AI Communication Model for Real-Time Language Feedback
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Solution Overview
Problem
Language barriers and varying language proficiency among international work teams hinder effective communication in virtual settings, as existing language training methods are often artificial and do not address real-time communication needs.
Innovation Solution
A method utilizing an AI-based communication model that captures and analyzes real-time communication content, including spoken language, gestures, and facial expressions, to provide personalized feedback and improve language skills within virtual communication services like Zoom or Slack, using a neural network trained by experts to recognize and classify communication patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If artificial communication inputs are used for language training, then training content can be standardized, but the training becomes unrealistic and does not reflect actual user communication needs
Solution Approach 1:
The patent captures actual communication inputs from users during real-world usage of the internet-based communication service. Instead of creating artificial training content, the system copies authentic communication data including speech, text, gestures, and facial expressions that users naturally produce, thereby ensuring training realism while maintaining standardization through systematic processing of these copied inputs
Solution Approach 2:
The system utilizes the user's own communication data as training material. By analyzing the user's actual communication patterns, errors, and behaviors during normal usage, the system generates personalized training content without requiring external artificial content creation, thus achieving both realism and automated standardization
2Productivity
If communication analysis is performed continuously in real-time, then language skills can be improved immediately, but system complexity and processing requirements increase
Solution Approach 1:
The patent implements a feedback routine that continuously analyzes communication content as it is generated during real-time usage. The system is pre-configured with communication models and analysis algorithms that automatically process speech, text, gestures, and facial expressions without requiring complex post-processing, enabling immediate feedback while managing system complexity through proactive analysis architecture
Solution Approach 2:
The system provides continuous real-time feedback to users about their communication performance by analyzing their inputs and generating appropriate responses. This feedback mechanism includes correcting language errors, suggesting improvements, and tracking progress, all processed through an integrated feedback routine that balances immediate responsiveness with manageable system complexity
3Adaptability or versatility
If multiple communication models for different languages and levels are provided, then user coverage is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal communication model framework that can handle multiple languages, proficiency levels, and communication modalities (speech, text, gestures, facial expressions) through a single integrated system. The communication models are designed with modular architecture that allows them to adapt to different languages and levels without requiring completely separate models for each combination, thus achieving broad coverage while controlling complexity
Data Source
Figure 1
AI summary
A method and system for virtual communication, in particular for language training, when using Internet-based communication services, comprising the following steps • Providing communication content of a user's communication input; • Providing a communication model on a computer unit; • Passing the communication content to the communication model, wherein the communication model has a feedback routine that analyzes the communication content and calculates a communication score and generate a hint for the user with regard to a correct usage of the communication content if the communication score is below an adjustable level; • Output of the hint on a terminal device of the user.