Spam Detection via Aggregated Communication Analysis
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Solution Overview
Problem
Current speech recognition systems are ineffective in identifying and mitigating spam communications, as users' blocking of spam numbers does not share across systems, and spammers use sophisticated methods to disguise their location, making it difficult to distinguish legitimate from spam communications.
Innovation Solution
A system that ingests blocked communications lists and contact lists to identify spam numbers by analyzing frequency and user markings, and uses audio characteristics to determine overlapping communications from the same source, thereby flagging and mitigating spam activity across a distributed network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speech recognition systems process individual user communications independently, then each user can block spam numbers locally, but blocking information does not share across the distributed network, allowing spammers to evade detection
Solution Approach 1:
The patent merges isolated user-level blocking data into a network-wide spam detection system. Multiple user communications are aggregated and analyzed collectively, allowing the system to identify spam patterns that individual users cannot detect alone. This combining approach enables shared blocking information across the distributed network while maintaining speech recognition processing.
Solution Approach 2:
The system creates a universal spam detection mechanism that serves all users in the distributed network. The speech recognition system performs multiple functions: individual user communication processing, network-wide spam pattern detection, and shared blocking information distribution. This multi-functionality allows blocking information to be shared across the network while maintaining individual user processing capabilities.
2Object-affected harmful factors
If spammers use sophisticated methods to disguise their location and communication patterns, then they can evade individual user blocking, but this increases the difficulty of distinguishing spam from legitimate communications across the network
Solution Approach 1:
The patent implements feedback mechanisms where the speech recognition system continuously learns from aggregated user communications and blocking decisions. The system analyzes patterns across multiple users, receives feedback about blocked spam communications, and refines its detection algorithms. This feedback loop enables the system to adapt to sophisticated spammer disguise methods by identifying patterns that individual users cannot detect.
Solution Approach 2:
The system performs excessive analysis by examining multiple dimensions of communications beyond what individual users need. It aggregates and analyzes communication patterns, audio characteristics, and metadata from across the network, even though individual users only need to block specific numbers. This excessive action enables detection of sophisticated spam patterns through collective intelligence.
3Measurement precision
If the system aggregates and analyzes communications from multiple users to identify spam patterns, then spam detection accuracy improves, but the system complexity and processing requirements increase
Solution Approach 1:
The patent segments the spam detection function into distributed components across the network. Each user's speech recognition system performs local processing and blocking, while spam pattern aggregation and analysis are distributed across multiple nodes. This segmentation maintains high detection accuracy through collective intelligence while reducing the complexity burden on any single system component.
Data Source
AI summary
Techniques for detecting spam accounts in a system are described. The system may analyze speech characteristics of communication content (e.g., telephone call content, VoIP content, audio messaging, etc.) to determine whether multiple devices or user profiles are associated with overlapping communications. The system may also analyze text transcriptions of communication content to determine whether multiple devices or user profiles are associated with overlapping communications. If so, the system may mitigate, such as throttling service, disabling accounts, and the like.