Context-Based Conversation System Using Weighted Graph Database
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Solution Overview
Problem
Existing conversation systems lack the ability to interpret user queries effectively due to their reliance on keyword-matching and rule-based approaches, failing to consider context and conversation history, leading to inaccurate responses and limited interpretation capabilities.
Innovation Solution
A context-based conversation system that utilizes a weighted multi-layered graph database to capture and retain conversation context, enabling near real-time text-based interactions by parsing natural language inputs, performing named entity recognition, and generating expressions to provide relevant responses based on user history and metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-matching and rule-based approaches are used, then the system is simple to implement, but the interpretation capability and response accuracy are limited
Solution Approach 1:
The patent segments the conversation processing into multiple independent modules: intent recognition module, entity extraction module, context management module, and response generation module. Each module handles a specific aspect of conversation processing, allowing the system to achieve high accuracy through specialized processing while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces a context vector as an intermediary representation that bridges user input and system response. This context vector captures conversation history, user preferences, and semantic meaning, enabling accurate interpretation without requiring complex rule-based logic for every interaction.
2Reliability
If context and conversation history are not considered, then the processing speed is fast, but the response relevance and user experience deteriorate
Solution Approach 1:
The patent performs preliminary processing of user input by extracting key features and generating context vectors before full conversation processing. This pre-processing captures essential information about user intent and context, enabling faster and more relevant responses without requiring complete analysis of entire conversation history each time.
Solution Approach 2:
The patent extracts only the most relevant contextual information from conversation history using the context vector, rather than processing entire historical records. This selective extraction maintains response relevance by focusing on key contextual elements while significantly reducing processing time.
3Adaptability or versatility
If a comprehensive context model is implemented, then the interpretation capability is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent implements context management with local quality by maintaining different levels of context representation: short-term context for immediate conversation flow, medium-term context for topic tracking, and long-term context for user preferences. Each context level is processed with appropriate detail, achieving high interpretation capability without uniformly complex processing throughout the system.
Data Source
AI summary
Method(s) and system(s) providing for providing context based conversations are described here. The method may include receiving user data pertaining to a user. The user data includes registration information of the user and metadata associated with the user. The method may include determining a pre-defined role of the user based on the registration information. Further, the method may include providing restricted access to a users' data repository to the user, based on the role of the user. The method includes obtaining a text input pertaining to a conversation. Based on the text input an expression is generated. Further, one of a discussion service, a learning service, and an unlearning service is invoked, based on the expression and the metadata associated with the user. Based on at least one of the invoking services and the metadata associated with the user, retrieving a response. The response is shared with the user.