Behavioral Biometric Cookies for Cross-Device User Tracking
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Solution Overview
Problem
Conventional cookie systems fail to reliably track users across multiple devices, differentiate between multiple users sharing the same device, and accurately measure unique visitors, due to limitations such as easy deletion, browser specificity, and inability to link usage sessions across different devices or software upgrades.
Innovation Solution
The development of behavioral biometric cookies that monitor and analyze user interactions to create unique user profiles, allowing differentiation between users and tracking across multiple devices, browsers, and IP addresses, even if cookies are deleted or changed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cookies are used for user tracking, then implementation is simple and compatible with all browsers, but users can easily delete them and they cannot reliably track users across multiple devices
Solution Approach 1:
The system segments user identification into multiple components: device identifiers, browser identifiers, IP addresses, and behavioral biometric profiles. Each component serves a specific tracking purpose, and together they form a robust multi-device user profile that cannot be easily deleted or spoofed
Solution Approach 2:
The patent introduces server-side stored behavioral biometric profiles as an intermediary between the user's device and the tracking system. These profiles are maintained on the server rather than stored locally in browser cookies, allowing reliable cross-device tracking while keeping the client-side implementation simple
2Measurement precision
If cookies are used to identify users, then user identification is straightforward, but they cannot differentiate between multiple users sharing the same device
Solution Approach 1:
The system applies local quality by creating distinct behavioral biometric profiles for each user based on their unique interaction patterns with the device. Each user's typing rhythm, mouse movements, and navigation behaviors are captured and stored as separate profiles, enabling precise differentiation even when using the same physical device
Solution Approach 2:
The patent changes the parameters used for user identification from static cookie values to dynamic behavioral biometric parameters such as typing speed, keystroke timing, mouse movement patterns, and navigation preferences. These parameters continuously evolve and provide high-precision user differentiation
3Reliability
If cookies are deleted or browsers are changed, then user privacy is protected, but tracking continuity is lost and unique visitor measurement becomes inaccurate
Solution Approach 1:
The system performs preliminary action by collecting and analyzing user behavioral data continuously to build and update behavioral biometric profiles before cookie deletion occurs. These profiles are stored server-side, ensuring tracking continuity even when local cookies are deleted or browsers are changed
Solution Approach 2:
The patent converts the harmful effect of cookie deletion into a benefit by designing a system that does not rely on persistent local storage. Instead, it uses ephemeral behavioral data captured during each session to maintain accurate user profiles on the server, making the system more resilient to privacy-conscious user actions
4Measurement precision
If behavioral biometric monitoring is implemented, then accurate user differentiation and cross-device tracking are achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential behavioral biometric features needed for user identification, such as typing rhythm, mouse movement patterns, and navigation preferences, rather than monitoring all possible user actions. This selective extraction reduces data processing requirements while maintaining high differentiation accuracy
Solution Approach 2:
The behavioral biometric monitoring system is self-configuring and automatically adapts to each user's behavior patterns without requiring manual setup or calibration. The system continuously learns and updates profiles in the background, reducing the operational complexity of managing the monitoring infrastructure
Data Source
AI summary
Devices, systems, and methods of generating and managing behavioral biometric cookies. The system monitors user-interactions of a user, that are performed via an input unit of an end-user device; and extracts a set of user-specific characteristics, which are used as a behavioral profile or behavioral signature. The set of user-specific characteristics are further used as a behavioral biometric cookie data-item, allowing the system to distinguish between two human users that utilize the same electronic device; and allowing the system to distinguish between a human user and an automated script. The system further allows creation and utilization of behavioral sub-cookies that distinguish among multiple users of the same device. The system also allows creation of a cross-device behavioral cookie, to track browsing history of a single user across multiple electronic devices.