Context-Aware Question Suggestion System for Conversation Detail Retention
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Solution Overview
Problem
During conversations, individuals often miss important details or overlook aspects, leading to forgotten questions, necessitating a solution to enhance question asking.
Innovation Solution
A computer-implemented method that identifies concepts in a conversation, links them to a knowledge base, retrieves and displays attributes and values, and generates context-dependent suggestions for follow-up questions, using natural language processing and inter-language links to provide domain-independent and language-independent assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users rely on manual memory and attention during conversation, then conversation simplicity is maintained, but important details are missed and questions are forgotten
Solution Approach 1:
The system performs preliminary actions by automatically identifying concepts, retrieving attributes, and generating follow-up questions in advance, before users need to ask questions manually. This proactive approach ensures important details are captured and questions are formulated ahead of time, improving reliability without requiring complex user intervention during the conversation.
Solution Approach 2:
The system serves itself by automatically monitoring the conversation, identifying key concepts, and generating relevant follow-up questions without requiring external tools or complex user management. The automated question generation system manages its own operation through natural language processing and knowledge base queries, maintaining simplicity while improving detail retention.
2Loss of information
If automated concept identification and knowledge base linking are implemented, then context understanding is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-loading and organizing knowledge base data, and by continuously monitoring conversation in real-time to identify concepts as they appear. This allows the system to have context understanding ready in advance, reducing the perceived processing time when generating follow-up questions, as the heavy lifting of knowledge retrieval is done proactively rather than reactively.
3Loss of information
If comprehensive attributes and values are displayed for linked concepts, then information completeness is improved, but display complexity increases
Solution Approach 1:
The system extracts and displays only the most relevant attributes and values from the knowledge base, rather than showing all possible information. By selectively taking out only the essential context needed for understanding the conversation and generating follow-up questions, the system maintains information completeness while avoiding display complexity and overwhelming the user with unnecessary details.
Data Source
AI summary
A method and system are provided for assisting users in a conversation. The method includes identifying concepts in the conversation. The method further includes linking identified concepts in the conversation by matching the identified concepts in the conversation to concepts in a knowledge base. The method also includes generating and displaying on the display device, one or more context dependent suggestions for the conversation based on attributes and values associated with the linked concepts in the knowledge base.