User-Centric AI Knowledge Base Graph Data Structure
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Solution Overview
Problem
Existing approaches for building and maintaining artificial intelligence knowledge bases are unsuitable for providing enhanced user interactions, as they pose a large computational burden and require extensive human oversight, and are not designed to handle user-centric data from multiple computer services.
Innovation Solution
A graph data structure for a user-centric artificial intelligence knowledge base that includes user-centric facts with an application-agnostic data format, allowing for centralized querying and efficient storage across multiple computer services, using a node-centric data structure that cross-references nodes and edges across different application-specific constituent graph structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing approaches for building and maintaining AI knowledge bases are used, then comprehensive data coverage can be achieved, but computational burden increases significantly and extensive human oversight is required
Solution Approach 1:
The patent segments the monolithic knowledge base into multiple application-specific constituent graph structures, each handling a specific application domain. This segmentation reduces the computational burden on any single structure while maintaining comprehensive data coverage across all applications. Each constituent graph can be independently processed, stored, and queried, eliminating the need to process the entire knowledge base for every operation.
Solution Approach 2:
The patent introduces a new dimensional organization by creating a hierarchical structure with multiple levels: application-specific constituent graphs at the lower level and an application-agnostic graph at the upper level. This dimensional change allows the system to navigate between application-specific details and general patterns, reducing computational complexity by allowing queries to be resolved at the most appropriate level without traversing the entire knowledge base.
2Quantity of substance
If existing approaches for building and maintaining AI knowledge bases are used, then data aggregation can be achieved, but extensive human oversight is required
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs data aggregation, validation, and integration across application-specific graphs without requiring human intervention. The application-agnostic graph automatically discovers and integrates patterns from constituent graphs, and the system autonomously handles data consistency, reducing the need for human oversight while maintaining comprehensive data aggregation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors and adjusts the knowledge base structure based on query patterns and data consistency requirements. Automatic feedback loops detect inconsistencies, validate data integration, and optimize the graph structures, replacing manual human oversight with automated self-correction and validation processes.
3Manufacturing precision
If data is stored in application-specific formats, then data fidelity for each application is maintained, but data integration across services becomes difficult
Solution Approach 1:
The patent creates an application-agnostic graph structure that serves multiple functions: it maintains application-specific data fidelity through constituent graphs while simultaneously providing universal data integration capabilities. The application-agnostic graph acts as a universal interface that can query and integrate data from any application-specific graph without requiring data conversion or loss of fidelity, enabling both precise application-specific storage and versatile cross-service integration.
Solution Approach 2:
The application-agnostic graph serves as an intermediary layer between application-specific data sources. It translates between different application-specific formats and a unified query interface, allowing data to remain in its native application-specific format (maintaining fidelity) while enabling seamless integration and querying across different services through the intermediary translation layer.
4Stability of the object's composition
If centralized knowledge base storage is used, then data consistency is maintained, but computational overhead and storage redundancy increase
Solution Approach 1:
The patent segments the centralized knowledge base into distributed application-specific constituent graphs, each storing only the data relevant to its application. This segmentation eliminates storage redundancy by avoiding duplicate storage of the same data across multiple applications. Data consistency is maintained through the graph structure's inherent relationships and the application-agnostic graph's ability to query across constituents, reducing computational overhead compared to a monolithic centralized storage system.
5Productivity
If application-specific data structures are used, then optimization for specific applications is achieved, but cross-application querying becomes inefficient
Solution Approach 1:
The patent adds another dimensional layer with the application-agnostic graph that sits above application-specific constituent graphs. This dimensional change enables efficient cross-application querying by allowing the system to resolve queries at the application-agnostic level without traversing through multiple application-specific structures. The application-agnostic graph maintains indexes and relationships that optimize cross-application queries while preserving the productivity benefits of application-specific optimizations at the constituent level.
Data Source
AI summary
A graph data structure for an artificial intelligence knowledge base includes a plurality of user-centric facts associated with a user. Each user-centric fact has an application-agnostic data format and includes a subject graph node, an object graph node, and an edge connecting the subject graph node to the object graph node. The graph data structure is designed to accommodate facts from different application-specific data providers using the same application-agnostic data format even when the different application-specific data providers use different native data formats.