Automated Telecom Plan Management Robot
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Solution Overview
Problem
Companies face challenges in managing telecom plans effectively, particularly with shared data pools, as usage can vary significantly from month to month, leading to potential overage charges and increased costs.
Innovation Solution
A system and method that utilize machine learning algorithms to monitor telecommunications usage and automatically modify rate plans to optimize pooled usage, ensuring cost savings by right-sizing plans dynamically within the billing month.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed data pool plan is purchased at the beginning of the billing cycle, then the plan provides stable coverage for data usage, but it cannot adapt to varying monthly usage patterns leading to overage charges or wasted capacity
Solution Approach 1:
The patent implements dynamic plan adjustment by allowing the data pool allocation to be automatically modified during the billing cycle based on real-time usage monitoring and machine learning predictions. The system transitions from static fixed plans to dynamic adjustable plans, enabling the pool size to expand or contract according to actual usage patterns while maintaining operational simplicity through automation.
Solution Approach 2:
The system employs machine learning algorithms that autonomously analyze usage data, predict future consumption patterns, and automatically adjust plan allocations without requiring manual intervention. The robotic process automation independently makes optimization decisions, allowing the system to self-manage the complexity of dynamic plan adjustment while delivering adaptability to usage variations.
2Productivity
If manual monitoring and adjustment of data pool plans is performed, then some optimization can be achieved, but it requires significant time and effort that cannot keep pace with real-time usage variations
Solution Approach 1:
The patent replaces manual mechanical processes of monitoring and adjusting plans with an automated robotic system that uses machine learning algorithms. This substitution eliminates the time-consuming manual effort while achieving real-time optimization, as the automated system continuously monitors usage data and instantly adjusts plan allocations based on predictive analytics without human intervention delays.
Solution Approach 2:
The system implements continuous feedback loops where usage data is constantly monitored, analyzed by machine learning models, and used to automatically adjust plan allocations. This closed-loop feedback mechanism enables rapid response to usage variations, achieving high productivity in plan adjustment while minimizing the time loss associated with manual monitoring through automated real-time optimization.
3Reliability
If a larger data pool is allocated to accommodate peak usage, then overage charges are avoided, but capacity is wasted during low-usage periods increasing overall costs
Solution Approach 1:
The patent dynamically changes the parameter of data pool size based on real-time usage monitoring and machine learning predictions. Instead of maintaining a fixed large pool to ensure coverage during peak usage, the system continuously adjusts the pool size parameter to match actual demand, ensuring adequate coverage when needed while minimizing wasted capacity during low-usage periods through automated optimization.
Solution Approach 2:
The system transitions from static data pool allocation to dynamic adjustment, allowing the pool size to flexibly expand during high-usage periods and contract during low-usage periods. This dynamic approach maintains reliability by ensuring sufficient capacity when needed while reducing waste by scaling down the allocated pool size during periods of lower demand, all managed through automated real-time optimization.
Data Source
AI summary
A system and computer implemented method which determines usage and available rate plans, at least some of which are pool eligible. Based on expected usage for a billing cycle, the system and method modifies rate plans for one or more devices to migrate devices in and out of the pool(s) and/or modify plans and data available to the pool in order to reduce cost of telecommunications services for the devices.