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題名 基於BERT分析專利訴訟風險之方法:以Apple Watch關聯訴訟為例
A Method for Analyzing Patent Litigation Risk Based on BERT: A Case Study of Litigations Related to Apple Watch作者 沈孟葳
Shen, Meng-Wei貢獻者 宋皇志
Sung, Huang-Chih
沈孟葳
Shen, Meng-Wei關鍵詞 專利分析
專利訴訟風險分析
自然語言處理
智慧型穿戴裝置
BERT預訓練模型
Patent Analysis
Patent Litigation Risk Analysis
Natural Language Processing
Smart Wearable Devices
BERT Pre-trained Model日期 2023 上傳時間 1-Sep-2023 14:51:51 (UTC+8) 摘要 專利訴訟的發生和結果，可能造成企業負擔高額訴訟費用、支出鉅額賠償金、產品遭政府禁售和產品喪失技術獨佔性等重大負面影響，因此專利訴訟風險的分析和預警，對於企業而言至關重要。在大數據時代，智慧型手錶、運動手環等具備生理數據追蹤功能的智慧型穿戴裝置，是在健康保健和醫療領域中大數據的重要貢獻者，裝置數據流結合大數據分析技術，為醫療保健領域帶來的革命性機會，因此吸引越來越多的公司投入競爭及相關技術的開發中。由於市場層面的競爭和巨大的潛在商機，專利侵權訴訟也頻繁發生。本研究旨在提出一種方法，使企業得以在更符合經濟效益的情形下，進行專利訴訟風險分析的工作。為此本研究基於深度學習模型BERT，提出能夠協助企業提前辨識出有較高可能性提出專利侵權訴訟之公司，以及篩選出有較高風險被用於提起訴訟之專利的方法。本研究並以關聯於智慧型穿戴裝置中的代表性產品Apple Watch的數宗法院或ITC專利侵權訴訟，以及PTAB複審案件，作為本研究個案，測試本研究分析方法的有效性。個案測試結果顯示，三家標的競爭公司皆排序於前0.5%，專利侵權訴訟中的15件涉訟專利有8件排序於前10%，25件複審案件中有20件至少有1件舉發用前案排序在前10%，顯示本研究提出的分析方法，可以幫助企業挑選出較有可能造成威脅的競爭公司及專利，同時聚焦公司產品或服務中較可能發生侵權風險的技術，協助專利訴訟風險分析工作的進程。
The occurrence and outcome of patent litigation may result in significant negative impacts on a business, such as bearing high litigation costs, paying substantial damages, facing governmental product bans, and losing product technical exclusivity. Therefore, the analysis and early warning of patent litigation risks are of paramount importance for businesses.In the era of big data, smart wearable devices such as smartwatches and fitness bands that have physiological data tracking functions are significant contributors to big data in the healthcare and medical fields. The combination of device data flow and big data analysis techniques brings revolutionary opportunities to the healthcare sector, attracting an increasing number of companies to enter the competition and engage in related technology development. Due to market competition and huge potential business opportunities, patent infringement lawsuits occur frequently.The aim of this study is to propose a method that allows businesses to conduct patent litigation risk analysis in a more cost-effective way. To this end, based on the deep learning model BERT, this study proposes a method that can help businesses identify in advance companies that are more likely to file patent infringement lawsuits, as well as screen for patents that are at higher risk of being used in litigation. This study further tests the effectiveness of this analytical method using several representative lawsuits related to the Apple Watch, a product associated with smart wearable devices, in court or the International Trade Commission (ITC) patent infringement litigation, as well as Patent Trial and Appeal Board (PTAB) review cases.The case study results show that all three targeted competitor companies rank in the top 0.5%, eight out of fifteen patents involved in the patent infringement lawsuits rank in the top 10%, and in 25 review cases, 20 cases have at least one citation from previous cases ranking in the top 10%. These results indicate that the analysis method proposed in this study can help businesses identify competitors or patents that are more likely to pose a threat, and focus on technologies within the company`s products or services that are more likely to incur infringement risks, assisting the process of patent litigation risk analysis.參考文獻 英文文獻Abbas, A., Zhang, L., & Khan, S. U. J. W. P. I. (2014). A literature review on the state-of-the-art in patent analysis. 37, 3-13.Beltagy, I., Peters, M. E., & Cohan, A. J. a. p. a. (2020). Longformer: The long-document transformer.Bergmann, I., Butzke, D., Walter, L., Fuerste, J. P., Moehrle, M. G., & Erdmann, V. A. (2008). Evaluating the risk of patent infringement by means of semantic patent analysis: the case of DNA chips. R&D Management, 38(5), 550-562. doi:10.1111/j.1467-9310.2008.00533.xBessen, J. E., & Meurer, M. J. (2008). The Private Costs of Patent Litigation. Boston University School of Law Working Paper No. 07-08, 2nd Annual Conference on Empirical Legal Studies Paper.Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. J. a. p. a. (2015). A large annotated corpus for learning natural language inference.Caragea, D., Chen, M., Cojoianu, T., Dobri, M., Glandt, K., & Mihaila, G. (2020). Identifying FinTech innovations using BERT. Paper presented at the 2020 IEEE International Conference on Big Data (Big Data).Chien, C. V. (2011). Predicting patent litigation. Texas Law Review, 90(2), 283-330.Chowdhary, K. R. (2020). Natural Language Processing. In Fundamentals of Artificial Intelligence (pp. 603-649). New Delhi: Springer India.Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1). doi:10.1186/s40537-019-0217-0De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. doi:10.1108/LR-06-2015-0061Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. J. a. p. a. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding.Gardella, G. H., & Berger, E. A. J. J. M. R. I. P. L. (2008). United States Reexamination Procedures: Recent Trends, Strategies and Impact on Patent Practice. 8, i.Koh, E. C. Y. (2013). Engineering design and intellectual property: where do they meet? Research in Engineering Design, 24(4), 325-329. doi:10.1007/s00163-013-0153-5Lanjouw, J. O., & Schankerman, M. (2001). Characteristics of Patent Litigation: A Window on Competition. The RAND Journal of Economics, 32(1), 129-151. doi:10.2307/2696401Lee, C., Song, B., & Park, Y. (2013). How to assess patent infringement risks: a semantic patent claim analysis using dependency relationships. Technology Analysis & Strategic Management, 25(1), 23-38. doi:10.1080/09537325.2012.748893Lee, J.-S., & Hsiang, J. J. a. p. a. (2019). Patentbert: Patent classification with fine-tuning a pre-trained bert model.Liu, H., Zhang, R., Liu, Y., & He, C. J. N. (2022). Unveiling Evolutionary Path of Nanogenerator Technology: A Novel Method Based on Sentence-BERT. 12(12), 2018.Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., . . . Stoyanov, V. J. a. p. a. (2019). Roberta: A robustly optimized bert pretraining approach.Maehara, Y., Kuku, A., & Osabe, Y. J. W. P. I. (2022). Macro analysis of decarbonization-related patent technologies by patent domain-specific BERT. 69, 102112.Mikolov, T., Chen, K., Corrado, G., & Dean, J. J. a. p. a. (2013). Efficient estimation of word representations in vector space.Miller, G. A. (1995). WordNet: a lexical database for English. 38(11 %J Commun. ACM), 39–41. doi:10.1145/219717.219748Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544-551. doi:10.1136/amiajnl-2011-000464 %J Journal of the American Medical Informatics AssociationPark, H., Ree, J. J., & Kim, K. J. E. s. w. a. (2013). Identification of promising patents for technology transfers using TRIZ evolution trends. 40(2), 736-743.Park, H., Yoon, J., & Kim, K. (2011). Identifying patent infringement using SAO based semantic technological similarities. Scientometrics, 90(2), 515-529. doi:10.1007/s11192-011-0522-7Raghu, T. S., Woo, W., Mohan, S. B., & Rao, H. R. (2007). Market reaction to patent infringement litigations in the information technology industry. Information Systems Frontiers, 10(1), 61-75. doi:10.1007/s10796-007-9036-5Reimers, N., & Gurevych, I. J. a. p. a. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks.Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017). Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief Bioinform, 18(1), 105-124. doi:10.1093/bib/bbv118Si, Y., Wang, J., Xu, H., & Roberts, K. J. J. o. t. A. M. I. A. (2019). Enhancing clinical concept extraction with contextual embeddings. 26(11), 1297-1304.Somaya, D. (2016). How patent strategy affects the timing and method of patent litigation resolution. In Strategy Beyond Markets (Vol. 34, pp. 471-504): Emerald Group Publishing Limited.Srebrovic, R., & Yonamine, J. J. W. p. (2020). Leveraging the BERT algorithm for Patents with TensorFlow and BigQuery.Su, H.-N., Chen, C. M.-L., & Lee, P.-C. (2012). Patent litigation precaution method: analyzing characteristics of US litigated and non-litigated patents from 1976 to 2010 %J Scientometrics Scientometrics. 92(1), 181-195. doi:https://doi.org/10.1007/s11192-012-0716-7Surdeanu, M., Nallapati, R., Gregory, G., Walker, J., & Manning, C. D. (2011). Risk analysis for intellectual property litigation. Paper presented at the Proceedings of the 13th International Conference on Artificial Intelligence and Law, Pittsburgh, Pennsylvania. https://doi.org/10.1145/2018358.2018375Tan, A.-H. (1999). Text mining: The state of the art and the challenges. Paper presented at the Proceedings of the pakdd 1999 workshop on knowledge disocovery from advanced databases.Trappey, A. J. C., Chen, L. W. L., & Trappey, C. V. (2015, 6-8 May 2015). Computer supported formal concept analysis to explore the evolution of patent litigation. Paper presented at the 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. J. A. i. n. i. p. s. (2017). Attention is all you need. 30.Voskuil, K., & Verberne, S. J. a. p. a. (2021). Improving reference mining in patents with BERT.Williams, A., Nangia, N., & Bowman, S. R. J. a. p. a. (2017). A broad-coverage challenge corpus for sentence understanding through inference.Willis, T. S. J. J. I. P. L. (2004). Patent Reexamination Post Litigation: It`s Time to Set the Rules Straight. 12, 597.WIPO. (2022). WIPO IP Facts and Figures 2022. Retrieved from https://www.wipo.int/edocs/pubdocs/en/wipo-pub-943-2022-en-wipo-ip-facts-and-figures-2022.pdfWongchaisuwat, P., Klabjan, D., & McGinnis, J. O. (2017). Predicting litigation likelihood and time to litigation for patents. Paper presented at the Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law, London, United Kingdom. https://doi.org/10.1145/3086512.3086545中文文獻曾元顯 (2004)。 專利文字之知識探勘: 技術與挑戰。鄭証元 (2021)。 專利訴訟風險的實證研究。 國立臺灣大學, Available from Airiti AiritiLibrary database. (2021年)。 描述 碩士
國立政治大學
科技管理與智慧財產研究所
110364202資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110364202 資料類型 thesis dc.contributor.advisor 宋皇志 zh_TW dc.contributor.advisor Sung, Huang-Chih en_US dc.contributor.author (Authors) 沈孟葳 zh_TW dc.contributor.author (Authors) Shen, Meng-Wei en_US dc.creator (作者) 沈孟葳 zh_TW dc.creator (作者) Shen, Meng-Wei en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 14:51:51 (UTC+8) - dc.date.available 1-Sep-2023 14:51:51 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 14:51:51 (UTC+8) - dc.identifier (Other Identifiers) G0110364202 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146881 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 科技管理與智慧財產研究所 zh_TW dc.description (描述) 110364202 zh_TW dc.description.abstract (摘要) 專利訴訟的發生和結果，可能造成企業負擔高額訴訟費用、支出鉅額賠償金、產品遭政府禁售和產品喪失技術獨佔性等重大負面影響，因此專利訴訟風險的分析和預警，對於企業而言至關重要。在大數據時代，智慧型手錶、運動手環等具備生理數據追蹤功能的智慧型穿戴裝置，是在健康保健和醫療領域中大數據的重要貢獻者，裝置數據流結合大數據分析技術，為醫療保健領域帶來的革命性機會，因此吸引越來越多的公司投入競爭及相關技術的開發中。由於市場層面的競爭和巨大的潛在商機，專利侵權訴訟也頻繁發生。本研究旨在提出一種方法，使企業得以在更符合經濟效益的情形下，進行專利訴訟風險分析的工作。為此本研究基於深度學習模型BERT，提出能夠協助企業提前辨識出有較高可能性提出專利侵權訴訟之公司，以及篩選出有較高風險被用於提起訴訟之專利的方法。本研究並以關聯於智慧型穿戴裝置中的代表性產品Apple Watch的數宗法院或ITC專利侵權訴訟，以及PTAB複審案件，作為本研究個案，測試本研究分析方法的有效性。個案測試結果顯示，三家標的競爭公司皆排序於前0.5%，專利侵權訴訟中的15件涉訟專利有8件排序於前10%，25件複審案件中有20件至少有1件舉發用前案排序在前10%，顯示本研究提出的分析方法，可以幫助企業挑選出較有可能造成威脅的競爭公司及專利，同時聚焦公司產品或服務中較可能發生侵權風險的技術，協助專利訴訟風險分析工作的進程。 zh_TW dc.description.abstract (摘要) The occurrence and outcome of patent litigation may result in significant negative impacts on a business, such as bearing high litigation costs, paying substantial damages, facing governmental product bans, and losing product technical exclusivity. Therefore, the analysis and early warning of patent litigation risks are of paramount importance for businesses.In the era of big data, smart wearable devices such as smartwatches and fitness bands that have physiological data tracking functions are significant contributors to big data in the healthcare and medical fields. The combination of device data flow and big data analysis techniques brings revolutionary opportunities to the healthcare sector, attracting an increasing number of companies to enter the competition and engage in related technology development. Due to market competition and huge potential business opportunities, patent infringement lawsuits occur frequently.The aim of this study is to propose a method that allows businesses to conduct patent litigation risk analysis in a more cost-effective way. To this end, based on the deep learning model BERT, this study proposes a method that can help businesses identify in advance companies that are more likely to file patent infringement lawsuits, as well as screen for patents that are at higher risk of being used in litigation. This study further tests the effectiveness of this analytical method using several representative lawsuits related to the Apple Watch, a product associated with smart wearable devices, in court or the International Trade Commission (ITC) patent infringement litigation, as well as Patent Trial and Appeal Board (PTAB) review cases.The case study results show that all three targeted competitor companies rank in the top 0.5%, eight out of fifteen patents involved in the patent infringement lawsuits rank in the top 10%, and in 25 review cases, 20 cases have at least one citation from previous cases ranking in the top 10%. These results indicate that the analysis method proposed in this study can help businesses identify competitors or patents that are more likely to pose a threat, and focus on technologies within the company`s products or services that are more likely to incur infringement risks, assisting the process of patent litigation risk analysis. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機 31.3 研究問題 5第二章 文獻探討 62.1 專利訴訟風險之意義 62.2 專利訴訟風險分析方法 82.3 BERT模型 122.4 小結 16第三章 研究方法 183.1 方法概述 183.2 個案測試 233.2.1 專利侵權訴訟 233.2.2 專利複審案件 253.3 資料蒐集 29第四章 研究結果 30第五章 結論與建議 40參考文獻 43附錄1：向量表示方法示例 48 zh_TW dc.format.extent 2549861 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110364202 en_US dc.subject (關鍵詞) 專利分析 zh_TW dc.subject (關鍵詞) 專利訴訟風險分析 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 智慧型穿戴裝置 zh_TW dc.subject (關鍵詞) BERT預訓練模型 zh_TW dc.subject (關鍵詞) Patent Analysis en_US dc.subject (關鍵詞) Patent Litigation Risk Analysis en_US dc.subject (關鍵詞) Natural Language Processing en_US dc.subject (關鍵詞) Smart Wearable Devices en_US dc.subject (關鍵詞) BERT Pre-trained Model en_US dc.title (題名) 基於BERT分析專利訴訟風險之方法:以Apple Watch關聯訴訟為例 zh_TW dc.title (題名) A Method for Analyzing Patent Litigation Risk Based on BERT: A Case Study of Litigations Related to Apple Watch en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 英文文獻Abbas, A., Zhang, L., & Khan, S. U. J. W. P. I. (2014). A literature review on the state-of-the-art in patent analysis. 37, 3-13.Beltagy, I., Peters, M. E., & Cohan, A. J. a. p. a. (2020). Longformer: The long-document transformer.Bergmann, I., Butzke, D., Walter, L., Fuerste, J. P., Moehrle, M. G., & Erdmann, V. A. (2008). Evaluating the risk of patent infringement by means of semantic patent analysis: the case of DNA chips. R&D Management, 38(5), 550-562. doi:10.1111/j.1467-9310.2008.00533.xBessen, J. E., & Meurer, M. J. (2008). The Private Costs of Patent Litigation. Boston University School of Law Working Paper No. 07-08, 2nd Annual Conference on Empirical Legal Studies Paper.Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. J. a. p. a. (2015). A large annotated corpus for learning natural language inference.Caragea, D., Chen, M., Cojoianu, T., Dobri, M., Glandt, K., & Mihaila, G. (2020). Identifying FinTech innovations using BERT. Paper presented at the 2020 IEEE International Conference on Big Data (Big Data).Chien, C. V. (2011). Predicting patent litigation. Texas Law Review, 90(2), 283-330.Chowdhary, K. R. (2020). Natural Language Processing. In Fundamentals of Artificial Intelligence (pp. 603-649). New Delhi: Springer India.Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1). doi:10.1186/s40537-019-0217-0De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135. doi:10.1108/LR-06-2015-0061Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. J. a. p. a. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding.Gardella, G. H., & Berger, E. A. J. J. M. R. I. P. L. (2008). United States Reexamination Procedures: Recent Trends, Strategies and Impact on Patent Practice. 8, i.Koh, E. C. Y. (2013). Engineering design and intellectual property: where do they meet? Research in Engineering Design, 24(4), 325-329. doi:10.1007/s00163-013-0153-5Lanjouw, J. O., & Schankerman, M. (2001). Characteristics of Patent Litigation: A Window on Competition. The RAND Journal of Economics, 32(1), 129-151. doi:10.2307/2696401Lee, C., Song, B., & Park, Y. (2013). How to assess patent infringement risks: a semantic patent claim analysis using dependency relationships. Technology Analysis & Strategic Management, 25(1), 23-38. doi:10.1080/09537325.2012.748893Lee, J.-S., & Hsiang, J. J. a. p. a. (2019). Patentbert: Patent classification with fine-tuning a pre-trained bert model.Liu, H., Zhang, R., Liu, Y., & He, C. J. N. (2022). Unveiling Evolutionary Path of Nanogenerator Technology: A Novel Method Based on Sentence-BERT. 12(12), 2018.Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., . . . Stoyanov, V. J. a. p. a. (2019). 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