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| Artificial Intelligence Technology: Novel Strategy for Patent Dataset Creation Based on Machine Learning |
| Chen Yue1, Song Kai1, Liu Anrong2, Cao Xiaoyang2 |
1.Institution of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116024
2.Chinese Academy of Engineering Innovation Strategy, Beijing 100089 |
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Abstract Disruptive technology is a technology group with a complex internal structure that spans multiple disciplines and fields, and from a spatial perspective, it includes leading, auxiliary, and supporting technologies. The use of scientometrics to evaluate disruptive technologies and explore the evolution of science and technology is facing challenges that manifest in data retrieval. This paper explores a novel strategy for patent dataset construction for complex technology based on machine learning, with a focus on the patent retrieval tasks (binary classification tasks) of machine learning. This is similar to query classification, which is based on active learning in information retrieval. Additionally, we propose an improved text classification method that combines feature maximization with the CNN model. In this paper, the technical domain of artificial intelligence (AI) is used as an example. The results show an accuracy of 98.01%, a recall rate of 97.04%, and an F1 value of 97.89%; this demonstrates that the proposed strategy accurately identifies AI patents, improves the accuracy and recall rate of patent searches, and facilitates the creation of accurate and comprehensive patent datasets for the technical domain of AI.
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Received: 07 July 2020
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