Device Identification and Activity Estimation in Computing Platforms
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Solution Overview
Problem
Conventional systems are limited in their ability to identify devices and predict their behavior as they transition between computing environments while maintaining user privacy, and they struggle to provide relevant web content efficiently and intelligently.
Innovation Solution
A system that identifies devices using device identifiers and generates keyword and probability assignments based on aggregated data events, enabling the prediction of user actions and anticipatory delivery of relevant web content, even in secure computing environments where interaction history is inaccessible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If device identifiers and aggregated data events are used to identify devices and predict behavior, then the ability to deliver relevant web content and predict user actions is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts only the necessary identifying features (device identifiers, browser type, operating system, screen resolution, language settings) from the complete device profile, separating these from detailed user interaction data. This extraction allows content delivery optimization without requiring access to sensitive user behavior patterns, thus maintaining privacy while improving content relevance.
Solution Approach 2:
The system performs preliminary device identification and capability assessment before content delivery, storing device profiles in advance. This preliminary action includes determining device type, browser capabilities, and preferred languages, allowing the system to prepare and deliver appropriately formatted content without needing to analyze real-time user behavior, thereby improving efficiency while preserving privacy.
2Adaptability or versatility
If device identification and behavior prediction systems are implemented, then the ability to provide personalized web content is improved, but system complexity increases
Solution Approach 1:
The patent segments the content delivery system into distinct modular components: device identification module, profile storage module, content selection module, and content delivery module. Each component handles a specific aspect of the personalization process independently. This segmentation allows the system to achieve high adaptability through coordinated modules while managing complexity by isolating functions and reducing interdependencies.
3Measurement precision
If comprehensive device data is collected and analyzed, then the accuracy of user behavior prediction is improved, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial action by collecting and analyzing only the most relevant device characteristics and interaction patterns needed for accurate content prediction, rather than comprehensively analyzing all possible device data. The system focuses on key identifiers (device type, browser, OS) and essential interaction metrics, achieving sufficient prediction accuracy while significantly reducing data processing time and computational resource requirements.
Data Source
AI summary
Systems, methods, and devices identify devices and assign keywords to such devices. Methods include retrieving data from at least one data source, the data comprising a plurality of data events associated with a plurality of devices, and generating a plurality of probability metrics for each of the plurality of devices based on device information and data event parameters included in the retrieved data. Methods also include generating an activity estimation parameter for each of the plurality of devices based on the plurality of probability metrics, the activity estimation parameter comprising an estimated probability of a subsequent data event being taken by a device.