Human-Computer Dialogue with Personal Cognitive-Level Adaptation
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Solution Overview
Problem
The cognitive gap between users and AI systems due to differing personal knowledge backgrounds and life experiences hinders effective communication, necessitating improved alignment of AI responses with user cognition.
Innovation Solution
A human-computer dialogue method that generates entity data from user input, queries a personal database for cognitive data, and determines output results based on this data to align AI responses with user cognition, utilizing techniques like natural language understanding, entity recognition, and database querying to enhance communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI uses generic response generation without user cognitive level adaptation, then system complexity is reduced, but communication effectiveness and user satisfaction deteriorate
Solution Approach 1:
The system performs preliminary actions by building a user cognitive level database before actual dialogue occurs. User data including education background, professional experience, and knowledge domains are collected and processed in advance to establish baseline cognitive profiles. This preliminary preparation enables the AI to quickly adapt to different users without requiring complex real-time analysis during interactions.
Solution Approach 2:
A cognitive level database acts as an intermediary between the user and the AI response generation system. This database stores processed user cognitive profiles and serves as a mediator that translates user characteristics into appropriate response parameters. The intermediary layer simplifies the overall system architecture by decoupling user analysis from response generation, allowing each component to operate independently and efficiently.
2Measurement precision
If AI requests explicit user input about cognitive level, then measurement precision of user cognition is improved, but ease of operation and user convenience deteriorate
Solution Approach 1:
The system implements self-service by automatically collecting and analyzing user data from multiple sources including social media profiles, educational records, and interaction history. The AI autonomously processes this information to determine user cognitive levels without requiring users to manually input or declare their knowledge backgrounds. This approach maintains high measurement precision while preserving user convenience.
Solution Approach 2:
The system uses feedback mechanisms to continuously refine cognitive level measurements. During interactions, the AI analyzes user responses, question quality, and engagement patterns to validate and adjust cognitive level assessments. This ongoing feedback loop ensures accurate measurement of user cognition while requiring minimal additional user input beyond normal interaction.
3Adaptability or versatility
If AI analyzes extensive user data to determine cognitive level, then adaptability to user cognition is improved, but loss of time and processing efficiency worsen
Solution Approach 1:
The user cognitive profile is segmented into distinct dimensions including education level, professional experience, knowledge domains, and cognitive style. Each dimension is independently analyzed and stored, allowing the system to efficiently retrieve and apply only the relevant segments for specific dialogue contexts. This segmentation reduces processing time while maintaining comprehensive adaptability to user characteristics.
Solution Approach 2:
User cognitive profiles are built and stored in advance before actual dialogue interactions occur. The system pre-processes user data from various sources, creates structured cognitive profiles, and makes them readily available for quick reference during interactions. This preliminary action eliminates the need for time-consuming real-time analysis, enabling fast adaptation to different users while maintaining high cognitive assessment accuracy.
Data Source
AI summary
A human-computer dialogue method includes receiving input data from a user, generating, based on the input data, entity data at least representing an object included in the input data, sending to a personal database a query request including the entity data, receiving cognitive data, sent by the personal database, corresponding to the entity data and representing a cognitive level of the user regarding the entity data, determining an output result based on the entity data and the cognitive data, and outputting the output result.