The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater due diligence to protect intangible assets such as brands and corporate logos which can easily be copied or distributed through grey channels and internet sales sites. Trademarks (TMs) are government registered intellectual property rights (IPRs) used to legally protect a companies’ identities and brand equity. The rapid growth of global trademark (TM) registrations and the number of TM infringement cases pose a great challenge for TM owners to detect infringement and take action to protect TMs, consumer trust, and market share. This research develops advanced TM similarity assessment models using machine learning (ML) approaches. Litigation principles over similarity follow US TM laws which are consistent with global TM protection convention under the World Intellectual Property Organization (WIPO). This research covers the similarity analysis of TM spelling, pronunciation, and images, which are most likely to cause TM confusion among customers. The research focuses on deploying machine learning for natural language (spelling and phonetic features) and image similarity analyses. The vector space modeling algorithms are trained and verified for the similarity analysis of TM wordings in both spelling and pronunciation. The convolutional neural network and Siamese neural network models are trained and verified for TM image similarity comparison. The training and testing sets consist of 250,000 and 20,000 different image pairs respectively. This research provides a significant contribution toward implementing intelligent and automated IPR protection. The system solution supports users (companies, TM attorneys, or IP officers) to identify similar registered TMs before registering new TMs ensuring uniqueness to avoid infringement disputes. The solution also supports automatic screening of online content to detect potential infringement of TM images and wording for effective global IPR protection.

寿命短的消费产品的快速发展，以及快速的全球电子商务和产品和服务的电子营销分销，需要进行更大的尽职调查，以保护无形资产，如品牌和公司徽标，可以通过以下方式轻松复制或分发灰色渠道和互联网销售网站。商标（TM）是政府注册的知识产权（IPR），用于合法地保护公司的身份和品牌资产。全球商标（TM）注册的快速增长和TM侵权案件的数量对TM所有者发现侵权并采取行动保护TMs，消费者信任和市场份额构成了巨大挑战。这项研究使用机器学习（ML）方法开发了先进的TM相似性评估模型。相似性诉讼原则遵循美国商标法，该法律与世界知识产权组织（WIPO）的全球商标保护公约相一致。这项研究涵盖了TM拼写，发音和图像的相似性分析，这很可能导致客户之间的TM混淆。该研究专注于为自然语言（拼写和语音特征）和图像相似性分析部署机器学习。训练和验证了向量空间建模算法，以用于TM单词在拼写和发音方面的相似性分析。对卷积神经网络和暹罗神经网络模型进行了训练和验证，以进行TM图像相似性比较。训练和测试集分别包含250,000和20,000不同的图像对。这项研究为实现智能和自动化的IPR保护做出了重大贡献。该系统解决方案支持用户（公司，TM律师或IP人员）在注册新的TM之前先确定类似的已注册TM，以确保唯一性以避免侵权纠纷。该解决方案还支持自动筛选在线内容，以检测对TM图像和文字的潜在侵权，以有效地保护全局IPR。