Stress Detection Authentication for Distress-Aware Transactions
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Solution Overview
Problem
Current authentication mechanisms fail to consider a user's emotional and behavioral state during transactions, leading to regretted actions due to fear, intoxication, or impairment, as they rely solely on traditional financial standards like ISO-20022.
Innovation Solution
A system that utilizes various sensors to capture user behavior data, converts it into feature vectors, and applies machine-learning models to generate a stress signal, determining a response based on this signal to adjust user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication mechanisms are used, then transaction processing is simple and fast, but user safety and transaction integrity are compromised due to inability to detect emotional distress
Solution Approach 1:
The patent combines multiple authentication mechanisms including biometric sensors (camera, microphone, keystroke sensors), machine learning models for stress detection, and traditional financial verification systems into a unified authentication framework. This integration allows the system to simultaneously process behavioral data from multiple sources while maintaining transaction processing capabilities, thereby improving transaction integrity without completely overhauling the existing system architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process raw behavioral data from sensors and transform it into actionable stress indicators. These models act as mediators between the physical user state (detected by sensors) and the authentication decision-making process, enabling the system to interpret complex behavioral patterns without requiring direct human intervention or overly complex rule-based systems.
2Measurement precision
If multiple sensors and machine-learning models are deployed, then stress detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by continuously collecting and pre-processing behavioral data from sensors in the background before a transaction is initiated. The system maintains ready-state machine learning models that can immediately analyze incoming data streams, eliminating the need for lengthy data collection and processing periods during the actual transaction. This allows accurate stress detection to occur in real-time without significantly delaying transaction processing.
Solution Approach 2:
The patent employs dynamic processing where the system adjusts the level of analysis based on transaction context and detected stress levels. For low-risk transactions with normal stress indicators, the system uses streamlined processing paths. For high-risk transactions or those showing stress indicators, the system dynamically activates more comprehensive analysis protocols, thereby optimizing processing time while maintaining detection accuracy when needed most.
Data Source
AI summary
Systems and methods of stress detection of and action response via a set of operations including receiving a request from a user behavior data associated with the user. The operations further include converting the behavior data into one or more groups of feature vectors and applying the one or more groups of features vectors to one or more machine-learning models to generate a stress signal associated with the user. The operations include determining a stress response based on the stress signal and performing an action based in part on the request and the stress response.