Device Identification via User Agent and Cookie Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Web service providers face difficulties in identifying whether multiple similar devices used by a single user are known or unknown, making it challenging to secure accounts and manage login processes effectively.
Innovation Solution
A system that analyzes user agent strings and scheduling cookies to cluster requests from electronic devices, generating profiles that include device characteristics, and uses interval scheduling to determine the number of devices that could have used specific cookies without any single device using multiple cookies simultaneously, thereby identifying known devices and providing tailored web service functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the service provider requests additional login information for unrecognized devices, then account security is improved, but user convenience deteriorates
Solution Approach 1:
The patent segments device identification into multiple dimensions: hardware identifiers, software environment (user agent strings), and behavioral patterns (request timing, frequency). This multi-faceted segmentation enables more accurate device recognition, allowing the system to distinguish between legitimate new devices and potentially malicious access attempts, thereby improving security while reducing false positives that would inconvenience users
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring device behavior patterns and adjusting authentication requirements dynamically. When a device exhibits normal usage patterns consistent with the user's historical behavior, the system provides feedback by granting streamlined access. When anomalies are detected, additional authentication is triggered, thus adapting security measures to actual risk levels rather than applying uniform restrictions
2Measurement precision
If the service provider collects and analyzes multiple data points about electronic devices, then device identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the device identification system into distinct modular components: data collection modules that gather various data points, clustering modules that group requests by device characteristics, and analysis modules that interpret user agent strings and behavioral patterns. This segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable and maintainable while achieving high identification accuracy
Solution Approach 2:
The patent creates universal data structures and processing pipelines that can handle multiple types of device data (hardware identifiers, software environments, behavioral patterns) through a single unified framework. The clustering mechanism serves multiple functions: grouping requests, identifying devices, and detecting anomalies, thereby reducing the need for separate specialized systems and lowering overall complexity
3Measurement precision
If the service provider uses clustering algorithms to group requests by device characteristics, then the ability to distinguish known from unknown devices is improved, but computational resources increase
Solution Approach 1:
The patent applies partial clustering by focusing computational resources on the most discriminative features of device characteristics rather than analyzing all possible data points equally. The system identifies and clusters based on key distinguishing factors in user agent strings and behavioral patterns, achieving effective device distinction while avoiding the computational overhead of exhaustive analysis of all available data
Solution Approach 2:
The patent dynamically adjusts clustering parameters such as similarity thresholds and grouping criteria based on the specific context and device types being analyzed. By changing these parameters adaptively, the system optimizes the balance between clustering accuracy and computational efficiency, reducing resource consumption while maintaining the ability to distinguish between known and unknown devices
Data Source
AI summary
A web service provider identifies electronic devices that are associated with a particular user by using device parameters, and user agents strings to cluster the user's web service requests determine whether the requests in each cluster originated from a single device or multiple devices. For any cluster having requests from multiple devices, the provider will analyze the scheduling cookies associated with each request to determine how many devices submitted the requests in each cluster. The system will then store identified devices and their characteristics in a profile for the user. Then, when the user submits a new web service request, the web service provider can compare data in the request such as device parameters, user agent strings and/or cookies to identify the device from which the new request originated.