Scalable visual patent analysis

The project "Scalable Visual Patent Analysis" is funded by the German Sciene Foundation (DFG) as part of the priority programme 1335: "Scalable Visual Analytics". It builds upon the research results of the EC project "PatExpert". The project is a cooperation of the Statistical Natural Language Processing group led be Prof. Ph.D. Schütze and the Graphical Interactive Systems group headed by Prof. Dr. rer. nat. Thomas Ertl.

Patent analysis has become an important economic task in recent years. Due to the multi-dimensional nature of patent data, their heterogeneity and often poor quality, exhaustive analysis of this data is time-consuming and error-prone. Exhaustiveness in terms of good data coverage (recall), however, is a key-factor for high-quality patent analysis, because missing important patent information can result in severe economic consequences.
The main idea of this project is therefore to research and develop new approaches for the integration of user-tailored interactive visual exploration methods and advanced text analytics methods in the field of intellectual property analysis. The power of interactive visualisation can be enhanced greatly if the underlying text data are analysed, filtered and classified using advanced text analytics methods. Classical information retrieval is not sufficient with respect to recall in the patent domain.
This project aims to develop new methods for patent analysis that greatly improve the state of the art in terms of effectiveness and reliability, thus establishing a new form of scalable visual patent analysis.