Intelligent Communication Error Correction System
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Solution Overview
Problem
In today's complex society, communication challenges arise due to cognitive dysfunctions, language barriers, and health-related learning and communication disabilities, leading to misunderstandings and miscommunications, especially in real-time interactions across distances.
Innovation Solution
A processor-based system that identifies communication errors using machine learning, automatically translates and interprets them, and provides feedback to enhance mutual understanding by classifying errors into categories such as incorrect words, pronunciation, or semantic substitutions, and suggests corrections based on contextual factors and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automatic translation and correction systems are implemented, then communication clarity and mutual understanding are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces an intermediary translation and correction system that acts as a mediator between users with different languages and cognitive abilities. This intermediary automatically translates speech, corrects communication errors, and provides contextual interpretations without requiring direct user intervention in the complex processing operations.
Solution Approach 2:
The system performs self-service by automatically detecting communication errors, translating speech in real-time, and providing corrections without requiring manual intervention. The machine learning models autonomously analyze communication patterns, identify errors, and generate appropriate translations and suggestions.
2Reliability
If real-time communication error identification and translation is performed, then understanding between users is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing communication patterns before actual translation is needed. Machine learning models are trained in advance on diverse communication data, and communication error detection rules are pre-established, enabling faster real-time translation and correction.
Solution Approach 2:
The translation and correction process operates continuously in real-time without interrupting the natural flow of communication. The system maintains continuous processing of speech streams, providing ongoing translation and error correction that adapts to the evolving conversation context.
Data Source
AI summary
Embodiments for intelligent facilitation of communication by a processor. A communication error in one or more selected portions of a communication of a user may be identified. The communication error in the one or more selected portions of the communication may be automatically translated.