Mobile Device Stay Detection Using Proximity and Duration Thresholds
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Solution Overview
Problem
Current location-based services require manual user input for check-ins and location tracking, leading to check-in fatigue and limited accuracy in determining user presence at specific locations, which hinders automatic monitoring and sharing of user stays.
Innovation Solution
A method and system for automatically detecting and sharing user stays on a mobile device by persistently collecting location information, determining user stays based on proximity and duration, and sharing this information with designated groups, allowing users to set privacy preferences for public or private notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual check-in is used, then user control over location sharing is maintained, but user fatigue increases and automation is reduced
Solution Approach 1:
The system performs automatic location monitoring and stay detection without requiring user intervention. The mobile device autonomously collects location data, determines when the user is staying at a location based on proximity and duration thresholds, and shares this information with contacts, eliminating manual check-in operations entirely
Solution Approach 2:
The system pre-configures sharing rules, contact groups, and location thresholds in advance. When a stay is detected, the predetermined rules automatically trigger notification sharing without requiring real-time user decisions, reducing operational burden while maintaining automation
2Adaptability or versatility
If geo-fencing and background location monitoring are used, then automatic stay detection is enabled, but the system cannot handle new places not in the existing database
Solution Approach 1:
The system transitions from relying solely on a predefined location database to using spatial dimension (coordinates) combined with temporal dimension (duration of stay). By calculating distance from coordinates and comparing against duration thresholds, the system can identify stays at any location regardless of whether it exists in a database, adding dimensional flexibility to the detection mechanism
3Measurement precision
If location information is persistently collected, then accurate user stay determination is achieved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic location sampling combined with threshold-based triggers. Location updates are collected at intervals, and stay detection is triggered only when proximity and duration thresholds are met, reducing unnecessary processing and energy consumption while maintaining detection accuracy
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on context. Location update frequency and detection thresholds can be modified based on user preferences, historical behavior patterns, and current activity states, optimizing the balance between measurement precision and energy consumption for different scenarios
Data Source
AI summary
Methods, systems and apparatuses for automatically detecting and sharing user stays of a mobile device of a user are disclosed. One method includes persistently collecting location information, determining a group of users for sharing the user stays, determining whether the mobile device is within a specific distance of a specific location for at least specific minimum duration based on the location information, determining a user stay based on the location information and the specific distance, determining if the user stay is private or public to at least a portion of the group of users based on preferences of the user and the location information, and sharing the user stay with the at least the portion of the group of users by sending notifications to the group of users if the user stay is determined to be public to the at least the portion of the group of users.