Geofencing via Location Duration and Crowdsourced Paths
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Solution Overview
Problem
Existing location-aware monitoring systems lack the ability to detect unscheduled stops or deviations in routes taken by tracked entities, such as delivery trucks, which can lead to security concerns like theft or improper behavior, as geofences may be too large and fail to identify intermediate stops.
Innovation Solution
A tracking service that registers trackable entities with location-aware devices, using crowdsourced data to identify common paths and intermediate stops between a point of origin and destination, and generates alerts for deviations from these established patterns, including unexpected stops or prolonged durations at specific locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If geofences are made large to cover the entire route between origin and destination, then the entity can travel freely between locations, but intermediate stops and deviations cannot be detected
Solution Approach 1:
The patent segments the large geofence area into multiple smaller monitoring zones along the expected route. Instead of treating the entire route as one geofence, the system divides it into sequential segments with defined intermediate stops. This allows the system to detect when the entity deviates from the expected sequence of stops while maintaining comprehensive coverage of the route area.
2Measurement precision
If geofences are made small to detect intermediate stops, then stop detection accuracy improves, but the entity cannot travel freely between locations
Solution Approach 1:
The patent implements dynamic geofencing where the monitoring zones are not static but adapt based on the entity's progress along the route. As the entity reaches expected intermediate stops, the system dynamically activates monitoring at those specific locations while allowing free movement through areas not designated as monitoring zones. This creates a flexible system that provides precise stop detection only where necessary.
3Measurement precision
If administrators manually define all possible routes and stops, then monitoring accuracy improves, but the setup complexity and time increase significantly
Solution Approach 1:
The patent implements self-service route definition where the system automatically generates monitoring policies based on historical data and crowd-sourced information. Instead of requiring administrators to manually define every route and stop, the system autonomously identifies common travel patterns, extracts intermediate stops from historical data, and creates geofence configurations automatically. This reduces setup complexity while maintaining monitoring accuracy.
4Measurement precision
If extensive administrator input is required to set up tracking policies, then monitoring accuracy improves, but the ease of operation decreases
Solution Approach 1:
The patent performs preliminary action by pre-processing historical movement data and crowd-sourced information to identify common routes and intermediate stops before the administrator needs to configure monitoring. The system pre-generates candidate geofence configurations and route patterns, so when the administrator sets up tracking, they are working with pre-analyzed data and suggested configurations rather than raw data, significantly reducing the effort required while maintaining accuracy.
Data Source
AI summary
A system and method for efficiently establishing patterns of behavior for location-aware monitoring applications. An administrator registers a trackable entity with a tracking service by providing identification of at least the trackable entity, a point of origin and a destination. To identify and select paths between the point of origin and the destination, the tracking service accesses crowdsourced information corresponding to the history of movements of trackable entities between the point of origin and the destination. The tracking service identifies intermediate locales along the selected paths and determines an expected duration of stay for each of the intermediate locales based on the history of movements. As the tracking service monitors the movement of the trackable entity, the tracking service generates alerts responsive to determining the trackable entity stopped at an unallowed intermediate locale or determining a duration of stay at an identified intermediate locale exceeds an expected duration of stay.