政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/155691

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 114504/145531 (79%)
Visitors : 53385863 Online Users : 3

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 > Item 140.119/155691

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155691

Title:	Using Patent Data and Language Models to Analyze Company Advantages and Reduce Attorney Workload
Authors:	蕭舜文 Hsiao, Shun-Wen;Jhuang, Yun-Yun
Contributors:	資管系
Keywords:	patent;language model;latent space
Date:	2024-12
Issue Date:	2025-02-14 10:47:04 (UTC+8)
Abstract:	This paper focuses on patent analysis by using deep learning and customized language model to help human beings to deal with labor-intensive, time-consuming. and knowledge-intensive paperwork. The idea is to train a customized language model. Pat-DistilRoBERTa, who is a patent specialist, and it can map a patent document to a proper high-dimensional vector for later mathematical analysis, therefore, we do not need to deal with text-based data afterward. We anticipate can better understand and represent a patent document. As a comparison, we employ a pre-trained DistilRoBERTa model as a generalist for patent analysis. We conducted numerous experiments on semiconductor patents of leading global companies, specifically TSMC and Samsung. These experiments show that we can easily leverage the language models to appropriately embed a professional technique document for different downstream tasks. For example, Pat-DistilRoBERTa improved the F1-score by 5% to 15% when classifying context and achieved 99% accuracy in classifying patents by IPC. We anticipate that such AI-assisted analysis with specialist language model can assist patent attorneys in reducing their workload as technology improves.
Relation:	2024 IEEE International Conference on Big Data, IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/BigData62323.2024.10825831
DOI:	10.1109/BigData62323.2024.10825831
Appears in Collections:	[資訊管理學系] 會議論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	1	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...