User Device Grouping by Proximity for Accurate User Counting
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Solution Overview
Problem
Existing methods inaccurately estimate the number of individuals in a location by counting multiple user devices instead of users, and provide unnecessary content to multiple devices associated with the same user, wasting network resources.
Innovation Solution
A computer-implemented method that monitors the locations of user devices and groups them based on proximity, associating multiple devices with a single user when they are within a threshold distance of each other for a threshold period of time, allowing for accurate user counting and targeted content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple user devices are counted to estimate the number of individuals in a location, then device detection is simplified, but user counting accuracy deteriorates because multiple devices may belong to a single user
Solution Approach 1:
The system dynamically groups user devices based on real-time location data, continuously monitoring device proximity and adjusting groups as devices move in or out of threshold distances. This dynamic approach allows accurate user counting while adapting to changing spatial relationships without requiring complex static classification rules
Solution Approach 2:
The server acts as an intermediary that receives location information from multiple user devices, processes proximity relationships, and determines group associations. This intermediary approach centralizes the complexity of grouping logic while keeping individual device functionality simple, resolving the contradiction between counting accuracy and system complexity
2Loss of energy
If content is provided to all user devices in a location, then content delivery is simplified, but network resource usage deteriorates due to redundant content delivery to multiple devices associated with the same user
Solution Approach 1:
The system merges multiple user devices into single user groups based on proximity thresholds, then delivers content to one representative device per group rather than all devices. This combining approach eliminates redundant content delivery while maintaining ease of operation through automated group management and representative device selection
3Measurement precision
If user devices are grouped based on proximity monitoring, then user identification accuracy is improved, but processing time and computational resources deteriorate due to continuous location monitoring
Solution Approach 1:
The system monitors location data at optimized intervals and only processes grouping logic when devices enter or exit threshold distances, rather than continuously recalculating all device relationships. This partial monitoring approach maintains accurate user identification while significantly reducing processing time and computational resource consumption
Data Source
AI summary
A computer-implemented method includes: monitoring, by a computing device, respective locations of a plurality of user devices; determining, by the computing device, that a particular group of user devices of the plurality of user devices are located within a threshold distance of each other based on monitoring the respective locations of the plurality of user devices; and storing or outputting, by the computing device, grouping information indicating that the particular group of user devices are associated with a single user based on the determining that the particular group of user devices are located within a threshold distance of each other.