Concept Graph Data Ingestion for Thematic Analysis
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Solution Overview
Problem
Business analysts face challenges in efficiently managing and analyzing vast amounts of data from various sources to correlate organizations with concepts or themes, often relying on personal knowledge due to the overwhelming volume of information, which limits their ability to identify relevant data and perform comprehensive analysis.
Innovation Solution
A system that generates concept graphs, entity graphs, and data graphs to associate thematic concepts with organizations, using ingestion models and portfolio models to process and correlate data from diverse sources, enabling the identification of relevant entities and themes through query-based reporting and portfolio management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually review and analyze vast amounts of information from multiple sources, then they can identify relevant data and perform comprehensive analysis, but it requires significant time and effort, making it nearly impossible to digest all available information
Solution Approach 1:
The patent introduces an automated system comprising data ingestion engines, concept graph generation engines, and portfolio management engines as an intermediary between raw information and analyst interpretation. This system automatically processes vast amounts of data from multiple sources, generates concept graphs representing relationships between entities and themes, and identifies relevant portfolios, thereby eliminating the need for analysts to manually review all available information while maintaining comprehensive analysis capability
Solution Approach 2:
The patent replaces the mechanical manual review process with automated computational systems. Data ingestion engines automatically collect and process information from multiple sources, concept graph generation engines automatically analyze relationships between entities, and portfolio management engines automatically identify relevant portfolios. This substitution of automated mechanical systems for manual human analysis resolves the contradiction by enabling comprehensive processing of all available information without time constraints
2Productivity
If analysts rely on personal knowledge or experience to guide research, then they can make decisions with limited information, but they miss out on relevant information that falls outside their existing knowledge base
Solution Approach 1:
The automated system acts as an intermediary that bridges analysts' existing knowledge with comprehensive data from multiple sources. The system ingests information beyond any single analyst's personal knowledge base, processes it through concept graph generation, and presents relevant findings, thereby expanding the effective knowledge base while maintaining research efficiency
Solution Approach 2:
The system performs multiple functions including data ingestion from diverse sources, concept extraction, relationship mapping, and portfolio identification. This multi-functional capability ensures that no relevant information is missed regardless of its source or format, while the automated nature maintains high productivity by processing all information types simultaneously rather than requiring specialized human expertise for each domain
3Loss of information
If the system processes and correlates data from multiple sources automatically, then it enables comprehensive analysis and identification of relevant entities, but it increases system complexity
Solution Approach 1:
The patent divides the complex data processing task into distinct modular components: data ingestion engines that collect information from multiple sources, concept graph generation engines that process and relationship-map data, and portfolio management engines that identify relevant portfolios. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining comprehensive information coverage
Solution Approach 2:
The system introduces intermediate data structures and processing layers, including concept graphs that serve as mediators between raw data and final analysis results. These intermediate representations simplify the complexity by organizing vast amounts of multi-source data into structured relationships that are easier to process and analyze, thereby enabling comprehensive information coverage without proportionally increasing system complexity
Data Source
AI summary
Concepts may be associated with each other based on information provided by data sources. Entities may be associated based on the information provided by the data sources and characteristics of the entities. A concept graph may be generated based on the concepts such that each edge in the concept graph corresponds to a relationship between two or more associated concepts. A data graph may be generated based on the concept graph and the entities such that each node in the data graph corresponds to a concept or an entity and the edges in the data graph correspond to relationships between two or more concepts and such that other relationships between two or more associated concepts are absent from the concept graph. In response to a query, traversing the data graph to determine entities that are related to the query and providing a report that includes those entities.