Multi-Dimensional Machine Learning for Resource Value Prediction
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Solution Overview
Problem
Conventional resource management techniques fail to accurately determine resource-related values, leading to inefficiencies and losses in resource-related processes.
Innovation Solution
Utilizing multi-dimensional machine learning techniques to predict resource-related values by processing data through a machine learning-based prediction engine, which includes a multi-output neural network trained on historical data to estimate resource values and automate actions based on predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional resource management techniques are used, then resource management processes are simple and easy to implement, but accuracy in determining resource-related values is poor leading to losses and inefficiencies
Solution Approach 1:
The patent replaces conventional resource management techniques with machine learning-based prediction techniques. Specifically, it uses neural network models and other ML algorithms to predict resource-related values, substituting traditional manual or rule-based methods with intelligent automated systems that can accurately determine resource values, activity outcomes, and other parameters without requiring complex manual intervention
Solution Approach 2:
The system enables self-service through automated machine learning models that independently predict resource-related values and activity outcomes without human intervention. The models automatically process input data, generate predictions for resource values and activity metrics, and can trigger automated actions based on these predictions, allowing the system to serve itself in determining resource management parameters
2Reliability
If machine learning techniques are implemented, then prediction accuracy of resource values is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the resource management problem into multiple prediction tasks handled by separate machine learning models. It implements distinct models for predicting resource values, activity outcomes, and other parameters, allowing each model to specialize in specific predictions. This segmentation improves overall reliability while managing complexity by dividing the system into manageable, independent components
Solution Approach 2:
The system performs preliminary actions by pre-processing input data and preparing features before feeding them to prediction models. It includes data cleaning, feature engineering, and model training phases that occur before actual resource management decisions are made. This preliminary preparation improves prediction accuracy while organizing the complexity into structured, repeatable processes
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for predicting resource-related values using multi-dimensional machine learning-based techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one resource-related activity involving at least one resource; predicting one or more values associated with the at least one resource by processing at least a portion of the obtained data using one or more machine learning techniques; predicting one or more values attributed to the at least one resource-related activity by processing the at least a portion of the obtained data using the one or more machine learning techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more predicted values associated with the at least one resource and at least a portion of the one or more predicted values attributed to the at least one resource-related activity.