Adaptive Security Settings Based on Occupant Behavior Patterns
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Solution Overview
Problem
Security systems often fail to adapt to changes in occupant behavior, leading to inefficient operation and potential security breaches, as their settings remain static and unresponsive to evolving usage patterns.
Innovation Solution
A method that collects and analyzes security data from various system components to identify patterns and deviations, using AI to dynamically adjust security settings and levels in response to changes in occupant behavior, ensuring real-time adaptation and enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If security system settings remain static during operation, then system simplicity and ease of operation are maintained, but the system cannot adapt to changes in occupant behavior, reducing security effectiveness
Solution Approach 1:
The patent implements dynamic security settings that automatically adjust based on real-time analysis of occupant behavior patterns. The system transitions from static configuration to dynamic adaptation by continuously collecting security data, identifying behavioral patterns, and modifying security parameters (such as alarm thresholds, access control settings, or monitoring intensity) in response to detected changes in occupant behavior, thereby resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The security system performs self-adjustment by automatically analyzing its own operational data and making configuration changes without requiring manual intervention. The system collects data from its own components, processes this data through pattern recognition algorithms, and autonomously modifies its security settings, enabling the system to serve itself in adapting to behavioral changes while maintaining operational simplicity for users.
2Reliability
If security system settings are frequently adjusted to match changing behavior patterns, then security effectiveness is improved, but system stability and reliability deteriorate
Solution Approach 1:
The system performs preliminary analysis of behavioral changes before implementing security setting adjustments. By collecting and analyzing security data over time to identify established patterns before making changes, the system ensures that adjustments are based on confirmed behavioral shifts rather than transient anomalies, thereby maintaining settings stability while improving security effectiveness through timely adaptations.
Solution Approach 2:
The system implements a feedback mechanism where security settings are adjusted based on continuous monitoring and analysis of occupant behavior patterns. The feedback loop collects security data, compares actual behavior against expected patterns, and only triggers setting changes when significant deviations are confirmed, ensuring that adjustments improve security while maintaining overall system stability through controlled, evidence-based modifications.
3Productivity
If manual reconfiguration of security settings is required to adapt to behavior changes, then system reliability is maintained through human oversight, but productivity and responsiveness to security threats are reduced
Solution Approach 1:
The security system automatically performs the entire adaptation process including data collection, pattern recognition, and setting adjustment without requiring manual reconfiguration. This self-service capability enables rapid response to behavioral changes while maintaining operational simplicity, as the system handles all complex analysis and adjustment tasks autonomously, improving productivity without compromising ease of operation for users.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated electronic analysis and adjustment systems. Instead of requiring operators to physically reconfigure security settings in response to behavioral changes, the system uses electronic data processing, pattern recognition algorithms, and automated control mechanisms to detect changes and adjust settings, thereby dramatically improving response speed while maintaining user-friendly operation.
Data Source
AI summary
A method includes collecting over time security data from each of at least some of a plurality of security system components. A pattern is identified based at least in part on the collected security data, the pattern identifying an expected behavior of one or more occupants of the facility. Live security data is received from at least some of the plurality of security system components, and is compared with the identified pattern to identify when the current behavior of one or more occupants of the facility deviates from the expected behavior by more than a threshold. When the current behavior of one or more occupants of the facility deviates from the expected behavior by more than the threshold, one or more security system settings of the security system are changed and the security system is operated using the one or more changed security system settings.