Dynamic Geofence Location Updates via Breach Data Analysis
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Solution Overview
Problem
Existing geofence systems often underperform in terms of breach frequency, leading to inaccurate tracking of targeted breaches, as they are not dynamically updated based on real-time breach data and location analysis.
Innovation Solution
A method and system that examines breach data from client devices to determine breach positions, updates the geofence location to areas with the highest predicted breach frequency, and notifies clients of breaches while optimizing geofence performance using machine learning and Natural Language Processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the geofence location remains static, then the system structure is simple and easy to maintain, but the breach frequency is low and tracking accuracy is poor
Solution Approach 1:
The geofence location is transformed from a static definition to a dynamic one that automatically updates based on real-time breach data. The system continuously examines breach positions and recalculates the geofence location to areas with highest predicted breach frequency, making the geofence adaptive to changing patterns rather than fixed in space.
Solution Approach 2:
The system implements a feedback loop where breach data is continuously collected, analyzed, and used to update the geofence location. The breach positions feed into a machine learning model that predicts optimal locations, which then updates the geofence, creating a closed-loop system that self-optimizes based on performance data.
2Productivity
If the geofence location is dynamically updated based on breach data, then the breach frequency and tracking accuracy improve, but the computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating predicted breach frequencies and identifying optimal geofence locations before actual breaches occur. The machine learning model analyzes historical breach data in advance to predict where breaches are most likely to happen, allowing the geofence to be proactively positioned rather than reactively adjusted.
Solution Approach 2:
The system changes key parameters including the geofence location coordinates, the time window for breach analysis, and the prediction model parameters based on incoming data. These parameter changes allow the system to adapt to different breach patterns and optimize performance without requiring complete system redesign.
3Measurement precision
If the geofence location is updated frequently to capture real-time breach patterns, then the breach detection accuracy improves, but the system response time and processing overhead increase
Solution Approach 1:
The system implements periodic updates of the geofence location based on accumulated breach data over specific time windows. Rather than continuously updating with every single breach event, the system aggregates breach positions over defined periods and performs location updates at regular intervals, balancing accuracy with processing efficiency.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining data of breaches of a geofence by client computer devices to determine respective positions of the breaches; establishing an updated location for the geofence using the determined respective positions of the breaches; updating a location of the geofence so that the location of the geofence is the updated location; obtaining data of a client computer breach of the geofence at the updated location; and providing one or more output in response to the obtaining data of a client computer breach of the geofence at the updated location.