Gameplay Session Interest Detection via User Input Analysis
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Solution Overview
Problem
Conventional methods for identifying interesting content in gameplay sessions are either manual and time-consuming or rely on error-prone automated object detection models that require extensive game-specific training data and frequent updates, making them inefficient and inaccurate.
Innovation Solution
The system uses user input data to determine high-interest durations in gameplay sessions by computing a user activity measurement and interest score, allowing for automatic identification and prediction of engaging content without the need for extensive video analysis or frequent model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video editing is used to identify interesting content, then accuracy of identification is improved, but time consumption and processing resources increase significantly
Solution Approach 1:
The system enables automatic identification of interesting content by analyzing user input data patterns, allowing the gameplay session data to identify itself without manual intervention. The processor automatically detects high-interest durations by evaluating input event frequency and patterns, eliminating the need for manual video editing while maintaining identification accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual video editing with an automated computational system that processes user input data. Instead of manually reviewing video footage, the system uses algorithms to analyze input event patterns, frequencies, and distributions to automatically identify interesting segments, substituting human mechanical editing with automated digital processing.
2Loss of time
If automated object detection models are used to identify interesting content, then time consumption is reduced, but accuracy decreases due to errors and mischaracterization
Solution Approach 1:
The patent changes the analytical parameters from visual object detection to user input data analysis. Instead of detecting objects in video frames, the system analyzes input event types, frequencies, patterns, and distributions. This parameter change enables more accurate identification of interesting content by focusing on user engagement metrics rather than visual elements, which are prone to mischaracterization.
Solution Approach 2:
The system uses user input data as an intermediary to identify interesting content, rather than directly analyzing video content. The input data serves as a mediator that correlates with interesting moments without requiring direct interpretation of visual elements, thereby improving accuracy while maintaining automated efficiency.
3Adaptability or versatility
If object detection models are retrained frequently to accommodate new game content, then adaptability is improved, but computing resources and processing time increase
Solution Approach 1:
The patent creates a universal analysis system based on user input data patterns that works across different games and content types without requiring game-specific training. The system analyzes fundamental input event patterns (frequency, distribution, timing) that are consistent across various game genres and content, making it adaptable to new game content without retraining while conserving computing resources.
Data Source
AI summary
In various examples, durations of relatively high user activity within a gameplay session may be determined from user input events using a running user activity measurement. Once a duration is identified, it may be further analyzed to merge the duration with one or more other durations and/or to determine or predict whether the duration would be of sufficient interest for further action. A user interest score for an identified duration may be computed based on a set of the user input events that occur in the duration and used to determine and/or predict whether the duration would be of sufficient interest for further action. In some cases, an action may be performed based on determining the user interest score is greater than a statistical value that is computed from user interest scores of multiple identified durations.