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研究生:鄭俊彥
研究生(外文):Cheng, Chun-Yen
論文名稱:透過專利文字探勘辨識潛在競爭者之方法:以金融科技產業為例
論文名稱(外文):A patent mining methodology of identifying potential competitors: A case study of FinTech industry
指導教授:宋皇志宋皇志引用關係
指導教授(外文):Sung, Huang-Chih
口試委員:林怡伶莊弘鈺
口試委員(外文):Lin, Yi-LingChuang, Hung-Yu
口試日期:2020-07-03
學位類別:碩士
校院名稱:國立政治大學
系所名稱:科技管理與智慧財產研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:115
中文關鍵詞:專利分析文字探勘自然語言處理競爭者辨識金融科技
外文關鍵詞:Patent AnalysisText MiningNatural Language ProcessingCompetitor IdentificationFinTech
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  現今各行各業皆致力導入新興科技輔助商業發展,使得產業界線逐漸模糊。例如原本受到傳統金融機構長期掌握的金融服務市場,隨著金融科技的蓬勃發展,越來越多科技公司加入競爭,對傳統金融機構造成龐大威脅。倘若企業未能掌握潛在競爭者動向,將可能錯失擬定應對策略的最佳時機,最終導致企業競爭地位下滑。其中潛在競爭的不確定性,是企業不願進行潛在競爭者辨識的主要原因。

  本研究旨在提供一個方法,使企業得以在威脅浮現前預警到可能存在的競爭者。過去研究多將專利方法與文字探勘方法分別應用於競爭者辨識,本研究將結合兩種方法並考量實務可行性,提出以非監督式學習之專利文字探勘方法,協助企業辨識潛在競爭者,從中探求本方法論應用於潛在競爭者辨識的可能性,以及比較本研究採用模型應用於分析專利文本的優劣。本研究以有導入金融科技的傳統金融機構作為案例產業,並由美國百大銀行中挑選專利布局最多的美國銀行 (Bank of America,BOA) 作為標的企業進行測試,從約115萬篇的大量專利中找尋相關的市場與專利競爭威脅。

  本研究之貢獻在於提供一個企業得實際應用之新方法論辨識潛在競爭者。具體而言,研究中透過美國銀行的侵權訴訟評估方法可行性,發現29篇涉及侵權專利有13篇排名於前10%,其中3篇致使美國銀行敗訴的專利均排名於前5%,顯示本辨識方法足以在龐大的專利數據中提升鑑別競爭對手的效率。並以專利相似度排名進行競爭者分析,顯現本方法可以有效找尋有威脅性、多元且具相似專利資源的競爭者。
  Nowadays, all industries have dedicated to introducing new technology to assist their business, further blurring the boundary of industries. For example, the financial service market has long been monopolized by traditional financial institutions. However, with the rapid development of financial technology, technology companies have also entered the very industry, along with a huge competition threat. If the company fails to identify the potential competitors, it may miss the best time to formulate a responsive strategy, which ultimately leads to a decline. Also, the uncertainty of potential competition is the main reason why companies are unwilling to identify their potential competitors.

  The purpose of this study is to provide a methodology for companies to identify potential competitors before threats emerge. In the past research, the patent method and the text mining method were mostly applied to the identification of competitors. However, this study combined the two methods and considered the practical feasibility. Therefore, this research provides the patent text mining with unsupervised learning method to identify potential competitors, explore its feasibility, and compare the advantages between different models applying to analyze patent texts. In this study, traditional financial institutions that have introduced Fintech are been reviewed as case for research, and the Bank of America, which has the largest amount of patents among the top 100 banks in the United States, is selected as the target company for testing.

  The contribution of this study is to provide a new methodology in the field of competitor identification which companies could apply in practice. Specifically, through examining infringement litigation of Bank of America, the study found that 13 of the 29 infringement patents were ranked in the top 10%, and 3 patents that Bank of America is the losing party in infringement litigation were ranked in the top 5%. It shows that this identification method is sufficient to limit the competitive scope in the huge patent data. Lastly, the analysis of competitors based on the ranking of patent similarity shows this methodology can find competitors that are threatening, diverse and have similar patent resources.
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的與問題 6
1.4 研究範圍 6

第二章 文獻探討 8
2.1 競爭者辨識之意義 8
2.2 競爭者辨識之方法 10
 2.2.1 非專利與文字探勘之方法 10
 2.2.2 專利方法 12
 2.2.3 文字探勘方法 14
2.3 專利文本之文字探勘方法 16
2.4 銀行業之潛在競爭者問題 21
 2.4.1 金融科技產業概況 21
 2.4.2 銀行業之專利布局 23
2.5 小結 25

第三章 研究方法 27
3.1 研究架構 28
3.2 資料蒐集 33
3.3 模型訓練 35
 3.3.1前處理 35
 3.3.2向量化與模型訓練 36
3.4 模型驗證 42
 3.4.1 第一步驟:資料處理 43
 3.4.2 第二步驟:模型訓練 43
 3.4.3 第三步驟:模型驗證 45
3.5 個案測試 48
 3.5.1 前置步驟 49
 3.5.2 分群階段 49
 3.5.3 排名階段 51

第四章 研究結果 53
4.1 訓練模型驗證 53
 4.1.1 分群任務 53
 4.1.2 排名任務 57
 4.1.3 驗證結果評析 62
4.2 個案測試-Bank of America 63
 4.2.1 專利分群與排名流程 64
 4.2.2 辨識方法之可行性評估 72
 4.2.3 潛在競爭者辨識結果 75

第五章 結論與建議 86
5.1 研究結論 86
5.2 研究限制與建議 88

參考文獻 90

附錄1:模型驗證-分群任務原始數據 100
附錄2:模型驗證-排名任務原始數據 103
附錄3:個案測試-決定分群數原始數據 106
附錄4:個案測試-IPO潛在競爭者原始數據 108
附錄5:個案測試-非IPO潛在競爭者原始數據 112
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