计算机科学
新兴技术
数据科学
引用
空格(标点符号)
技术开发
人工智能
工程类
制造工程
万维网
操作系统
作者
Zhipeng Qiu,Zheng Wang
标识
DOI:10.1016/j.compind.2022.103829
摘要
In the past 20 years, with the rapid development of technology, the number of granted patents has also been increasing over years. How to utilize these data effectively is very important for making R&D policies and assisting in designing new technologies. In this paper, we map patents into a low-dimensional vector space, which is constructed by International Patent Classification (IPC) codes, through a deep learning model, i.e., Bidirectional Encoder Representations from Transformers (BERT). Then, this research makes the following contributions: first, we find that the generated vectors can describe the new technologies’ invention perspectives of patents accurately according to their texts; second, these vectors are combined with the physical meaning of patent citations (technological application) for the first time to solve some issues in designing new technologies from a different view; third, the citation relations and vectors of patents are adopted to explore the development rules of technology in terms of new technology’s invention perspective; fourth, an approach is raised to assist inventors in designing new technologies through the forward citations of patents, whose vectors have great similarities to the initial ones’; finally, the patents granted by USPTO in the past 20 years are used to verify the effectiveness of our framework.
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