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District Circuits

Second Circuit Likelihood of Confusion Factors

Second Circuit Likelihood of Confusion Factors

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Each of the 13 federal courts of appeal have their own test for evaluating whether a likelihood of confusion exists between two trademarks. Although the tests are not identical, most of them are substantially similar and use many of the same factors. And the factors are non-exclusive.

 

The Second Circuit exercises federal jurisdiction in:

The Second Circuit considers the Eight Polaroid Factors in likelihood of confusion cases:

  1. Strength of the trademark
  2. Similarity of marks
  3. Proximity of the products and their competitiveness with one another
  4. Evidence that the senior user may bridge the gap by developing a product for sale in the market of the alleged infringer’s product
  5. Evidence of actual consumer confusion
  6. Evidence that the imitative mark was adopted in bad faith
  7. Respective quality of the products
  8. Sophistication of consumers in the relevant market

Polaroid Corp. v. Polarad Electronics Corp., 287 F.2d 492, 495 (2d Cir. 1961).


Rhonda Harper MBA, Expert Witness

In the Second Circuit, Rhonda Harper has been retained in six cases filed in the:

  • New York State Supreme Court Manhattan
  • Western District of New York
  • Southern District of New YorkRhonda HarperRetained by 115+ 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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