IP Geolocation via Traceroute Learning Model

The patent transforms the geolocation problem from using physical propagation parameters (time, distance, speed) to using network topology parameters (traceroute paths, hop counts, routing information). This parameter transformation allows the system to work within the non-linear network environment while maintaining prediction accuracy through statistical learning models that capture network-specific patterns.

The patent replaces traditional physics-based geolocation methods (which rely on linear signal propagation through space) with a data-driven machine learning approach. Instead of using echo timing and triangulation based on physical laws, the system uses traceroute information and statistical models to predict geographic coordinates, substituting mechanical/physical measurement systems with computational prediction systems.

The system continuously updates its prediction model by incorporating new traceroute data and observed geographic locations. This feedback mechanism allows the model to adapt to changing network conditions and correct inaccuracies in the registry data, improving prediction accuracy over time while maintaining the simplicity of the lookup process.

The patent performs preliminary traceroute measurements and builds prediction models in advance, so that when a geolocation query is made, the system can quickly retrieve predictions based on pre-computed statistical models rather than performing complex real-time measurements or relying on static registry data.

The patent segments the geolocation system into distinct functional components: a training phase that builds statistical models from traceroute data, and a prediction phase that applies these models to new IP addresses. This segmentation allows the complex learning process to be separated from the simple query process, maintaining high accuracy while reducing operational complexity.

The patent introduces statistical learning models as intermediaries between raw traceroute data and geographic location predictions. These models capture complex network patterns and relationships, serving as a bridge that transforms difficult-to-interpret network measurements into accurate geographic predictions without requiring complex real-time processing.