Weidong Liu , Yu Zhang , Xiangfeng Luo , Yan Cao , Keqin Gan , Fuming Ye , Wei Tang , Minglong Zhang
{"title":"Patent transformation prediction: When a patent can be transformed","authors":"Weidong Liu , Yu Zhang , Xiangfeng Luo , Yan Cao , Keqin Gan , Fuming Ye , Wei Tang , Minglong Zhang","doi":"10.1016/j.ipm.2024.103872","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324002310","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.