Entity Household Generation via Attribute-Pair Matching

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Solution Overview

Problem

Existing methods for grouping entity profiles based on relationships often lead to misclassification due to manual data entry errors and reliance on hardcoded attributes, resulting in incomplete or incorrect analyses, especially when identifying profiles associated with the same household or group entity.

Innovation Solution

A method and system that automatically update entity profiles with group entity labels by applying predetermined sets of rules to attribute-value pairs, using combinations of exact and fuzzy matches to identify matching profiles and avoid misclassification, and synthesizing profiles based on shared attributes to accurately link related entities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If profiles are grouped based on manually input attributes using hardcoded filters, then the grouping process is simple to implement, but misclassification occurs due to typographical errors and incorrect data

Engineering Contradiction:

Improveease of implementationVSAvoidgrouping accuracy

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The system performs self-correction by automatically detecting and correcting typographical errors in attribute values. The computer implements its own data validation and correction mechanisms without external intervention, comparing profiles against each other to identify and fix errors in manually entered data, thereby improving grouping accuracy while maintaining ease of implementation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual filtering mechanisms with an automated computer-based system that uses algorithmic comparison and statistical analysis. Instead of relying on hardcoded filters and manual attribute matching, the system automatically processes profiles, identifies relationships, and corrects errors through computational methods, significantly improving reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If computers filter profiles based on hardcoded attributes, then processing is efficient, but profiles with typographical errors are misclassified or excluded from correct groupings

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidclassification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The computer system automatically detects and corrects typographical errors in profile attributes during processing. By implementing self-correction mechanisms that compare and validate data against established patterns and other profiles, the system maintains high processing efficiency while improving classification accuracy without requiring manual intervention for each error.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback loops where classification results are continuously evaluated and used to improve future classifications. When typographical errors cause misclassification, the system learns from these errors and adjusts its filtering and matching algorithms accordingly, maintaining efficiency while progressively improving accuracy through iterative refinement.

Inventive Principle:

Principle #23Feedback

3Reliability

If multiple sets of rules are applied to identify matching profiles, then grouping accuracy improves, but processing time and computational resources increase

Engineering Contradiction:

Improvegrouping accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary filtering and preprocessing of profiles before applying the full set of matching rules. By pre-processing data to identify obvious matches and eliminate clearly non-matching profiles first, the system reduces the computational burden of subsequent rule applications, maintaining high grouping accuracy while minimizing processing time through staged evaluation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent divides the rule application process into multiple stages or segments, where different sets of rules are applied in sequence based on profile characteristics. This segmentation allows the system to apply computationally intensive rules only to relevant profile subsets, improving accuracy through comprehensive rule application while reducing overall processing time through selective execution.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11860909B2Entity household generation based on attribute-pair matching

Publication Date: 2024.01.02 U S BANCORP NAT ASSOC

AI summary

A method comprises storing a plurality of entity profiles; applying a predetermined first set of rules to a plurality of attribute-value pairs of the plurality of entity profiles; identifying a first set of entity profiles that satisfy the predetermined first set of rules; identifying a second set of entity profiles that satisfy the predetermined second set of rules; and updating a first attribute-value pair of the first and second sets of entity profiles by adding a first label to the first attribute-value pair responsive to determining the first set of entity profiles and the second set of entity profiles share a common entity profile.