Organization Semantics Model for Multi-Source Data Navigation
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Solution Overview
Problem
Organizations face challenges in managing and navigating large volumes of structured and unstructured data, including inefficiencies in finding similar or related data, leading to operational inefficiencies and increased costs due to extensive manual searching and reviewing.
Innovation Solution
An organization semantics system utilizing a semantics-based model trained on organization-specific data to generate responses tailored to the organization's context, enabling efficient data management, quality control, and natural language conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations manage large volumes of structured and unstructured data, then data comprehensive coverage is improved, but data navigation and organization difficulty increases
Solution Approach 1:
The patent introduces an intermediary semantic layer between raw data and user queries. This semantic layer translates user-friendly natural language queries into data retrieval operations, enabling intuitive navigation through large datasets without requiring users to understand complex data structures or navigation mechanisms.
Solution Approach 2:
The patent replaces traditional mechanical search and navigation mechanisms with AI-powered semantic understanding. Instead of requiring users to manually browse through large datasets using conventional search interfaces, the system uses natural language processing and semantic models to automatically interpret queries and retrieve relevant data, substituting manual mechanical navigation with intelligent automated processing.
2Quantity of substance
If organizations store data from multiple sources, then data comprehensiveness is improved, but data organization and retrieval efficiency deteriorates
Solution Approach 1:
The patent implements a universal semantic understanding layer that handles multiple data sources and types through a single integrated interface. This multi-functional system can process structured data from databases, unstructured data from documents, and data from various formats, retrieving relevant information across all sources simultaneously through natural language queries, thereby improving retrieval efficiency despite data comprehensiveness.
Solution Approach 2:
The patent changes the fundamental parameters of data retrieval by transitioning from keyword-based search to semantic meaning-based retrieval. The system transforms queries from literal string matching to understanding contextual meaning, enabling efficient retrieval of relevant data across multiple sources by focusing on semantic relevance rather than superficial data characteristics.
3Reliability
If users manually search and review documents, then data accuracy can be verified, but time consumption increases
Solution Approach 1:
The patent implements feedback mechanisms where the semantic model continuously refines its understanding based on user interactions and data context. The system learns from user feedback patterns to improve its retrieval accuracy over time, enabling automated verification that maintains high data accuracy while significantly reducing the time users need to manually search and review documents.
Solution Approach 2:
The patent enables the system to perform self-service data verification through semantic understanding and contextual analysis. The system automatically determines data relevance and accuracy by interpreting semantic meaning and contextual relationships, reducing or eliminating the need for manual verification while maintaining high reliability standards.
Data Source
AI summary
The present disclosure relates to a method. The method includes receiving resource reference data corresponding to oil and gas resources. The method also includes obtaining, from a first database, a first plurality of resource data associated with a first organization. Further, the method includes obtaining, from a second database different than the first database, a second plurality of resource data associated with a second organization. Further still, the method includes generating an organization semantics model based on the reference data, the first plurality of resource data, and the second plurality of resource data, wherein the organization semantics model is a language-learning model configured to generate a first response based on a received query corresponding to the first organization, and wherein the organization semantics model is configured to generate a second response based on the received query corresponding to the second organization.