Guard Feedback Training for Context-Aware Security AI Alerts
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Solution Overview
Problem
Existing AI systems struggle with false positives, performance variability due to scene changes, and inability to produce human-like outputs in subjective or contextual scenarios, particularly in physical security applications.
Innovation Solution
An AI system that integrates human guard feedback to improve event classification and escalation by tracking and evaluating human responses to security alerts, using predictive models and deep learning neural networks to refine decision-making based on guard interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If software-only AI systems are used for security analysis, then system complexity is reduced, but measurement precision and reliability deteriorate due to false positives and inability to handle subjective contextual scenarios
Solution Approach 1:
The patent combines software-only AI systems with human security guard analysis to create a hybrid system. The AI system processes security footage and generates alerts, while human guards review and validate these alerts. This merging of automated and human analysis improves detection accuracy by reducing false positives while maintaining system complexity at acceptable levels.
2Ease of operation
If software-only AI systems are used for security analysis, then ease of operation is improved, but reliability deteriorates due to performance variability and brittleness to scene changes
Solution Approach 1:
The patent implements a feedback mechanism where human guard responses to AI-generated alerts are collected and used to retrain and refine the AI model. This continuous feedback loop allows the system to adapt to different scene types and circumstances, improving performance consistency and reliability while maintaining ease of operation through automated processing.
3Productivity
If traditional AI systems are used without human feedback, then productivity is improved through automated analysis, but measurement precision deteriorates due to false alarms and inability to produce human-like outputs
Solution Approach 1:
The system maintains high productivity through automated AI analysis while improving alert accuracy by incorporating human guard feedback. Guards review AI-generated alerts and provide corrections or confirmations, which are then used to retrain the model. This feedback mechanism progressively improves measurement precision without significantly reducing analysis throughput.
Solution Approach 2:
Human security guards serve as an intermediary between the AI system and the final security decisions. The AI generates preliminary alerts, human guards validate and refine these alerts, and the combined output achieves higher accuracy. This intermediary role allows the system to maintain automated productivity while achieving human-like judgment quality.
Data Source
AI summary
A system and method for training an AI model. A recorded video is divided into video frames that are input and read by a processor that identifies objects in the video frames using the object's latent characteristics. The processor further classifies an event based on the identified object, the latent characteristics, and surrounding factors at the time the object is identified. Video frames are annotated based on the identified object and classified event. A user's responses to annotated frames are tracked and the latent characteristics are adjusted based on the user's responses.