Voice-Command Authentication with Covert Duress Detection
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Solution Overview
Problem
Existing IVR systems lack the ability for users to covertly signal duress during transactions and initiate secure alarm protocols, and fail to automatically inform emergency services of the user's location and trigger necessary security responses.
Innovation Solution
A voice-command based authentication system with a user-definable emergency response, utilizing a mobile device to receive gestures and voice inputs, access legacy information, and trigger an AI engine to generate emergency responses when thresholds are exceeded, including communication channels to security centers or law enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IVR systems provide automated tools for interacting with callers, then the system can efficiently handle routine transactions, but the system lacks the ability to detect duress situations and initiate emergency responses
Solution Approach 1:
The system performs preliminary actions by establishing baseline behavioral patterns during normal interactions and pre-configuring emergency response protocols. When duress is detected, the system has already prepared the necessary emergency procedures, allowing for rapid response without adding complex real-time decision-making infrastructure.
Solution Approach 2:
The system introduces an intermediary AI engine that acts as a mediator between the IVR system and emergency services. This intermediary analyzes caller behavior, detects duress situations, and triggers appropriate emergency responses, thereby adding emergency capability without directly complicating the core IVR transaction processing architecture.
2Reliability
If the system monitors user inputs to detect duress, then emergency situations can be identified, but the complexity of analyzing and responding to various input patterns increases
Solution Approach 1:
The system employs self-service mechanisms by having the AI engine automatically analyze caller inputs, compare them against established baselines, and autonomously determine whether duress is present. This self-analyzing capability reduces the need for complex external monitoring systems while maintaining high detection accuracy through continuous behavioral pattern recognition.
Solution Approach 2:
The system implements feedback loops where the AI engine continuously monitors caller responses, compares them to baseline behavior, and adjusts its detection algorithms in real-time. This feedback mechanism enables accurate duress detection without requiring overly complex upfront system design, as the system learns and adapts during normal operations.
3Reliability
If the system automatically triggers emergency responses and contacts security centers, then user safety is enhanced, but the system requires integration with multiple external emergency services and protocols
Solution Approach 1:
The system achieves universality by designing a multi-functional AI engine that can handle multiple emergency scenarios (duress, medical emergencies, security threats) through a single integrated platform. This engine can interface with various emergency services (police, fire, medical) using standardized protocols, thereby enhancing user safety without requiring separate specialized systems for each emergency type.
4Reliability
If the system processes user transactions and simultaneously monitors for emergency signals, then comprehensive protection is provided, but the processing time and system resources increase
Solution Approach 1:
The system maintains continuity of useful action by performing emergency signal monitoring continuously during normal transaction processing rather than as a separate step. The AI engine analyzes caller inputs in real-time as part of the ongoing interaction, allowing the system to provide comprehensive protection without adding discrete processing delays to transaction completion.
Data Source
AI summary
A method triggers a user-definable emergency response in a voice-command based authentication system. The method receives gestures on a mobile device, and provides an interface at the mobile device to trigger an automatic interaction with an artificial intelligence (“AI”) engine stored on the mobile device. The AI accesses a database for storing legacy information associated with the caller and runs an application program interface that provides the AI engine access to the legacy information. The AI engine receives the gestures generated by the caller, accesses the database storing legacy information generated by the caller and constructs an emergency profile of the caller. The emergency profile may be based on the legacy information, a machine learning model; and/or a user training session that initializes an emergency response signal threshold level. When the gestures exceed the emergency response signal threshold level, the method may generate the emergency response.