Identity Verification via Neural Network Profile Clustering
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Solution Overview
Problem
Current identity verification systems for user accounts are cumbersome and inefficient, relying on user-defined security measures and failing to effectively utilize available data points from user behavior and device interactions, leading to unacceptably inconvenient access experiences.
Innovation Solution
A system utilizing machine learning techniques, specifically artificial neural networks, to analyze and weigh data subsets from user interactions, including active and passive data from devices and biometric information, to generate profiles and determine the likelihood of user access, thereby providing seamless and secure account access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multi-factor verification (username/password, device confirmation, biometric data) is implemented, then security is improved, but user convenience deteriorates due to burdensome verification procedures
Solution Approach 1:
The system automatically collects and analyzes verification data without requiring active user participation. The verification system performs self-service by continuously gathering device information, behavioral patterns, and biometric data in the background, eliminating the need for users to manually complete verification steps while maintaining security through automated profile comparison
Solution Approach 2:
The system pre-collects and stores user verification data including device characteristics, behavioral patterns, and biometric information during normal account usage. This preliminary data collection creates a baseline user profile that enables rapid verification without requiring users to undergo time-consuming verification procedures when access is needed
2Measurement precision
If comprehensive data collection from user interactions is implemented, then verification accuracy is improved, but system complexity increases due to processing multiple data subsets
Solution Approach 1:
The verification system segments collected data into distinct clusters including device information, behavioral patterns, and biometric data. Each cluster is analyzed separately using specialized algorithms, and the results are combined to produce the final verification decision. This segmentation approach improves verification accuracy by applying appropriate analysis methods to each data type while managing system complexity through modular processing
Solution Approach 2:
The system introduces cluster analysis as an intermediary processing layer between raw data collection and final verification decisions. The cluster analysis component aggregates and synthesizes multiple data subsets, transforming complex raw data into meaningful verification signals that can be compared against stored user profiles, thereby simplifying the overall system architecture
3Reliability
If passive device information and user behavior data are analyzed, then identity verification reliability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary analysis of device information and behavioral patterns during normal account interactions, building and updating user profiles in the background without interrupting user activities. This pre-processing approach ensures that when verification is needed, the system can quickly compare current data against pre-analyzed profiles, maintaining high verification reliability while minimizing processing time
Solution Approach 2:
The verification system operates continuously in the background, constantly collecting and analyzing device data and user behavior patterns. This continuous operation allows the system to accumulate verification-relevant information over time without requiring dedicated verification sessions, thereby improving reliability through comprehensive data analysis while maintaining seamless user experience with no noticeable time delays
Data Source
AI summary
A system for verifying a user identity is disclosed. The system is configured to generate a first profile related to the user and receive an indication that the account is accessed by an accessor through an accessor device. The system receives, from the accessor device, identity data associated with the accessor. The system is configured to store the identity data subsets in clusters, analyze the clusters, and output the cluster analyses to node instances that weighs the cluster analyses outputs. The system also generates a second profile related to the accessor based on the received identity data and weighted cluster analysis. The system determines a likelihood factor that the accessor is the user based on a comparison of the first profile and the second profile.