Automated Multi-User System Detection via Confidence Scoring
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Solution Overview
Problem
Existing systems lack an efficient method to reliably distinguish between multi-user and single-user computing devices, posing security and privacy risks, as manual user identification is unreliable and cumbersome.
Innovation Solution
An automated multi-user system detection system that evaluates various characteristics, such as user agent strings, cookie behavior, and geolocation data, to generate confidence scores and classify devices as multi-user or single-user, thereby customizing network resource access and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user identification is used to distinguish multi-user systems, then user awareness of system type is achieved, but reliability and ease of operation deteriorate due to unreliability and user burden
Solution Approach 1:
The system automatically detects and classifies multi-user systems by analyzing device characteristics, user behavior patterns, and system properties without requiring manual user input. The detection system serves itself by gathering evidence from multiple sources and making automated classification decisions, eliminating the need for users to manually identify their system type while improving classification reliability through objective analysis
Solution Approach 2:
The patent replaces the manual mechanical process of user self-identification with an automated electronic detection system that analyzes device fingerprints, user agent strings, cookie behavior, and other digital signals. This substitution transforms the identification process from a manual user action into an automated system-based detection mechanism, improving both reliability and ease of operation
2Measurement precision
If automated detection characteristics are evaluated to generate confidence scores, then classification accuracy improves, but device complexity increases
Solution Approach 1:
The detection system is divided into modular components that independently evaluate different characteristics: user agent string analysis, cookie behavior detection, geolocation data processing, and device fingerprinting. Each module generates partial confidence scores that are aggregated into an overall classification decision. This segmentation allows the system to achieve high measurement precision through multiple specialized detectors while managing complexity through modular architecture
Solution Approach 2:
The system employs a unified confidence score generation mechanism that processes multiple different detection characteristics through a common evaluation framework. The same confidence scoring algorithm handles diverse inputs including user agent strings, cookie patterns, geolocation data, and device identifiers, creating a universal detection approach that improves classification accuracy without proportionally increasing system complexity
Data Source
AI summary
Disclosed are various embodiments for automated detection of multi-user computing devices such as kiosks, public terminals, and so on. Network resource requests are obtained from a client computing device. It is determined whether the client computing device is a multi-user system based at least in part on whether the network resource requests embody characteristics associated with multi-user systems. The resulting classification is stored and may be used to customize generation of requested network resources.