Contextual Semantic Derivation With an Iterative Knowledge Graph
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Solution Overview
Problem
Current systems rely heavily on human judgment and intervention to derive relationships among data within and across documents due to the difficulty of discerning context, leading to errors, increased costs, and inefficiencies in processes such as financial transactions, despite advancements in computerization.
Innovation Solution
An autonomous event-driven integrated system that employs contextual semantic derivation (CSD) to automatically process documents, identify data interrelationships, and iteratively update knowledge graphs to perform tasks on an ongoing basis, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human judgment and intervention are used to derive relationships among data within and across documents, then accuracy and contextual understanding are improved, but costs and time consumption increase
Solution Approach 1:
The patent introduces an intermediary system comprising a document ingestion module, processing module, and output module that acts as a mediator between raw documents and human users. This automated processing system derives data relationships using machine learning models and knowledge graphs, reducing the need for direct human intervention while maintaining accuracy through iterative processing and confidence scoring mechanisms.
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated electronic processing system that uses optical character recognition (OCR), machine learning models, and knowledge graphs to derive relationships among data. This substitution maintains the ability to derive contextual relationships while eliminating the time consumption associated with manual human analysis.
2Reliability
If human judgment is employed to resolve difficult-to-derive relationships, then contextual meaning is accurately captured, but errors and inconsistencies increase
Solution Approach 1:
The patent implements a self-service automated processing system that independently derives relationships among data without requiring human intervention. The system uses machine learning models trained on document data to automatically identify and resolve difficult-to-derive relationships, thereby eliminating human errors while maintaining reliability through iterative processing and confidence-based validation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the processing module continuously refines its derivations by analyzing confidence scores and iteratively improving relationship identification. The system learns from processed documents and adjusts its processing approach, reducing errors through continuous improvement rather than relying on fallible human judgment.
3Productivity
If automated systems are used to process documents and derive relationships, then productivity and efficiency are improved, but ability to discern hidden context deteriorates
Solution Approach 1:
The patent transitions from traditional two-dimensional document processing to a multi-dimensional approach by implementing iterative processing cycles and confidence-based validation layers. The system processes documents through multiple passes, each deriving relationships at different levels of abstraction, thereby capturing hidden context that single-pass systems miss while maintaining high productivity through parallel processing capabilities.
Solution Approach 2:
The patent performs preliminary actions by pre-processing documents through OCR and initial relationship identification before main processing. The system prepares data structures and pre-identifies potential relationships that need further validation, enabling the main processing module to focus on deriving hidden context efficiently without sacrificing productivity.
4Measurement precision
If more human resources are allocated to document review and relationship derivation, then accuracy of data relationships is improved, but operational costs increase
Solution Approach 1:
The patent replaces the mechanical system of human document review with an automated electronic processing system that uses machine learning models and knowledge graphs. This substitution eliminates the need to allocate additional human resources while maintaining or improving accuracy through sophisticated algorithms that can process and derive relationships from documents more effectively than manual review.
Solution Approach 2:
The patent implements a universal processing system that handles multiple document types and relationship derivation tasks through a single automated platform. The system's multi-functional capabilities allow it to derive various types of data relationships (entity relationships, transaction relationships, contextual relationships) without requiring specialized human expertise for each task type, thereby reducing operational costs while maintaining accuracy.
Data Source
AI summary
The present invention includes novel methods and systems for deriving meaning from context, enabling the automation of processes that currently require significant human judgment and intervention. An autonomous event-driven system runs on a continuous basis over time, detecting and responding to new events as new information is obtained (including the mere passage of time) to implement virtually any scenario in which relationships among data within and across documents are difficult to discern (without human intervention) from the explicit information contained in the documents (DDRs). Trained models perform contextual semantic derivation (CSD) to derive meaning from context within and across documents in the form of DDRs and other relationships stored in an iteratively updated knowledge graph, which is leveraged to perform lower-level document processing tasks (capture, classification, matching, reconciliation, etc.) as well as higher-level tasks (natural-language interrogation, anomaly detection and resolution, decisioning and analytics).