Transfer Data Analysis Using Encrypted User Identification
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Solution Overview
Problem
Digital gaming environments face challenges in efficiently managing and analyzing transfer data between user accounts, particularly in identifying illegitimate transfers and optimizing data transmission to conserve resources and enhance user experience, while addressing the complexity of authenticating user accounts across disparate systems.
Innovation Solution
Implementing systems and methods for analyzing transfer data using encryption-based identification and probability calculations to determine expected transfer values, flagging deviations, and applying cryptographic techniques for unique user identification, with processing circuitry to manage and authenticate user accounts across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all transfers are subjected to intensive scrutiny, then detection accuracy of illegitimate transfers is improved, but computational resources and data processing load increase
Solution Approach 1:
The patent applies local quality by differentiating monitoring intensity based on transfer characteristics. Transfers are categorized into high-risk (e.g., large amounts, frequent transactions, unusual patterns) and low-risk types, with intensive scrutiny applied only to high-risk transfers while standard monitoring covers low-risk ones. This selective approach maintains detection accuracy for problematic transfers while reducing overall computational burden.
Solution Approach 2:
The system performs preliminary analysis by establishing baseline transfer patterns and risk thresholds before actual monitoring occurs. Historical data is used to pre-calculate expected transfer values and identify anomalous patterns, enabling the system to quickly flag suspicious transfers without requiring intensive real-time analysis of all transfers.
2Productivity
If data transmission volume is reduced to conserve bandwidth, then network efficiency is improved, but authentication accuracy may deteriorate
Solution Approach 1:
The patent extracts only the essential authentication data needed for verification, separating critical identification information from unnecessary transfer details. By extracting and transmitting only fundamental user identifiers and transfer metadata rather than complete transaction records, the system reduces bandwidth consumption while maintaining sufficient information for accurate authentication and monitoring.
Solution Approach 2:
The system creates simplified representations or copies of transfer data that capture essential characteristics without transmitting full detailed records. These condensed data copies enable authentication and risk assessment while significantly reducing the volume of information transmitted across the network.
3Speed
If transfer analysis complexity is reduced, then system responsiveness is improved, but detection capability for complex transfer chains decreases
Solution Approach 1:
The patent segments the complex transfer analysis into distinct processing stages: data collection, pattern recognition, risk assessment, and flagging. Each stage handles a specific aspect of analysis independently, allowing the system to process transfers quickly through standardized checks while maintaining the ability to detect complex patterns through multi-stage evaluation of segmented data.
Solution Approach 2:
The system applies partial analysis by implementing multiple layers of detection with increasing complexity. Simple transfers undergo basic validation for rapid processing, while complex transfer chains trigger deeper analysis only when initial checks indicate potential issues. This partial action approach maintains responsiveness for most transfers while preserving detection capability for complex scenarios.
Data Source
AI summary
The present disclosure relates to systems and methods for analyzing transfer data within digital environments and to improve network security through user identification mechanisms. This disclosure focuses on techniques for identifying and evaluating transfers patterns between user accounts, which may involve intermediary accounts. The systems and methods detailed herein aim to identify irregular transfer patterns by analyzing transfer data and implementing unique user identification processes.