Game Gaze Estimation for Focus-Based Content Rating
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Solution Overview
Problem
Existing methods for evaluating and enhancing electronic game content are inaccurate and rely heavily on intuition, making it difficult to identify the right design elements to improve player engagement and interest.
Innovation Solution
Monitoring player eye movements and gaze estimation during gameplay to create a player focus model, which can rate and modify game content based on player attention and engagement, using techniques such as image segmentation and machine learning to adjust visual elements, NPCs, lighting, and game events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If market performance is used to evaluate game content, then evaluation can be conducted, but accuracy of evaluation is poor and relies on intuition
Solution Approach 1:
The patent replaces intuitive market performance analysis with an automated eye-tracking system that objectively measures player attention. The system uses cameras to capture eye movements, processes this data through algorithms, and generates quantitative ratings of game content effectiveness, substituting human intuition with automated measurement technology.
Solution Approach 2:
The patent introduces eye-tracking technology as an intermediary between players and game content evaluation. Instead of directly analyzing market performance, the system uses eye movement data as a mediator to infer player engagement and content effectiveness, providing a more accurate indirect measurement of player experience.
2Manufacturing precision
If game content is modified based on intuition, then modifications can be made, but the process is error-prone and lacks precision
Solution Approach 1:
The patent implements a feedback loop where eye-tracking data is collected during gameplay, processed to identify which game elements capture player attention, and used to guide content modifications. This closed-loop feedback system enables precise, data-driven content optimization rather than intuition-based changes.
Solution Approach 2:
The system performs preliminary analysis of eye movement patterns to predict which content elements will be most effective before finalizing game design decisions. By pre-identifying high-attention areas through data analysis, the development team can prioritize modifications that are most likely to improve player engagement.
3Measurement precision
If eye movement monitoring is implemented, then accurate focus estimation is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex eye-tracking system into separate functional components: cameras for data collection, processing units for analysis, and rating generation modules. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high measurement precision.
Data Source
AI summary
Embodiments of the present disclosure are directed to monitoring and rating content of an electronic game based on player eye movement and gaze estimation during game play. According to one embodiment, monitoring content of an electronic game can comprise maintaining a player focus model defining eye movements and changes related to a location of focus of players of the electronic game. Game play information related to the content of the electronic game can be received from a gaming system while being played on the gaming system. Eye movement information for a player of the electronic game can also be received from the gaming system. A focus of the player can be determined based on the player focus model and the received eye movement information and the content of the electronic game can be rated based on the determined focus of the player and the received game play information.