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Secondary Meaning – Get an Expert Survey

Secondary Meaning

Get an Expert Survey.

The concept of secondary meaning is straightforward in intellectual property matters. A secondary meaning survey typically seeks to assess whether a “significant” or “substantial part” of the customer class associates a trademark or trade dress with a single source.

Secondary meaning may be measured through questions such as the following:
  1. Have you ever heard of [the trademark] with regard to [the context]?
  2. (If “yes” above:) Do you associate [the trademark] with one company, more than one company, or no company?
  3. (If “one company” above:) What company do you associate with [the trademark]?

Secondary meaning exists if respondents associate the trademark or trade dress with only one company. Although not required (a.k.a "anonymous source rule), secondary meaning surveys may ask the third question (“what company”) as a way to identify whether the trademark or trade dress is typically associated with the plaintiff or the defendant.

The measurements from these questions can be compared against standard sources or past precedents, to evaluate whether or not secondary meaning is present in sufficient quantity as to be considered substantial or relevant.

Rhonda Harper MBA, Trademark Infringement Survey Expert, Expert Witness

Retained by more than 125 law firms, Ms. Rhonda Harper is courtroom proven in virtually every Circuit as well as JAMS and TTAB.  Ms. Harper's 30+ year career includes serving as a Fortune 100 Chief Marketing Officer and an Adjunct MBA Marketing Professor. In addition to providing litigation consulting and research in the areas of business, licensing, marketing, advertising, in-store merchandising, and strategy, Ms. Harper is routinely retained to formulate expert surveys, conduct rebuttal critiques, or construct rebuttal surveys to show the potential difference in results with properly designed and executed surveys. She has extensive experience and a deep understanding of survey design, sampling, question construction, data analysis, and methodological pitfalls that introduce bias or systematic error.

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