Encoder-Driven Dynamic Machine Learning Frameworks for Model Consolidation
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Solution Overview
Problem
Existing predictive data analysis solutions face inefficiencies and reliability issues due to the need for multiple machine learning models and high storage costs associated with model definition data, particularly in biometric and biosimulator applications.
Innovation Solution
A dynamically parameterized machine learning framework using an encoder and decoder neural network to generate parameters for a target model, reducing the need for parallel implementations and normalizing outputs, thereby improving computational efficiency and reducing storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are used for predictive data analysis, then prediction accuracy and reliability are improved, but device complexity and storage requirements increase
Solution Approach 1:
The patent applies universality by creating a single machine learning model that can perform multiple prediction tasks through dynamic parameter adjustment. The model is designed to handle different prediction scenarios by modifying parameters such as time windows, feature selections, and model architecture configurations, eliminating the need for separate specialized models for each task.
Solution Approach 2:
The patent implements dynamics by enabling the machine learning model to adapt its parameters and structure in real-time based on the specific prediction task requirements. The system dynamically adjusts model parameters, time windows, and computational resources according to the input data characteristics and prediction objectives, allowing one model to effectively serve multiple purposes.
2Reliability
If multiple machine learning models are implemented in parallel, then prediction reliability is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent merges the functionality of multiple parallel models into a single integrated model that can handle diverse prediction tasks. By combining the strengths of different model approaches within one unified framework, the system achieves reliable predictions without the computational overhead of running multiple separate models simultaneously.
Solution Approach 2:
The system dynamically selects and adjusts model parameters based on the specific prediction task, avoiding the need for parallel model implementations. The dynamic parameter adjustment allows the single model to adapt its computational requirements to match the prediction reliability needs, optimizing the balance between reliability and computational efficiency.
3Reliability
If model definition data is stored for multiple machine learning models, then prediction accuracy is maintained, but storage costs increase
Solution Approach 1:
The patent creates a universal model framework that can perform multiple prediction tasks through parameter adjustment rather than requiring separate models. This universality eliminates the need to store definition data for multiple models, as a single model with configurable parameters suffices for various prediction scenarios.
Solution Approach 2:
The patent relies on parameter changes to adapt the model for different prediction tasks rather than storing multiple model definitions. By modifying parameters such as time windows, feature weights, and architectural configurations, the system achieves prediction accuracy equivalent to multiple specialized models without the storage cost of storing their definition data.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by dynamically parameterized machine learning frameworks, such as a dynamically parameterized machine learning framework comprising an encoder machine learning model that is configured to generate dynamically generated parameters for a target machine learning model of the dynamically parameterized machine learning framework.