Document Similarity via Heterogeneous Meta-Paths
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Solution Overview
Problem
Traditional document similarity checking methods are limited in accuracy and scalability, particularly in distinguishing sophisticated fraudulent electronic documents.
Innovation Solution
The system utilizes contextual information such as user behavior data, device data, IP addresses, and profile data to build Meta-Groups and determine document similarity by analyzing Meta-Paths in a heterogeneous document network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document similarity checking methods are used, then the process is simple to implement, but the accuracy in distinguishing fraudulent documents is insufficient
Solution Approach 1:
The patent segments the document verification process into multiple independent components: text similarity analysis, image similarity analysis, metadata analysis, and contextual relationship analysis. Each component processes specific aspects of document verification independently, then results are aggregated to form a comprehensive fraud detection assessment. This segmentation enables high accuracy through multi-faceted analysis while managing system complexity through modular design.
Solution Approach 2:
The patent transitions from traditional two-dimensional text-based similarity comparison to multi-dimensional analysis by incorporating image data, metadata, user behavior patterns, device information, and network relationship graphs. This dimensional expansion allows the system to detect fraudulent documents through multiple independent vectors, significantly improving detection accuracy while the structured integration of these dimensions prevents exponential complexity growth.
2Reliability
If traditional text-based similarity algorithms are used, then the processing speed is fast, but the ability to detect sophisticated fraud is limited
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing document embeddings, image features, and metadata signatures in vector databases before verification is needed. When a document verification request occurs, the system performs fast similarity searches using these pre-processed representations rather than analyzing raw documents in real-time. User behavior patterns and device fingerprints are also pre-established and stored for rapid comparison, enabling high-speed fraud detection without sacrificing reliability.
Solution Approach 2:
The patent introduces intermediary components including vector databases for efficient similarity search, graph neural networks for relationship analysis, and fingerprinting systems for device identification. These intermediaries act as mediators between raw input documents and final fraud detection decisions, enabling parallel processing and caching strategies that maintain high processing throughput while incorporating sophisticated multi-modal analysis for reliable fraud detection.
3Measurement precision
If multi-modal data analysis is implemented, then the fraud detection accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by implementing a tiered verification approach where not all analysis modalities are applied to every document. The system first performs lightweight text-based filtering using pre-computed embeddings, then applies more computationally intensive image analysis, graph neural networks, and behavioral pattern matching only to documents that show potential fraud indicators in preliminary screening. This selective application of analysis depth maintains high fraud detection accuracy for suspicious documents while minimizing computational resource consumption for legitimate documents.
Data Source
AI summary
A system may include a processor and a non-transitory computer readable medium having stored thereon instructions for performing operations including obtaining a first dataset including a plurality of electronic documents and a plurality of entities, extracting, based on one or more rules, one or more subgraphs including representations between one or more electronic documents and one or more entities from the first dataset, identifying one or more groups in the one or more subgraphs, each group including electronic documents and entities selectively identified from a corresponding subgraph based on the representations, and learning the representations associated with the electronic documents and the entities based on the one or more groups and updating the representations in the first dataset. The first dataset may include data corresponding to contextual information associated with the plurality of entities and the representations may be determined based on this contextual information.