Server User Grouping for Service Response Time
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Solution Overview
Problem
Existing electronic apparatuses face challenges in efficiently identifying and managing large numbers of external devices with similar tendencies for customized service provision, leading to increased server burden and delay in data comparison.
Innovation Solution
The electronic apparatus employs a method to collect and analyze user behavior data, classify terminals based on similarity, and provide services to users with characteristics matching a designated pattern by using a server that connects to multiple terminals through a network, utilizing a processor to select and group users with similar behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server compares data of each client one to one to identify clients with certain tendencies, then the service can be customized for each client, but the server burden increases and causes much delay in time
Solution Approach 1:
The patent segments the large client data into multiple groups based on similarity of tendencies. Instead of comparing each client individually against all others, the system divides clients into manageable segments (groups) where each group contains clients with similar characteristics. This segmentation reduces the comparison burden while maintaining identification accuracy within each group context.
Solution Approach 2:
The patent performs preliminary grouping of clients based on their tendencies before service delivery. By pre-organizing clients into groups according to their similar characteristics, the system prepares the data structure in advance, avoiding the need for time-consuming one-to-one comparisons when services need to be delivered. This preliminary classification action significantly reduces response time.
2Quantity of substance
If the server manages tens or hundreds of clients to provide customized services, then the service coverage increases, but it becomes not easy to identify the client which has a certain tendency
Solution Approach 1:
The patent merges clients with similar tendencies into the same group. By combining multiple clients who share similar characteristics into unified groups, the system makes it easier to identify and manage clients with certain tendencies. Instead of dealing with individual clients separately, the merged groups represent consolidated entities with clear tendency profiles, simplifying the detection process.
Solution Approach 2:
The system performs preliminary classification of clients into groups based on their tendencies before service delivery. This advance organization creates a structured framework where clients are already sorted by their characteristics, making subsequent identification and service matching much easier and more efficient.
3Adaptability or versatility
If the server provides services to all external apparatuses individually, then each apparatus receives customized service, but the server load becomes excessively high
Solution Approach 1:
The patent segments the client base into groups based on similarity of tendencies. By dividing the large number of individual clients into smaller manageable groups, the server can process and deliver customized services more efficiently. Each group can be handled as a unit with similar service requirements, reducing the overall processing load while still providing customization within each segment.
Solution Approach 2:
The patent merges clients with similar service requirements into the same group. By combining multiple clients who need similar customized services into unified groups, the server can process these clients more efficiently. The merging reduces redundant processing operations while maintaining the ability to provide adapted services to each client within the group context.
Data Source
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AI summary
An electronic apparatus is configured to: obtain through the communicator characteristic data of each of the external apparatuses regarding a plurality of user characteristics, classify the plurality of external apparatuses into a plurality of groups, whose user characteristic are similar, based on the obtained characteristic data, in response to designating one or more first external apparatuses among the plurality of external apparatuses, calculate similarity of the user characteristic between the first external apparatus and a plurality of second external apparatuses of the group in which the first external apparatus is included among the plurality of groups, and select one or more second external apparatuses whose similarity are relatively high among the plurality of second external apparatuses.