Patent transformation prediction: When a patent can be transformed

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Weidong Liu , Yu Zhang , Xiangfeng Luo , Yan Cao , Keqin Gan , Fuming Ye , Wei Tang , Minglong Zhang
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引用次数: 0

Abstract

Patent transformation is a pivotal pathway for realizing technological advancements, and patent transformation prediction is a potential strategy for improving the patent transformation rate. Existing automated patent transformation prediction models do not predict the transformation time, causing invalid conclusions for these valid patents. In this study, we propose a patent transformation prediction model to predict patent transformation time. (1) To obtain patent features in different time periods, the years elapsed since the patent application are segmented into multiple time slots; (2) For each patent, we extract static features and dynamic features of each time slot after constructing and embedding a dynamic graph of the patent; (3) The features for each time slot are concatenated as the input of the dynamic model which utilizes a neural network to predict the patent transformation of the time slot. We measure the model in diverse domains, each of which includes 10,000 patent transformation data. The experimental results show that precision, recall, and F1 scores are approximately 80% for predicting patent transformation in the next 3 years. Additionally, our study yields some novel findings: (1) later applied patents have a higher transformation speed; (2) over 90% of patent transformations occur within 13 years since the patent application; (3) dynamic features, especially dynamic structured features, have a significantly greater impact on patent transformation prediction compared to static features; (4) our model performs stably on different experiment data.

专利转化预测:专利何时可以转化
专利转化是实现技术进步的关键途径,而专利转化预测则是提高专利转化率的潜在策略。现有的专利转化自动预测模型无法预测专利转化时间,导致对这些有效专利的预测结论无效。在本研究中,我们提出了一种专利转化预测模型来预测专利转化时间。(1)为了获取不同时间段的专利特征,我们将专利申请后的年限划分为多个时间段;(2)对于每项专利,我们在构建并嵌入专利动态图后,提取每个时间段的静态特征和动态特征;(3)将每个时间段的特征串联起来,作为动态模型的输入,该模型利用神经网络预测该时间段的专利转化情况。我们在不同领域对模型进行了测评,每个领域包括 10,000 个专利转化数据。实验结果表明,预测未来 3 年专利转化的精确度、召回率和 F1 分数约为 80%。此外,我们的研究还得出了一些新发现:(1)后期申请的专利具有更高的转化速度;(2)超过 90% 的专利转化发生在专利申请后的 13 年内;(3)与静态特征相比,动态特征,尤其是动态结构化特征,对专利转化预测的影响更大;(4)我们的模型在不同的实验数据上表现稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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