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Quantifying the genericness of trademarks using natural language processing: an introduction with suggested metrics

Quantifying the genericness of trademarks using natural language processing: an introduction with... If a trademark (“mark”) becomes a generic term, it may be cancelled under trademark law, a process known as genericide. Typically, in genericide cases, consumer surveys are brought into evidence to establish a mark’s semantic status as generic or distinctive. Some drawbacks of surveys are cost, delay, small sample size, lack of reproducibility, and observer bias. Today, however, much discourse involving marks is online. As a potential complement to consumer surveys, therefore, we explore an artificial intelligence approach based chiefly on word embeddings: mathematical models of meaning based on distributional semantics that can be trained on texts selected for jurisdictional and temporal relevance. After identifying two main factors in mark genericness, we first offer a simple screening metric based on the ngram frequency of uncapitalized variants of a mark. We then add two word embedding metrics: one addressing contextual similarity of uncapitalized variants, and one comparing the neighborhood density of marks and known generic terms in a category. For clarity and validation, we illustrate our metrics with examples of genericized, somewhat generic, and distinctive marks such as, respectively, DUMPSTER, DOBRO, and ROLEX. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence and Law Springer Journals

Quantifying the genericness of trademarks using natural language processing: an introduction with suggested metrics

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature B.V. 2021
ISSN
0924-8463
eISSN
1572-8382
DOI
10.1007/s10506-021-09291-7
Publisher site
See Article on Publisher Site

Abstract

If a trademark (“mark”) becomes a generic term, it may be cancelled under trademark law, a process known as genericide. Typically, in genericide cases, consumer surveys are brought into evidence to establish a mark’s semantic status as generic or distinctive. Some drawbacks of surveys are cost, delay, small sample size, lack of reproducibility, and observer bias. Today, however, much discourse involving marks is online. As a potential complement to consumer surveys, therefore, we explore an artificial intelligence approach based chiefly on word embeddings: mathematical models of meaning based on distributional semantics that can be trained on texts selected for jurisdictional and temporal relevance. After identifying two main factors in mark genericness, we first offer a simple screening metric based on the ngram frequency of uncapitalized variants of a mark. We then add two word embedding metrics: one addressing contextual similarity of uncapitalized variants, and one comparing the neighborhood density of marks and known generic terms in a category. For clarity and validation, we illustrate our metrics with examples of genericized, somewhat generic, and distinctive marks such as, respectively, DUMPSTER, DOBRO, and ROLEX.

Journal

Artificial Intelligence and LawSpringer Journals

Published: Jun 1, 2022

Keywords: Generic trademark; Genericide; Genericness metric; Word embeddings; Orthographic variation; Semantic neighborhood density

References