Cognitive Ergonomic System for Voice Processing
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Solution Overview
Problem
Current language processing systems rely on statistical methods that fail to understand the conceptual meaning of verbal messages, leading to errors in interpreting incomplete or ambiguous statements, and are limited in their ability to control technical systems effectively.
Innovation Solution
The Cognitive Ergonomic System (CES) simulates human language comprehension by reconstructing the meaning of verbal inputs using a conceptual knowledge base and intelligence module, allowing for the identification of logically false statements, inference of consequences, and context-dependent processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical methods are used for language processing, then voice recognition can be implemented, but the system fails to understand conceptual meaning and makes errors with incomplete or ambiguous statements
Solution Approach 1:
The patent introduces an intermediary layer between acoustic signal processing and lexical assignment. This intermediary performs meaning-based processing that reconstructs verbal messages and ascends to conceptual level, acting as a mediator that preserves conceptual meaning while enabling statistical voice recognition to function effectively
Solution Approach 2:
The system segments the language processing task into distinct stages: acoustic signal processing, meaning-based reconstruction, conceptual level processing, and lexical assignment. This segmentation allows each stage to specialize, with the conceptual level handling ambiguous statements and incomplete information separately from statistical recognition
2Reliability
If meaning-based processing is implemented, then accurate interpretation of verbal inputs is achieved, but system complexity increases
Solution Approach 1:
The patent creates a universal processing module that handles multiple functions: reconstructing verbal messages, removing ambiguity, ascending to conceptual level, and generating situation models. This multi-functional module reduces overall system complexity by consolidating what could be separate complex components into a single integrated unit
3Measurement precision
If contextual processing is added to remove ambiguity, then interpretation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary construction of situation models that capture contextual information in advance. By pre-processing and storing contextual relationships, the system can quickly resolve ambiguity during actual language processing without requiring extensive real-time computation
Data Source
AI summary
A language-processing system has an input for language in text or audio, as a message, an extractor operating to separate words and phrases from the input, to consult a knowledge base, and to assign a concept to individual ones of the words or phrases, and a connector operating to link the concepts to form a statement. In some cases there is a situation model updated as language is processed. The system may be used for controlling technical systems, such as robotic systems.