Adaptive M2M Billing via Usage Learning

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Solution Overview

Problem

Existing M2M billing systems lack accuracy and flexibility, requiring customer feedback and manual adjustments, which delays billing adjustments and misses opportunities for optimizing pricing based on network element usage.

Innovation Solution

A computer-implemented method and system that provides a guaranteed price plan for a predetermined time period, learns network element usage to create a new price plan, and adapts pricing dynamically, minimizing customer feedback and identifying new billing opportunities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a guaranteed price plan is provided for a predetermined time period, then billing accuracy is improved, but flexibility to adjust pricing based on usage patterns deteriorates

Engineering Contradiction:

Improvebilling accuracyVSAvoidpricing flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system transitions from static guaranteed price plans to dynamic adaptive pricing by implementing a learning mechanism that automatically adjusts price plans based on monitored network element usage patterns. The billing system evolves from fixed predetermined pricing to flexible pricing that adapts in real-time based on actual usage data collected during the guaranteed price period.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements a feedback loop where network element usage is continuously monitored and fed back to the billing system. This usage data serves as input for the learning mechanism that generates optimized price plans, creating a closed-loop system where billing accuracy improves while maintaining adaptability through automatic adjustment based on actual usage patterns.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If manual adjustments and customer feedback are required for billing changes, then pricing accuracy is improved, but processing time and system efficiency deteriorate

Engineering Contradiction:

Improvepricing accuracyVSAvoidbilling processing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The billing system performs self-adjustment through an automated learning mechanism that monitors usage data and generates optimized price plans without requiring manual intervention. The system serves itself by automatically detecting usage patterns, calculating optimal pricing, and implementing price plan adjustments, thereby eliminating the need for manual billing adjustments and customer feedback loops.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces the mechanical process of manual billing adjustments and customer feedback with an automated electronic learning mechanism. The learning algorithm processes usage data and generates price plan optimizations automatically, substituting human manual operations with computational processes that improve both accuracy and processing efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If pricing is adjusted dynamically based on usage patterns, then billing system adaptability is improved, but system complexity deteriorates

Engineering Contradiction:

Improvebilling adaptabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The learning mechanism serves multiple functions within the billing system: it monitors network element usage, analyzes usage patterns, generates optimized price plans, and implements pricing adjustments. This multi-functional component handles diverse billing adaptability requirements through a single unified system, managing complexity by consolidating multiple functions rather than creating separate systems for each function.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If learning mechanisms are implemented to create new price plans, then billing optimization opportunities are improved, but computational requirements and processing load deteriorate

Engineering Contradiction:

Improvebilling optimizationVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The learning mechanism implements partial action by focusing computational resources on analyzing only the specific usage patterns that impact billing optimization, rather than processing all possible data. The system applies learning algorithms selectively to network element usage data that is most relevant for price plan optimization, reducing unnecessary computational energy consumption while maintaining effective billing optimization.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11039017B2Adaptive M2M billing

Publication Date: 2021.06.15 AERIS COMM INC

AI summary

A computer implemented method is disclosed. The computer implemented method comprises providing a guaranteed price plan to a customer for a predetermined time period and learning about network element 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 elements.