Terms of Service Violation Detection via Multi-Tiered Enforcement
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Solution Overview
Problem
Current systems are inadequate in detecting and addressing violations of mobile network terms of service, such as tethering and unauthorized device usage, which result in significant financial losses for carriers and poor customer experiences.
Innovation Solution
Implementing a system that detects terms of service violations through data analysis and machine learning, scoring the severity of violations, and applying corrective actions such as throttling or terminating service, using a multi-tiered approach to enforce compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tethering detection methods are used, then detection simplicity is maintained, but detection accuracy and completeness deteriorate, resulting in missed violations and financial losses
Solution Approach 1:
The detection system is segmented into multiple independent analysis modules: traffic volume analysis, data usage pattern analysis, device behavior analysis, and machine learning classification. Each module processes specific aspects of network traffic independently, then combines results to achieve high detection accuracy without requiring a single complex detection system
Solution Approach 2:
A machine learning model serves as an intermediary between raw network traffic data and violation detection decisions. The model processes complex traffic patterns, device behaviors, and usage statistics to classify tethering violations, bridging the gap between simple data collection and accurate violation identification
2Loss of energy
If strict enforcement actions are taken immediately upon detecting violations, then network profitability is improved, but customer experience deteriorates due to lack of warning and appeal opportunities
Solution Approach 1:
The system performs preliminary actions by sending warning notifications to customers before applying enforcement measures. When tethering violations are detected, the system first issues warnings through multiple channels (SMS, in-app notifications, emails) giving customers opportunity to correct behavior before service termination or throttling occurs
Solution Approach 2:
The enforcement mechanism is made dynamic and adaptive rather than static. The system adjusts enforcement severity based on violation patterns, customer history, and current network conditions. First-time or minor violations receive warnings, while persistent or severe violations trigger progressively stricter enforcement actions
3Ease of operation
If multi-tiered enforcement actions are implemented, then customer experience is improved through progressive warnings, but enforcement time and processing complexity increase
Solution Approach 1:
Enforcement action templates and decision rules are pre-configured and stored in the system. When violations are detected, the system retrieves and executes pre-defined action sequences (warning notifications, service throttling, termination procedures) without requiring real-time complex decision-making, significantly reducing processing time
Solution Approach 2:
The system automatically executes the multi-tiered enforcement sequence without requiring manual intervention at each stage. Automated workflows handle warning generation, delivery tracking, and progression to stricter enforcement actions, eliminating manual processing delays while maintaining the progressive enforcement approach
4Productivity
If automated detection and enforcement systems are deployed, then operational efficiency is improved, but system complexity and implementation cost increase
Solution Approach 1:
The enforcement system is designed as a universal multi-functional platform that can detect and enforce multiple types of violations (tethering, unauthorized devices, data cap exceedances, roaming violations) through a single integrated system. This eliminates the need for separate detection and enforcement systems for each violation type, improving efficiency while managing complexity through consolidation
Solution Approach 2:
The system implements continuous feedback loops where detection results inform enforcement actions, which in turn generate new detection opportunities. Machine learning models learn from enforcement outcomes and traffic patterns to improve detection accuracy over time. This self-improving feedback mechanism increases operational efficiency while the automated nature reduces manual complexity
Data Source
AI summary
Aspects of the subject disclosure may include, for example, determining whether a past throughput of past traffic communicated between a communication device and a communication network meets a first threshold value, resulting in a first determination; responsive to the first determination being that the past throughput meets the first threshold, transmitting an instruction to the communication device to restrict a first subsequent throughput of first subsequent traffic to no greater than a second threshold value; determining whether the first subsequent throughput has been restricted, resulting in a second determination; and responsive to the second determination being that the first subsequent throughput has not been restricted, taking one or more actions to enforce a second subsequent throughput of second subsequent traffic to no greater than a third threshold value, wherein the third threshold value is lower than the second threshold value. Other embodiments are disclosed.