Generative AI Model Parameters for Real-Time Stream Adaptation
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Solution Overview
Problem
Existing technologies face challenges in adapting machine learning models to learn from streaming data in an agile manner, particularly in wireless communication networks, due to varying data patterns and the need for real-time adaptation to changing data distributions.
Innovation Solution
A system utilizing a generative artificial intelligence model to detect change points in data streams and generate model parameters and normalization statistics for a machine learning model, enabling it to learn from and adapt to variable data patterns in real-time, facilitating predictions such as network traffic metrics, interference levels, and dynamic spectrum allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used to learn from streaming data, then the model can process data in real-time, but the model fails to adapt to varying data patterns and distributions
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating model parameters and normalization statistics as new data segments arrive. The system detects change points in data patterns and adjusts the machine learning model accordingly, transforming it from a static to a dynamic system that evolves with incoming data while maintaining prediction reliability through controlled adaptation mechanisms.
Solution Approach 2:
The patent changes the parameters of the machine learning model and normalization statistics based on detected data pattern shifts. By modifying model parameters and normalization constants in response to change points, the system adapts to varying data distributions while preserving the core model structure and ensuring reliable predictions through parameter evolution rather than complete model replacement.
2Loss of information
If machine learning models store raw data for learning, then the model can learn from historical patterns, but data storage requirements increase and privacy concerns arise
Solution Approach 1:
The patent extracts only the essential learning information from raw data streams by computing and storing normalization statistics (such as mean and standard deviation) and model parameters instead of retaining the actual raw data. This extraction approach allows the model to learn historical patterns and adapt to new data while minimizing storage requirements and eliminating privacy concerns associated with storing sensitive raw data.
Solution Approach 2:
The patent creates simplified copies of the essential data characteristics through normalization statistics and model parameters rather than storing the complete raw data sets. These statistical copies capture the necessary information for pattern recognition and model adaptation while occupying minimal storage space and containing no personally identifiable or sensitive information from the original data streams.
3Reliability
If the machine learning model is updated frequently to adapt to new data patterns, then the model remains accurate, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the continuous data stream into discrete segments and processes each segment independently to update model parameters and normalization statistics. This segmentation allows the system to perform incremental updates at controlled intervals rather than continuous complex computations, maintaining prediction accuracy through regular updates while reducing overall computational complexity by processing smaller, manageable data portions separately.
Solution Approach 2:
The patent performs partial updates of model parameters and normalization statistics based on each data segment rather than complete model retraining. This partial action approach maintains sufficient prediction accuracy by updating only the necessary components incrementally, significantly reducing computational complexity compared to full model retraining while still adapting to new data patterns effectively.
Data Source
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AI summary
Disclosed is a method comprising receiving a data stream divided into segments with variable data patterns; detecting a change point in the data stream, the change point corresponding to a shift in the data patterns; generating, based on the detection, by utilizing a generative artificial intelligence model, model parameters and normalization statistics for a machine learning model based on one or more previously learned segments of the data stream, the machine learning model being configured at least to learn from the data stream; updating the machine learning model based on the model parameters and the normalization statistics generated with the generative artificial intelligence model; and performing one or more predictions with the updated machine learning model.