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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77732完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏志平 | |
| dc.contributor.author | Yan-Syun Chen | en |
| dc.contributor.author | 陳彥勳 | zh_TW |
| dc.date.accessioned | 2021-07-10T22:18:32Z | - |
| dc.date.available | 2021-07-10T22:18:32Z | - |
| dc.date.copyright | 2017-08-30 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-09 | |
| dc.identifier.citation | U.S. Patent and Trademark Office. (2015). U.S. patent statistics chart–calendar years 1963–2015. Retrieved Dec 25, 2016, from http://www.uspto.gov/web/offices/ac/ido/oeip/taf/us_stat.htm
Yu, K. S. (2014). Patent invalidity search: A learning to rank approach. Unpublished Master Thesis, Department of Information Management, National Taiwan University, Taipei, Taiwan. Foglia, P. (2007). Patentability search strategies and the reformed IPC: A patent office perspective. World Patent Information, 29(1), 33-53. Wei, C.P., Yang, C.S., Yu, C.C., Lin, Y.K., & Chen, H.C. (2012). Searching for a needle in a haystack: A summary-based patent prior art retrieval technique. Working Paper, Department of Information Management, National Taiwan University, Taipei, Taiwan. Yoon, J., Choi, S., & Kim, K. (2011). Invention property-function network analysis of patents: A case of silicon-based thin film solar cells. Scientometrics, 86(3), 687–703. Yoon, J., & Kim, K. (2011). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213-228. Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., & Gatford, M. (1994). Okapi at TREC-3. In Proceedings of the Third Text Retrieval Conference (pp. 109-126). Gaithersburg, MD: NIST. Iwayama, M., Fujii, A., Kando, N., & Marukawa, Y. (2003). An empirical study on retrieval models for different document genres: Patents and newspaper articles. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 251-258. Lopez, P. & Romary, L. (2010). PATATRAS: Retrieval model combination and regression models for prior art search. Lecture Notes in Computer Science, 6241, 430-437. Xue, X. & Croft, W.B. (2009). Automatic query generation for patent search. In Proceeding of the 18th ACM Conference on Information and Knowledge Management, 2037-2040. Fujii, A. (2007). Enhancing patent retrieval by citation analysis. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 793-794. Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1-7), 107-117. Takeuchi, H., Uramoto, N., & Takeda, K. (2004). Experiments on patent retrieval at NTCIR-4 workshop. In Proceedings of the Fourth NII Test Collection for IR Systems (NTCIR-4) Workshop. Tokyo, Japan: National Institute of Informatics. Moehrle, M. G., Walter, L., Geritz, A., & Muller, S. (2005). Patent-based inventor profiles as a basis for human resource decisions in research and development. R&D Management, 35(5), 513-524. Yoon, J., & Kim, K. (2011). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213-228. Yoon, J., Park, H., & Kim, K. (2013). Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics, 94(1), 313-331. Stanford. (2016). The Stanford Parser: A statistical parser. Retrieved Dec 25, 2016, from http://nlp.stanford.edu/software/lex-parser.shtml Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(1), 993-1022. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The annals of mathematical statistics, 22(1), 79-86. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77732 | - |
| dc.description.abstract | 專利數量從1997年開始迅速上升。因此，專利辦公室，如：美國專利局需要雇用更多的審查員、人力來處理這些大幅上升的專利申請案。而與此同時，也代表需要發展一個更有效率的專利前案檢索技術。而過往的研究，大多都是著重在詞袋模型，然而詞袋模型難以展現出專利中的一些特徵、關鍵概念、詞與詞之間的關係，因此，我們提出一個基於SAO結構的專利前案檢索技術，最後結果也顯示，我們提出的方法，比過去研究提出的技術，確實有更好的表現。 | zh_TW |
| dc.description.abstract | The number of patent applications increases rapidly from 1997. At the same time, the number of patent applications increases rapidly that makes patent offices, such as United States Patent and Trademark Office (USPTO), have to recruit more patent examiners for dealing with the ever-increasing number of patent applications. Hence, an effective patent prior art retrieval from a huge patent repository becomes essential. In this research, we review several existing patent prior art retrieval approaches. These approaches typically involve extracting textual content by information retrieval technique called bag-of-words. However, bag-of-words cannot reflect the structural relationships among components. In order to solve this problem, we propose an SAO-based approach to conduct patent prior art retrieval. SAO structures can represent explicit relations among components used in a patent, and are considered to represent key concepts of the patent. Overall, the proposed SAO-based approach outperforms our performance benchmarks. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T22:18:32Z (GMT). No. of bitstreams: 1 ntu-106-R04725024-1.pdf: 2111840 bytes, checksum: ee18c78f4441b64a907b464533a40a85 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation and Objectives 3 Chapter 2 Literature Review 8 2.1 Text-based Approach 8 2.1.1 Feature-Reduction-Based Approach 8 2.1.2 Summary-Based Approach 10 2.2 Hybrid Approach 11 2.2.1 Citation-Reranking Technique 11 2.2.2 Class-Reweighting Technique 12 2.3 SAO Structure and Applications 13 Chapter 3 Design of Our Proposed Technique 15 3.1 Text-based Similarity Calculation 17 3.2 SAO-based Method 18 3.2.1 SAO Extraction 18 3.2.2 SAO Selection 21 3.3 LDA 22 3.3.1 LDA Topic Modeling 22 3.4 Similarity Aggregation 23 Chapter 4 Evaluation Design and Results 25 4.1 Data Collection 25 4.2 Evaluation Criterion 26 4.3 Parameter Tuning Experiments and Results 27 4.4 Comparative Evaluation Results 31 4.5 Effects of Citation-Reranking 32 4.5.1 Parameter Tuning Experiences and Results 33 4.5.2 Evaluation Results 35 4.6 Effects of LDA topic modeling 37 Chapter 5 Conclusion and Future Work 39 References 41 Appendix 1 44 1.1 Parameter Tuning Experiences and Results 44 1.2 Comparative Evaluation Results 46 1.3 Effects of Citation-Reranking 47 1.4 Parameter Tuning Experiences and Results 48 1.5 Evaluation Results 50 1.6 Effects of LDA topic modeling 52 | |
| dc.language.iso | en | |
| dc.title | 利用SAO方法進行專利前案檢索 | zh_TW |
| dc.title | Development of an SAO-based Approach to Supporting Patent Prior Art Retrieval | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳彥良,盧信銘 | |
| dc.subject.keyword | 專利前案檢索,基於SAO結構的專利前案檢索,SAO結構,LDA,專利探勘,專利檢索, | zh_TW |
| dc.subject.keyword | Prior Art Retrieval,SAO-based Prior Art Retrieval,SAO structure,LDA,Patent Mining,Patent Retrieval, | en |
| dc.relation.page | 52 | |
| dc.identifier.doi | 10.6342/NTU201702830 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-08-09 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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