<![CDATA[This study introduces a novel framework for trademark similarity analysis that integrates large language models (LLMs) to assess lexical, phonetic, and semantic relationships between trademark names without reliance on large precompiled databases or retraining. The primary motivation is to address the growing need for efficient and transparent preliminary trademark screening, which is often constrained by the limitations of traditional rule-based or string-matching approaches. To achieve this, a web-based system was developed using the Gemini API, allowing users to input trademark pairs for automated analysis. The workflow includes text normalization, phonetic conversion, multi-dimensional similarity computation, and the generation of interpretative explanations for each pair. A test dataset of ten diverse trademark name pairs was designed to capture variations in lexical overlap, phonetic similarity, and semantic association. The system’s outputs were evaluated both in terms of processing efficiency and expert assessment. Quantitative results show that the system can process a pair in under a second on average, handling 3,229 tokens across ten pairs with minimal computational overhead. Qualitative evaluation by five trademark and intellectual property experts using a 5-point Likert scale yielded mean scores of 4.4 for relevance, 3.8 for explanation quality, and 4.2 for practical usefulness, confirming strong alignment between the LLM outputs and expert intuition. The novelty of this research lies in demonstrating that LLMs can provide not only accurate similarity assessments but also human-readable interpretative reasoning, bridging the gap between automation and expert judgment in trademark evaluation. This approach offers a transparent and scalable solution for early-stage brand screening, significantly reducing the reliance on extensive databases and manual effort. The findings indicate a clear potential for integration into industrial-scale trademark examination workflows, paving the way for future developments in batch processing, recommendation systems, and enhanced interpretability in AI-assisted intellectual property management.]]>
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