Network Service Availability Prediction Across Providers and Service Levels
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Solution Overview
Problem
Determining network connectivity options and service levels at different locations is inefficient due to varying availability and unreliable pre-qualification data from multiple service providers, leading to significant delays and inaccuracies.
Innovation Solution
A system utilizing multiple machine learning models trained on pre-qualification and actual installation data to predict service availability and levels, reducing reliance on service provider responses by using a combination of first, second, and third machine learning models to identify suitable service providers and technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-qualification data is obtained from multiple service providers to determine network service availability, then the accuracy and completeness of service information is improved, but the time required to receive responses and make decisions is significantly increased
Solution Approach 1:
The system performs preliminary actions by collecting pre-qualification data from multiple service providers in advance and storing it in a database before it is needed. This allows the machine learning model to be trained on historical data and make predictions without requiring real-time responses from service providers, thus resolving the contradiction between obtaining accurate information and the time it takes to receive it.
Solution Approach 2:
The patent creates a copy of the service availability determination process by training a machine learning model on historical pre-qualification data. The model learns patterns from past data and can predict service availability without actually contacting service providers in real-time, effectively copying the decision-making process and eliminating the time delay associated with obtaining actual pre-qualification responses.
2Adaptability or versatility
If pre-qualification data is collected from multiple service providers for different locations, then the coverage and applicability of service information is improved, but the complexity of coordinating communication among systems increases
Solution Approach 1:
The patent introduces a centralized database as an intermediary that collects and stores pre-qualification data from multiple service providers. This intermediary component simplifies the coordination complexity by providing a single repository that the machine learning model can access, eliminating the need for complex real-time communication protocols between multiple systems while maintaining comprehensive service coverage.
Solution Approach 2:
The patent extracts the coordination complexity from the real-time communication process by separating data collection (historical pre-qualification data stored in database) from data usage (machine learning predictions). This extraction allows the system to handle diverse service information across multiple locations without requiring complex coordinated communication at the time of prediction.
3Productivity
If machine learning models are trained on historical pre-qualification data to predict service availability, then the speed of prediction is improved, but the accuracy may be reduced due to data sparsity or imprecision
Solution Approach 1:
The patent applies parameter changes by transforming the raw pre-qualification data into features suitable for machine learning training. The system processes historical data to extract relevant parameters and patterns, adjusting the data representation to improve prediction accuracy while maintaining fast prediction speeds. This parameter transformation allows the model to work effectively even with sparse or imprecise historical data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining availability of network service. In some implementations, a request indicating a location and a communication service level is received. A first subset of service providers or communication technologies is determined based on outputs generated by multiple first machine learning models each trained to predict service availability for different service providers or communication technologies. A second subset is selected from the first subset based on outputs generated by multiple second machine learning models trained to predict availability of different communication service levels for different service providers or communication technologies. At least one service provider or communication technology is selected from the second subset based on output generated by a third machine learning model. A response to the request indicating the selected service provider or communication technology is provided.