Knowledge Representation and Intelligent Recommender System for Trademark Protection and Litigation Analysis
| Autor: | Lin, Kevin, 林凱文 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Digital marketing has flourished and digital content has covered the world with advertisements, slogans and trademarked products. As digital diffusion continues to propagate, trademark infringement is difficult to discover, document and prosecute. Companies are at a disadvantage since the financial damage of trademark infringement is enormous. Most trademark owners cannot continuously monitor all of the content on the web and do not have the ability to determine whether other competitors are in fact infringing the trademark. As a result, this research proposes an intelligent trademark protection and infringement analysis system to ensure that the trademark owner is protected in the field of digital marketing. This study integrates legal case content and trademark law to construct a macroscopic ontology of trademark infringement. The ontology is used to analyze the source data of potential infringement cases retrieved by the front-end digital marketing system, find similar or corresponding cases from the infringement case legal libraries, infer the possibility of infringement in the case, and make suggestions to the trademark owner. This study plans to construct the ontology of infringement cases and develop the system. The intelligent case-based recommendation system will use the Python programming language to implement deep AI learning algorithms to recommend similar prior litigation cases and to identify infringing digital content. The results form the basis to develop systems extendable to patent infringement defenses analysis systems which are more complicated in scope. This case law recommendation system will provide complete trademark protection and assist users to quickly explore similar, relevant cases, reduce search time, and improve infringement claim accuracy. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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