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| Title: | 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
A patent mining methodology of identifying potential competitors: A case study of FinTech industry |
| Authors: | 鄭俊彥
Cheng, Chun-Yen |
| Contributors: | 宋皇志
Sung, Huang-Chih
鄭俊彥
Cheng, Chun-Yen |
| Keywords: | 專利分析
文字探勘
自然語言處理
競爭者辨識
金融科技
Patent Analysis
Text Mining
Natural Language Processing
Competitor Identification
FinTech |
| Date: | 2020 |
| Issue Date: | 2020-08-03 18:37:26 (UTC+8) |
| Abstract: | 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. |
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| Description: | 碩士
國立政治大學
科技管理與智慧財產研究所
107364206 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107364206 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001096 |
| Appears in Collections: | [科技管理與智慧財產研究所] 學位論文
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