Eye-Tracking Gameplay Analysis for Video Game Error Detection
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Solution Overview
Problem
Software developers face challenges in determining user engagement and identifying errors in complex video games due to the subjective nature of engagement and the difficulty in pinpointing when and where errors occur.
Innovation Solution
Implementing a system that captures eye tracking and positional information using sensors like cameras and LIDAR to associate these data with video game frames, generating heatmaps, and using machine learning models to identify features of interest, allowing for real-time game configuration adjustments and error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eye tracking and sensor data are collected and processed to identify features of interest, then user engagement measurement and error detection capability are improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of feature identification into distinct processing stages: capturing eye tracking data, capturing game frame data, associating the two datasets, identifying features of interest, and generating feedback. This segmentation allows each component to be optimized independently while working together to solve the overall complex problem of precise engagement measurement.
Solution Approach 2:
The patent introduces an intermediary processing layer that associates eye tracking information with game frame data through temporal synchronization and spatial mapping. This intermediary mechanism bridges the gap between raw sensor data and meaningful feature identification, enabling precise measurement without requiring direct complex analysis of all raw data simultaneously.
2Speed
If real-time processing of eye tracking data and game frames is implemented, then feedback speed is improved, but computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing eye tracking data to identify gaze bursts and pre-filtering game frames based on temporal windows. This preliminary processing reduces the amount of data that requires full analysis in real-time, enabling fast feedback generation while reducing computational burden during critical real-time processing moments.
Solution Approach 2:
The patent applies partial action by processing only the portions of data that are most relevant to feature identification - specifically, eye tracking data within temporal windows surrounding detected features of interest, rather than processing all captured data continuously. This selective processing maintains real-time feedback speed while conserving computational resources.
3Measurement precision
If multiple sensors and processing components are integrated, then feature identification accuracy is improved, but ease of operation decreases
Solution Approach 1:
The system implements self-service by automatically performing the complex tasks of data association, feature identification, and feedback generation without requiring manual intervention. The integrated components work autonomously to process sensor data and game frames, transforming raw inputs into meaningful insights automatically, thus maintaining high accuracy while simplifying operation for users.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances user interaction capture and utilization for better video game testing and increased configurability by identifying engaging features and errors, improving the gaming experience.
Implementation Method 1
the one or more sensors includes a light detection and ranging ('LIDAR') system
Data Source
AI summary
A device may as implemented by an interactive computing system configured with specific computer-executable instructions, capturing one or more image frames of a video game, receiving, from one or more sensors, eye tracking information associated with a user playing the video game, associating the eye tracking information with the one or more image frames, identifying at least a first frame based at least in part on the eye tracking information, identifying at least one feature of interest within the first frame based on the eye tracking information, and outputting an indication associated with the at least one feature of interest.