Harper Litigation Consulting and Research : 214-244-4608

District Circuits

Tenth Circuit Likelihood of Confusion Factors

Tenth Circuit Likelihood of Confusion Factors

Get the expertise you need plus the experience you want.

Each of the thirteen 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 Tenth Circuit

The Tenth Circuit includes the following jurisdictions:

  • District of Colorado
  • District of Kansas
  • District of New Mexico
  • Eastern District of Oklahoma
  • Northern District of Oklahoma
  • Western District of Oklahoma
  • District of Utah
  • District of Wyoming

The Tenth Circuit uses the Six Factors in likelihood of confusion cases:

  1. The degree of similarity between the marks
  2. The intent of the alleged infringer in using the mark
  3. Evidence of actual confusion
  4. Similarity of products and manner of marketing
  5. The degree of care likely to be exercised by purchasers
  6. The strength or weakness of the mark

Rhonda Harper MBA, Likelihood of Confusion Survey Expert, Expert Witness

In the Tenth Circuit, Ms. Rhonda Harper has been engaged in the District of Colorado, Denver.

Retained by more than 140 law firms, Ms. Harper is courtroom proven in virtually every circuit along with JAMS and TTAB.  Her 30+ year career includes serving as a Fortune 100 chief marketing officer and an adjunct 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 the methodological pitfalls that can introduce bias or systematic error.

Professional Memberships