Content Resource Execution Analyzer for Interactive Media
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Solution Overview
Problem
Current content distribution networks face challenges in analyzing the execution of interactive content resources across client devices and selecting appropriate content for specific execution devices based on user feedback and performance levels, leading to suboptimal content delivery and user experience.
Innovation Solution
The implementation of techniques that analyze content resource execution data using singular vector decomposition to determine correlations between content executors, calculate similarity index values, and aggregate data to select relevant interactive content resources for execution, ensuring personalized content delivery based on user feedback and performance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content distribution networks provide vast and diverse content resources to users, then the quantity and variety of content available is improved, but the difficulty of analyzing execution data and selecting appropriate content for specific devices increases
Solution Approach 1:
The patent introduces an intermediary system that includes servers and algorithms to analyze content resource execution data, user feedback, and device characteristics. This intermediary layer processes the complex data relationships between content, users, and devices, automatically selecting appropriate content without requiring direct manual analysis of the vast content library.
Solution Approach 2:
The system implements feedback mechanisms where user responses and execution data are collected, analyzed, and used to improve future content selection. The feedback loop enables the system to learn from past content delivery outcomes and continuously refine its selection process, making the complex analysis task more efficient over time.
2Reliability
If the system analyzes user feedback and performance levels to personalize content delivery, then the quality of user experience is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the content selection process into distinct functional modules: data collection from users and devices, execution analysis, feedback processing, and content recommendation. Each module handles specific aspects of the complex analysis task, distributing computational complexity across multiple specialized components rather than requiring one monolithic processing system.
Solution Approach 2:
The system performs preliminary analysis of user profiles, device characteristics, and content metadata before actual content delivery. By pre-processing and organizing data in advance, the system reduces the computational burden during real-time content selection, enabling personalized delivery without excessive processing requirements at the moment of delivery.
3Measurement precision
If content resource execution data is collected and analyzed from multiple client devices, then the accuracy of content selection is improved, but the amount of data to be processed and stored increases
Solution Approach 1:
The patent extracts only the most relevant features and patterns from the vast execution data collected from multiple devices, rather than processing every raw data point. The system identifies and extracts key performance indicators, user preference patterns, and device characteristic features that are most predictive of successful content delivery, discarding redundant information.
Solution Approach 2:
The system merges and aggregates execution data from multiple client devices to create consolidated profiles and patterns. By combining data across devices, the system achieves higher accuracy through larger sample sizes while managing data volume through aggregation and synthesis rather than individual device analysis.
Data Source
AI summary
Techniques described herein relate to analyzing executions of content resources within networks of execution client devices, and selecting sets of interactive content resources for execution on particular execution devices based on such analyses. Content resource execution data may be received from various execution client devices on which content resources have been executed and provided to end users. Such data may be analyzed to determine correlations between a first content executor and additional content executors based on the their respective content resource execution data, and the content resource execution data of correlated content executors may be aggregated and analyzed to select particular interactive content resources for the first content executor. Such selections may be provided to first content executor during a content execution session following an authenticated login by the first content executor.