Dynamic ML Model Reconfiguration for Context-Specific Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models face challenges when predicting across a wide range of contexts, leading to models that are either too large or ineffective due to contradictory data, resulting in increased false positives and negatives.
Innovation Solution
A system that dynamically reconfigures machine learning models based on external data, allowing for the use of multiple models trained for specific contexts, deployed in standby mode, and configured through a configuration database to process prediction requests efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is trained to cover a wide range of contexts, then the model can handle diverse prediction scenarios, but the model becomes too large and unwieldy
Solution Approach 1:
The patent divides a single large model into multiple smaller context-specific models. Each model is trained on data from a specific context (e.g., home network, vacation, business travel), and the system selects the appropriate model based on the current context. This segmentation reduces the size of each individual model while maintaining overall prediction coverage across diverse scenarios.
2Adaptability or versatility
If a model is trained with contradictory data from different contexts, then the model can potentially handle all contexts, but the model becomes ineffective with all contexts due to conflicting patterns
Solution Approach 1:
The patent applies local quality by training each model with data specific to its designated context, allowing each model to develop specialized patterns optimized for that context. For example, the home network model learns patterns specific to household devices, while the vacation model learns patterns from travel locations. This context-specific training eliminates the contradiction problem by ensuring each model operates within its domain of expertise.
Solution Approach 2:
The system dynamically selects which model to use based on the current context, determined through context classification. This dynamic approach allows the system to adapt to changing conditions by switching between models rather than relying on a single static model that must accommodate all contexts simultaneously.
3Reliability
If multiple context-specific models are maintained, then prediction accuracy for each context improves, but the system complexity increases
Solution Approach 1:
The system performs preliminary context classification before making predictions. By first determining the current context (e.g., home, vacation, business) and then selecting the appropriate pre-trained model, the system avoids the complexity of managing multiple models simultaneously. The context classification step acts as a gateway that simplifies model selection and management.
4Reliability
If a model is optimized for a particular context, then prediction accuracy for that context improves, but the model becomes ineffective for other contexts
Solution Approach 1:
The patent creates a universal prediction system that incorporates multiple context-specific models. While each individual model is optimized for a particular context, the overall system achieves universality by providing prediction capabilities across multiple contexts through model selection. The system acts as a multi-functional platform that routes different contexts to appropriate specialized models.
Data Source
AI summary
Systems and methods of reconfiguring machine learning models are disclosed. A method includes receiving, by a server computer, external data and determining, by the server computer, a machine learning model from a plurality of machine learning models, based on the external data. The plurality of machine learning models are in standby mode. The server computer configures an AI engine with the machine learning model. The method also includes receiving, by the server computer, a prediction request from a client computer, processing the prediction request with the AI engine to form a prediction response, and sending the prediction response to the client computer.