Predictive Access Control Using Movement Pattern Analysis
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Solution Overview
Problem
Existing access control systems fail to provide secure and seamless access to physical resources as they rely solely on proximity and identity verification, lacking the ability to predictively grant access based on user movement patterns, which can lead to unintended access by unauthorized users.
Innovation Solution
An access control system that evaluates user movement data to predict access attempts by analyzing current and prior path and movement patterns, using machine learning models and beacon data to authenticate and authorize access without the need for proximity sensors or biometric inputs, allowing automatic unlocking of resources when a high likelihood of access is determined.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access control systems rely solely on proximity and identity verification, then unauthorized users may gain unintended access, but the system lacks the ability to predictively grant access based on user movement patterns
Solution Approach 1:
The system performs preliminary analysis of movement patterns and predicts access intent before the user actually reaches the resource. By evaluating historical and current movement data in advance, the system determines whether to grant access proactively, rather than reactively responding only when the user is at the door.
Solution Approach 2:
The system dynamically adjusts access decisions based on real-time movement pattern analysis. Instead of static proximity-based access, the system continuously evaluates changing movement characteristics to determine access intent, allowing flexible and context-aware access control.
2Ease of operation
If the system automatically unlocks resources when a user approaches, then authorized users gain seamless access, but unauthorized users nearby may also gain access
Solution Approach 1:
The system uses feedback from movement pattern analysis to make informed access decisions. By continuously monitoring and evaluating movement characteristics against established patterns, the system receives feedback on whether the current approach matches legitimate access behavior, enabling secure automatic access only when patterns confirm intent.
Solution Approach 2:
The system introduces movement pattern analysis as an intermediary layer between proximity detection and access grant. This intermediary evaluation step analyzes whether the proximity is due to legitimate access intent or merely incidental presence, preventing unauthorized access while maintaining convenient access for authorized users.
3Reliability
If the system requires manual input or biometric verification for access, then security is maintained, but the access process becomes intrusive and less seamless
Solution Approach 1:
The system performs self-service access control by automatically analyzing movement patterns and making access decisions without requiring user intervention. The system serves itself by evaluating its own collected movement data to determine access intent, eliminating the need for manual codes, cards, or biometric inputs while maintaining security.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for predictively providing access to resources. In some implementations, a method includes receiving movement data indicating movement of a mobile device associated with a user while the mobile device approaches a resource is received. A credential of the user authorizes access to the resource. Based on the movement data, the movement of the mobile device is classified as corresponding to an attempt to access the resource. The mobile device is determined to be in proximity to the resource. Before the user interacts with the resource, the resource is caused to be unlocked or opened in response to determining that the credential of the user authorizes access to the resource, classifying the movement of the mobile device as corresponding to an attempt to access the resource, and determining that the mobile device is in proximity to the resource.