User Identity Data Conversion Through Feature-Based Field Matching

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Solution Overview

Problem

Conventional methods for identifying user identity information in large data sets are inefficient and prone to high error rates due to complex data structures and large data sizes, making it difficult to accurately and securely process user identities.

Innovation Solution

A method involving scanning data tables to extract features of each field, matching these features with user identity rules, and converting identified user identity data into third-party accounts while keeping non-identity data unchanged, using a terminal with a processor and non-volatile storage medium to improve accuracy and security.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional automated methods (fuzzy searching, limiting data value range, matching based on traversing all registration data) are used to identify user identity, then the process can be automated, but the error rate is high and efficiency is low

Engineering Contradiction:

Improveautomation of user identity identificationVSAvoidaccuracy of user identity identification

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The patent segments the user identity identification process into distinct phases: data collection from multiple sources, feature extraction from segmented data, feature matching against known identity patterns, and verification. This segmentation allows each phase to be optimized independently, improving both automation and accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces feature extraction and feature matching as intermediary steps between raw data collection and final identity identification. These intermediaries process and refine the data, enabling automated identification while maintaining high accuracy through systematic feature comparison rather than direct matching.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If manual processing is used to identify user identity information in large data sets, then accuracy can be maintained, but efficiency is low due to the complexity and size of data tables

Engineering Contradiction:

Improveaccuracy of user identity identificationVSAvoidefficiency of user identity identification

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical processing with an automated system that uses feature extraction and pattern matching algorithms. This substitution maintains accuracy by systematically analyzing data features while dramatically improving productivity through automated processing of large data sets.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the processing parameters from examining entire data records to analyzing specific extracted features. This parameter change reduces the computational complexity from processing complete data tables to comparing key feature sets, thereby improving efficiency while maintaining identification accuracy.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If user identity data is stored and processed directly, then complete information is available for processing, but security is compromised due to direct exposure to third-party platforms

Engineering Contradiction:

Improvecompleteness of user identity informationVSAvoidsecurity risk of user identity exposure

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

The patent extracts only the necessary identifying features from complete user identity data for processing purposes. By taking out only the essential feature sets needed for identification rather than processing complete identity information, the system maintains processing effectiveness while reducing security risks associated with exposing sensitive data.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent introduces feature extraction as an intermediary layer between complete user identity data and processing systems. This intermediary preserves the necessary information for accurate identification while protecting sensitive identity details from direct exposure to third-party platforms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10878121B2Method and device for converting data containing user identity

Publication Date: 2020.12.29 TENCENT TECHNOLOGY (SHENZHEN) CO LTD

AI summary

A method for automatically converting electronic data is disclosed. The method comprises scanning a source data table containing data fields; determining a feature for each of the data fields of the source data table; comparing the feature for each of the data fields with a feature rule for identifying user-identity-containing data fields in the source data table; identifying a first data field of the source data table as containing user identity when the feature of the first data field matches the feature rule; identifying a second data field of the source data table as containing no user identity when the feature of the second data field fails to match the feature rule; converting the source data table by replacing data items of the first data field in the source data table identified as containing user identity with corresponding third-party user accounts, and keeping the second data field in the source data table identified as not containing user identity unaltered; and storing the converted data table in a storage medium.