TMID : A Comprehensive Real-world Dataset for Trademark Infringement Detection in E -Commerce (2023)

Abstract: Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world’s largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere.

## background and problem definition a registration in a chinese online e-commerce platform breaches the chinese trademark law (outlined in appendix a.1), if it violates the corresponding statutes to protect the ip rights of the existing trademark owners, who can be individuals, businesses, or other legal entities. statutes are the legal rules codified in legislation. because china employs a civil law system, legal decisions are made mainly based on legal rules. an example registration is depicted in fig. 1 . it consists of a registration name "pande delivery center", a description of provided goods or services, the registered merchant name, and optionally also a slogan. it is an infringement because i) the registration name is similar to a protected trademark "depan delivery" by only swapping two chinese characters de and pan, ii) the service provided by the merchant is almost the same as the one provided by the company of the protected trademark, and iii) the company "sky delivery company" is not affiliated with or has a business relation with the company "depan delivery". our legal experts usually consider four factors to determine if there is an infringement or not. when a registration name is similar to a protected trademark, the similarity between goods and services is also important, because it may not be an infringement if the provided services and goods are substantially different from the trademark owners. moreover, if the merchant is a subsidiary of the trademark owner, it is legal to use a registration name similar to the trademark. therefore, trademark infringement detection is beyond only measuring similarity between logos, as done in prior works @xcite . we manually analyze 192 registrations annotated with reasoning traces, which do not involve business relations with any trademark owners. among them, 139 registrations involve infringements because those registrations are filtered by a recalloriented deployed ensemble method detailed in section 3.3. all of the infringed registrations use registration names or words in descriptions similar to protected trademarks. as the majority are service providers, 88.49% of infringements provide similar services, while only 10.07% of them provide similar goods. formally, the target task is to predict if a registration x r s infringes the trademark law or not. ## related work existing research in automatic trademark ip infringement detection typically simplify the problem definition to logo image similarity @xcite @xcite @xcite @xcite or textual similarity detection @xcite , subsequently proposing methods using diverse machine learning models like convolutional neural networks @xcite or recurrent neural networks @xcite . other studies delved into constructing trademark ontologies @xcite or developing logo similarity detection datasets @xcite . distinct from these studies, our work directly addresses a real-world issue of trademark ip infringement detection on the e-commerce platform, providing comprehensive textual data with legal annotations based on statute laws. ## conclusion in this work, we present the first dataset, coined tmid, on trademark infringement detection in the merchant information registered to online ecommerce platforms. the target task requires legal reasoning over registrations, information about the merchant to register, statutes in trademark laws, protected trademarks and auxiliary information about trademark owners. our empirical study shows that i) llms greatly benefit from the training data and the contextual information from our dataset; ii) powerful gpt3.5 still fails to generate reasoning traces aligning with those from legal experts but are able to reach an f1 over 95% if the reasoning traces are correct. this work does not only provides a useful resource but also sheds light on the limitations of llms on complex reasoning.