Neural Resource Navigation for Low-Latency Graph Search

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Solution Overview

Problem

Existing information retrieval systems struggle to efficiently search graph data structures, particularly when dealing with unindexed, fire-walled, or frequently updated web sites, leading to inefficiencies in bandwidth, latency, and inaccurate results.

Innovation Solution

A neural network-based system that utilizes a query encoder, resource encoder, and navigation policy neural network to navigate through a collection of resources, generating scores for outgoing links to efficiently identify relevant information without human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional information retrieval systems search graph data structures, then they can retrieve information from web sites, but they incur high bandwidth usage, high latency, and inaccurate results when dealing with unindexed, fire-walled, or frequently updated web sites

Engineering Contradiction:

Improvesearch accuracyVSAvoidsearch latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system pre-generates embeddings for graph nodes and pre-computes navigation policies during training phases, so that during actual search operations, the neural network can quickly retrieve and apply pre-computed information rather than calculating everything from scratch. This preliminary preparation significantly reduces search latency while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces neural network embeddings as an intermediary representation layer between the graph structure and the search query. Instead of directly traversing the graph or crawling web pages, the system uses neural embeddings to mediate the search process, enabling faster and more accurate information retrieval especially for unindexed or fire-walled sites.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If traditional information retrieval systems crawl and index web pages, then they can search for information, but they consume significant bandwidth and compute resources

Engineering Contradiction:

Improveinformation retrieval efficiencyVSAvoidcompute bandwidth requirements

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system creates neural network embeddings as compact copies of the graph node information. Instead of storing and processing full web page content or maintaining complete graph indexes, the system uses these compressed embedding representations that capture essential information while requiring minimal storage and compute resources for search operations.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent transforms the graph data structure into a different parameter space using neural network embeddings. By changing the representation from raw graph data or full web content to compressed embedding vectors, the system enables efficient similarity search and navigation with significantly reduced computational requirements.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250363121A1Resource navigation using neural networks

Publication Date: 2025.11.27 GDM HOLDING LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for resource navigation using neural networks.