Visual Session Transfer with Behavioral Identity Verification
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Solution Overview
Problem
Existing systems lack seamless, secure, and efficient methods for transferring active sessions and content across multiple devices, particularly in scenarios involving real-time media or screen-specific content, and they fail to provide robust identity verification based on contextual cues.
Innovation Solution
A system that enables secure, AI-assisted transfer of sessions using visual detection and behavioral authentication, allowing users to initiate session transfer by pointing a mobile device at another screen, leveraging optical character recognition (OCR), image classification, and AI-based interaction recognition to identify actionable regions, and verifying user identity based on historical patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional copy-paste mechanisms or QR codes are used for cross-device transfer, then implementation is simple, but user experience is brittle and friction is high
Solution Approach 1:
The patent replaces mechanical/copy-paste transfer mechanisms with optical recognition systems. Users point a mobile device camera at screen content, and the system automatically detects and transfers sessions through visual recognition, eliminating manual copy-paste operations and QR code scanning friction while improving transfer reliability through AI-based content identification
Solution Approach 2:
The system creates visual copies of screen content through camera capture and uses AI models to recognize and replicate session states. The visual detection module captures screen content and generates representations that can be reconstructed on different devices, enabling seamless session transfer without requiring direct connection or manual data entry
2Reliability
If static credentials or out-of-band verification are used for authentication, then security is maintained, but speed and adaptability to fast transitions are poor
Solution Approach 1:
The system performs preliminary authentication by analyzing behavioral patterns and interaction styles before the actual transfer occurs. The AI model continuously monitors user behavior during sessions and builds profiles that enable rapid verification during fast transitions, eliminating the need for slow password entry or multi-step authentication processes
Solution Approach 2:
The patent replaces traditional mechanical authentication mechanisms (passwords, codes) with AI-based behavioral analysis. The system uses machine learning models to verify identity through pattern recognition of user interactions, providing both security and speed by continuously adapting to user behavior rather than relying on static credentials
3Ease of operation
If camera-based visual detection is implemented, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary AI-based visual detection module that bridges the simple camera capture and the complex session transfer logic. This intermediary layer processes visual input through pre-trained models, identifies actionable content, and triggers appropriate transfer actions, abstracting away the complexity from the user while managing system resources efficiently
Solution Approach 2:
The system segments the complex transfer process into distinct modules: visual detection, content recognition, session identification, and transfer execution. Each module handles specific tasks independently, making the overall system more manageable and maintainable while enabling the camera-based initiation to work through coordinated functionality
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates seamless, secure, and continuous multi-device interactions without manual login, preserving session state and identity verification, enhancing usability and security through adaptive authentication.
Implementation Method 1
using a combination of optical character recognition (OCR), image classification, and AI-based interaction recognition
Implementation Method 2
using a combination of optical character recognition (OCR), image classification, and AI-based interaction recognition
Data Source
AI summary
A system and method are provided for secure, AI-driven session transfer between devices using visual input and behavioral authentication. A user may use a mobile device to capture an image or video of another device's screen, including live streams, video calls, or form sessions. The system detects and classifies actionable interface content from the captured media and initiates a secure session handoff or mirroring workflow to the capturing device. The system further uses AI to verify user identity based on historical behavioral patterns and conversational interaction style. Secure hyperlinks, masking logic, and persistent session context are maintained throughout the transfer. The system enables seamless, privacy-conscious transfers of digital sessions across devices using camera-based detection and behavioral AI.