Recommended Reading: The Trademark Reporter, November-December 2023 Issue
The November-December 2023 (Vol. 113 No. 6) issue of the Trademark Reporter has been published. [pdf here]. Willard Knox, Editor-in-Chief, summarizes the contents as follows (and below): "This issue offers our readers an article proposing an innovative, neuroscience-based approach to establishing likelihood of confusion, an article detailing the astonishing reconstruction of an historical trademark registry and sharing what lessons can be learned from the registry’s new (and old) data, and a commentary challenging how leading survey formats assess consumer uncertainty and offering modifications to these formats that could yield valuable data."
A New Addition to the Trademark Litigator’s Tool Kit: A Neuroscientific Index of Mark Similarity, Mark Bartholomew, Zhihao Zhang, Ming Hsu, Andrew Kayser, and Femke van Horen. With trademark law always striving to keep abreast of new developments in science and technology, the authors of this article propose an innovative, neuroscience-based approach to answering the time-honored question of whether likelihood of consumer confusion exists in a particular dispute.
Reconstructing the Trademark Registry of Mandate Palestine and What Historical Data Can Reveal, Michael Birnhack. The author of this unique piece of legal scholarship shares with readers the story of his extraordinary reconstruction of an historical trademark registry and limns how the registry’s new (and old) trademark data might aid the modern practitioner and academic in their own work.
Commentary: Incorporating Uncertainty in Trademark Surveys: Do Respondents Really Know What They Are Talking About?, Barton Beebe, Roy Germano, Christopher Jon Sprigman, and Joel H. Steckel. In this provocative and practical commentary, the authors home in on how leading survey formats currently fail to address all aspects of consumer uncertainty; the authors then propose straightforward and cost-effective modifications to those same formats that could yield more complete and useful data.
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Text Copyright John L. Welch 2023.
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