Network Service Growth Forecasting with Propensity Models
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Solution Overview
Problem
Current techniques for forecasting network service deployment face challenges in accurately predicting customer growth due to diverse technologies, capacity constraints, and dynamic market conditions, leading to excessive capital investment or missed opportunities for improved service performance.
Innovation Solution
A prediction system using machine learning and state transition models to quantify growth opportunities by processing consumer data, determining propensities, and calculating probabilities for network service utilization, allowing for optimized deployment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting techniques are used for network service deployment, then capital investment decisions can be made, but forecasting accuracy deteriorates leading to excessive capital investment or missed opportunities
Solution Approach 1:
The patent replaces traditional mechanical forecasting techniques (spreadsheets, manual analysis) with an AI-based machine learning system that automatically processes consumer data, determines propensities, and calculates probabilities for network service utilization. This substitution enables accurate forecasting of customer growth and network service adoption, allowing ISPs to make precise capital investment decisions without overestimating or underestimating potential utilization.
Solution Approach 2:
The patent introduces an intermediary AI prediction system that acts as a mediator between raw consumer data and capital investment decisions. This system processes consumer characteristics, behaviors, and network service data through propensity models and state transition models to generate accurate forecasts, serving as a bridge that transforms unprocessed data into actionable investment insights.
2Productivity
If traditional forecasting techniques are used, then deployment decisions can be made, but resource consumption increases due to inaccurate forecasting requiring repeated analysis
Solution Approach 1:
The patent performs preliminary action by pre-processing consumer data and training propensity models in advance using historical data. The system pre-calculates propensities and state transition probabilities, so when deployment decisions are needed, the AI system can quickly generate accurate forecasts without requiring repeated intensive analysis of raw data, thereby reducing computing resource consumption during decision-making cycles.
Solution Approach 2:
The patent creates a computational copy of consumer behavior patterns through machine learning models that replicate real-world consumer decision-making processes. These models capture the essence of consumer propensities and state transitions, allowing the system to simulate and predict future scenarios without repeatedly processing actual consumer data, thus reducing computing resource usage while maintaining forecast accuracy.
Data Source
AI summary
A device may receive data identifying characteristics, location dependent characteristics, and a network service associated with target consumers, such as households or businesses, and may process the data, with one or more propensity models, to determine propensities of the target consumers to utilize the network service. The device may process the data and the propensities, with a state transition model, to calculate probabilities that the target households will utilize the network service, and may determine utilization states of the target consumers over time based on the probabilities that the target consumers will utilize the network service. The device may aggregate the utilization states of the target consumers to determine penetration rates for the network service, and may perform one or more actions based on the penetration rates for the network service.