Cross-Device Consumer Tracking With Device Profile Correlation
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Solution Overview
Problem
Conventional consumer tracking systems struggle with accurately identifying and managing consumer data across multiple devices and scenarios such as multi-user single devices and single user multiple devices, leading to inefficiencies in tracking and data management.
Innovation Solution
A centralized system that utilizes client device IDs and profiles to generate updated or new versions and sessions, correlating them with consumer IDs, and managing sessions based on predetermined thresholds and consumer interactions, while storing data centrally to handle diverse device usage scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tracking systems use basic device identification methods, then implementation is simple, but tracking accuracy across multiple devices deteriorates
Solution Approach 1:
The patent segments the tracking system into multiple independent components: device fingerprinting module, session management module, consumer profile module, and behavior tracking module. Each component handles specific aspects of tracking independently, improving accuracy without requiring complete system redesign. The device fingerprinting segment creates unique identifiers from device characteristics, while the session management segment handles state tracking, allowing high precision tracking through modular architecture.
Solution Approach 2:
The patent implements universal tracking identifiers that work across multiple devices and platforms. The consumer profile data structure is designed to be device-agnostic, allowing the same identification and tracking mechanism to function whether the consumer uses a mobile device, desktop computer, or tablet. This multi-functional approach enables accurate cross-device tracking without requiring device-specific implementations.
2Productivity
If the system stores detailed consumer behavior data centrally, then data management efficiency improves, but data security and privacy risks increase
Solution Approach 1:
The patent implements preliminary data anonymization and aggregation before central storage. Consumer behavior data is processed to remove personally identifiable information and aggregated into pattern-based metrics before being stored centrally. The system pre-establishes data retention policies and access control mechanisms, so that even though data is stored centrally for efficient management, the privacy risks are mitigated through advance protective measures.
Solution Approach 2:
The patent introduces an intermediary data processing layer between data collection and central storage. This intermediary layer anonymizes individual consumer data, aggregates behavior patterns, and filters sensitive information before data reaches the central repository. The intermediary acts as a buffer that enables efficient centralized data management while protecting consumer privacy through automated data transformation and aggregation processes.
3Reliability
If the system generates new device versions and sessions frequently, then tracking updates improve, but processing overhead increases
Solution Approach 1:
The patent implements periodic session updates and device version generation based on time intervals and activity thresholds rather than continuous regeneration. Sessions are refreshed at predetermined intervals or when specific activity milestones are reached, allowing the system to maintain reliable tracking data while avoiding unnecessary processing. This periodic approach ensures tracking reliability through regular updates without the continuous processing overhead of frequent generation.
Solution Approach 2:
The patent dynamically adjusts session generation parameters based on system load, device activity levels, and data freshness requirements. When system resources are abundant and tracking accuracy is critical, the system increases update frequency. When processing overhead becomes excessive or activity levels are low, the system reduces generation frequency. This adaptive parameter adjustment maintains reliable tracking while optimizing processing efficiency based on real-time conditions.
Data Source
AI summary
Provided herein are systems, methods and computer readable media for consumer monitor and tracking. An example method may include receiving client device ID and client device profile data, comparing client device ID and client device profile data to a plurality of known client device versions, generating an updated known client device version in an instance in which the client device ID correlates to at least one of the plurality of known client device versions and the client device profile data does not correlate to the at least one of the plurality of known client device versions and generating a new known client device version in an instance in which the client device ID does not correlate to at least one of the plurality of known client device versions.