Query-Adaptive Machine Learning Models With Supplemental Data
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Solution Overview
Problem
Current language models do not customize their activation or use to the particular needs of user queries, leading to inaccurate responses, especially when external data is required, and lack post-processing to ensure confidentiality and quality.
Innovation Solution
A system that modifies the activation pattern of a machine learning model based on user query needs, incorporates supplemental data, and performs post-processing to ensure accurate and confidential responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model processes all user queries using the same mechanism, then the system complexity is low and ease of operation is maintained, but the response accuracy and adaptability to different query types deteriorate
Solution Approach 1:
The patent segments the query processing system into multiple specialized machine learning models, each trained to handle specific types of queries (e.g., classification models, extraction models, generation models). This segmentation allows each model to specialize in particular query types, improving adaptability and response accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent applies local quality by assigning different processing mechanisms and model configurations to different query types. Each query type receives customized processing with appropriate models activated based on the specific requirements, rather than applying a uniform processing mechanism to all queries. This enables optimized performance for each query category.
2Measurement precision
If supplemental data is integrated into the machine learning model input, then the response accuracy and completeness improve, but the data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing supplemental data before it reaches the machine learning models. Data is retrieved, validated, and structured in advance based on query type predictions, reducing the computational burden during actual query processing and minimizing additional processing time while maintaining improved accuracy.
Solution Approach 2:
The patent applies partial action by selectively retrieving and processing only the supplemental data that is relevant to the specific query type and context. Rather than always processing all available supplemental data, the system determines the minimum necessary data required to answer each query accurately, reducing processing time while maintaining response quality.
3Reliability
If post-processing is performed to ensure confidentiality and quality standards, then the response reliability and confidentiality are improved, but the processing time and system complexity increase
Solution Approach 1:
The patent implements feedback mechanisms in post-processing where the system evaluates generated responses against confidentiality and quality standards, then iteratively refines the output. Automated validation checks and confidence scoring provide feedback loops that ensure reliable responses while maintaining manageable processing complexity through systematic evaluation protocols.
4Measurement precision
If multiple specialized machine learning models are used for different query types, then the response accuracy for complex queries improves, but the device complexity and model management difficulty increase
Solution Approach 1:
The patent applies universality by designing a unified query processing framework that manages multiple specialized models through a common interface and coordination mechanism. The system architecture provides multi-functional capabilities, handling classification, extraction, generation, and validation through integrated model management that simplifies coordination while maintaining the specialized capabilities of individual models.
Data Source
AI summary
Systems and methods for customizing a machine learning model for responding to a user query. The system receives a user query from a user of a conversational program. The system determines a set of model requirements for a first machine learning model. Based on the set of model requirements, the system determines first supplemental data corresponding to the required input data and a formatting requirement corresponding to an activation pattern. The system performs pre-processing to obtain the first supplemental data from a first supporting software program and generates the first response using the first machine learning model.