Semantic Graph Query Response With Explainable ML Reasoning

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Solution Overview

Problem

Current data management systems struggle with data overload, fragmented data integration, lack of semantic understanding, and inflexible schemas, leading to performance bottlenecks, data quality issues, and inability to adapt to changing data landscapes.

Innovation Solution

A system integrating a semantic graph database with flexible graph schema and advanced data ingestion capabilities, utilizing AI, ML, and LLMs for seamless data integration, semantic understanding, and adaptive knowledge representation, enabling efficient data traversal and query response.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If traditional data management systems are used, then basic data storage and retrieval are achieved, but data overload and fragmented integration cause performance bottlenecks and loss of meaningful insights

Engineering Contradiction:

Improveloss of meaningful insightsVSAvoiddata processing efficiency

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The patent merges multiple data sources and types into a unified semantic graph database structure, integrating disparate data fragments into a cohesive knowledge framework that preserves meaningful insights while improving processing efficiency through centralized management

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The semantic graph database acts as an intermediary layer between raw data sources and analysis tools, transforming fragmented data into structured knowledge representations that maintain information integrity while enabling efficient querying and insight extraction

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If rigid schema structures are used, then data organization is simplified, but adaptability to changing data landscapes deteriorates

Engineering Contradiction:

Improveadaptability to changing data landscapesVSAvoidschema flexibility complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic schema evolution in the semantic graph database, allowing the data structure to adapt automatically to changing requirements while maintaining organized access patterns, thus achieving both adaptability and manageable complexity through flexible yet structured design

Inventive Principle:

Principle #15Dynamics

3Loss of information

If advanced analytical techniques are added, then deeper insights are extracted, but system complexity and computational requirements increase

Engineering Contradiction:

Improvedepth of insights extractedVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent performs preliminary organization of data into semantic graph structures before analysis, pre-processing data into meaningful relationships and contexts that enable deeper insights to be extracted more efficiently, reducing the computational complexity required for advanced analytics

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250390491A1Accuracy and providing explainability and transparency for query response using machine learning models

Publication Date: 2025.12.25 DATA SQUARED USA INC

AI summary

The implementations herein disclose advanced systems and methods for integrating, analyzing, and reasoning over heterogeneous data at scale. In some implementations, the system comprises a synergistic data processing infrastructure featuring: a graph database core for unified data representation; specialized loaders for concurrent ingestion and processing of structured, unstructured, and time series data; a natural language reasoning engine leveraging large language models; and a multi-modal user interface.