研究生: |
劉柏宏 Liu, Bo-Hong. |
---|---|
論文名稱: |
商標侵權判決分析與判決書推薦系統建立 Analysis of trademark infringement cases and identification of legal precedent |
指導教授: |
張瑞芬
Trappey, Amy J.C. |
口試委員: |
施翠倚
張力元 Trappey, Vincent.Charles |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 中文 |
論文頁數: | 77 |
中文關鍵詞: | 商標侵權 、判決分群 、潛在狄利克雷分配 、判決分析 、判決推薦平台 |
外文關鍵詞: | trademark infringement, precedence clustering, Latent Dirichlet Allocation, precedence analysis, recommendation platform |
相關次數: | 點閱:1 下載:0 |
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商標圖像容易被模仿,相似商標無論在拼字、發音或圖像等特徵上若有混淆之事實,將使商標所有權人遭受損害。尤其在網路社群等電子化媒介的快速普及其使用人數高速增長下,此類商標侵權的影響程度更為巨大。本研究計畫以機器學習方法為基,進行商標侵權案件之文字探勘分析,並進一步建構侵權判決的推薦系統,針對使用者對特定之商標侵權議題,提供推薦判決書,以利快速精準匹配出共同特徵內容的判決,作為法律攻防之輔助資料。本研究首先完成商標侵權知識本體論的建構,作為商標侵權案件之文本探勘基礎,並以美國地方及聯邦法院商標案例建構商標判決文本模型,將判決書文字轉換成文本向量,作為推薦高相關性判決與分群及主題分析解讀之基礎。並發展非監督式機器學習判決推薦方法論,並以判決書分群及主題模型分析提取該集群之關鍵詞彙,使主題的定義在分群本身更具代表性,增加推薦與搜索的準確性。增加推薦判決之對應集群的解釋力與分析價值。本研究成果建立之人機互動介面提供使用者宏觀與微觀的檢視,作為事由的評估與結果預測。
Former legal precedents of content cases have a considerable impact for the development of legal strategies. This research uses machine learning approaches to analyze and identify the most relevant legal precedential judgements in the domain of US trademark (TM) litigations. The TM judgement narratives (text) are from Westlaw corpus. The documents are vectorized based on trained neural network model. The highly correlated precedents and their features are automatically identified by comparing the given judgement with the historical case judgements in the corpus. The non-supervised machine learning methods, including clustering and Latent Dirichlet Allocation (LDA), generate the legal document clusters, topics, and topic key terminology distributions, to better discover the case descriptions. The advantage is to make the definition of the clusters, topics and corresponding key terms more representative and self-explanatory, enhancing the accurate interpretations toward the recommended case judgements. Further, the non-supervised machine learning approach is used to analyze the TM litigation trends based on TM judgement corpus, providing users with both macro and micro views to evaluate the causes and predict the judgement results.
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