Communication Platform Account Scoring for Robocall Detection
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Solution Overview
Problem
Communication services platforms face challenges in identifying and preventing non-compliant voice calls, such as robocalls, to comply with acceptable use policies and reduce resource consumption.
Innovation Solution
Implementing systems and methods that analyze voice call metrics, including average duration, answer-seizure ratio, and interaction with do-not-call and robocall surveillance systems, to identify potentially non-compliant accounts and take mitigating actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the communication services platform monitors and analyzes voice call metrics to identify non-compliant accounts, then the ability to detect and prevent robocalls improves, but the computational resources and processing time required increase
Solution Approach 1:
The system segments the voice call monitoring process into distinct analytical components: answer-seizure ratio calculation, average duration analysis, and do-not-call list matching. Each metric is computed independently and then integrated to form a comprehensive compliance assessment, allowing for optimized resource allocation across different detection functions
Solution Approach 2:
The system performs preliminary filtering by comparing call metrics against pre-established thresholds and do-not-call lists before initiating full account analysis. Accounts that clearly meet compliance criteria are quickly identified and excluded from further intensive processing, reducing overall computational burden while maintaining detection accuracy
2Reliability
If the platform implements comprehensive voice call monitoring and analysis of multiple metrics, then compliance with acceptable use policies improves, but the complexity of the system increases
Solution Approach 1:
The system employs a universal analytical framework that processes multiple voice call metrics (answer-seizure ratio, average duration, call frequency) through a single integrated compliance assessment engine. This multi-functional approach ensures comprehensive policy compliance checking while avoiding the need for separate specialized systems for each metric type
Solution Approach 2:
The system implements continuous feedback loops where compliance assessment results are fed back into the monitoring process. Identified non-compliant accounts trigger automated responses and updated monitoring parameters, creating a self-regulating system that adapts to emerging compliance patterns without requiring manual intervention or system redesign
3Object-generated harmful factors
If the communication services platform takes immediate mitigating actions against identified non-compliant accounts, then the prevention of harmful robocalls improves, but the potential for false positives and user disruption increases
Solution Approach 1:
The system applies preliminary compliance filtering by comparing voice call metrics against established compliance thresholds before taking mitigating actions. Accounts are assessed against multiple criteria (answer-seizure ratio, average duration, do-not-call list matches) to build a compliance profile that predicts non-compliant behavior, allowing preventive action against true violators while minimizing disruption to legitimate users
Data Source
AI summary
A method for identifying accounts used to make noncompliant calls (e.g., robocalls or voice spam calls) includes obtaining, by a communication services platform, one or more metrics for voice calls associated with a first identifier of a first communication endpoint. The one or more metrics include an average call duration for the first identifier or a predetermined signaling protocol code. The method includes determining, based on the metrics, a risk score associated with the first identifier. The method includes performing, based on the risk score, a corrective action with respect to an account of the communication services platform. The account can be associated with the first communication endpoint.