Video Game Inactivity Prediction and Mitigation
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Solution Overview
Problem
Users often experience difficulty re-engaging with video games after a period of inactivity, leading to a decline in familiarity and playing ability, which can deter them from revisiting previously played games.
Innovation Solution
A data processing apparatus that predicts mitigation actions for video games based on inactivity periods using machine learning models, generating video game mitigation information such as reduced difficulty settings, notifications, tutorials, and auto-complete functions to assist users in re-acclimating to the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users return to a video game after a period of inactivity, then they can continue playing the game, but their familiarity and playing ability have declined
Solution Approach 1:
The system performs preliminary actions by detecting inactivity periods and automatically adjusting game parameters (such as enemy health, attack power, or quest difficulty) before the user returns to gameplay. This preemptive adjustment compensates for the user's declined skills without requiring the user to manually reconfigure settings or complete extensive tutorial content.
Solution Approach 2:
The game difficulty parameters are made dynamic and automatically adapt based on the detected inactivity period. The system continuously monitors user activity and adjusts game parameters in real-time, allowing the difficulty to fluctuate according to the user's current skill level rather than remaining static.
2Ease of operation
If difficulty settings are reduced to help returning users, then usability improves, but the challenge and engagement may decrease
Solution Approach 1:
The difficulty settings are made dynamic rather than static, automatically adjusting based on the user's inactivity period and performance metrics. This allows the game to provide easier conditions when needed while maintaining challenge through adaptive elements that respond to user performance.
Solution Approach 2:
The system changes multiple game parameters simultaneously (enemy statistics, quest requirements, resource availability) rather than simply reducing overall difficulty. This creates a balanced adjustment that maintains game integrity while accommodating returning users' reduced skill levels.
3Ease of operation
If the game automatically adjusts parameters based on inactivity, then user experience improves, but system complexity increases
Solution Approach 1:
The game system serves itself by automatically detecting inactivity periods and adjusting parameters without requiring external intervention or complex user input. The system monitors its own usage data and makes autonomous decisions about parameter adjustments, reducing the need for additional complexity in user interfaces or manual configuration.
Solution Approach 2:
The system implements feedback loops where user performance and activity patterns are continuously monitored, and this information feeds back into automatic parameter adjustments. This closed-loop control allows the system to adapt to user needs while maintaining relatively simple architecture through iterative refinement rather than complex upfront design.
Data Source
AI summary
An apparatus comprises receiving circuitry to receive user information indicative of an inactivity period for one or more video games previously played by a user, prediction circuitry to predict a mitigation action associated with a respective video game of the one or more video games previously played by the user in dependence on at least an inactivity period for the respective video game and generate video game mitigation information for the mitigation action associated with the respective video game, the prediction circuitry comprising one or more trained machine learning models to predict the mitigation action in dependence on at least the inactivity period for the respective video game, and output circuitry to output the video game mitigation information.