Real-Time Fraud Distress Detection and Security Control
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Solution Overview
Problem
Individuals receiving fraudulent calls often experience distress, leading to the disclosure of personal information, and existing technologies lack effective real-time solutions to mitigate this distress.
Innovation Solution
A system that uses a processor to detect incoming calls, analyze conversations in real-time, monitor physical metrics, and classify calls as fraud risks, triggering notifications and security controls such as muting or transferring calls to prevent further distress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time conversation analysis and physical metric monitoring are implemented to detect fraud risk and distress, then the ability to detect and respond to fraudulent calls is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The system divides fraud detection into multiple independent modules: conversation analysis module, physical metric monitoring module, distress detection module, and security control module. Each module processes specific aspects separately, improving detection accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges the callee's device and the support individual. This intermediary analyzes conversations and physical metrics, detects distress signals, and coordinates security controls, thereby improving reliability without directly increasing the callee's device complexity.
2Object-affected harmful factors
If multiple security controls (mute, interrupt prompt, forced disconnect, automated transfer) are activated during fraud risk calls, then the protection of the callee is improved, but the extent of automation and system control increases
Solution Approach 1:
The system pre-configures multiple security controls (mute, interrupt prompt, forced disconnect, automated transfer) that can be rapidly deployed based on detected distress signals. This preliminary preparation allows immediate protection without complex real-time decision-making, reducing the perceived automation complexity while maintaining high protection levels.
Solution Approach 2:
The security controls are designed to be dynamically activated based on the severity and type of distress detected. The system adjusts the level and type of automated intervention according to real-time conditions, allowing flexible protection that adapts to different fraud scenarios without requiring fixed, overly complex automation rules.
3Measurement precision
If biometric data is collected from sensors and wearable devices to monitor physical metrics, then the precision of distress detection is improved, but the quantity of data processed and privacy concerns increase
Solution Approach 1:
The system extracts only the essential physical metrics (heart rate, skin conductance, temperature) needed for distress detection from the available biometric data. By selecting and monitoring only the most relevant parameters rather than processing all available biometric information, the system achieves high measurement precision while minimizing data volume and privacy concerns.
4Speed
If the system selects and notifies an individual associated with the callee in real-time based on availability, then the responsiveness of distress assistance is improved, but the complexity of individual selection and notification increases
Solution Approach 1:
The system pre-establishes a list of associated individuals and their availability status before distress events occur. When distress is detected, the system quickly matches the situation with the most appropriate pre-identified contact, enabling rapid notification without complex real-time selection algorithms, thus improving speed while managing complexity.
Data Source
AI summary
Disclosed aspects and embodiments pertain to customer distress assistance. A call can be detected to a callee from a caller. The call can be analyzed and determined to be a fraud or spam risk. In response, callee monitoring is triggered. The monitoring can capture biometrics or speech of the callee. Distress in the callee can be determined through analysis of the biometrics or speech and comparison to reference biometrics or speech of the callee. An individual associated with the callee can be selected based on determining the distress in the customer. The individual can be contacted through a notification or a call to assist the callee in distress.