Adaptive M2M Billing via Usage Learning
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Solution Overview
Problem
Existing M2M/IoT billing systems lack accuracy and flexibility, requiring customer feedback to adjust billing plans, and fail to identify new billing opportunities effectively.
Innovation Solution
A computer-implemented method and system that provides a guaranteed price plan, learns from network device usage over a predetermined time period, and adapts to determine a new pricing plan, minimizing customer feedback and identifying new billing opportunities by using network elements like Packet Data Network Gateways, Traffic Control Functions, and Authentication and Accounting servers in 4G and 5G networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a guaranteed price plan is provided to customers for M2M/IoT networks, then billing accuracy is improved, but the system lacks flexibility and requires customer feedback to adjust plans
Solution Approach 1:
The system implements automated feedback loops where usage data from network elements is continuously collected and fed back to the billing system. This enables the system to automatically adjust and learn optimal pricing plans without requiring manual customer feedback, resolving the contradiction between billing accuracy and plan flexibility
Solution Approach 2:
The billing system performs self-learning and self-adjustment by automatically analyzing usage patterns from network devices and generating optimized pricing plans. This eliminates the need for customer intervention while maintaining both accuracy and flexibility in billing plan adaptation
2Ease of manufacture
If traditional billing systems are used for M2M/IoT networks, then implementation is straightforward, but they fail to identify new billing opportunities effectively
Solution Approach 1:
The system performs preliminary learning and analysis of usage patterns before finalizing billing plans. By pre-processing and analyzing data from network elements in advance, the system identifies billing opportunities proactively rather than reactively, improving productivity while maintaining implementation feasibility
Solution Approach 2:
The billing system transitions from static, predefined plans to dynamic, adaptive pricing that automatically adjusts based on learned usage patterns. This dynamic approach enables continuous identification of new billing opportunities while building upon traditional billing infrastructure
3Measurement precision
If customer feedback is required to adjust billing plans, then plan accuracy is maintained, but customer feedback reduction is needed for scalability
Solution Approach 1:
The system introduces automated learning algorithms and data analytics as intermediaries between usage data and billing plan adjustment. This intermediary layer processes and interprets usage patterns automatically, maintaining pricing accuracy while eliminating the need for direct customer feedback and reducing time delays
Data Source
AI summary
A computer implemented method, system and computer readable medium for use in machine to machine (M2M) or internet of Things (IoT) network including network devices are disclosed. The computer implemented method comprises providing a guaranteed price plan to a customer for a predetermined time period and learning about network device usage over the predetermined time period to provide a learned price plan. The computer implemented method also comprises determining a new price plan based on the learned price plan. Finally, the computer implemented method comprises utilizing the new price plan with customer's network devices.