基于专利挖掘和Gompertz模型的颠覆性技术识别方法研究

Research on identification method of disruptive technology based on patent mining and Gompertz Model

颠覆性技术已经成为推动新一轮技术变革的重要抓手,颠覆性技术的精准识别对于把握技术变革机遇、布局技术创新战略具有重要意义。现有研究多集中于围绕技术或市场的单一维度特征进行颠覆性技术识别,而对于综合“技术-市场”维度的系统性识别方法仍处于探索阶段。为此,本文从颠覆性技术特征出发,构建基于专利挖掘和Gompertz模型的系统性识别方法,并进行实证研究和稳健性检验。研究发现:(1)颠覆性技术具有“技术-市场”双重维度特征,具体可围绕前沿性、独特性、影响力和扩散性四个基本特征进行颠覆性技术“漏斗式”逐层识别;(2)综合专利挖掘和Gompertz模型构建系统性的颠覆性技术识别方法,可实现对颠覆性技术的系统识别;(3)动力锂电池技术的实证研究表明,本文构建的颠覆性技术识别方法可提升识别过程的效率和识别结果的准确性,有效克服主观判断的局限性。该方法突破了已有识别方法中特征信息获取不全面、识别结果准确性较低的局限性,可以为颠覆性技术的早期识别提供更加科学的方法支持。

Disruptive technology has become an important starting point to promote a new round of technological change.Accurate identification of disruptive technology is of great significance to grasp the opportunities of technological change and then to lay out technological innovation strategies.Existing research focuses on the single-dimensional characteristics of technology or market,and the systematic identification method of the comprehensive"technology-market"dimension is still in the exploratory stage.Therefore,this paper started from the characteristics of disruptive technologies,constructed a systematic identification method based on patent mining and Gompertz model,and conducted empirical research and robustness testing.The research findings are as follows:(1)Disruptive technology has the dual dimension feature of the"technology market",which can be identified layer by layer in a"funnel"manner around four basic characteristics:cutting-edge,uniqueness,influence,and diffusion.(2)A systematic identification method for disruptive technologies was adopted based on patent mining and the Gompertz model,which can achieve systematic identification of disruptive technologies.(3)The empirical research on power lithium battery technology showed that the disruptive technology identification method constructed in this paper can improve the efficiency of the identification process and the accuracy of the identification results,effectively overcoming the limitations of subjective judgment.This method has made a breakthrough to the limitations of incomplete feature information acquisition and low accuracy of recognition results in existing recognition methods,aiming to provide more scientific method support for early recognition of disruptive technologies.