近年來，商標侵權問題已經引起了公眾的關注。商標在商業中有著重要的作用，不但代表公司的品牌形象，也展示其產品的商業價值。然而，關於商標侵權的爭議層出不窮，不道德的商人仿造並使用類似的著名商標，吸引消費者的關注以獲取不當利潤。因此，越來越多商標所有權者提起訴訟來捍衛自己的權益。面對繁雜的訴訟準備過程，蒐集訴訟相關的前案是最耗費時間也難以省略的步驟之一。為了提高搜索和閱讀商標判決的時間和效率，本研究提出以知識本體為基之商標侵權判例電腦輔助推薦系統，將判決文件知識結構化，推薦並提供使用者作為前案蒐集之參考依據。本研究首先以法律上案件摘要(Case Brief)的觀點建構商標侵權知識本體，將本體知識分為五個類別，包含基本資料、案件背景、爭點、法條、裁決，並以美國地方法院及聯邦法院之商標侵權判例為分析文本，針對非結構化的文字資料定義相應的規則，利用自然語言處理提取判例的關鍵特徵，構建商標侵權判決領域的知識本體資料庫。最後透過主題模型(Topic Model)分析判例文本，分群並定義判例的內容主題，對應法律爭點和分類主題之集群，比對判例之間的相似性，提供最符合法律閱讀角度的前案推薦結果。

A registered trademark (TM) distinctively represents a company and the products or services the company sells. TM is a form of intellectual property (IP) legally protected by the IP laws in the country where it is officially registered. Thus, TM related legal cases have grown rapidly due to the exponential growth of globally registered TMs. In this paper, an intelligent decision support system automatically discovers similar TM related legal judgments for any given seed case, is developed to support legal research. This study constructs the semantic network (i.e., an ontology schema) representing TM legal scope and terminologies. In the research, 3580 US trademark infringement cases litigated in the US district and federal court are collected as our experimental corpus. The system extracts critical features of each legal document, in compliance with the TM legal semantic ontology schema. The system can identify matching precedents according to the critical features (representing these legal briefs), e.g., case facts, TM-related issues under disputes, judgment holdings, and applicable laws. Text mining and topic modeling techniques (LDA, DTM) are deployed to discover relevant precedents. The objective is to assist users in searching the most relevant cases in terms of different corresponding rules and laws in those litigation documents. Through the analysis of the similarity between the issues and the topics, the system can provide relevant precedents based on the reasonable legal opinion.

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