Ensemble Network Identity Validation via Neural Fusion
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Solution Overview
Problem
Current identity verification systems are limited in providing both trust and confidence scores for digital identities across diverse, unrelated enterprises, often resulting in inaccurate merged results due to the use of summing or averaging methods, and lack the integration of behavioral and emotional data for dynamic identity validation.
Innovation Solution
A system and method that utilize a counting machine for generating event-relevant identifications with Trust and Confidence Scores through a Managed Secure-Immutable-Nonreputable-Replicated-Verifiable and Fault-Tolerant Distributed Datastore via an Ensemble-Based Network, integrating historical and current third-party data, behavioral, and emotional data for fusion over a peer-to-peer blockchain system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If summing or averaging methods are used to merge identity verification results from multiple organizations, then the verification process can be completed, but the accuracy of the merged results deteriorates
Solution Approach 1:
The patent replaces the mechanical arithmetic operations (summing/averaging) with a neural network-based ensemble system. The neural network learns optimal weighting and fusion strategies from data, substituting the simple mechanical combination method with an intelligent system that can capture non-linear relationships and interactions between different verification signals, thereby improving accuracy while maintaining processing efficiency.
2Device complexity
If traditional identity verification systems are used, then the process is simple, but the system cannot provide both trust and confidence scores across diverse enterprises
Solution Approach 1:
The patent creates a universal ensemble network that serves multiple functions: it processes identity verification data from diverse enterprises, generates both trust scores (indicating identity authenticity) and confidence scores (indicating verification reliability), and adapts to different data formats and verification methods. This multi-functional system replaces simple single-purpose verification systems, enabling cross-enterprise compatibility while maintaining manageable complexity through standardized interfaces.
3Ease of operation
If static identity verification is performed, then the verification is straightforward, but the system cannot dynamically update trust and confidence scores in real-time
Solution Approach 1:
The patent transforms the static verification process into a dynamic system where the neural network continuously processes incoming verification data and updates trust and confidence scores in real-time. The system adapts its predictions based on new information, allowing identity verification results to evolve as additional data becomes available, thereby improving reliability while maintaining operational simplicity through automated continuous processing.
Data Source
AI summary
A Counting Machine for Manufacturing and Validating Event-Relevant IDs, tagged with both Trust and Confidence Scores, for Specific Entities (Individuals and Enterprises) and their Prosoponyms using a Managed Secure-Immutable-Nonreputable-Replicated-Verifiable and Fault-Tolerant Distributed Datastore via an Ensemble-Based Network of both Existing and Newly-Created Heterogenous Single- and Multi-Factor ID Validation Services, the Ensemble consisting of both Commercial Organizations (known as Members) requiring valid Entity IDs for use during various events, such as purchase transactions, and Service Providers (known as Partners) who supply technology services for ID validation (as standalone services or as licensed by Members).