Automated User Information Extraction via Layered Matching
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Solution Overview
Problem
Traditional methods for collecting user information from mobile and related computing systems are inefficient, requiring manual entry and being resource-intensive, especially when dealing with large volumes of data, and are limited by the use of offline methods and limited network functionality.
Innovation Solution
A computer-implemented method using layered matching of textual information from user services with a preset background identification list, allowing for different matching methods to extract user information, including exact, fuzzy, and similarity-based matching, reducing the need for manual input and improving data quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual entry methods are used to collect user information, then data accuracy can be ensured, but collection efficiency is low and resource consumption is high
Solution Approach 1:
The system automatically extracts user information from service data without requiring manual entry by users or operators. The automated extraction process identifies and collects user background information from available service records, eliminating the need for manual data input while maintaining data accuracy through structured extraction rules.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated computational methods. Information extraction algorithms and data processing systems substitute human operators, enabling high-speed automated collection of user information from service data while reducing resource consumption.
2Adaptability or versatility
If traditional offline methods are used for data collection, then system complexity is reduced, but adaptability to mobile internet technologies is limited
Solution Approach 1:
The system is designed to handle multiple data sources and service types through a unified information extraction framework. The same extraction mechanisms work across different service platforms and data formats, enabling the system to adapt to mobile internet technologies while maintaining a relatively simple core architecture through standardized processing approaches.
3Measurement precision
If extensive manual processing is performed on user data, then data quality can be maintained, but resource consumption increases
Solution Approach 1:
The system performs preliminary organization and structuring of service data before extraction, pre-processing the information into formats that facilitate accurate automated extraction. This preliminary action reduces the computational complexity of the extraction process itself, maintaining data accuracy while reducing overall resource consumption.
Data Source
AI summary
Textual information related to user information from user service information is identified. A layered matching is performed on the textual information based on preset background identification information in a preset list, wherein the layered matching includes different matching methods, and the preset list includes a plurality of entries storing different preset background identification information related to the user information. The user information is determined based on the layered matching.