Voice Communications System Acoustic Ringback Analysis
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Solution Overview
Problem
Contemporary techniques for identifying call devices and geographical locations are often circumvented or unsupported, leading to inaccuracies in fraud detection, as malicious actors can spoof caller IDs and geographical indicators, and existing methods may not accurately verify device identities or locations.
Innovation Solution
A voice communications computer system identifies call devices by analyzing frequency components in ring signals, comparing them to stored identification characteristics, and generating fraud estimation data to determine the likelihood of fraudulent calls, thereby improving identification accuracy and reducing spoofing attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FSK encoding techniques are used to identify call devices, then device identification can be achieved, but the technique may be circumvented or unavailable
Solution Approach 1:
The patent replaces the mechanical/electronic FSK encoding system with an acoustic-based identification system. The call device's speaker generates audible ringback tones that pass through the telephone network's acoustic channel, and the microphone captures these tones for analysis. This acoustic substitution bypasses the need for FSK encoding infrastructure while achieving device identification through analysis of the acoustic characteristics of the ringback signal.
2Ease of manufacture
If traditional identification methods are used, then implementation is simple, but fraud detection accuracy is insufficient due to spoofing
Solution Approach 1:
The patent uses acoustic fingerprinting to create a unique 'acoustic signature' or 'color' for each call device. By analyzing the frequency spectrum, harmonics, and temporal characteristics of the ringback signal, the system generates an acoustic profile that is unique to each device's speaker and acoustic path. This acoustic 'color' cannot be easily spoofed and provides robust fraud detection while maintaining implementation feasibility through standard audio processing techniques.
3Measurement precision
If frequency component analysis is performed on ring signals, then device identification accuracy improves, but analysis complexity increases
Solution Approach 1:
The patent segments the frequency analysis into distinct processing stages: (1) capturing the ringback signal through the microphone, (2) performing Fourier transform to obtain frequency spectrum, (3) identifying dominant frequency components and harmonics, (4) extracting temporal characteristics, and (5) comparing against stored profiles. This segmentation of the analysis process reduces overall complexity by breaking down the sophisticated frequency analysis into manageable, sequential processing steps that can be implemented with standard audio processing tools.
Data Source
AI summary
A voice communications computer system (“VCCS”) receives a ring signal from a call device having unverified device identification data. The VCCS identifies an audible frequency component and an electronic frequency component of the ring signal. The VCCS identifies a device identification characteristic or a geographic location characteristic based on the audible or electronic frequency components, and identifies a stored identification characteristic associated with the device identification data. Based on a comparison of the stored identification characteristic with the device identification characteristic or geographic location characteristic, the VCCS generates fraud estimation data. In some cases, the VCCS generates call status data based on the fraud estimation data. The VCCS provides the fraud estimation data or the call status data to a user interface device, which is configured to display data or perform a call action for a call associated with the ring signal.