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| Topic Prediction for Disruptive Technologies Based on Patent Literature—A Case Study of Artificial Intelligence Patents |
| Chen Yuxin1, Lu Jun1, Han Yi2 |
1.College of Computer and Information Science, Southwest University, Chongqing 400715
2.Business College of Southwest University, Chongqing 402460 |
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Abstract As a driving force of economic development and an important starting point and breakthrough in technological innovation, disruptive technology is of great significance for a country or enterprise to optimize its R&D layout and actively seize the commanding heights of science and technology by detecting its dynamic development process and realizing the identification and prediction of disruptive technology. From the perspective of technology and market, a measurement index of disruptive potential was constructed, and a dynamic trend identification method to detect the disruptive potential of technology topics was constructed by combining a time series sliding window and latent Dirichlet allocation (LDA). Patents in the field of artificial intelligence in the United States were taken as a sample to verify the usability of this method in identifying and predicting disruptive technologies. Combined with the top 10 international patent classification (IPC) groups with high correlation intensity of identified disruptive technology topics, the contents of disruptive technology were characterized, and the practical value of the method was further tested. Deep learning and image recognition and processing are disruptive technologies in the field of artificial intelligence. They are closely related and have an obvious collaborative development trend. Deep learning technology focuses on the field of electronic digital data processing, while image recognition and processing is applied to mainstream fields such as automatic driving, medical diagnosis, and television communication. The sample empirical data shows that the multi-index fusion method has more advantages for the identification of disruptive technologies. Closely combining the multi-index fusion method of sample data with the development trend prediction and exploring the influence of historical development inertia through the iterative continuity of time series can better reveal the evolutionary details of disruptive technologies, their development trend, and the internal dependence of its various elements.
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Received: 27 October 2021
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