Trademark Opposition Proceedings in Switzerland: Empirical Insights (UZH)

The first part of this project is an empirical analysis of legal reasoning involved in trademark opposition proceedings in Switzerland (Widerspruchsverfahren). We examine a novel dataset on trademark opposition proceedings brought before the Swiss Federal Institute of Intellectual Property (IPI).

In these proceedings, the likelihood of confusion between two (or more) trademarks is assessed drawing on several legal factors (for example, trademark similarity, similarity of goods and services and the level of attention). Our dataset contains information on 2453 cases relating to proceedings between June 2002 and August 2018. In particular, we examine which legal factors drive the outcome of these decisions and we assess their relative importance.

Principal Investigators

Research Team

  • Daniel Gerber
  • Zoé Gianocca
  • Manuela Kälin
  • Angel Morger

Funding: Swiss Federal Institute of Intellectual Property (partial)
Status: Part 1 completed.

Part 2: Empirical Analysis of Extralegal Factors involved in Trademark Opposition Proceedings in Switzerland

The second part of this project looks more closely at the extralegal factors associated with winning or losing in trademark opposition proceedings before the IPI.

Principal Investigators

Research Team

  • Daniel Gerber
  • Zoé Gianocca
  • Manuela Kälin
  • Angel Morger

Funding: Swiss Federal Institute of Intellectual Property (partial)
Status: Part 2 ongoing.

Part 3: Analyzing Trademark Similarity Decisions using Machine Learning

Registered trademarks enjoy legal protection and can therefore take legal measures against (newer) trademarks that might be confused with the original trademark. Hence, the question whether two trademarks are similar is crucial for the trademark office’s decision. However, it is not yet entirely clear which criteria/features of two trademarks have an impact on the trademark office’s decision.

In collaboration with Prof. Dr. Lena Jäger (Department of Computational Linguistics) and Prof. Dr. Florent Thouvenin (Faculty of Law), we aim at developing a machine learning algorithm that predicts decisions of trademark offices regarding the similarity of two trademarks. In a second step, we will analyse the algorithms behaviour in order to assess which features are informative with respect to the trademark office’s decision.

Principal Investigators

Research Team

We are currently looking for a Master thesis student to work on this project.

Status: Part 3 ongoing.