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Therefore, intelligent approaches are urgently needed to analyze intrinsic patterns of patents. However, a long-standing obstacle is the lack of effective methods for modeling the dynamic and diverse examination process of patent applications, which can benefit a wide range of downstream tasks for patent management. In fact, the major challenges lie in how to discover and integrate domain-specific properties from large-scale unlabeled examination data. To this end, in this article, we propose a Self-supervised Examination Process Modeling framework to learn the contextualized embedding for patents through modeling their examination processes. Specifically, we first design a multi-aspect event embedding layer, which leverages the fine-tuned language model, frequent-pattern embedding, and time encoding to capture the semantic, frequent-pattern and temporal information of examination events, respectively. Then, a mutual-information-aware integration layer is applied to fuse the extracted features into multi-aspect embedding considering their mutual interactions. Further, we develop a multi-objective sequential neural network for learning the contextualized patent representation, which is achieved through jointly learning two self-supervised objectives, namely, event code and event lag auto-regression. To explore the application potential of SEPM, we fine-tune the well-trained model for three important downstream tasks of patent management, including the prediction of next events, patent classification, and grant prediction. In the end, extensive experiments with real-world data from the US Patent and Trademark Office verify the effectiveness and application prospects of the proposed framework.<\/jats:p>","DOI":"10.1145\/3712309","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:25:12Z","timestamp":1736853912000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Examination Process Modeling for Intelligent Patent Management: A Multi-aspect Neural Sequential Approach"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-0625","authenticated-orcid":false,"given":"Han","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-9651","authenticated-orcid":false,"given":"Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology - Guangzhou Campus, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2013.12.006"},{"key":"e_1_3_1_3_2","first-page":"5360","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP\u201919)","author":"Alexei Baevski","year":"2019","unstructured":"Baevski Alexei, Edunov Sergey, Liu Yinhan, and Auli Michael. 2019. 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