Cloud Gaming Engagement Model Using Multi-Sensor Reaction Data
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Solution Overview
Problem
Current methods for enhancing user engagement in video games through recommendation systems are often biased or inaccurate, leading to decreased user interaction due to mismatched game recommendations.
Innovation Solution
A system that captures player reactions during gameplay using multiple sensors, processes the sensor data to identify significant reactions, and uses an engagement model to generate personalized recommendations based on user interests and preferences, aligning sensor data to remove delays and correlate it with interactive game data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to capture user reactions during gameplay, then measurement precision of user engagement is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (facial expression cameras, voice microphones, body movement sensors, controller input sensors) into a unified sensor system that captures various user reactions simultaneously. This merging approach improves measurement precision by gathering comprehensive engagement data while managing complexity through integrated processing.
Solution Approach 2:
The sensor system is designed to perform multiple functions: capturing facial expressions, voice reactions, body movements, and controller inputs all through a single integrated system. This multi-functionality improves measurement precision across different reaction types while avoiding the complexity of separate independent sensor systems.
2Loss of information
If sensor data from multiple sources is collected and processed in real-time, then user engagement understanding is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously capturing and pre-processing sensor data during gameplay, organizing and synchronizing data from multiple sensors before analysis. This preliminary processing ensures data completeness is maintained while reducing the computational burden during real-time engagement analysis, thus managing processing time effectively.
Solution Approach 2:
The system implements feedback loops where sensor data is continuously processed, analyzed for engagement patterns, and used to adjust processing priorities in real-time. This feedback mechanism ensures that the most relevant user reactions are processed with higher priority, maintaining data completeness while optimizing processing time through adaptive resource allocation.
3Measurement precision
If sensor data is synchronized to remove delays, then data alignment accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent introduces a central server as an intermediary that receives, synchronizes, and aligns sensor data from multiple sources. This intermediary handles the complex task of removing delays and aligning data timestamps, improving measurement precision through accurate temporal correlation while isolating the synchronization complexity within the server rather than requiring complex coordination between multiple distributed systems.
Data Source
AI summary
Methods and systems are provided for generating recommendations related to a game. One example method is for generating recommendations for a game executed by a cloud gaming service. The method includes receiving, by a server, sensor data captured during gameplay of the game by a plurality of user, and each of the plurality of sensor data includes intensity information related to reactions made by respective users. The method includes processing, by the server, features from the sensor data and the interactive data from the game when the users played the game. The features are classified and used to build an engagement model that identifies relationships between specific ones of the plurality of sensor data and the interactive data. The method includes processing, by the server, sensor data captured during a current gameplay by a user using the engagement model. The processing is configured to generate a recommendation to the user regarding an action to take to progress in the game during said current gameplay.