Abnormal Document Self-Discovery via Knowledge Graph Analysis
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Solution Overview
Problem
Existing systems lack the automated speed and accuracy to detect abnormal documents, which often differ from normal documents by minor capacities such as page layout or content, leading to time-consuming and error-prone manual sorting processes.
Innovation Solution
A system utilizing a knowledge graph with vectors representing page layouts, generated through object detection and multimodal embedding, and graph neural networks for anomaly detection, comparing structures across documents to identify abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting processes are used to detect abnormal documents, then accuracy in detecting minor differences can be maintained, but productivity is reduced and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical sorting processes with an automated computer-based system that uses machine learning models (including graph neural networks and layout analysis) to detect abnormal documents. This substitution maintains high detection accuracy for minor differences while dramatically improving processing speed and productivity by eliminating human manual intervention.
Solution Approach 2:
The system enables automated self-detection of abnormal documents through intelligent algorithms that automatically analyze document layouts, compare structures, and identify anomalies without requiring manual review. The computer-executable instructions autonomously perform the detection task, achieving both high accuracy and improved productivity.
2Reliability
If manual sorting processes are used to detect abnormal documents, then detection thoroughness can be maintained, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming manual sorting with automated computer-based analysis using machine learning models. The system thoroughly examines document structures, layouts, and content to detect abnormalities with high reliability, while completing the analysis in fractions of the time required for manual review.
Solution Approach 2:
The system performs preliminary automated analysis of document structures and layouts before final abnormality determination. By pre-processing documents through layout analysis and feature extraction, the system maintains thorough detection capabilities while reducing overall time consumption through efficient staged processing.
3Productivity
If automated detection systems are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional automated detection system that handles various document types and anomaly detection tasks through a unified platform. The system performs layout analysis, structure comparison, and abnormality detection using integrated machine learning models, reducing the need for multiple separate systems while maintaining high productivity.
Solution Approach 2:
The system uses intermediate processing layers including layout analysis modules and feature extraction components that bridge raw document input and final abnormality detection. These intermediary components organize complex processing tasks into manageable stages, improving productivity while managing system complexity through structured modular architecture.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate abnormal document self-discovery. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an object detection component that can generate a knowledge graph with vectors corresponding with nodes representative of a page layout of a document. Additionally, the computer executable components can comprise an evaluation component that can compare the vectors to identify whether an edge is present between corresponding vectors of the knowledge graph; an encoder component that can re-code the knowledge graph; and a comparison component that can compare a structure of the knowledge graph with one or more other knowledge graphs corresponding to one or more other documents to determine if the document is abnormal.