Dynamic Geofence Definition Using User Contextual Data
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Solution Overview
Problem
Current systems lack the ability to dynamically define and manage geofences based on the geo-locations and attributes of users within a network-based system, limiting targeted marketing and service delivery to specific populations.
Innovation Solution
A system that receives contextual information and geolocation data from users to identify common elements, allowing for the dynamic definition and adjustment of geofence boundaries to include or exclude users based on their characteristics and location, enabling targeted offers and advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If geofence boundaries are fixed and static, then system complexity is reduced, but the ability to target specific user populations dynamically is limited
Solution Approach 1:
The geofence boundaries are transformed from static to dynamic by continuously adjusting them based on real-time geolocation data and contextual information from multiple users. The system monitors user locations and automatically redefines geofence perimeters to encompass populations sharing common characteristics, enabling adaptive targeting without manual intervention.
Solution Approach 2:
The system implements feedback loops by continuously receiving geolocation and contextual data from portable electronic devices, analyzing this information to identify common elements among user populations, and using these insights to dynamically adjust geofence boundaries. This closed-loop process ensures the geofences remain aligned with current user distributions and characteristics.
2Measurement precision
If geofence boundaries are adjusted frequently to track user movements, then targeting precision is improved, but computational resources and processing time increase
Solution Approach 1:
Instead of continuously adjusting geofence boundaries at every possible moment or in response to every location update, the system applies adjustments selectively based on significant changes in user population distributions. The system monitors for meaningful shifts in contextual information and geolocation patterns, triggering boundary redefinitions only when necessary to maintain accurate targeting, thus reducing unnecessary computational overhead.
3Measurement precision
If the system monitors contextual information from all users continuously, then user population identification accuracy is improved, but data processing load increases
Solution Approach 1:
The system extracts and focuses on specific contextual elements and common themes from user data rather than processing all raw information uniformly. By identifying and isolating key characteristics that define user populations (such as shared interests, behaviors, or attributes), the system reduces the effective data volume requiring intensive processing while maintaining accurate population identification.
Data Source
AI summary
In one embodiment, a method comprises receiving, via a mobile station, contextual information or geographic location data relating to a plurality of members of the population within the geographic region, identifying a common element in the received contextual information relating to at least two members of the population as a basis for defining a geofence to include the at least two members, wherein the common element is identified upon a comparison of the first and second contextual information, and defining the boundary of the geofence.