Security Robot Threat Filtering for Selective Third-Party Alerts
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Solution Overview
Problem
Robotic vehicles have not been configured to intelligently determine when to notify a third party about potential threats to a target entity, leading to inefficiencies and potential desensitization due to false-positive notifications.
Innovation Solution
A robotic vehicle is equipped with detection and threat criteria to identify potential threats and selectively notify a third party based on predefined or dynamically established thresholds, using sensors and communication systems to capture and analyze audiovisual data, and perform actions upon receiving a command from the third party.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robotic vehicle notifies the third party of every detected object, then the third party is kept informed of all potential threats, but false-positive notifications cause alert fatigue and desensitization
Solution Approach 1:
The system changes the parameters of threat evaluation by introducing multiple criteria (detection criteria, threat criteria, notification criteria) with associated thresholds. Instead of notifying on any detection, the system evaluates objects against dynamic thresholds that adjust based on context, object characteristics, and threat level assessments, thereby filtering out false positives while maintaining reliable threat detection.
Solution Approach 2:
The system implements feedback loops where notification criteria are dynamically adjusted based on previous notifications and their outcomes. The third party's responses and the system's learning from detected patterns allow the thresholds and criteria to evolve, reducing false alarms over time while maintaining sensitivity to actual threats.
2Reliability
If the robotic vehicle uses multiple criteria and thresholds for threat assessment, then false-positive notifications are reduced, but the device complexity increases
Solution Approach 1:
The complex threat assessment system is segmented into distinct modular criteria: detection criteria for identifying objects, threat criteria for evaluating potential danger, and notification criteria for determining when to alert the third party. Each criterion has its own thresholds and evaluation logic, allowing the system to manage complexity through structured decomposition while maintaining high notification accuracy.
Solution Approach 2:
The thresholds and criteria are designed to be dynamic rather than static. The system can adjust detection thresholds, threat evaluation parameters, and notification criteria based on contextual factors, object characteristics, and learned patterns. This dynamic adaptation allows the complex system to optimize its performance automatically without requiring manual reconfiguration.
3Adaptability or versatility
If the robotic vehicle dynamically identifies and establishes detection and threat criteria, then the system adapts to different situations, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing template criteria and threshold ranges for common threat scenarios. When a new object is detected, the system quickly matches it against these pre-configured criteria rather than building evaluation frameworks from scratch. This allows dynamic adaptation to different situations while minimizing the time and computational resources required for criteria establishment.
Solution Approach 2:
The robotic vehicle's system serves itself by automatically identifying and establishing detection and threat criteria based on object characteristics and contextual information. The dynamic criteria system uses self-learning mechanisms where the vehicle accumulates experience from detected objects and automatically refines its evaluation parameters without external intervention, reducing processing time through automated adaptation.
Data Source
AI summary
Various methods for monitoring a target user by a robotic vehicle include tracking the target user by the robotic vehicle, detecting an object in the presence of the target user based on one or more detection criteria, determining whether the object is a potential threat to the target user based on one or more threat criteria, determining whether to notify the third party of the potential threat to the target user based on one or more notification criteria in response to determining that the object is a potential threat, and notifying the third party of the potential threat to the target user in response to determining that the third party should be notified.