ML Architecture for Resource Allocation Prediction
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Solution Overview
Problem
Current machine learning architectures struggle to accurately predict resource allocations over time, particularly for recurring resources like electricity consumption, leading to inefficiencies in resource management and potential overdrawn or insufficient resource pools.
Innovation Solution
A machine learning architecture utilizing a residual long short-term memory (LSTM) network with self-attention mechanisms processes historical resource allocation data to generate prospective resource allocations, allowing for dynamic weighted averages of prior observations and user-adjustable parameters to control resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning architectures are used for resource allocation prediction, then the system is simpler to implement, but prediction accuracy deteriorates leading to inefficiencies in resource management
Solution Approach 1:
The model segments the resource allocation prediction task into two distinct components: amount prediction (how much resource to allocate) and date prediction (when the allocation will occur). This segmentation allows each component to be optimized independently, improving overall prediction accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent transitions from single-dimensional prediction (only amount or only date) to multi-dimensional prediction by simultaneously modeling both amount and date dimensions. This dimensional expansion captures the temporal and quantitative aspects of recurring resource allocations, significantly improving prediction accuracy for recurring resources like electricity consumption.
2Measurement precision
If dynamic weighted averages of prior observations are used, then prediction accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing historical resource allocation data and training the machine learning model offline before deployment. This preliminary training phase computes the dynamic weighted averages and optimizes model parameters in advance, reducing real-time computational requirements while maintaining high prediction accuracy during actual resource allocation operations.
3Adaptability or versatility
If user-adjustable parameters are implemented for controlling resource consumption, then user control and management capability is improved, but system complexity increases
Solution Approach 1:
The patent implements user-adjustable parameters that allow users to modify key variables such as prediction time horizons, resource allocation thresholds, and consumption control limits. By providing intuitive parameter adjustment capabilities, the system enhances user control and adaptability while managing complexity through standardized parameter interfaces and automated processing of user inputs.
Data Source
AI summary
A system and method for machine learning architecture for prospective resource allocations are described. The method may include: receiving data records representing historical resource allocations from a user account associated with a first identifier to a resource account associated with a second identifier; deriving input features based on the data records; computing, using a trained neural network architecture, a predicted resource allocation amount and a predicted resource allocation date for the predicted resource allocation amount based on the derived input features; determining, using the trained neural network architecture, a first selection score associated with the predicted resource allocation amount and a second selection score associated with the predicted resource allocation date; and when the first or second selection score is above a minimum threshold, causing to display, at a display device, the associated resource allocation amount or date corresponding to the second identifier.