User Identification Marking via Server-Side Classification Analysis
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Solution Overview
Problem
Users face challenges in accurately marking user identifications, such as phone numbers or email accounts, due to confusion in classification, resulting in low marking rates and accuracy.
Innovation Solution
A user identification marking method and system that analyzes classification basis information, including historical records, call data, and regional distribution, to provide possible classifications and rankings to clients for guided marking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users perform manual classification and marking of user identifications based on subjective judgment, then the system allows user autonomy and simple operation, but the marking accuracy rate is low and marking rate is low
Solution Approach 1:
The server performs preliminary analysis of user identification data before the user needs to mark, pre-calculating classification basis information including marking historical records, call quantities, time periods, and regional distributions. This preliminary processing provides users with ready-to-use classification suggestions, improving marking accuracy without requiring users to perform complex analysis themselves.
Solution Approach 2:
The server acts as an intermediary between raw user identification data and user marking decisions. It processes the data, generates classification basis information and suggestions, and presents them to users. This intermediary role transforms complex data analysis into user-friendly suggestions, maintaining user autonomy while improving marking accuracy.
2Productivity
If users perform manual marking without guidance, then the operation process is simple, but the marking rate is low due to user confusion
Solution Approach 1:
The system provides feedback to users in the form of classification suggestions and rankings based on analyzed data. Users receive feedback about what classification would be most appropriate for each user identification, along with supporting evidence from the data analysis. This feedback loop guides users toward accurate markings without requiring them to be experts in classification, thereby increasing marking rate while maintaining ease of operation.
Solution Approach 2:
The server performs preliminary analysis and prepares classification suggestions before users need to make marking decisions. By having the analysis ready in advance, users are presented with clear, pre-processed information that guides their marking actions, reducing confusion and increasing marking rate without adding operational complexity.
3Measurement precision
If comprehensive classification basis information is analyzed, then the marking accuracy improves, but the data processing complexity and time increase
Solution Approach 1:
The server performs comprehensive data analysis in advance, before users need to make marking decisions. By pre-processing the classification basis information and storing it ready for retrieval, the system eliminates the need for real-time analysis during user marking operations. This approach maintains high marking accuracy through thorough analysis while minimizing the time users spend on marking tasks.
Solution Approach 2:
The system creates copies of processed classification basis information and stores them for quick retrieval. Instead of re-analyzing raw data each time a user needs to mark, the system serves pre-analyzed classification suggestions from stored copies. This copying approach maintains accuracy based on comprehensive analysis while dramatically reducing the time required for actual marking operations.
Data Source
AI summary
The present disclosure provides a user identification marking method. The method includes determining a user identification that needs a classification analysis; obtaining classification basis information of the user identification; analyzing the user identification according to the classification basis information, to obtain possible classifications of the user identification and a ranking thereof in each of the possible classifications; and providing the possible classifications and the rankings to a client.