Server Orchestrated Fine Grain Copresence Detection
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Solution Overview
Problem
Existing systems for determining copresence between user devices are inefficient due to power-intensive GPS and other location detection methods, which can result in stale data and poor battery life, and are prone to inaccuracies and spoofing, especially indoors.
Innovation Solution
A system that uses a processor to transmit wakeup signals based on coarse grain location information, requesting devices to transmit tokens using various communication technologies like Bluetooth, Wi-Fi, or audio to determine fine grain copresence, refining the location determination without constant device activation, thereby reducing battery drain and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used to locate user devices, then location data can be obtained, but power consumption increases and data may become stale
Solution Approach 1:
The system implements periodic wake-up signals sent at intervals (e.g., every 5-30 minutes) to activate devices only when needed for location refinement, rather than continuously monitoring. This periodic activation maintains location accuracy while dramatically reducing power consumption compared to constant GPS operation.
Solution Approach 2:
The system performs preliminary coarse-grain location determination using low-power methods (cell tower triangulation, Wi-Fi scanning) to identify potential copresence. Only when coarse-grain analysis suggests devices might be copresent does the system activate high-precision fine-grain detection, avoiding unnecessary power consumption from continuous high-precision monitoring.
2Measurement precision
If GPS hardware is used for location detection, then location data can be obtained, but accuracy is insufficient for true copresence determination
Solution Approach 1:
The system segments location detection into two distinct stages: coarse-grain detection using GPS/cell towers for general area identification, and fine-grain detection using Bluetooth/audio/Wi-Fi for precise copresence verification. This segmentation allows each method to operate at its optimal accuracy level, with fine-grain methods confirming true copresence that GPS alone cannot reliably determine.
Solution Approach 2:
The system introduces intermediate detection methods (Bluetooth, audio signals, Wi-Fi) that act as mediators between coarse GPS location data and final copresence determination. These intermediary technologies provide the additional precision needed to confirm true copresence, bridging the gap between approximate GPS locations and accurate device-to-device proximity verification.
3Measurement precision
If GPS is used indoors, then location detection can continue, but signal availability decreases and accuracy drops
Solution Approach 1:
The system uses alternative detection methods that create copies or substitutes for GPS functionality in indoor environments. Bluetooth and audio-based detection create virtual location signatures that replicate the copresence detection capability of GPS, but work effectively indoors where GPS signals are blocked by building structures.
Solution Approach 2:
The system changes the detection parameters and physics when transitioning from outdoor to indoor environments. Instead of relying on satellite radio wave propagation (GPS), the system switches to methods utilizing different physical principles: Bluetooth radio propagation through building materials, audio wave propagation through air and structures, and Wi-Fi signal characteristics, all of which maintain effectiveness indoors where GPS fails.
4Measurement precision
If copresence detection technologies are constantly left running, then detection accuracy improves, but battery life deteriorates
Solution Approach 1:
The system implements periodic wake-up signals sent at intervals (e.g., every 5-30 minutes) to activate devices only when needed for location refinement, rather than continuously monitoring. This periodic activation maintains location accuracy while dramatically reducing power consumption compared to constant high-precision detection operation.
Solution Approach 2:
The system dynamically adjusts detection intensity and activation frequency based on contextual factors such as device movement patterns, time of day, and copresence probability. Detection resources are concentrated during periods when copresence is most likely and dispersed during low-probability periods, optimizing the balance between detection accuracy and battery conservation through adaptive, dynamic operation.
Data Source
AI summary
The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: transmit a wakeup signal to a plurality of devices based on coarse grain location information; send a request to a first device of the plurality of devices to transmit a token using a first communication technology to determine fine grain copresence; receive a first token acknowledgment from a first subset of the plurality of devices; send a request to a second device of the first subset of the plurality of devices to transmit the token using a second communication technology to determine fine grain copresence; receive a second token acknowledgment from a second subset of the plurality of devices; and refine copresence based on receiving the first and second token acknowledgment.