Identity Authentication Device Using Neural Network Browsing Habit Matching

The patent segments browsing records into multiple dimensions including URL categories, time patterns, device information, and behavioral features. This segmentation allows the system to analyze specific aspects of browsing behavior without exposing complete user identity information, thereby balancing authentication accuracy with privacy protection.

The patent introduces browsing behavior profiles as an intermediary between raw browsing data and user identity identification. Instead of directly linking browsing records to user identities, the system creates behavioral profiles that serve as a mediator, enabling law enforcement to identify users through matched behavior patterns without direct exposure of personal information.

The patent performs preliminary actions by pre-processing browsing records into structured features and pre-training neural network models with browsing behavior data. This preliminary preparation reduces the complexity of real-time analysis by converting raw data into standardized formats that can be efficiently processed during authentication operations.

The patent replaces traditional mechanical or rule-based analysis systems with neural network-based machine learning models. This substitution enables the system to handle complex, multi-dimensional browsing data more efficiently, achieving high measurement precision without proportionally increasing system complexity through automated pattern recognition.