ML-Based Geo-Location Selection for Wireless Infrastructure
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Solution Overview
Problem
Current cellular network management systems struggle to accurately predict user payment behavior, service costs, and churn rates, leading to inefficient resource allocation and potential loss of high-value customers.
Innovation Solution
The implementation of machine learning models that analyze user data, including payment history, data usage, service costs, device type, data plan, relationship longevity, and demographic features, to estimate predicted payment amounts, service costs, and churn rates, thereby calculating a customer lifetime value (CLV) metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network management systems are used to monitor and manage cellular networks, then basic network operations can be maintained, but the systems cannot accurately predict user payment behavior, service costs, and churn rates, leading to inefficient resource allocation
Solution Approach 1:
The patent replaces traditional mechanical/network management systems with machine learning models that use artificial intelligence to predict user behavior. The ML models analyze multiple data features (payment history, data usage, service costs, device type, data plan, relationship longevity, and demographic features) to generate predictions about payment behavior, service costs, and churn rates, achieving high measurement precision without proportionally increasing system complexity
Solution Approach 2:
The patent introduces customer lifetime value (CLV) as an intermediary metric that synthesizes multiple prediction outputs (payment behavior, service costs, churn rates) into a single actionable indicator. This CLV metric serves as a mediator between complex ML predictions and practical network management decisions, enabling efficient resource allocation based on high-value customer identification
2Loss of energy
If bandwidth throttling is implemented to limit data usage and reduce costs, then short-term costs are reduced, but user experience deteriorates and churn rate increases
Solution Approach 1:
The patent applies differentiated quality treatment to different customer segments based on their CLV. High-value customers (high CLV) receive priority resource allocation, additional data usage quotas, and enhanced service quality, while lower-value customers receive standard or reduced service. This local quality approach ensures cost reduction measures do not negatively impact high-value customers' retention
Solution Approach 2:
The patent dynamically changes network resource allocation parameters (data usage quotas, bandwidth limits, service priorities) based on predicted customer behavior and CLV metrics. Instead of uniform throttling, the system adjusts parameters individually for each customer segment, allocating additional data quotas to high-value customers predicted to churn, thereby maintaining their retention while controlling overall costs
3Area of stationary object
If network infrastructure is expanded without data-driven location selection, then network coverage is improved, but resource allocation efficiency decreases and profitability is reduced
Solution Approach 1:
The patent performs preliminary analysis of aggregate CLV metrics for potential geolocation areas before making infrastructure expansion decisions. By pre-identifying high-value geolocations where customers are likely to reside or work based on their CLV scores, the network provider can plan and allocate resources for infrastructure expansion in advance, ensuring that new coverage areas will be profitable
Solution Approach 2:
The patent creates a universal CLV-based decision framework that can be applied across multiple geolocations and infrastructure types (cell towers, retail locations, distribution centers). This multi-functional approach allows the same ML model and CLV metric to guide various expansion decisions, improving resource allocation efficiency across the entire network while expanding coverage strategically
Data Source
AI summary
A method includes estimating using one or more machine learning models, for each wireless subscriber of a plurality of wireless subscribers, a score that is indicative of an expected profitability associated with the corresponding wireless subscriber. The method also includes identifying a geolocation, wherein within a pre-defined radius from the geo-location, there is at least a threshold data usage level by a subset of the plurality of wireless subscribers. The method also includes determining an aggregate profitability metric associated with the geolocation based upon the estimated scores corresponding to wireless subscribers included in the subset of the plurality of wireless subscribers. The method also includes determining that the aggregate profitability metric associated with the geolocation satisfies a threshold condition, and responsive to determining that the aggregate profitability metric associated with the geolocation satisfies the threshold condition, selecting the identified geolocation as a candidate location for wireless network infrastructure.