Video Analytics Device for User Engagement Measurement
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Solution Overview
Problem
Content providers face inaccuracies in determining user engagement and content popularity due to incomplete or erroneous data from server-side log files, leading to inefficient resource allocation and poor user experience.
Innovation Solution
A system that processes data from user devices to generate an analytical data model of user engagement, using machine learning techniques to clean and analyze content data, identify user preferences, and adjust content delivery based on user behavior, thereby improving content recommendations and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-side log files are used to determine user engagement and content popularity, then data collection is simple and centralized, but the data is incomplete or erroneous leading to inaccuracies
Solution Approach 1:
The patent introduces an intermediary analytical system that sits between the server-side log files and the content delivery mechanism. This intermediary processes and validates the data, using multiple data sources including client-side analytics to cross-verify information and eliminate errors, thereby improving measurement precision without directly modifying the simple server-side logging approach
Solution Approach 2:
The patent segments the data collection and processing system into multiple independent components: server-side logging, client-side analytics, and an intermediary analytical processing layer. Each segment handles specific tasks, allowing the system to maintain simplicity in data collection while achieving high accuracy through distributed validation and processing
2Productivity
If content is provided to all users without personalization, then content delivery is simple and fast, but resource allocation is inefficient leading to waste
Solution Approach 1:
The patent changes the parameters used for content delivery from generic one-size-fits-all approaches to personalized parameters derived from analytical data models. These models incorporate user engagement metrics, content popularity scores, and behavioral patterns to dynamically adjust content selection, timing, and delivery parameters, improving resource allocation efficiency while maintaining manageable system complexity through algorithmic automation
Solution Approach 2:
The system implements self-service mechanisms where the analytical data models automatically generate personalized content recommendations and delivery schedules without requiring manual intervention. The system serves itself by using its own collected data to optimize its content delivery, reducing operational complexity while achieving high productivity in resource allocation
3Measurement precision
If multiple layers of analysis are performed using machine learning techniques, then user engagement accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-aggregating data into analytical data models before actual content delivery decisions are made. Machine learning models are trained in advance on historical data, and user engagement patterns are pre-analyzed to create ready-to-use recommendation frameworks. This reduces real-time processing time while maintaining high accuracy through pre-computed insights
Solution Approach 2:
The system implements partial action by applying multiple layers of machine learning analysis selectively rather than uniformly to all data. High-value or ambiguous cases receive full multi-layer analysis, while clear-cut cases use simplified processing paths. This approach achieves high measurement precision for critical decisions while minimizing unnecessary processing time for routine cases
Data Source
AI summary
A device may communicate with a group of devices to obtain data regarding a set of events occurring for the group of devices. The device may process the data regarding the set of events to remove a subset of data entries, from the data, that is associated with an anomalous event. A first layer of analysis may relate to the group of devices, a second layer of analysis relating to a set of sessions of operating a user interface via the group of devices, and a third layer of analysis relating to information provided via the user interface. The device may perform the multiple layers of analysis via a machine learning technique to identify an alteration relating to the information provided via the user interface. The device may alter the information provided via the user interface based on performing the multiple layers of analysis.