Dynamic ML Model Updates via Configuration Files
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Solution Overview
Problem
Existing machine learning models for generating conversational responses face challenges in accurately determining user intent in real-time, requiring large amounts of labeled training data and being prone to errors due to unsupervised learning and the need for frequent updates.
Innovation Solution
The use of a configuration file that defines modifications and changes to the machine learning model, allowing for dynamic updates and improvements, such as through a mutation algorithm, to enhance the model's ability to determine user intent and generate timely and pertinent responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained to determine user intent with high precision, then measurement precision improves, but loss of time increases due to extensive training data requirements and long learning periods
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline with extensive labeled training data before deployment. The models are trained in advance to recognize user intents across multiple categories, so that during real-time operation, the pre-learned knowledge can be quickly applied without requiring extensive training time during actual use.
Solution Approach 2:
The patent implements dynamics through configurable parameters that allow the system to adapt between different operating modes. The system can dynamically adjust the balance between using pre-trained model predictions versus performing additional real-time analysis, enabling flexibility in responding to different computational resource availability and accuracy requirements during deployment.
2Adaptability or versatility
If machine learning models use unsupervised learning to detect patterns, then adaptability improves, but reliability worsens due to inability to explain learned patterns and increased error proneness
Solution Approach 1:
The patent applies segmentation by dividing the intent classification task into multiple distinct categories and training separate machine learning models for each intent category. This segmentation allows the system to focus on detecting specific, well-defined patterns for each category rather than attempting to detect all patterns in a single unsupervised model, thereby improving both reliability and interpretability while maintaining adaptability through the multi-category approach.
3Adaptability or versatility
If machine learning models are updated frequently to reflect new intents, then adaptability improves, but device complexity increases due to multiple updates and retraining processes
Solution Approach 1:
The patent applies preliminary action by preparing and training models for multiple intent categories in advance during an offline phase. Configuration files containing labeled training data for various intent categories are created beforehand, allowing the system to quickly adapt to new intents by loading pre-configured data and models rather than performing complex retraining processes in real-time.
Solution Approach 2:
The patent implements parameter changes through configurable parameters that control model behavior and intent classification thresholds. By adjusting these parameters, the system can adapt to new intents and categories without requiring fundamental changes to the model architecture or extensive retraining, thereby reducing deployment complexity while maintaining high adaptability.
Data Source
AI summary
Methods and systems for dynamically updating machine learning models that provide conversational responses through the use of a configuration file that defines modifications and changes to the machine learning model are disclosed. For example, the configuration file may be used to define an expected behavior and required attributes for instituting modifications and changes (e.g., via a mutation algorithm) to the machine learning model.