Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies. (August 2020)

• Record Type: Journal Article
• Authors: Trappey, Charles V.; Trappey, Amy J.C.; Lin, Sam C.-C.
• Is Part Of: Advanced engineering informatics. Volume 45(2020)
• Publication Date: 2020-08
• Subjects: Convolutional neural network -- Siamese neural network -- Trademark similarity assessment -- Trademark (TM) infringement -- Vector space model; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101120
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: An integrated machine learning approach auto-detects trademark (TM) similarities. Performs multi-featured TM (spelling, pronunciation and image) similarity analyses. The intelligent system supports rapid global TM rights protection. Abstract: 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.