ML Residence Prediction via Location Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems are unable to accurately predict when a person will change their residence, making it difficult to update user data records proactively.
Innovation Solution
The use of machine learning-based systems that analyze location data from mobile devices and telematics data to generate predictions by creating graph representations of location patterns and training classifiers on ground truth data to determine the likelihood of a residence change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems analyze location data to predict residence changes, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct components: location data collection from multiple sources, graph representation construction, feature extraction, and machine learning classification. This modular segmentation allows each component to be optimized independently while maintaining overall prediction accuracy.
Solution Approach 2:
The patent introduces graph representations as an intermediary structure between raw location data and the machine learning classifier. The graph serves as a mediator that transforms complex location patterns into structured features (nodes and edges) that are more suitable for classification, thereby improving prediction accuracy while managing system complexity.
2Reliability
If multiple location data sources are integrated, then prediction reliability improves, but data processing complexity increases
Solution Approach 1:
The system merges multiple location data sources (GPS coordinates, map applications, telematics data) into a unified graph representation. By combining these diverse data sources into a single structured format with consistent nodes and edges, the system improves prediction reliability while managing the complexity of processing multiple sources through standardization.
Solution Approach 2:
The graph representation structure serves as a universal data model that can accommodate multiple types of location data sources. The same graph framework handles GPS coordinates, map application data, and telematics information uniformly, allowing the system to process diverse data sources through a single multi-functional architecture.
Data Source
AI summary
Systems and methods can use a variety of computing devices to obtain location data that can be used to generate a prediction of a likelihood that a person will move his or her residence. The location data can be generated based on location data captured from a mobile computing device and/or based on telematics data captured during the operation of a vehicle and/or from a computing device. The location data may be compiled into graphs comprising locations of visited by the person and relationships between the locations visited by the person, such as instances of the person traveling between the two locations. The likelihood to change residence can be determined based on the amount of time the person spent at a given location, the distance between the various locations that are the most significant in the graph, and/or the frequency of visits to particular locations.