Conversational Mediator Architecture for Context-Rich AI Responses
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Solution Overview
Problem
AI-based conversational applications have constrained knowledge bases, limited interaction memory, and lack access to user-specific data, leading to generalized and context-insensitive responses.
Innovation Solution
A conversational mediator system that acts as an intermediary, generating mediator inputs based on user data and contextual information, querying multiple sources, aggregating responses, and updating user data for personalized and context-aware interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If AI-based conversational applications use pre-trained data only, then the system complexity is low, but the knowledge base completeness deteriorates
Solution Approach 1:
The system segments the knowledge base into multiple components: pre-trained model knowledge, user-specific data, and contextual information. Each segment is processed and integrated separately through the mediator system, allowing comprehensive knowledge coverage while managing complexity through modular architecture.
Solution Approach 2:
A mediator system is introduced as an intermediary layer between the user and the conversational application. The mediator aggregates information from multiple sources including pre-trained data, user data, and contextual information, then synthesizes comprehensive responses without requiring the core AI model to directly handle all data sources.
2Reliability
If AI-based conversational applications have limited interaction memory, then the processing speed is high, but the contextual responsiveness deteriorates
Solution Approach 1:
The system extracts and separates contextual information from the main processing flow. Contextual data including conversation history and user preferences is extracted and stored in external storage, allowing the AI model to access relevant context without processing entire conversation histories, thus maintaining fast response times while improving contextual responsiveness.
Solution Approach 2:
Contextual information and user preferences are pre-processed and stored before interactions occur. The mediator system prepares and organizes contextual data in advance, so that during actual conversations, the AI can quickly retrieve relevant context without time-consuming processing, thereby maintaining high processing speed while improving contextual responsiveness.
3Reliability
If AI-based conversational applications lack access to user-specific data, then the privacy protection is simple, but the response personalization deteriorates
Solution Approach 1:
The mediator system serves as an intermediary that selectively accesses and processes user-specific data. It retrieves relevant user preferences and personal information from external storage, integrates this data with conversational context, and provides personalized responses without requiring the AI model to directly manage complex user data structures.
Solution Approach 2:
The system applies local quality by customizing responses based on specific user characteristics and preferences. The mediator system retrieves and applies user-specific data locally for each interaction, allowing personalized responses while keeping the overall system architecture relatively simple through targeted data access rather than comprehensive data management.
Data Source
AI summary
A conversational mediator system includes: a mediator input generating module, a context management module and a response aggregation module. The mediator input generating module is configured to generate a mediator input based on a user input and auxiliary information associated with the user input, and accordingly send the mediator input to at least one conversational application. The context management module is configured to extract the auxiliary information from user data and update the user data based on responses to the mediator input that is generated by the at least one conversational application. The response aggregation module is configured to summarize and aggregate the responses to the mediator input that is generated by the at least one conversational application to generate a consolidated response.