Gameplay Event Identification Using Biometric Telemetry
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Solution Overview
Problem
Users face challenges in creating engaging gameplay videos from long gameplay sessions, as they need to manually identify and edit interesting events, which is time-consuming and inefficient.
Innovation Solution
A system that analyzes gameplay session data to automatically identify and index interesting events, using telemetric and biometric data, and generates a digital storyboard, allowing users to modify and output the content for sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and editing of interesting events is used, then video creation quality can be maintained, but time consumption and effort increase significantly
Solution Approach 1:
The system enables automatic self-service by having the gameplay session data automatically analyzed by the event identification model to identify interesting events without requiring manual review. The system self-generates the digital storyboard by automatically selecting, sequencing, and formatting video segments based on the identified events, eliminating the need for manual video editing while maintaining quality through automated quality metrics evaluation.
2Productivity
If automatic event identification using machine learning model is implemented, then video creation time is reduced, but system complexity increases
Solution Approach 1:
The system segments the complex video creation process into distinct modular components: gameplay session data collection, event identification model analysis, interesting events selection, digital storyboard generation, and video compilation. Each component operates independently with well-defined interfaces, allowing the complex automated process to be managed through manageable segments while maintaining high productivity.
3Measurement precision
If comprehensive gameplay session data analysis is performed, then event identification accuracy improves, but data processing requirements increase
Solution Approach 1:
The system applies local quality by focusing computational resources on analyzing specific portions of gameplay session data that are most likely to contain interesting events. The event identification model evaluates data locally around detected event candidates rather than uniformly processing entire gameplay sessions, thereby improving event detection accuracy while reducing overall data processing energy requirements through targeted analysis.
Data Source
AI summary
The present disclosure provides a system that automatically analyzes telemetric data, biometric data, and other data associated with a gameplay session to identify events occurring during the gameplay session. The telemetric data is generated by the game application during the gameplay session. The biometric data can be generated by input devices and can generate data associated with the user. The system can be configured to identify the segments associated with recorded gameplay events from the gameplay session and use the gameplay data associated with the events to create and output video data for a gameplay segment.