Identity Verification System Using Behavioral and Emotional Data Analysis
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Solution Overview
Problem
Current identity management solutions have limitations in qualifying collected data for decision-making, particularly in analyzing behavioral and emotional metrics, which are essential for accurate identity verification and trust assessment.
Innovation Solution
A system and method that utilize behavior data analysis and emotional, mood, and feelings (EMF) analysis to qualify online data, assigning Trust and Confidence Scores to entities through a Managed Secure-Immutable-Nonreputable-Replicated-Verifiable and Fault-Tolerant Distributed Datastore via an Ensemble-Based Network, incorporating both existing and newly-created heterogenous Single- and Multi-Factor ID Validation Services, and a peer-to-peer blockchain system for continuous validation and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional identity management solutions are used, then basic authentication can be performed, but the accuracy of identity verification is limited due to inability to analyze behavioral and emotional metrics
Solution Approach 1:
The patent combines multiple data sources including behavioral metrics, emotional metrics, and traditional identity data into a unified analysis framework. This merging of diverse data types enables comprehensive identity verification that goes beyond basic authentication capabilities.
Solution Approach 2:
The system changes the parameters of identity verification by introducing new measurable parameters such as behavioral metrics (typing patterns, mouse movements) and emotional metrics (facial expressions, voice tone). These new parameters transform the verification process from static document checking to dynamic behavior analysis.
2Reliability
If data collection is expanded to include behavioral and emotional metrics, then trust assessment accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex trust assessment system into distinct modules: behavioral metric collection, emotional metric collection, data qualification, and trust score calculation. Each module handles specific tasks independently, making the overall complex system manageable and maintainable.
Solution Approach 2:
The system introduces intermediary components such as the data qualification module that processes and validates data from multiple sources before feeding into the trust assessment engine. This intermediary layer simplifies the interaction between complex components by standardizing data formats and validation rules.
3Reliability
If real-time behavioral and emotional analysis is implemented, then fraud detection capability improves, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing behavioral and emotional metrics during normal interactions. This ongoing data collection and qualification prepares the system for rapid fraud detection without requiring intensive real-time processing when fraud is detected.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors behavioral patterns and compares them against established baselines. This feedback loop enables real-time fraud detection by identifying deviations from normal behavior patterns without requiring complete re-analysis of all data.
4Measurement precision
If multiple data sources are integrated for comprehensive identity verification, then confidence score accuracy improves, but data qualification difficulty increases
Solution Approach 1:
The patent applies homogeneity by standardizing the qualification criteria for different data sources. The data qualification module uses consistent validation rules and formatting standards across all data types, making it easier to process and compare data from diverse sources without increasing complexity.
Data Source
AI summary
An improvement to a system for identity verification is provided in which data records are continuously updates to provide the validation, verification and trusted confidence values of an entity (an individual person or organization) for each type and level of identification needed. In addition to the iterative updating of conventional data, the historical change in recorded data is compared with newly received entity identification verification parameters, with changes and an analysis of the changes also iteratively tracked and stored as part of the data record with continuous updating. The data record may also include emotional, mood or feelings responses to emotional, mood or feeling prompts. Similarly, the historical change in emotional, mood or feeling responses is compared with newly received responses to similar or different prompts, with changes and an analysis of the changes also iteratively tracked and stored as part of the data record with continuous updating.