{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:25:15Z","timestamp":1774355115544,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-026-01382-z","type":"journal-article","created":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T18:03:16Z","timestamp":1771178596000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A patent push method and system based on the product life cycle multi-dimensional classification"],"prefix":"10.1186","volume":"13","author":[{"given":"Zhen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"key":"1382_CR1","doi-asserted-by":"publisher","unstructured":"Mart\u00edn-Rojas AG-MR, V\u00edctor, Garc\u00eda-Morales J et al. The key role of innovation and organizational resilience in improving business performance: A mixed-methods approach. International Journal of Information Management. 2024;77:102777. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2024.102777","DOI":"10.1016\/j.ijinfomgt.2024.102777"},{"key":"1382_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101349","volume":"49","author":"Z Nie","year":"2021","unstructured":"Nie Z, Zhang P, Wang F, et al. Sustainable innovation pathway for mechanical products by inducing characteristic parameters. Adv Eng Inform. 2021;49:101349. https:\/\/doi.org\/10.1016\/j.aei.2021.101349.","journal-title":"Adv Eng Inform"},{"key":"1382_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.101887","volume":"55","author":"Z Huang","year":"2023","unstructured":"Huang Z, Guo X, Liu Y, et al. A smart conflict resolution model using multi-layer knowledge graph for conceptual design. Adv Eng Inform. 2023;55:101887. https:\/\/doi.org\/10.1016\/j.aei.2023.101887.","journal-title":"Adv Eng Inform"},{"key":"1382_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124895","volume":"256","author":"Z-X Chang","year":"2024","unstructured":"Chang Z-X, Guo W, Wang L, et al. A novel patent technology characterization method based on heterogeneous network message passing algorithm and patent classification system. Expert Syst Appl. 2024;256:124895. https:\/\/doi.org\/10.1016\/j.eswa.2024.124895.","journal-title":"Expert Syst Appl"},{"key":"1382_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102530","volume":"61","author":"S Jiang","year":"2024","unstructured":"Jiang S, Yang J, Xie J. Product innovation design approach driven by implicit relationship completion via patent knowledge graph. Adv Eng Inform. 2024;61:102530. https:\/\/doi.org\/10.1016\/j.aei.2024.102530.","journal-title":"Adv Eng Inform"},{"key":"1382_CR6","doi-asserted-by":"publisher","first-page":"103154","DOI":"10.1016\/j.compind.2019.103154","volume":"115","author":"L Liu","year":"2020","unstructured":"Liu L, Li Y, Xiong Y, et al. A new function-based patent knowledge retrieval tool for conceptual design of innovative products. Comput Ind. 2020;115:103154. https:\/\/doi.org\/10.1016\/j.compind.2019.103154.","journal-title":"Comput Ind"},{"issue":"1","key":"1382_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2024.101616","volume":"19","author":"M Li","year":"2025","unstructured":"Li M, Wang L. Leveraging patent classification based on deep learning: the case study on smart cities and industrial Internet of Things. J Informetr. 2025;19(1):101616. https:\/\/doi.org\/10.1016\/j.joi.2024.101616.","journal-title":"J Informetr"},{"key":"1382_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106636","volume":"147","author":"J Yun","year":"2020","unstructured":"Yun J, Geum Y. Automated classification of patents: a topic modeling approach. Comput Ind Eng. 2020;147:106636. https:\/\/doi.org\/10.1016\/j.cie.2020.106636.","journal-title":"Comput Ind Eng"},{"key":"1382_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2020.101968","volume":"61","author":"C Cassidy","year":"2020","unstructured":"Cassidy C. Parameter tuning Na\u00efve Bayes for automatic patent classification. World Pat Inf. 2020;61:101968. https:\/\/doi.org\/10.1016\/j.wpi.2020.101968.","journal-title":"World Pat Inf"},{"key":"1382_CR10","doi-asserted-by":"publisher","first-page":"123498","DOI":"10.1016\/j.techfore.2024.123498","volume":"205","author":"P Shao","year":"2024","unstructured":"Shao P, Tan R, Qingjin Peng, et al. Scenario-based anticipatory failure determination and patent technology inspiration for product innovation design. Technological Forecast Social Change. 2024;205:123498. https:\/\/doi.org\/10.1016\/j.techfore.2024.123498.","journal-title":"Technological Forecast Social Change"},{"key":"1382_CR11","doi-asserted-by":"publisher","unstructured":"Xiao Y, Li C, Th\u00fcrer M. A patent recommendation method based on KG representation learning. Eng Appl Artif Intell 126(A). 2023;106722. https:\/\/doi.org\/10.1016\/j.engappai.2023.106722.","DOI":"10.1016\/j.engappai.2023.106722"},{"key":"1382_CR12","doi-asserted-by":"publisher","first-page":"120659","DOI":"10.1016\/j.ins.2024.120659","volume":"672","author":"C Li","year":"2024","unstructured":"Li C, Li W, Yida Hong, et al. A patent retrieval method and system based on double classification. Inf Sci. 2024;672:120659. https:\/\/doi.org\/10.1016\/j.ins.2024.120659.","journal-title":"Inf Sci"},{"key":"1382_CR13","doi-asserted-by":"publisher","first-page":"104167","DOI":"10.1016\/j.compind.2024.104167","volume":"164","author":"C Li","year":"2025","unstructured":"Li C, Li W, Xiang H, et al. A technical patent map construction method and system based on multi-dimensional technical feature extraction. Comput Ind. 2025;164:104167. https:\/\/doi.org\/10.1016\/j.compind.2024.104167.","journal-title":"Comput Ind"},{"key":"1382_CR14","doi-asserted-by":"publisher","first-page":"120511","DOI":"10.1016\/j.techfore.2020.120511","volume":"164","author":"CV Amy Trappey","year":"2021","unstructured":"Amy Trappey CV, Trappey. An intelligent patent recommender adopting machine learning approach for natural Language processing: A case study for smart machinery technology mining. Technol Forecast Soc Chang. 2021;164:120511. https:\/\/doi.org\/10.1016\/j.techfore.2020.120511.","journal-title":"Technol Forecast Soc Chang"},{"key":"1382_CR15","doi-asserted-by":"publisher","unstructured":"Ascione GS, Vezzulli A. 2024. Leveraging NLP and web knowledge graphs to harmonize locations: A case study on US patent transactions. World Patent Information. 126(A), 106722. https:\/\/doi.org\/10.1016\/j.engappai.2023.106722","DOI":"10.1016\/j.engappai.2023.106722"},{"key":"1382_CR16","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.asoc.2016.01.020","volume":"41","author":"J-L Wu","year":"2016","unstructured":"Wu J-L, Chang P-C, Cheng-Chin Tsao, et al. A patent quality analysis and classification system using self-organizing maps with support vector machine. Appl Soft Comput. 2016;41:305\u201316. https:\/\/doi.org\/10.1016\/j.asoc.2016.01.020.","journal-title":"Appl Soft Comput"},{"key":"1382_CR17","doi-asserted-by":"publisher","first-page":"121477","DOI":"10.1016\/j.techfore.2022.121477","volume":"176","author":"K Chen Zhu","year":"2022","unstructured":"Chen Zhu K, Motohashi. Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach. Technol Forecast Soc Chang. 2022;176:121477. https:\/\/doi.org\/10.1016\/j.techfore.2022.121477.","journal-title":"Technol Forecast Soc Chang"},{"issue":"1","key":"1382_CR18","doi-asserted-by":"publisher","first-page":"103908","DOI":"10.1016\/j.ipm.2024.103908","volume":"62","author":"R Zhang","year":"2025","unstructured":"Zhang R, Yu X, Zhang B. Discovering technology opportunities of latecomers based on RGNN and patent data: the example of Huawei in self-driving vehicle industry. Inf Process Manag. 2025;62(1):103908. https:\/\/doi.org\/10.1016\/j.ipm.2024.103908.","journal-title":"Inf Process Manag"},{"key":"1382_CR19","doi-asserted-by":"publisher","first-page":"100941","DOI":"10.1016\/j.wpi.2021.102060","volume":"42","author":"J Wang","year":"2019","unstructured":"Wang J, Chen Y-J. A novelty detection patent mining approach for analyzing technological opportunities. Adv Eng Inform. 2019;42:100941. https:\/\/doi.org\/10.1016\/j.wpi.2021.102060.","journal-title":"Adv Eng Inform"},{"key":"1382_CR20","doi-asserted-by":"publisher","first-page":"108395","DOI":"10.1016\/j.cie.2022.108395","volume":"171","author":"Wonchul Seo","year":"2022","unstructured":"Wonchul Seo. A patent-based approach to identifying potential technology opportunities realizable from a firm\u2019s internal capabilities. Comput Ind Eng. 2022;171:108395. https:\/\/doi.org\/10.1016\/j.cie.2022.108395.","journal-title":"Comput Ind Eng"},{"issue":"3","key":"1382_CR21","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1016\/j.cie.2022.108395","volume":"37","author":"HT Cong He","year":"2010","unstructured":"Cong He HT, Loh. Pattern-oriented associative rule-based patent classification. Expert Syst Appl. 2010;37(3):2395\u2013404. https:\/\/doi.org\/10.1016\/j.cie.2022.108395.","journal-title":"Expert Syst Appl"},{"key":"1382_CR22","doi-asserted-by":"publisher","first-page":"102060","DOI":"10.1016\/j.wpi.2021.102060","volume":"66","author":"Mustafa Sofean","year":"2021","unstructured":"Mustafa Sofean. Deep learning based pipeline with multichannel inputs for patent classification. World Patent Inf. 2021;66:102060. https:\/\/doi.org\/10.1016\/j.wpi.2021.102060.","journal-title":"World Patent Inf"},{"key":"1382_CR23","doi-asserted-by":"publisher","first-page":"122576","DOI":"10.1016\/j.techfore.2023.122576","volume":"193","author":"A Mohsen Ghaffari","year":"2024","unstructured":"Mohsen Ghaffari A, Aliahmadi A, Khalkhali, et al. Topic-based technology mapping using patent data analysis: A case study of vehicle tires. Technol Forecast Soc Chang. 2024;193:122576. https:\/\/doi.org\/10.1016\/j.techfore.2023.122576.","journal-title":"Technol Forecast Soc Chang"},{"key":"1382_CR24","doi-asserted-by":"publisher","first-page":"102238","DOI":"10.1016\/j.wpi.2023.102238","volume":"75","author":"H Meiyun Wang","year":"2023","unstructured":"Meiyun Wang H, Sakaji H, Higashitani, et al. Discovering new applications: Cross-domain exploration of patent documents using causal extraction and similarity analysis. World Patent Inf. 2023;75:102238. https:\/\/doi.org\/10.1016\/j.wpi.2023.102238.","journal-title":"World Patent Inf"},{"key":"1382_CR25","doi-asserted-by":"publisher","first-page":"144385","DOI":"10.1016\/j.jclepro.2024.144385","volume":"486","author":"A Rainville","year":"2025","unstructured":"Rainville A, Dikker I, Buggenhagen M, et al. Tracking innovation via green patent classification systems: are we truly capturing circular economy progress? J Clean Prod. 2025;486:144385. https:\/\/doi.org\/10.1016\/j.jclepro.2024.144385.","journal-title":"J Clean Prod"},{"key":"1382_CR26","doi-asserted-by":"publisher","first-page":"101502","DOI":"10.1016\/j.aei.2021.101502","volume":"51","author":"K Qiyu Liu","year":"2022","unstructured":"Qiyu Liu K, Wang Y, Li, et al. A novel function-structure concept network construction and analysis method for a smart product design system. Adv Eng Inform. 2022;51:101502. https:\/\/doi.org\/10.1016\/j.aei.2021.101502.","journal-title":"Adv Eng Inform"},{"key":"1382_CR27","doi-asserted-by":"publisher","first-page":"136896","DOI":"10.1016\/j.jclepro.2023.136896","volume":"403","author":"F Shuqin","year":"2023","unstructured":"Shuqin F, Liu G, Tu Y, et al. Improved multi-criteria decision making method integrating machine learning for patent competitive potential evaluation: A case study in water pollution abatement technology. J Clean Prod. 2023;403:136896. https:\/\/doi.org\/10.1016\/j.jclepro.2023.136896.","journal-title":"J Clean Prod"},{"key":"1382_CR28","doi-asserted-by":"publisher","first-page":"102084","DOI":"10.1016\/j.wpi.2021.102084","volume":"67","author":"X Zhao Ruijie","year":"2021","unstructured":"Zhao Ruijie X, Ying J Shuaichen, et al. Patent text modeling strategy and its classification based on structural features. World Patent Inf. 2021;67:102084. https:\/\/doi.org\/10.1016\/j.wpi.2021.102084.","journal-title":"World Patent Inf"},{"key":"1382_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2024.102301","volume":"78","author":"A Wambsganss","year":"2024","unstructured":"Wambsganss A, Tomidei L, Sick N, et al. Machine learning-based method to cluster a converging technology system: the case of printed electronics. World Pat Inf. 2024;78:102301. https:\/\/doi.org\/10.1016\/j.wpi.2024.102301.","journal-title":"World Pat Inf"},{"key":"1382_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/IDAP.2018.8620929","author":"S Yucesoy","year":"2018","unstructured":"Yucesoy S, Dereli T, Durmusoglu A, et al. Patent classification via textual analysis. Which sections to be included? 2018 Int Conf Artif Intell Data Process (IDAP). 2018. https:\/\/doi.org\/10.1109\/IDAP.2018.8620929.","journal-title":"2018 Int Conf Artif Intell Data Process (IDAP)"},{"key":"1382_CR31","doi-asserted-by":"publisher","first-page":"49","DOI":"10.2478\/jdis-2022-0015","volume":"7","author":"R Henriques","year":"2022","unstructured":"Henriques R, Ferreira A, Castelli, et al. A use case of patent classification using deep learning with transfer learning. J Data Inform Sci. 2022;7:49\u201370. https:\/\/doi.org\/10.2478\/jdis-2022-0015.","journal-title":"J Data Inform Sci"},{"issue":"1","key":"1382_CR32","doi-asserted-by":"publisher","first-page":"54","DOI":"10.14569\/IJACSA.2023.0140107","volume":"14","author":"R Li","year":"2023","unstructured":"Li R, Yu W, Huang Q, et al. Patent text classification based on deep learning and vocabulary network. Int J Adv Comput Sci Appl. 2023;14(1):54\u201361. https:\/\/doi.org\/10.14569\/IJACSA.2023.0140107.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"1382_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2020.101965","volume":"61","author":"J-S Lee","year":"2020","unstructured":"Lee J-S, Hsiang J. Patent classification by fine-tuning BERT language model. World Pat Inf. 2020;61:101965. https:\/\/doi.org\/10.1016\/j.wpi.2020.101965.","journal-title":"World Pat Inf"},{"key":"1382_CR34","doi-asserted-by":"publisher","first-page":"2182","DOI":"10.1109\/ACCESS.2022.3229490","volume":"11","author":"G Yue","year":"2023","unstructured":"Yue G, Liu J, Hou Y, et al. A novel patent knowledge extraction method for innovative design. IEEE Access. 2023;11:2182\u201398. https:\/\/doi.org\/10.1109\/ACCESS.2022.3229490.","journal-title":"IEEE Access"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-026-01382-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-026-01382-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-026-01382-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:19:48Z","timestamp":1774351188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40537-026-01382-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,15]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1382"],"URL":"https:\/\/doi.org\/10.1186\/s40537-026-01382-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,15]]},"assertion":[{"value":"7 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The Ethical and informed consent for data used are not applicable for this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent for publication"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"45"}}