Graph Neural Network Query Response System
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Solution Overview
Problem
Conventional query response systems are inflexible and inaccurate in generating digital responses, as they rigidly rely on individual phrases and fail to identify relevant information across multiple text phrases or passages.
Innovation Solution
A graph neural network is used to generate a graph topology connecting text phrases and a query, allowing for iterative information propagation to identify supporting text phrases across passages, enabling flexible and accurate digital response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems rigidly generate digital responses based on individual phrases, then the system structure is simple, but the accuracy in identifying relevant support for digital responses deteriorates
Solution Approach 1:
The patent transitions from analyzing individual text phrases in isolation to representing phrases as nodes in a graph structure with inter-phraser relations as edges. This dimensional change from 1D (individual phrases) to 2D/3D (graph topology with multiple relationships) enables the system to capture contextual relationships and propagate information across the graph, significantly improving accuracy in identifying relevant supporting phrases while managing complexity through structured representation.
Solution Approach 2:
The graph structure serves as an intermediary between the input text and the query response generation. By introducing graph nodes (representing text phrases) and edges (representing relational connections), the system creates a mediating layer that enables information propagation and contextual analysis, improving the accuracy of identifying relevant support without directly complicating the core response generation mechanism.
2Adaptability or versatility
If conventional systems analyze text passages individually, then the processing is straightforward, but the flexibility in identifying supporting text phrases across multiple passages deteriorates
Solution Approach 1:
The patent merges multiple text passages into a unified graph structure where nodes from different passages can be connected through inter-phraser relations. This combining approach allows the system to flexibly identify supporting text phrases across multiple passages by propagating information through the graph connections, enabling cross-passage analysis while managing complexity through the integrated graph representation.
Solution Approach 2:
By representing text phrases from multiple passages as nodes in a graph with relational edges, the system adds dimensional structure to the analysis process. This enables flexible identification of supporting phrases across passages through graph traversal and information propagation, transforming straightforward but limited individual passage analysis into versatile multi-passage analysis.
3Measurement precision
If conventional systems use rigid phrase-based response generation, then the implementation is simple, but the accuracy of digital responses deteriorates
Solution Approach 1:
The graph neural network implements feedback mechanisms where information propagates iteratively through the graph structure. Node representations are updated based on aggregated information from neighboring nodes, creating a feedback loop that refines the understanding of each text phrase's relevance to the query. This feedback-driven approach improves response accuracy by continuously refining phrase representations based on contextual relationships.
Solution Approach 2:
The system transitions from static phrase-based analysis to dynamic graph-based analysis where node representations evolve through iterative information propagation. The graph neural network dynamically updates phrase embeddings based on their relationships with other phrases in the graph, enabling more accurate and context-aware response generation while managing complexity through structured dynamic processing.
Data Source
AI summary
The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.