{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:31:03Z","timestamp":1779928263068,"version":"3.53.1"},"reference-count":39,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1016\/j.ipm.2024.103872","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T07:35:25Z","timestamp":1724744125000},"page":"103872","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"title":["Patent transformation prediction: When a patent can be transformed"],"prefix":"10.1016","volume":"61","author":[{"given":"Weidong","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6388-2479","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangfeng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqin","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuming","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minglong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2024.103872_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2024.109909","article-title":"Predictive modeling for technology convergence: A patent data-driven approach through technology topic networks","volume":"188","author":"Afifuddin","year":"2024","journal-title":"Computers & Industrial Engineering"},{"key":"10.1016\/j.ipm.2024.103872_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101509","article-title":"Establish a patent risk prediction model for emerging technologies using deep learning and data augmentation","volume":"52","author":"Chi","year":"2022","journal-title":"Advanced Engineering Informatics"},{"issue":"12","key":"10.1016\/j.ipm.2024.103872_b3","doi-asserted-by":"crossref","first-page":"16175","DOI":"10.3390\/su71215809","article-title":"A predictive model of technology transfer using patent analysis","volume":"7","author":"Choi","year":"2015","journal-title":"Sustainability"},{"issue":"9","key":"10.1016\/j.ipm.2024.103872_b4","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.respol.2017.07.015","article-title":"Global markets for technology: Evidence from patent transactions","volume":"46","author":"De Marco","year":"2017","journal-title":"Research Policy"},{"key":"10.1016\/j.ipm.2024.103872_b5","series-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"10.1016\/j.ipm.2024.103872_b6","series-title":"2022 IEEE 25th international conference on computer supported cooperative work in design","first-page":"1443","article-title":"Technical function-effect based patent multi-to-one negation game model","author":"Du","year":"2022"},{"key":"10.1016\/j.ipm.2024.103872_b7","series-title":"struc2vec: Learning node representations from structural identity","author":"Figueiredo","year":"2017"},{"key":"10.1016\/j.ipm.2024.103872_b8","series-title":"Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers)","first-page":"349","article-title":"Towards comprehensive patent approval predictions:Beyond traditional document classification","author":"Gao","year":"2022"},{"key":"10.1016\/j.ipm.2024.103872_b9","doi-asserted-by":"crossref","unstructured":"Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 855\u2013864).","DOI":"10.1145\/2939672.2939754"},{"key":"10.1016\/j.ipm.2024.103872_b10","series-title":"2018 portland international conference on management of engineering and technology","first-page":"1","article-title":"Analyzing patent transactions with patent-based measures","author":"Huang","year":"2018"},{"key":"10.1016\/j.ipm.2024.103872_b11","series-title":"2023 26th international conference on computer supported cooperative work in design","first-page":"1439","article-title":"Exploring patent transformation event: Forecasting patent transfer time","author":"Huo","year":"2023"},{"issue":"2","key":"10.1016\/j.ipm.2024.103872_b12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/joitmc7020119","article-title":"Patent value and survival of patents","volume":"7","author":"Hwang","year":"2021","journal-title":"Journal of Open Innovation: Technology, Market, and Complexity"},{"issue":"2","key":"10.1016\/j.ipm.2024.103872_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.joi.2023.101402","article-title":"Deep learning for predicting patent application outcome: The fusion of text and network embeddings","volume":"17","author":"Jiang","year":"2023","journal-title":"Journal of Informetrics"},{"key":"10.1016\/j.ipm.2024.103872_b14","doi-asserted-by":"crossref","first-page":"188833","DOI":"10.1109\/ACCESS.2020.3030960","article-title":"Predicting patent transactions using patent-based machine learning techniques","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.ipm.2024.103872_b15","series-title":"Distributed representations of sentences and documents","author":"Le","year":"2014"},{"key":"10.1016\/j.ipm.2024.103872_b16","doi-asserted-by":"crossref","first-page":"135196","DOI":"10.1109\/ACCESS.2023.3337830","article-title":"Technology-transferability analysis of universities and public research institutes using deep neural networks","volume":"11","author":"Lee","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.ipm.2024.103872_b17","doi-asserted-by":"crossref","first-page":"10177","DOI":"10.1007\/s11042-019-7422-x","article-title":"Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model","volume":"79","author":"Liu","year":"2020","journal-title":"Multimedia Tools and Applications"},{"issue":"3","key":"10.1016\/j.ipm.2024.103872_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103327","article-title":"Multi-task learning based high-value patent and standard-essential patent identification model","volume":"60","author":"Liu","year":"2023","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2024.103872_b19","series-title":"2023 international joint conference on neural networks","first-page":"1","article-title":"Graph convolutional networks based standard essential patents identification","author":"Liu","year":"2023"},{"key":"10.1016\/j.ipm.2024.103872_b20","series-title":"2020 international joint conference on neural networks","first-page":"1","article-title":"Bayesian graph convolutional neural network based patent valuation model","author":"Liu","year":"2020"},{"key":"10.1016\/j.ipm.2024.103872_b21","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.patrec.2020.07.023","article-title":"Probabilistic graph-based valuation model for measuring the relative patent value in a valuation scenario","volume":"138","author":"Liu","year":"2020","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.ipm.2024.103872_b22","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s00521-020-04964-x","article-title":"Discovering the realistic paths towards the realization of patent valuation from technical perspectives: defense, implementation or transfer","volume":"33","author":"Liu","year":"2021","journal-title":"Neural Computing and Applications"},{"issue":"4","key":"10.1016\/j.ipm.2024.103872_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102582","article-title":"Patent transformation opportunity to realize patent value: Discussion about the conditions to be used or exchanged","volume":"58","author":"Liu","year":"2021","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2024.103872_b24","series-title":"IJCAI","first-page":"5052","article-title":"Patent litigation prediction: A convolutional tensor factorization approach","author":"Liu","year":"2018"},{"key":"10.1016\/j.ipm.2024.103872_b25","doi-asserted-by":"crossref","unstructured":"Liu, X., Yan, J., Xiao, S., Wang, X., Zha, H., & Chu, S. (2017). On predictive patent valuation: Forecasting patent citations and their types. Vol. 31, In Proceedings of the AAAI conference on artificial intelligence. (1).","DOI":"10.1609\/aaai.v31i1.10722"},{"issue":"3","key":"10.1016\/j.ipm.2024.103872_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.102908","article-title":"Discovering the influences of the patent innovations on the stock market","volume":"59","author":"Liu","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2024.103872_b27","series-title":"2022 international joint conference on neural networks","first-page":"1","article-title":"Innovation hierarchy based patent representation model","author":"Liu","year":"2022"},{"key":"10.1016\/j.ipm.2024.103872_b28","series-title":"2022 international joint conference on neural networks","first-page":"1","article-title":"Dirichlet mixture model of hawkes processes based patent user role discovery model","author":"Liu","year":"2022"},{"key":"10.1016\/j.ipm.2024.103872_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.techfore.2024.123390","article-title":"A study on patent term prediction by survival time analysis using neural hazard model","volume":"203","author":"Marusaki","year":"2024","journal-title":"Technological Forecasting and Social Change"},{"key":"10.1016\/j.ipm.2024.103872_b30","series-title":"2018 IEEE international conference on industrial engineering and engineering management","first-page":"1884","article-title":"Community detection and growth potential prediction using the stochastic block model and the long short-term memory from patent citation networks","author":"Nakai","year":"2018"},{"key":"10.1016\/j.ipm.2024.103872_b31","series-title":"The 35th international conference on software engineering and knowledge engineering, SEKE 2023, KSIR virtual conference center, USA, July 1-10, 2023","first-page":"14","article-title":"Convolution neural network based patent infringement detection method","author":"Pei","year":"2023"},{"key":"10.1016\/j.ipm.2024.103872_b32","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 701\u2013710).","DOI":"10.1145\/2623330.2623732"},{"key":"10.1016\/j.ipm.2024.103872_b33","unstructured":"Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 workshop on graph representation learning."},{"key":"10.1016\/j.ipm.2024.103872_b34","doi-asserted-by":"crossref","unstructured":"Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H. (2020). Dysat: Deep neural representation learning on dynamic graphs via self-attention networks. In Proceedings of the 13th international conference on web search and data mining (pp. 519\u2013527).","DOI":"10.1145\/3336191.3371845"},{"issue":"4","key":"10.1016\/j.ipm.2024.103872_b35","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1111\/j.1756-2171.2010.00117.x","article-title":"The dynamics of the transfer and renewal of patents","volume":"41","author":"Serrano","year":"2010","journal-title":"Rand Journal of Economics"},{"key":"10.1016\/j.ipm.2024.103872_b36","article-title":"A multi-aspect neural tensor factorization framework for patent litigation prediction","author":"Wu","year":"2023","journal-title":"IEEE Transactions on Big Data"},{"key":"10.1016\/j.ipm.2024.103872_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.techfore.2023.122637","article-title":"Which type of dynamic indicators should be preferred to predict patent commercial potential?","volume":"193","author":"Yang","year":"2023","journal-title":"Technological Forecasting and Social Change"},{"key":"10.1016\/j.ipm.2024.103872_b38","series-title":"2023 26th international conference on computer supported cooperative work in design","first-page":"1336","article-title":"Research on patent recommendation method based on graph neural network","author":"Zhang","year":"2023"},{"key":"10.1016\/j.ipm.2024.103872_b39","series-title":"Encoder-decoder architecture for supervised dynamic graph learning: A survey","author":"Zhu","year":"2022"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457324002310?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457324002310?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T23:49:37Z","timestamp":1726012177000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457324002310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":39,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["S0306457324002310"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2024.103872","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Patent transformation prediction: When a patent can be transformed","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2024.103872","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103872"}}