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A Quantitative Evaluation of Trademark Search Engines' Performances through Large-Scale Statistical Analysis

Published: 07 September 2023 Publication History

Abstract

Intellectual Property Offices now offer their users trademark search engines to help them identify earlier trademarks in their register. Such tools have proven to be extremely useful given the growing number of trademarks registered but have never been subjected to thorough evaluation, despite the necessity for openness and accountability in justice systems. Additionally, their performance is unknown, in particular the reliability of their results pertaining to applicable legal rules. In fact, their "black box nature" makes automatic and at-scale evaluation hard to perform directly, which is why we propose a novel method for evaluating their performance using settled case-law for ground truth, and at-scale analysis. Based on this methodology, we evidence the performance for two such systems, the Benelux Office of Intellectual Property (BOIP) and European Union Intellectual Property Office (EUIPO), using 8 126 opposition division decisions from the EUIPO. We show important disparities between the two systems, along with surprisingly good results for EUIPO's system.

References

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Ilanah Fhima and Dev Gangjee. 2019. The confusion test in European trade mark law (first edition ed.). Oxford University Press, Oxford.
[2]
Dev S. Gangjee. 2021. Eye, Robot: Artificial Intelligence and Trade Mark Registers. In Transition and Coherence in Intellectual Property Law (1 ed.), Niklas Bruun, Graeme B. Dinwoodie, Marianne Levin, and Ansgar Ohly (Eds.). Cambridge University Press, 174--190. https://doi.org/10.1017/9781108688529.020
[3]
Dev S. Gangjee. 2022. A Quotidian Revolution: Artificial Intelligence and Trade Mark Law. SSRN Electronic Journal (2022). https://doi.org/10.2139/ssrn.4081317
[4]
Sonia K. Katyal and Aniket Kesari. 2020. Trademark Search, Artificial Intelligence, and the Role of the Private Sector. (2020). https://doi.org/10.15779/Z380V89H87 Publisher: Berkeley Technology Law Journal.
[5]
Daryl Lim. 2022. Trademark Confusion Revealed: An Empirical Analysis. American University Law Review 71 (2022), 1285--1365.
[6]
Anke Moerland and Conrado Freitas. 2021. Artificial Intelligence and Trade Mark Assessment. In Artificial Intelligence and Intellectual Property. Oxford University Press, 266--291.
[7]
Benelux Office of Intellectual Property. [n.d.]. BOIP and Darts-IP. https://www.boip.int/en/darts-ip
[8]
European Union Intellectual Property Office. [n. d.]. New AI solution for image search (the AI solution is on the tool image search). https://euipo.europa.eu/ohimportal/en/web/guest/-/news/new-ai-solution-for-images-search
[9]
European Union Intellectual Property Office. [n. d.]. Strategic Plan 2025. https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/contentPdfs/about_euipo/strategic_plan/SP2025_en.pdf
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European Union Intellectual Property Office. 2023. EUIPO Statistics for European Union Trade Marks, 1996-01 to 2022-12 Evolution. https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/contentPdfs/about_euipo/the_office/statistics-of-european-union-trade-marks_en.pdf
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Claudio A. Perez, Pablo A Estevez, Francisco J. Galdames, Daniel A. Schulz, Juan P. Perez, Diego Bastias, and Daniel R. Vilar. 2018. Trademark Image Retrieval Using a Combination of Deep Convolutional Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janeiro, 1--7. https://doi.org/10.1109/IJCNN.2018.8489045
[12]
Charles V. Trappey, Amy J.C. Trappey, and Sam C.-C. Lin. 2020. Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies. Advanced Engineering Informatics 45 (Aug. 2020), 101120. https://doi.org/10.1016/j.aei.2020.101120
[13]
Osman Tursun, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes, and Sandra Mau. 2020. Component-based Attention for Large-scale Trademark Retrieval. IEEE Transactions on Information Forensics and Security (2020), 1--1. https://doi.org/10.1109/TIFS.2019.2959921 arXiv: 1811.02746.

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  1. A Quantitative Evaluation of Trademark Search Engines' Performances through Large-Scale Statistical Analysis

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      ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law
      June 2023
      499 pages
      ISBN:9798400701979
      DOI:10.1145/3594536
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      New York, NY, United States

      Publication History

      Published: 07 September 2023

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      Author Tags

      1. BOIP
      2. EUIPO
      3. Intellectual Property
      4. Likelihood of Confusion
      5. Search Engine
      6. Trademark

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      • Fédération Wallonie-Bruxelles (Actions de recherches concertées)
      • Université Libre de Bruxelles

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      ICAIL 2023
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      • IAAIL

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      Overall Acceptance Rate 69 of 169 submissions, 41%

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