Video Game Error Detection Using Predicted Gameplay Frames
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Solution Overview
Problem
Existing video game quality assurance testing is labor-intensive and inefficient, with many errors going undetected, leading to poor functionality and reduced immersion.
Innovation Solution
A data processing apparatus utilizing a trained machine learning model to predict video game sequences and detect errors by comparing actual game footage with expected outcomes, incorporating receiving circuitry, prediction circuitry, and error detection circuitry to analyze video images and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assurance testing is used, then human judgment and immersion can be maintained, but testing time and labor effort increase significantly
Solution Approach 1:
The patent replaces manual human testing with an automated computer vision system that uses machine learning models to detect errors in video game footage. The system processes gameplay videos automatically, eliminating the need for human testers to watch and log errors manually, thus reducing testing time while maintaining detection reliability through AI-based error identification.
Solution Approach 2:
The system enables self-service error detection where the gameplay video itself serves as the data source for automated analysis. The machine learning model processes the video footage independently, allowing the system to detect errors autonomously without requiring human intervention for each error detection task, thereby significantly reducing the time investment required for quality assurance.
2Measurement precision
If manual error logging is performed, then contextual understanding is maintained, but productivity decreases due to labor intensity
Solution Approach 1:
The patent substitutes manual error logging with an automated computer vision system that uses trained machine learning models to identify and log errors. The system processes gameplay videos automatically, extracting error information and contextual data without human intervention, thereby maintaining identification accuracy while significantly improving testing efficiency and productivity.
Solution Approach 2:
The machine learning model acts as an intermediary between the gameplay video and the error detection process. It analyzes the video footage, identifies errors, and extracts contextual information automatically, serving as a mediator that maintains the precision of error identification while eliminating the labor-intensive manual logging process and boosting overall productivity.
3Productivity
If automated error detection is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent implements automated error detection using machine learning models that process gameplay videos automatically. The system uses pre-trained models that can be applied to different video games without requiring complex custom development for each game title, thereby achieving high productivity in error detection while managing system complexity through the use of general-purpose AI models rather than game-specific complex systems.
Data Source
AI summary
A data processing apparatus for detecting one or more errors for one or more video images for a video game, the data processing apparatus comprising receiving circuitry to receive a sequence of video images for the video game and one or more action inputs associated with the sequence of video images, prediction circuitry to generate a predicted video image in dependence on at least one video image of the sequence of video images and an action input associated with the at least one video image, the prediction circuitry comprising a trained machine learning model to generate the predicted video image, and error detection circuitry to detect, for one or more video images subsequent to the at least one video image, one or more errors in dependence on the predicted video image.