Dynamic Chat Translation via Machine Learning and Eye Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gaming systems lack the ability to dynamically translate in-game chat and communications in real-time, accommodating users with different language preferences and cultural backgrounds, which can lead to misunderstandings and a less immersive gaming experience.
Innovation Solution
The implementation of a system that uses a machine learning model for language processing, coupled with user-specific localization settings, to dynamically translate communications such as voice, text, and character outputs in real-time, while also detecting and adapting to user reactions through eye tracking data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-time language translation is implemented using machine learning models, then communication accessibility across different languages is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that sits between the communication input and output systems. This model acts as a mediator that automatically translates and adapts communications between different languages and cultural contexts, resolving the language compatibility issue without requiring direct integration between different language systems, thus managing the complexity through a dedicated intermediary layer
Solution Approach 2:
The system performs preliminary localization processing by pre-configuring cultural models and language translation frameworks before actual communication occurs. The machine learning model is pre-trained with cultural nuances and language patterns, allowing it to rapidly process translations in real-time without requiring complex runtime decision-making, thereby reducing the perceived system complexity during operation
2Adaptability or versatility
If dynamic localization settings are implemented for each user, then user experience and cultural adaptability are improved, but data processing requirements and system resources increase
Solution Approach 1:
The patent implements local quality by applying different localization settings and cultural models to different users based on their specific preferences and cultural backgrounds. Instead of a uniform translation approach, each user receives customized localization processing tailored to their individual needs, allowing the system to handle diverse data processing requirements efficiently through differentiated treatment of user-specific parameters
3Object-affected harmful factors
If offensive language filtering and replacement is implemented, then user safety and communication quality are improved, but loss of original communication content increases
Solution Approach 1:
The patent converts potentially harmful offensive language into beneficial localized alternatives by using cultural models to identify and replace inappropriate content with culturally appropriate equivalents. Rather than simply blocking or censoring offensive language, the system transforms it into acceptable communication that maintains the original intent while adhering to cultural norms, thus converting a harmful factor into a beneficial localization feature
Data Source
AI summary
System, process, and device configurations are provided for dynamic chat translation and interactive game control. Methods can include receiving communications for a user of an electronic game and converting communications using a localization setting and a machine learning model for language processing to replace one or more segments of the communication. Updated communications may be output with replacement segments to provide automatic conversion of information in a user's understanding. By automatically adapting outputting voice or text of a game chat and/or game output, such as non-player character (NPC) chat, users may have improved understanding. In addition, systems and methods include detecting user reactions, including use of eye tracking data, to assess communication conversion and to update processes and models for conversion of communications.