US20250177871
2025-06-05
Human necessities
A63F13/87
The patent application discusses a system designed for dynamic chat translation and interactive game control. It involves receiving user communications in electronic games and converting these using localization settings alongside machine learning models for language processing. This conversion process replaces segments of communication to enhance user understanding. The system is capable of adapting both voice and text outputs, including those from non-player characters (NPCs), to improve user interaction and comprehension in gaming environments.
The invention addresses the growing need for enhanced communication in increasingly immersive gaming environments. With players often located in diverse regions, there is a demand for systems that facilitate better communication through dynamic translation. Existing systems with static menu or pre-programmed settings often fall short in meeting all user requirements, highlighting the necessity for more adaptable configurations that cater to different linguistic and cultural needs.
The system utilizes localization settings and machine learning models to process communications. It can handle various forms of communication, such as voice, text, and audio data, converting segments into more understandable formats for users. The model can adjust translations to account for cultural differences, ensuring that terms like measurement units or slang are appropriately adapted. It also includes functionality to filter out offensive language, making it suitable for different age groups and audiences.
Machine learning models play a crucial role in this system by facilitating accurate language processing and dynamic chat translation. These models are trained using diverse datasets that include regional and cultural parameters. The system continuously updates these models based on user reactions, detected through eye tracking and other behavioral analyses, ensuring that translations remain relevant and accurate over time.
User reactions are integral to refining the translation process. The system detects reactions such as facial expressions and body movements to identify discrepancies in translation accuracy. Eye tracking data is particularly useful for spotting incorrect translations, prompting updates to the machine learning models. This feedback loop ensures that the system can adapt to changing user needs and improve its translation capabilities continuously.