Trademark Search, Artificial Intelligence and the Role of the Private Sector

2026-01-22

AI Summary

This study addresses a critical gap in trademark scholarship, which has predominantly focused on consumer search costs while overlooking the substantial burdens faced by applicants and the transformative role of artificial intelligence (AI) in trademark creation and retrieval. Through empirical experiments, the paper evaluates leading AI-powered trademark search engines, assessing their performance in judging trademark similarity. It proposes a reconceptualization of the trademark analysis framework from a supply-side perspective, foregrounding AI as a central actor in trademark selection. The findings demonstrate that AI tools significantly enhance both the efficiency and accuracy of trademark searches, thereby advancing trademark law toward a more balanced paradigm that simultaneously promotes innovation incentives and institutional efficiency.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

Abstract

Almost every industry today confronts the potential role of artificial intelligence and machine learning in its future. While many studies examine AI in consumer marketing, less attention addresses AI's role in creating and selecting trademarks that are distinctive, recognizable, and meaningful to consumers. Traditional economic approaches to trademarks focus almost exclusively on consumer-based, demand-side considerations regarding search. However, these approaches are incomplete because they fail to account for substantial costs faced not just by consumers, but by trademark applicants as well. Given AI's rapidly increasing role in trademark search and similarity analysis, lawyers and scholars should understand its dramatic implications. This paper proposes that AI should interest anyone studying trademarks and their role in economic decision-making. We examine how machine learning techniques will transform the application and interpretation of foundational trademark doctrines, producing significant implications for the trademark ecosystem. We run empirical experiments regarding trademark search to assess the efficacy of various trademark search engines, many of which employ machine learning methods. Through comparative analysis, we evaluate how these AI-powered tools function in practice. In an age where artificial intelligence increasingly governs trademark selection, the classic division between consumers and trademark owners deserves an updated, supply-side framework. This insight has transformative potential for encouraging both innovation and efficiency in trademark law and practice.

Problem

• trademark search
• artificial intelligence
• machine learning
• trademark law
• supply-side framework

Innovation

• artificial intelligence
• trademark search
• machine learning
• supply-side framework
• trademark similarity analysis