Contextual Device Unlocking via Temporal and Proximity Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current device unlocking methods require manual input or stringent security measures, which can reduce usability and increase the risk of inappropriate access, especially in familiar secure locations where the user's presence can be trusted.
Innovation Solution
A device learns contextual information over time to determine secure locations and automatically unlock when the authorized user is present, using a combination of temporal, location, and proximity data, while locking when the user is not nearby to maintain security in unfamiliar areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual unlocking methods are used, then security is maintained, but usability deteriorates
Solution Approach 1:
The unlocking method transitions from static manual requirement to dynamic contextual-based automatic unlocking. The system continuously monitors contextual information (location, time, device state) and adjusts unlocking behavior accordingly, allowing automatic unlocking in secure contexts while maintaining manual requirement in uncertain situations.
Solution Approach 2:
The device performs self-validation by automatically monitoring its own contextual information and making unlocking decisions without requiring user intervention. The system monitors its location, time, and state to determine when automatic unlocking is appropriate, serving itself rather than requiring constant user authentication.
2Ease of operation
If automatic unlocking is implemented, then usability is improved, but security deteriorates
Solution Approach 1:
The system continuously monitors contextual information and uses this feedback to adjust unlocking behavior. By monitoring location, time, and device state, the system receives feedback about the current situation and decides whether automatic unlocking is appropriate, thereby maintaining security while enabling usability improvements.
Solution Approach 2:
The system changes the parameters for unlocking based on contextual conditions. Instead of a fixed unlocking requirement, the system adjusts the unlocking behavior based on parameters such as location, time of day, device state, and historical patterns, allowing automatic unlocking when parameters indicate security is not compromised.
3Measurement precision
If contextual monitoring is continuous, then unlocking accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system performs periodic sampling of contextual information at intervals. The system monitors location, time, and device state at regular intervals rather than continuously, reducing energy consumption while maintaining sufficient accuracy for unlocking decisions.
Solution Approach 2:
The system monitors only the necessary contextual parameters required for unlocking decisions rather than all possible data. By selectively monitoring location, time, and device state, the system performs partial monitoring that is sufficient for accurate unlocking while minimizing energy consumption.
Data Source
AI summary
Examples associated with contextual device unlocking are described. One example storing sets of contextual state information associated with unlock events associated with a device. A first contextual state of the device is detected. The first contextual state of the device is compared to sets of contextual state information. The device is unlocked based on the comparison of the first contextual state of device to the sets of contextual state information when the device is in a secure location with a nearby authorized user.