Voice Call Content Detection for Sophisticated Scam Screening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing sophistication of scammers in spoofing telephone numbers and evading scam alerting mechanisms in telecommunication networks poses a challenge for network operators, who are held responsible for protecting subscribers from fraudulent calls.
Innovation Solution
Integrating a cloud service provider with a telecommunication operator's voice network to transcribe calls using speech-to-text technology and evaluate the transcript with a language model like GPT to determine fraudulent activity, enabling proactive notifications and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scam alerting mechanisms using telephone number databases are used, then the system is simple to implement, but it cannot detect sophisticated spoofing and has incomplete coverage of suspicious telephones
Solution Approach 1:
The patent introduces an intermediary cloud-based AI service that bridges the telecommunication operator's network and fraud detection capabilities. The cloud service provider receives call data, processes it through AI models, and returns fraud assessments to the operator, enabling sophisticated detection without requiring complex on-premises infrastructure.
Solution Approach 2:
The patent replaces traditional mechanical database lookup systems with AI-based machine learning models that analyze call content, metadata, and patterns. This substitution enables the system to detect sophisticated spoofing and evade techniques that traditional number databases cannot identify.
2Reliability
If AI-based content analysis is implemented, then fraud detection capability is enhanced, but processing time and computational resources increase
Solution Approach 1:
The patent segments the fraud detection process into multiple stages: initial filtering using simpler rules and heuristics, followed by AI-based analysis only for calls that pass the initial filter. This segmentation reduces the computational burden and processing time while maintaining high detection accuracy for fraudulent calls.
Solution Approach 2:
The patent applies partial action by analyzing only specific segments of call data (such as transcripts, metadata, and call patterns) rather than processing every aspect of the call in detail. This selective analysis reduces processing time while maintaining sufficient detection capability.
3Reliability
If cloud-based AI services are integrated with operator networks, then detection accuracy improves, but security and privacy of phone call content may be compromised
Solution Approach 1:
The patent uses a cloud service provider as an intermediary that processes call data externally to the operator's network. This intermediary approach allows the operator to benefit from advanced AI detection capabilities without directly storing or processing sensitive call content on their own infrastructure, thereby reducing security and privacy risks.
Solution Approach 2:
The patent extracts sensitive call content analysis from the operator's internal network and places it in a secure cloud environment. Only necessary metadata and anonymized call patterns are processed by the AI models, while the actual call content remains protected and is not stored permanently, reducing privacy risks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the ability of network operators to detect and respond to fraudulent calls effectively, providing timely notifications and preventing potential scams while maintaining security and privacy.
Implementation Method 1
transcribe calls (using, for example, speech-to-text technology)
Implementation Method 2
injected speech (e.g., via AI text-to-speech technology)
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Characteristics of a voice call in a telecommunications network are identified. A voice call that is in process in the telecommunications network is accessed and, for a specified segment of the voice call, a sample of the voice call is analyzed. Based on the analysis, a prompt is generated for input to a machine learning model. The prompt is usable to prompt the machine learning model to determine a likelihood that the voice call meets one or more characteristics. The prompt includes an example of a different voice call that meets the one or more characteristics.