Context Nexus Query Handling via ML Embeddings
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Solution Overview
Problem
Automated query response systems fail to provide accurate responses to queries that deviate from expected patterns and struggle to maintain context across multiple related queries, relying on naive regex techniques and lacking context-aware search capabilities.
Innovation Solution
Implementing unsupervised machine learning models to compute contextual parameters for query embeddings, enabling context-informed searches in an n-dimensional space, and updating context nexus parameters based on user inputs to generate accurate and relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If regex or similar search techniques are used for automated query response, then the system operation is simple, but the system fails to provide responses to non-conforming queries and cannot maintain context across multiple queries
Solution Approach 1:
The patent replaces traditional regex-based mechanical search techniques with machine learning models that compute contextual parameters and perform semantic analysis. This substitution enables the system to handle non-conforming queries by understanding their intent rather than relying on pattern matching, directly resolving the contradiction between handling versatility and system complexity.
Solution Approach 2:
The system transforms query processing from fixed pattern matching to dynamic parameter-based search. By computing contextual parameters (category, sub-category, entity type) from query embeddings and using these to dynamically generate search logic, the system adapts to various query formats while maintaining manageable complexity through structured parameter handling.
2Loss of information
If traditional search techniques are used, then the system structure is simple, but the system cannot identify or maintain context of multiple related queries
Solution Approach 1:
The patent introduces context parameters as intermediary elements that bridge multiple queries. By computing and storing contextual parameters (category, sub-category, entity type) for each query and using them to inform subsequent searches, the system maintains context across query sequences without requiring complex state management, as the context is encoded in reusable parameters.
Solution Approach 2:
The system performs preliminary computation of contextual parameters from query embeddings before executing searches. This advance preparation of context information enables the system to maintain and leverage context across multiple queries, as the contextual understanding is established beforehand and can be applied to subsequent query processing.
3Measurement precision
If machine learning-based context generation is implemented, then predictive accuracy and response relevance improve, but computational complexity increases
Solution Approach 1:
The patent segments the complex task of query understanding into distinct computational stages: computing query embeddings, extracting contextual parameters (category, sub-category, entity type), generating search logic, and executing searches. This segmentation manages computational complexity by breaking down the ML process into manageable, modular steps while maintaining high predictive accuracy through comprehensive analysis at each stage.
Data Source
AI summary
A system and method for improving a predictive accuracy of a machine learning-based virtual conversational agent that includes computing an antecedent context nexus based on query embeddings computed for a preceding query input by a user, wherein the antecedent context nexus includes a pairing of a categorial parameter and a sub-categorical parameter derived based on the query embeddings of the preceding query; creating search logic parameters based on the categorical parameter and the sub-categorical parameter of the antecedent context nexus; executing a context nexus-informed search of a corpus of structured data using at least the search logic parameters; extracting a response candidate from the corpus of structured data based on the execution of the context nexus-informed search; constructing a response to the preceding query based on the extracted response candidate; and returning, via a user interface, the response to the preceding query.