Dynamic NLP System Contextual Inference Segmentation
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Solution Overview
Problem
Current AI conversational systems lack the ability to process information and respond contextually, empathetically, and personally, often failing to understand and address the nuanced needs and desires of users, leading to limitations in human-like conversational capabilities.
Innovation Solution
A dynamic natural language processing system that accesses a Knowledgebase of Knowledge Records, Terms, and Relationship Types to facilitate user interaction, employing relationship properties for inference, classifying terms, and generating responses based on Criteria-Value Rating Pairs and user traits, while integrating learnings from interactions and external data, and utilizing a Large Language Model for personalized and contextually relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current AI conversational systems process information based on literal words and provided context, then system complexity is reduced, but contextual understanding capability deteriorates
Solution Approach 1:
The system segments contextual understanding into multiple layers: literal word processing, provided context analysis, and inferred context generation. Each layer handles specific aspects of understanding independently, allowing the system to maintain manageable complexity while achieving comprehensive contextual comprehension through the combination of segmented processing stages.
2Ease of operation
If AI systems provide standardized responses, then ease of operation is improved, but personalization capability deteriorates
Solution Approach 1:
The response generation system dynamically adapts between standardized and personalized responses based on the analyzed user profile and context. The system maintains a baseline of efficient standardized responses while dynamically injecting personalized elements when user-specific context is available, allowing operational efficiency and personalization capability to coexist through dynamic adjustment of response generation mode.
3Speed
If AI systems process only provided context, then processing speed is improved, but empathetic engagement capability deteriorates
Solution Approach 1:
The system performs preliminary analysis of user input to identify contextual cues that indicate emotional state or personal significance. This preliminary action enables the system to quickly flag cases requiring empathetic engagement while maintaining fast processing for routine queries, thus preserving processing speed while enhancing empathetic engagement capability when needed.
Data Source
AI summary
Systems and methods for contextual inference and response generation in natural language processing are disclosed. A data structure containing Knowledge Records, terms, and Relationship Types is accessed to facilitate interaction and inference. Knowledge states and uses relationship properties are employed for inference, classifying terms to generate responses. Logic and inference mechanism link Knowledge Records and Terms for response generation. Responses are tailored based on Criteria-Value Rating Pairs and user Traits, with the system adapting through learnings from user interactions and external data. The method includes storing interaction graphs and features modules for natural language understanding, interaction, response generation, and user engagement, executed on a processing unit. The system dynamically updates its Knowledgebase and refines response mechanisms, offering personalized and contextually relevant interactions in natural language. Additionally, the system provides structured contextual and criteria-value data to third-party systems such as Large Language Models (LLM) s via APIs, enhancing contextual understanding and interaction.