Map Data Change Detection via Mobile Device Activity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing map data often becomes outdated due to changes in the real world, such as road configurations and user behavior, making it difficult and costly to detect and update map data accurately, especially in mobile device applications, leading to navigation errors and inefficient data processing.
Innovation Solution
A computing device analyzes device activity data from mobile devices to detect changes in map data by correlating location, speed, and application usage data with geospatial entities, enabling quicker updates and improving map data quality by identifying areas of high change and prioritizing updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical data capture teams drive through geographic areas to detect changes, then map data accuracy can be improved, but the cost and time required for data collection increases significantly
Solution Approach 1:
The patent replaces the mechanical system of physical data capture teams driving through geographic areas with an automated electronic system that collects and analyzes device activity data from mobile devices. This substitution eliminates the need for manual field surveys while maintaining or improving measurement precision through automated processing of location, speed, and application usage data.
Solution Approach 2:
The patent creates a virtual copy of the physical data collection process by using device activity data from mobile devices to represent real-world conditions. Instead of physically traveling to collect data, the system uses digital traces left by mobile devices to infer map changes, achieving the same objective through data replication rather than physical presence.
2Measurement precision
If physical data capture teams are dispatched to detect road changes, then map data quality can be improved, but the complexity and cost of the operation increases
Solution Approach 1:
The patent makes mobile devices perform multiple functions: they serve as location sensors, speed sensors, and behavior indicators simultaneously. By leveraging the existing multi-functional nature of mobile devices, the system avoids the complexity of deploying specialized equipment while maintaining high measurement precision for detecting map changes.
Solution Approach 2:
The system uses device activity data that is already being collected by mobile devices for their own operational purposes. This self-service approach means the data is generated autonomously by the devices themselves without requiring external intervention or complex collection infrastructure, thereby reducing system complexity while improving data quality.
3Reliability
If map data is updated frequently to reflect real-world changes, then user experience can be improved, but processing resources and transmission costs increase
Solution Approach 1:
The patent performs preliminary analysis of device activity data to identify potential map changes before full updates are triggered. By pre-processing and filtering data to detect anomalies or patterns indicating changes, the system can prioritize which areas need updates, reducing unnecessary processing and transmission while maintaining high reliability for user experience.
Solution Approach 2:
The patent applies updates selectively to specific geographic areas where changes are detected rather than performing blanket updates across all map data. This localized approach concentrates processing resources only where needed, reducing overall energy consumption and transmission costs while maintaining high reliability in updated regions.
4Measurement precision
If comprehensive device activity data is collected to improve change detection accuracy, then map update precision can be improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the relevant features from comprehensive device activity data that are necessary for change detection. Instead of processing all raw data, the system identifies and extracts key indicators such as location patterns, speed variations, and application usage changes, thereby maintaining high measurement precision while reducing processing complexity by eliminating unnecessary data elements.
Data Source
AI summary
In some implementations, a computing device can detect changes in map data based on device activity data received from a mobile device. For example, the device activity data can include location data that describes locations where the mobile devices have traveled, direction, speed, and/or other data. Based on the received location data, the computing device can determine whether stored map data for a particular area accurately reflects the real world characteristics of the particular area. The device activity data can identify user behavior with respect to the mobile device. For example, the characteristics of a real world geographic area may influence how users use their mobile devices (e.g., which applications are used) in the geographic area. The computing device can analyze the user behavior identified in the device activity data to detect changes in the real world characteristics of the geographic area.