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作者(中文):劉柏宏
作者(外文):Liu, Bo-Hong.
論文名稱(中文):商標侵權判決分析與判決書推薦系統建立
論文名稱(外文):Analysis of trademark infringement cases and identification of legal precedent
指導教授(中文):張瑞芬
指導教授(外文):Trappey, Amy J.C.
口試委員(中文):施翠倚
張力元
口試委員(外文):Trappey, Vincent.Charles
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學號:106034558
出版年(民國):108
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 infringementprecedence clusteringLatent Dirichlet Allocationprecedence analysisrecommendation platform
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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.
致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
壹、緒論 1
1.1 研究背景與動機 1
1.2 研究範圍與目的 3
1.3 研究方法與步驟 3
貳、文獻探討 6
2.1 商標的定義與商標侵權 6
2.2 商標類型與註冊保護 7
2.3.1 美國司法制度 8
2.3.2 司法管轄權(Jurisdiction) 9
2.3.3 美國聯邦地方法院(U.S. District Court) 9
2.3.4 美國聯邦上訴法院(United State Courts of Appeals or Circuit courts) 10
2.3.5 美國聯邦最高法院(The U.S. Supreme Court) 10
2.4 商標法規與判決特徵 11
2.4.1 美國聯邦商標法(Lanham law) 11
2.4.2 商標侵權的補救措施 11
2.4.3 商標侵權判決書特徵 12
2.5 電子偵測 (Electronic Discovery) 13
2.6 推薦系統 13
2.6.1 內容式過濾 (Content-based Filtering) 14
2.6.2 協同式過濾系統 (Collaborative Filtering, CF ) 14
2.7 自然語言處理 15
2.7.1自然語言理解 15
2.7.2 遞迴神經網路Recurrent neural network (RNN) 16
2.7.3 Sequence to Sequence 16
2.7.4 神經網路的語言模型 17
2.7.5 詞向量(Woed2vec) 18
2.8 主題模型(Topic model) 19
2.8.1 LSI (Latent Semantic Indexing) 19
2.8.2 LDA (Latent Dirichlet Allocation) 20
2.9 分群技術 21
2.9.1 階層式分群法 (Hierarchical clustering) 21
2.9.2 分區方法 (Partitioning) 22
2.9.3 K值決定法 23
2.10 商標侵權知識本體論 24
2.10.1 知識本體論 24
2.10.2 商標侵權領域知識本體論 25
2.10.3 商標判決書文本技術領域知識本體論 26
參、方法論 28
3.1 建立商標判決書文本模型 30
3.1.1商標判決書選定 30
3.1.2 資料前處理 31
3.1.3文本向量化 31
3.2 以非監督學習建構判決書分群 33
3.3 主題模型建立 35
3.4 提取判決書文本特徵 37
3.5 建立智能商標推薦判決平台 39
肆、案例研究分析 41
4.1 判決書檢索 41
4.2 以文本模型推薦相似判決 41
4.3 判決書分群 42
4.4 特徵提取結果 44
4.5主題模型分析結果 48
4.6 案例分析 52
4.7 推薦系統驗證 58
4.7.1 法條驗證 58
4.7.2 集群驗證 60
4.7.3 綜合驗證結果 60
伍、結論 62
參考資料 65
Appendix 71
Appendix A- The United States Code (U.S.C) 71
Appendix B -集群法條引用數 74
Appendix C 驗證判決書 75
Appendix D 群集與法條驗證結果 77
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