Intelligent Conversational System Using Celestial Architecture
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Solution Overview
Problem
Current computer systems lack the ability to simulate human intelligence, such as logical deductions, inferences, and creative responses, and are unable to recognize or react to human languages or learn from experiences, limiting their ability to communicate in a human-like manner.
Innovation Solution
An intelligent conversational system using a celestial architecture that decouples backend computing from front-end processing, employing modules for natural language processing, knowledge engines, and biometric analysis to recognize user intent, generate personalized responses, and adapt to user generation and background, enabling conversations that mimic human speech.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computer systems are used to execute specific tasks, then computational efficiency and reliability are maintained, but the ability to simulate human intelligence, recognize human languages, and communicate in a human-like manner is lost
Solution Approach 1:
The system divides the conversational AI functionality into separate modules: a natural language processing module that handles language understanding, a knowledge engine module that processes information, and a dialogue management module that coordinates interactions. This segmentation allows each module to specialize in specific tasks while maintaining overall system efficiency and reducing complexity through modular design.
Solution Approach 2:
The patent introduces a dialogue management module as an intermediary between the natural language processing module and the knowledge engine. This mediator coordinates the flow of information, manages conversation state, and orchestrates the interaction between different components, enabling human-like communication while maintaining computational efficiency through structured mediation.
2Adaptability or versatility
If computers are designed to recognize and react to human languages, then communication capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of user input through the natural language processing module, which pre-parses and structures the language data before passing it to the knowledge engine. This preliminary action prepares the data in advance, reducing the computational burden during the main processing phase and thereby decreasing overall processing time while maintaining comprehensive language recognition capabilities.
3Ease of operation
If the system is designed to be personalized and adaptive to user background, then user experience improves, but system complexity and data processing requirements increase
Solution Approach 1:
The knowledge engine is designed to process information locally within its module, maintaining user context and personalization data without requiring complex global coordination. Each module operates with its own localized processing capabilities, adapting to user background and preferences through distributed intelligence rather than centralized complexity, thereby improving user experience while managing system complexity.
Data Source
AI summary
A system and method simulates conversation with a human user. The system and method receive media, convert the media into a system-specific format, and compare the converted media to a vocabulary. The system and method generate a plurality of intents and a plurality of sub-entities and transform them into a pre-defined format. The system and method route intents and the sub-entities to a first selected knowledge engine and a second knowledge engine. The first selected knowledge engine selects the second knowledge engine and each active grammar in the vocabulary uniquely identifies each of the knowledge engines.