents. Among them, the disruptive convergence field (G06K9, G06N3) contains 443 disruptive patents, involving in four major disruptive directions: smart healthcare, smart transportation, smart security, and smart manufacturing. It can provide reference for the technological layout and strategic formulation of stakeholders such as the government, industry, and enterprises.

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