Robocall Detection Using Machine Learning Pattern Analysis
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Solution Overview
Problem
Current methods for detecting and classifying robocalls are inefficient and costly, relying on large, proprietary databases that require active customer feedback and are vulnerable to robocaller number changes, and do not effectively mitigate disruption without access to these databases.
Innovation Solution
A system using machine learning to identify robocalls based on call characteristics and patterns, storing suspect numbers in a compact, frequently updated database that does not require proprietary database access, allowing for real-time classification and mitigation of robocalls without customer reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proprietary databases of known robocaller numbers are used for detection, then detection accuracy is improved, but system cost and complexity increase due to database licensing and maintenance
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between raw call data and detection decisions. This model processes call characteristics and patterns to generate detection results without requiring direct access to proprietary databases, thereby reducing system complexity and licensing costs while maintaining detection accuracy through learned patterns from training data
Solution Approach 2:
The patent replaces the mechanical database lookup system with a machine learning-based detection system. Instead of mechanically querying and matching against proprietary databases, the system uses trained ML models to analyze call characteristics and patterns, substituting the mechanical database access mechanism with an intelligent inference mechanism that reduces dependency on external proprietary resources
2Measurement precision
If customer feedback reporting is required for database maintenance, then database quality is improved, but loss of time and productivity increase due to reliance on active customer reporting
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using historical call data and customer feedback. The ML models are trained in advance to recognize robocall patterns, so when actual calls are processed, the system can immediately apply these pre-learned patterns without waiting for new customer reports. This preliminary training phase separates database quality improvement from real-time detection, eliminating the time delay
Solution Approach 2:
The system implements self-service by enabling the machine learning model to automatically learn and adapt to new robocall patterns from incoming call data. The model continuously improves its detection capabilities by processing new calls and updating its internal representations, reducing dependency on manual customer feedback collection and processing while maintaining high database quality
3Measurement precision
If large proprietary databases are maintained by each carrier or service, then detection coverage is improved, but loss of information occurs as databases are fragmented and proprietary
Solution Approach 1:
The patent creates a universal machine learning model that can be deployed across multiple carriers and services. The model is trained on diverse call data and learns generalizable robocall patterns that apply across different networks and regions. This universal model replaces multiple fragmented proprietary databases, enabling information sharing and consistent detection coverage across all deploying entities without requiring each to maintain separate databases
Solution Approach 2:
The patent merges multiple fragmented detection approaches into a single unified machine learning system. By combining training data from multiple sources and creating a centralized model architecture, the system consolidates the functionality of multiple proprietary databases into one shared resource that all entities can access, eliminating information loss due to fragmentation while maintaining comprehensive detection coverage
4Speed
If machine learning models are trained offline and deployed for real-time detection, then processing speed is improved, but manufacturing precision decreases due to model training complexity
Solution Approach 1:
The patent segments the detection system into two distinct phases: an offline training phase and an online inference phase. The complex model training process is separated from real-time call processing, allowing sophisticated ML models to be trained with high precision using extensive historical data without impacting real-time performance. Once trained, the models are deployed for fast real-time detection, achieving both high processing speed and training precision through temporal segmentation
Data Source
AI summary
The present invention relates to methods, systems and apparatus for identifying and acting upon suspect robocalls. An exemplary method embodiment includes the steps of processing call records of a customer to identify calls which are possibly from a robocaller, based on at least one of i) a call characteristic or ii) a call pattern; storing calling party source identification information of the identified calls in a suspect robocall database; processing an incoming call, said processing including comparing calling party source identification information of an incoming call to the calling party source identification information in the suspect robocall database; and completing the incoming call in a standard manner if the incoming call is not in the suspect robocall database; and handling the call as a suspect robocall if the incoming calling party source identification information is in the suspect robocall database.