Automated Personal Data Categorization System

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Solution Overview

Problem

Entities face challenges in efficiently capturing and categorizing personal data from a large quantity of diverse data sources, due to the complexity and volume of these sources.

Innovation Solution

The method involves automatically identifying tables containing personal identifying information across multiple data sources, extracting this information, and categorizing it by individual, using a processor with content analysis, data extraction, and data organization components.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual data processing is used to handle personal data from multiple data sources, then data accuracy can be maintained through human verification, but the time consumption and labor requirements increase significantly

Engineering Contradiction:

Improvedata accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent introduces an automated data processing system that acts as an intermediary between raw personal data from multiple sources and the final organized profiles. This system uses automated identification, extraction, and categorization components to process data without manual intervention, thereby reducing time consumption while maintaining accuracy through systematic processing methods.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical data processing with an automated electronic system. The processor executes automated identification of tables, extraction of personal data, and categorization into profiles, substituting human labor with machine-based operations that are both faster and equally accurate when properly designed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated data extraction is implemented across multiple data sources, then productivity and speed of data capture improve, but the complexity of the system increases

Engineering Contradiction:

Improvedata capture efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the data processing system into distinct functional modules: an identification component that locates tables containing personal data, an extraction component that retrieves the actual data, and a categorization component that organizes data into profiles. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by creating clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal data processing system that can handle multiple types of data sources (websites, documents, databases) and multiple formats of personal data through a single integrated platform. The system is designed to work with diverse input sources without requiring separate processing mechanisms for each source type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of information

If comprehensive data collection from all available sources is performed, then the completeness and thoroughness of personal data profiles improve, but the quantity of data to be processed and stored increases significantly

Engineering Contradiction:

Improvedata completenessVSAvoiddata volume

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The patent extracts only the specific personal data elements that are relevant and useful for creating comprehensive profiles, rather than collecting and storing all available data. The extraction component selectively identifies and pulls out pertinent information from tables, filtering out unnecessary data to maintain completeness of essential information while controlling data volume.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies different processing and storage approaches to different types of data based on their specific characteristics and importance. The categorization component organizes data into structured profiles with appropriate levels of detail for each personal data element, ensuring high quality and completeness for critical information while using more compact representations for less critical data.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12265559B2System and methods for categorizing captured data

Publication Date: 2025.04.01 STRIDER TECHNOLOGIES INC

AI summary

An entity may want to capture personal data associated with one or more individuals. Personal data can include, for example, phone number(s), physical address(es), job titles, email address(es), or any other item of personal identifying information. The entity may be able to find such personal data via a variety of different data sources. For example, the entity may be able to find such personal data via tens, hundreds, thousands, or millions of different websites, documents, files, etc. The entity may also want to categorize the captured personal data by individual. For example, the entity may capture first personal data associated with a first individual from a number of different data sources and second personal data associated with a second individual from a number of different data sources.