Dynamic Querying With ML Explainability for Data Drift Adaptation
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Solution Overview
Problem
Existing predictive models face challenges with dynamic data handling, data drift, and domain-specific knowledge interpretation, leading to performance deficiencies and limited adaptability in machine learning applications.
Innovation Solution
Integrating a machine learning pipeline with feature engineering, model explainability, and reinforcement training techniques to enhance model inputs and adapt to dynamic querying processes, enabling continuous improvement and targeted query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional batch processing is used for structured data, then processing simplicity is maintained, but the ability to capture complex dynamic situations and adapt to changing data trends deteriorates
Solution Approach 1:
The patent implements dynamic querying processes that continuously adapt to changing data trends and patterns. The system transitions from static batch processing to dynamic, real-time querying that automatically adjusts to new information, enabling the predictive model to maintain accuracy in evolving environments without requiring complete system redesign
Solution Approach 2:
The system incorporates feedback loops where query responses are analyzed and used to refine future queries and model predictions. This continuous feedback mechanism allows the system to learn from incoming data patterns and automatically adjust its processing approach, balancing adaptability with manageable complexity through iterative improvement
2Measurement precision
If generic algorithms are used in predictive models, then ease of implementation is improved, but the ability to leverage domain-specific knowledge and contextual information deteriorates
Solution Approach 1:
The patent applies local quality by tailoring the querying and processing approach to specific domain requirements and contextual needs. Different query strategies and processing methods are employed based on the particular domain knowledge and data characteristics, allowing high prediction accuracy in specialized fields while maintaining a unified system architecture that manages overall complexity
3Adaptability or versatility
If encoders and tokenizers are used to convert unstructured data to numerical format, then machine learning model compatibility is improved, but information loss and interpretation limitations occur
Solution Approach 1:
The system introduces an intermediary dynamic querying layer between unstructured data and the machine learning model. This intermediary process extracts and structures relevant information through targeted queries before model input, preserving more contextual meaning and reducing information loss compared to direct encoding, while still maintaining model compatibility through structured output formats
Data Source
AI summary
Various embodiments of the present disclosure provide a machine learning framework integrated within a dynamic querying process that improves the functionality of a computer in various aspects. The techniques comprise receiving a model input comprising a set of entity attributes and a set of initial query responses. The techniques comprise generating, using a machine learned model, a model prediction based on the model input and determining, based on the model prediction, an influential parameter from the first subset of independent parameters for the model prediction. The techniques comprise providing a set of subsequent queries based on the influential parameter to receive a set of subsequent query responses that correspond to a second subset of independent parameters and generating, using the machine learned model, an updated model prediction based on the model input and the set of subsequent query responses.