Server Knowledge Database Segmentation for Response Accuracy
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Solution Overview
Problem
Conventional AI systems using knowledge databases for user inquiries are limited in providing responses due to their reliance on limited knowledge information, restricting the variety and accuracy of user interactions.
Innovation Solution
A server system that utilizes both personal and global knowledge databases to store and manage user knowledge information, allowing for the filtering and validation of responses based on feedback, enabling more comprehensive and accurate responses to user inquiries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional knowledge database with limited knowledge information is used, then the system structure is simple, but the variety and accuracy of responses to user inquiries are limited
Solution Approach 1:
The knowledge database is segmented into multiple types (first knowledge database, second knowledge database, third knowledge database) with different scopes and purposes. The first database stores general knowledge, the second stores user-specific knowledge, and the third stores validated high-quality knowledge. This segmentation allows the system to provide diverse responses by selecting from appropriate database types while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The patent adds a temporal dimension to knowledge validation by introducing a verification process that evolves knowledge from unvalidated to validated status over time. Knowledge transitions through stages: initially stored in the first database, then moved to the second database upon user interaction, and finally to the third database after verification. This dimensional approach to knowledge lifecycle management enables response variety without overwhelming system complexity.
2Reliability
If a knowledge database with limited knowledge information is used, then the system is easy to maintain, but the accuracy of responses is limited
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with responses trigger verification processes. When users engage with knowledge from the first database, the system moves this knowledge to the second database for validation. Subsequent verification determines whether knowledge should be promoted to the third database (validated high-quality knowledge) or discarded. This feedback loop continuously improves response accuracy by prioritizing validated knowledge while managing complexity through automated verification workflows.
3Adaptability or versatility
If personal and global knowledge databases are used, then comprehensive and accurate responses are provided, but the system complexity increases
Solution Approach 1:
The patent merges personal knowledge (second database) and global knowledge (third database) operations within a unified server architecture. The server integrates multiple database types, manages knowledge transitions between them, and coordinates verification processes. This merging approach provides comprehensive responses by accessing both personal and global knowledge pools while managing system complexity through centralized control and standardized interfaces rather than distributed independent systems.
Data Source
AI summary
A server and a control method thereof are disclosed. The control method of a server includes receiving knowledge information from a first electronic device, storing the received knowledge information in a personal knowledge database corresponding to a user using the first electronic device, transmitting a response to an inquiry to obtain the knowledge information to at least one second electronic device based on the knowledge information stored in the personal knowledge database, based on the inquiry being received from the at least one second electronic device, receiving feedback information to the response from the at least one second electronic device, and storing the knowledge information in a global knowledge database based on the feedback information. At least a part of a method of allowing a server to provide a response to a user inquiry may use an artificial intelligence model learned according to at least one of machine learning, neural networks, or deep learning algorithms.