Dynamic Hierarchical Learning Engine Matrix for Real-Time User Authentication
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Solution Overview
Problem
Current AI and machine learning-based systems for determining user authenticity face challenges in accurately identifying users due to variations in resource distribution patterns, health care data, and interactions over time, making real-time authenticity verification difficult.
Innovation Solution
A dynamic hierarchical learning engine matrix is employed to model users from behavioral, temporal, intent, event, and anomaly-based perspectives, utilizing event-driven, intent-driven, and temporal-based modeling to generate user profiles and optimize authenticity identification by clustering patterns and transitions in user event history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AI and machine learning algorithms are used for determining user authenticity, then the system can process user data, but the accuracy of user identification deteriorates due to variations in user event history and resource distribution patterns
Solution Approach 1:
The patent implements dynamic user profiling that continuously adapts to user behavior changes over time. The system updates user profiles based on evolving event history and resource distribution patterns, allowing the authentication mechanism to remain accurate despite user variations. This dynamic adaptation resolves the contradiction by making the system both precise and adaptable simultaneously.
Solution Approach 2:
The system changes multiple parameters including time windows for event history analysis, weighting factors for different user attributes, and threshold values for authentication decisions. By dynamically adjusting these parameters based on observed user patterns, the system maintains high identification accuracy across diverse user behaviors and changing conditions.
2Measurement precision
If multiple modeling perspectives (behavioral, temporal, intent, event, anomaly) are used simultaneously, then user authenticity identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the authentication system into five distinct modeling perspectives: behavioral modeling, temporal modeling, intent modeling, event modeling, and anomaly modeling. Each perspective processes specific aspects of user data independently, then their results are integrated to form a comprehensive authenticity assessment. This segmentation manages complexity by organizing multiple models into structured, manageable components with clear responsibilities.
Solution Approach 2:
The system creates a universal authentication framework where a single multi-perspective modeling system handles diverse authentication scenarios. The same five modeling perspectives serve multiple functions: detecting fraud, verifying user identity, analyzing behavior patterns, and preventing misappropriation. This multi-functionality reduces overall system complexity by using a unified approach rather than separate specialized systems.
3Productivity
If real-time authenticity identification is implemented, then user authentication speed improves, but the ability to analyze comprehensive user patterns deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously building and updating user profiles in the background as users interact with the system. Event history, behavioral patterns, and user preferences are pre-analyzed and stored before authentication is needed. During real-time authentication, the system queries these pre-processed profiles rather than analyzing raw data from scratch, enabling fast authentication without sacrificing pattern analysis completeness.
Solution Approach 2:
The patent implements continuous user profiling that operates alongside normal system operations. The modeling perspectives continuously analyze user events, update profiles, and refine understanding of user patterns without interrupting service. This continuous action ensures comprehensive pattern analysis is maintained while authentication decisions can be made in real-time based on the continuously updated information.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for identification of normal state authenticity indicators for user and entity authentication into applications in real-time to prevent misappropriation at the point of authenticity. In this way, the system uses multiple modeling processes for identification of authentic access requests to prevent misappropriation including utilizing phase-based characterization of different perspectives to make real-time determinations on authenticity of an interaction and/or misappropriation likelihood. The invention relies on multiple characteristics and models in simultaneous utilization for real-time authenticity decisions.