Dynamic Chatting Server Matching for Natural Language Input
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Solution Overview
Problem
Conventional chatting servers are inefficient in processing user utterances as they rely on pre-stored responses and lack the ability to dynamically adjust resources based on the complexity of natural language input, leading to unnecessary resource utilization.
Innovation Solution
An electronic apparatus that evaluates the difficulty level of natural language input using AI technology and dynamically matches the user with an optimal chatting server, reducing resource usage by selecting from a range of servers with varying specifications and response models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all chatting servers are embodied with the same specification, then reliability is improved, but resource utilization deteriorates due to overly used resources even for simple questions
Solution Approach 1:
The patent applies parameter changes by varying the specification parameters of chatting servers (e.g., processing power, memory, response model complexity) to match the difficulty level of user utterances. Simple questions are routed to servers with lower specifications, while complex questions are routed to servers with higher specifications, thereby optimizing resource utilization while maintaining service reliability.
Solution Approach 2:
The patent segments the chatting server pool into multiple groups with different specifications based on their processing capabilities. This segmentation allows the system to selectively assign user queries to appropriate server groups based on the complexity of the natural language input, avoiding the use of high-specification servers for simple tasks and thus reducing overall resource consumption.
2Device complexity
If a single chatting server is used for all user inputs, then device complexity is reduced, but productivity deteriorates due to inefficient resource allocation
Solution Approach 1:
The patent implements dynamics by making the chatting server assignment dynamic rather than static. The system evaluates the difficulty level of each user utterance in real-time and dynamically selects the most appropriate chatting server from the pool. This dynamic approach improves processing efficiency by matching server capabilities to task requirements, while the underlying server pool structure remains manageable.
Solution Approach 2:
The patent introduces an intermediary component (the difficulty level evaluation module and server matching mechanism) that sits between the user input and the chatting server pool. This intermediary evaluates the natural language input, determines the appropriate difficulty level, and selects the suitable server, thereby improving productivity without significantly increasing overall system complexity.
3Power
If high-specification chatting servers are used for all queries, then processing capability is improved, but resource consumption increases unnecessarily
Solution Approach 1:
The patent applies local quality by assigning different processing capabilities to different servers in the pool based on their specifications. Instead of uniformly using high-specification servers, the system matches the local quality (processing power) of each server to the local quality (complexity) of the specific query, thereby improving processing capability where needed while reducing resource consumption where not needed.
Data Source
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AI summary
An electronic apparatus includes an input unit comprising input circuitry configured to receive a natural language input, a communicator comprising communication circuitry configured to perform communication with a plurality of external chatting servers, and a processor configured to analyze a characteristic of the natural language and a characteristic of the user and to identify a chatting server corresponding to the natural language from among the plurality of chatting servers, and to control the communicator to transmit the natural language to the identified chatting server in order to receive a response with respect to the natural language.