Distributed Server Network for Detecting Multi-Dimensional Linkage in Resource Transfers
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Solution Overview
Problem
Current systems lack an efficient and secure method to detect unauthorized resource transfers and entities across multi-dimensional linkages and layering, which are often hidden and complex, making it difficult to identify potential unauthorized activity.
Innovation Solution
A distributed server network system utilizing deep learning-based graph processing algorithms to analyze resource transfer data, identifying multi-dimensional linkages between users, accounts, and transfers by generating a graph database that includes source, layering, and destination agents, and performing time-series tracking to recognize patterns and unauthorized activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify unauthorized resource transfers, then the system is simpler to implement, but the detection precision and ability to uncover hidden relationships deteriorates
Solution Approach 1:
The patent introduces a distributed server network as an intermediary system that coordinates detection across multiple entities. Each entity operates local detection systems, but the distributed network aggregates data and coordinates analysis, enabling higher detection precision through collaborative multi-entity analysis while distributing system complexity across participants rather than concentrating it in a single complex system
Solution Approach 2:
The patent replaces traditional mechanical or rule-based detection systems with deep learning-based graph processing algorithms. These algorithms automatically analyze complex relationships and patterns in resource transfer data, achieving superior detection precision by substituting manual or simple automated methods with intelligent systems that can uncover hidden linkages and layering patterns
2Reliability
If comprehensive data analysis is performed to identify multi-dimensional linkages, then the detection capability improves, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the comprehensive data analysis task across multiple distributed server nodes. Each node processes specific portions of the resource transfer data and contributes to the overall detection, enabling parallel processing that maintains high detection capability while reducing total processing time through distributed computation
Solution Approach 2:
The system performs preliminary data collection and preprocessing by entities before submitting to the distributed network. Data is prepared and structured in advance, allowing the deep learning algorithms to focus computational resources on the most critical analysis tasks, thereby reducing overall processing time while maintaining detection reliability
3Measurement precision
If deep learning algorithms are used to identify hidden relationships, then the detection precision improves, but the computational complexity and resource requirements worsen
Solution Approach 1:
The patent applies graph processing algorithms that transform resource transfer data into multi-dimensional graph structures where entities and transactions become nodes and edges. This dimensional transformation allows deep learning algorithms to efficiently capture complex relationships and patterns that would be difficult to detect in traditional tabular formats, achieving high detection precision while providing a structured framework for computation
4Reliability
If distributed server network is implemented across multiple entities, then the detection coverage and reliability improve, but the system complexity and coordination requirements worsen
Solution Approach 1:
The patent implements a universal distributed server network protocol that enables different entities to participate in the detection system regardless of their specific implementations. Each entity can contribute resource transfer data and detection results using standardized interfaces, allowing the system to achieve high detection reliability through diverse participant coverage while reducing coordination complexity through universal communication standards
Data Source
AI summary
A system is provided for detecting multi-dimensional linkage and layering of resource transfers using a distributed server network. In particular, the system may comprise a plurality of distributed server nodes that each host a copy of a distributed register, where each of the nodes may be operated by an entity. Each distributed server node may submit, to the distributed register, data records that may contain data regarding potential unauthorized users, accounts, and/or resource transfers. Based on the information within the distributed register, along with various other data inputs, the system may use a deep learning-based graph processing algorithm to identify a multi-dimensional linkage between the users, accounts, and/or resource transfers to extract hidden relationships and potentially unauthorized activity.