Processor User Tendency Identification via Cluster Segmentation
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Solution Overview
Problem
Existing electronic apparatuses face challenges in efficiently identifying user tendencies among a large number of clients to provide customized services, as current methods become burdensome and time-consuming due to the comparison of vast amounts of data.
Innovation Solution
An electronic apparatus equipped with a processor that acquires characteristic data from users, identifies specific user tendencies by categorizing and analyzing data using reference characteristics, and assigns weights to categories to determine similarity, allowing for the identification of users with tendencies matching the reference characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server compares data on user tendency appearing in input information with data of each client managed by the server on a one-to-one basis, then the accuracy of identifying clients' specific tendency is improved, but the processing time and server burden increase significantly
Solution Approach 1:
The patent segments the large-scale one-to-one comparison task into two phases: first, cluster analysis groups clients into segments based on similarity; second, tendency comparison is performed only between input information and cluster representatives. This segmentation reduces the computational complexity from O(n*m) to O(k*m) where k << n, thereby resolving the contradiction between accuracy and processing time.
Solution Approach 2:
The patent introduces cluster analysis as an intermediary step between raw client data and tendency identification. By creating cluster representatives as intermediaries, the system avoids direct one-to-one comparison between all clients and input information, reducing time consumption while maintaining identification accuracy through the intermediary's representative characteristics.
2Quantity of substance
If the server manages vast amounts of big data on clients, then the coverage of user base is improved, but the computational burden and time delay increase
Solution Approach 1:
The patent applies segmentation by dividing the vast client database into meaningful clusters based on behavior patterns and characteristics. This allows the server to manage large quantities of client data efficiently by processing cluster-level representations rather than individual client records, thereby maintaining high coverage while improving productivity.
Solution Approach 2:
The patent creates cluster representatives as simplified copies or summaries of groups of clients. These representative profiles capture the essential characteristics of entire client segments, allowing the server to process and analyze data much more efficiently while still representing the full diversity of the user base.
3Adaptability or versatility
If the server provides customized services based on detailed user tendency analysis, then the service quality is improved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modules: data collection, cluster analysis, tendency identification, and service matching. This modular segmentation reduces overall system complexity by breaking down the intricate process into manageable, independent components that can be processed sequentially with well-defined interfaces.
Solution Approach 2:
The patent introduces cluster analysis as an intermediary layer that simplifies the complexity between raw client data and customized service delivery. This intermediary processing step transforms complex individual client data into simplified cluster representations, making subsequent tendency analysis and service customization more manageable while preserving adaptability.
Data Source
AI summary
An electronic apparatus includes a processor configured to acquire a plurality of characteristic data of a plurality of users through a communication interface circuitry; identify a plurality of categories and reference characteristics for analyzing the plurality of characteristic data according to an input received through the communication interface circuitry; identify specific characteristic data that corresponds to the reference characteristics, among the plurality of characteristic data for each of the plurality of categories; identify a specific user having the specific characteristic data, among the plurality of users; and output an analysis result of the specific characteristic data of the specific user.