Client Device Association via Clickstream Similarity Scoring
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Solution Overview
Problem
Existing methods struggle to identify different client devices used by the same user based on public clickstream data without relying on private user information, especially when users operate multiple devices.
Innovation Solution
A method and system that computes a similarity score between data items from various client devices using geolocation, networking, and HTTP parameters, determining if devices are operated by the same user if the score reaches a predetermined value, and combining scores for different data types to reach a total threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If private user information is used to identify devices, then user identification accuracy is improved, but user privacy protection deteriorates
Solution Approach 1:
The patent extracts and removes private user information from the data collection process, relying solely on public clickstream data for device identification. This extraction principle resolves the contradiction by eliminating privacy exposure while maintaining identification capability through alternative public data sources.
Solution Approach 2:
The patent introduces similarity scoring algorithms and data analysis intermediaries that process public clickstream data to infer user-device relationships without directly accessing private information. These intermediaries enable accurate device association while preserving user privacy by acting as a buffer between public data and user identification.
2Measurement precision
If multiple data parameters are collected to improve identification accuracy, then device association accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the identification process into distinct analytical components: geolocation parameter analysis, networking parameter analysis, HTTP parameter analysis, and temporal pattern analysis. Each segment processes specific data types independently using dedicated algorithms, reducing overall system complexity while maintaining comprehensive identification accuracy.
Solution Approach 2:
The patent transforms multiple raw data parameters into a unified similarity score through standardized processing functions. By changing the parameter representation from diverse raw data to a consolidated similarity metric, the system simplifies data processing while preserving the informational content needed for accurate device association.
3Measurement precision
If similarity scoring algorithms are applied to public data, then device identification capability is improved, but computational resources increase
Solution Approach 1:
The patent applies partial action by implementing threshold-based filtering that stops detailed similarity analysis once a predetermined confidence level is reached. This prevents excessive computational resources from being consumed on already-solved cases while maintaining high identification capability for ambiguous cases that require full analysis.
Solution Approach 2:
The patent implements continuous data collection and incremental similarity scoring that processes clickstream data in real-time as it becomes available. This continuous approach distributes computational load over time rather than concentrating it in batch processing, reducing peak resource consumption while maintaining identification accuracy.
Data Source
AI summary
A method for associating a device with a user including the action of receiving from a first computerized device operated by a first user a first data content including a plurality of first data items, including clickstream data. An action of setting rules for computing scores representing similarity between a first data item received from the first computerized device and a second data item received from a second computerized device. An action of collecting the plurality of the first data items and the second data items, from the plurality of computerized devices of the plurality of computerized devices. An action of computing the score representing similarity between at least one pair of computerized devices respectively providing the first and the second data items. And an action of determining that the pair of computerized devices are operated by a same user if the score reaches a predetermine value.