Abstract Legal case precedents have a considerable impact on the development of litigation strategies. This research uses the neural network language modeling (NNLM) approach to analyze and identify judgment documents of US trademark (TM) litigation cases as precedents of a given target case. In this research, the NNLM has been trained using 4835 TM litigation documents. There are more than 800,000 words in the entire training text set including more than 150,000 vocabularies. The words in TM legal documents are vectorized to train the NN model for e-discovery of semantically correlated precedents and their features. Specifically, non-supervised machine learning (ML) methods, including clustering and Latent Dirichlet Allocation (LDA), are applied to form the TM legal document clusters, topics, and key terminologies used to characterize the TM case descriptions and precedents. The definition of the clusters, topics and corresponding key terms enhance the ability of the system to recommend and explain similar case judgments for any given TM case of interest or a cease and desist letter with detailed claims of infringement. Further, the intelligent approach provides macro and micro views for companies to research TM litigation trends as a means to better protect their brand equity.

中文翻译：

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识别商标法律案例先例 - 使用机器学习实现判断的语义分析

摘要 法律判例对诉讼策略的发展有相当大的影响。本研究使用神经网络语言建模 (NNLM) 方法来分析和识别美国商标 (TM) 诉讼案件的判决文件作为给定目标案件的先例。在这项研究中，NNLM 已经使用 4835 TM 诉讼文件进行了训练。整个训练文本集中有超过 800,000 个单词，包括超过 150,000 个词汇表。TM 法律文件中的词被向量化以训练 NN 模型，用于语义相关先例及其特征的电子发现。具体来说，非监督机器学习 (ML) 方法，包括聚类和潜在狄利克雷分配 (LDA)，被应用于形成 TM 法律文档集群、主题、以及用于表征 TM 案例描述和先例的关键术语。集群、主题和相应的关键术语的定义增强了系统为任何给定的 TM 感兴趣的案件或带有详细侵权索赔的停止和终止信件推荐和解释类似案件判决的能力。此外，智能方法为公司研究 TM 诉讼趋势提供宏观和微观视角，作为更好地保护其品牌资产的手段。