Machine Learning Model for Unobservable Capacity Prediction
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Solution Overview
Problem
Existing approaches to assessing the capacity of systems or entities for handling tasks are inaccurate and do not consider the dynamic nature of these systems, as they rely on observable outputs and rule-based comparisons.
Innovation Solution
The use of machine learning models trained with predictor variables, performance indicators, and task quantities to predict unobservable capacity, where the loss function is defined to minimize penalties for both capacity deficits and excesses, allowing for a more accurate and dynamic estimation of capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to predict observable outputs only, then the model training is straightforward and reliable, but the model cannot predict unobservable capacity which is needed for accurate task assignment
Solution Approach 1:
The patent introduces an intermediary variable (capacity) that bridges the gap between observable inputs (predictor variables) and observable outputs (performance indicators). The machine learning model learns the relationship through this intermediary, allowing prediction of unobservable capacity from observable data without directly measuring capacity.
Solution Approach 2:
The patent replaces traditional rule-based capacity assessment mechanisms with a machine learning-based predictive system. Instead of using explicit rules and thresholds to determine capacity, the system uses trained neural networks to infer capacity from patterns in historical performance data.
2Ease of manufacture
If rule-based methods are used to assess system capacity, then the assessment process is simple and interpretable, but the accuracy is insufficient and does not capture dynamic system behavior
Solution Approach 1:
The patent transitions from static rule-based assessment to dynamic machine learning-based prediction. The model captures dynamic system behavior by learning from historical performance data under varying conditions, allowing capacity assessment to adapt to changing system states rather than relying on fixed rules.
Solution Approach 2:
The patent changes the fundamental parameters of the assessment system by moving from explicit rule parameters to learned model parameters. The machine learning model learns optimal decision boundaries and relationships from data, replacing hand-crafted rules with data-driven parameters that better reflect actual system behavior.
3Productivity
If tasks are assigned without accurate capacity prediction, then task assignment is simple and fast, but system failures occur and resource utilization is suboptimal
Solution Approach 1:
The patent performs capacity prediction as a preliminary action before task assignment. By estimating system capacity in advance using the trained machine learning model, the system can make informed task assignment decisions that prevent overload and failures, rather than reacting to failures after they occur.
Solution Approach 2:
The patent implements a feedback loop where performance indicators from executed tasks are fed back into the machine learning model to continuously refine capacity predictions. This feedback mechanism allows the system to learn from past performance and improve future task assignment decisions, reducing failures and optimizing resource utilization.
Data Source
AI summary
In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.