Configuration Attribute Optimization Using ML and Shortest-Path Search
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Solution Overview
Problem
Conventional methods for configuring entities with multiple attributes, such as telecommunications plans, are inefficient and resource-intensive, relying on manual and brute force approaches that fail to identify the optimal combination of configuration values for maximizing performance metrics like revenue and customer retention.
Innovation Solution
A system utilizing machine learning models and graph-based representations to predict and optimize configuration values, reducing computational complexity by identifying a set of configuration values that maximize efficiency based on specified priorities, using a graph to determine the shortest path that achieves the highest efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and brute force methods are used to select configuration options, then configuration accuracy is improved, but resource consumption and time overhead increase significantly
Solution Approach 1:
The patent replaces manual mechanical configuration selection with an automated machine learning system. The ML model predicts optimal configuration values by learning from historical data and patterns, substituting human manual analysis and brute-force computational searches with an intelligent automated system that achieves both accuracy and efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical configuration data and performance metrics before actual configuration selection is needed. This pre-computed knowledge enables rapid, accurate configuration recommendations without requiring resource-intensive real-time analysis or manual trial-and-error processes.
2Measurement precision
If manual adjustments and iterations are performed based on feedback, then configuration accuracy is improved, but time overhead increases significantly
Solution Approach 1:
The patent implements automated feedback loops where the machine learning model continuously learns from configuration performance data and customer metrics. The system automatically adjusts configuration recommendations based on observed outcomes, eliminating the need for manual feedback analysis and iterative adjustments while maintaining high configuration accuracy.
Solution Approach 2:
The configuration system serves itself by automatically generating, evaluating, and refining configuration recommendations without human intervention. The ML model autonomously processes performance feedback and updates its predictions, enabling the system to improve configuration accuracy independently without requiring manual time investment for analysis and adjustment.
3Measurement precision
If all possible plan configurations are evaluated, then optimal configuration identification is improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts and leverages patterns from historical configuration data and performance metrics to guide the search for optimal configurations. Instead of evaluating all possible configurations, the ML model identifies and focuses on promising configuration spaces based on learned patterns, extracting useful signals from historical data to reduce the evaluation scope while maintaining identification accuracy.
Solution Approach 2:
The system changes the approach from exhaustive configuration evaluation to predictive modeling. By transforming the problem from searching through configuration space to predicting optimal configurations based on learned parameter relationships, the patent reduces computational complexity from exponential to polynomial time while maintaining or improving optimal configuration identification.
Data Source
AI summary
Techniques for configuring an entity are disclosed. The techniques include generating, based on multiple sets of features inputted into a machine learning model, predictions of multiple sets of performance metrics for multiple configurations of the entity. The techniques also include calculating a change in efficiency between each configuration in the multiple configurations and the base configuration based on a first set of performance metrics for the base configuration and a second set of performance metrics generated by the machine learning model for the configuration. The techniques further include determining, based on values of the change in efficiency calculated for the multiple configurations, a second set of configuration values with a highest increase in efficiency over the base configuration. The techniques additionally include causing the second set of configuration values to be outputted in association with the entity.