Entity Attribute Representation Using Validated ML Pipelines

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Solution Overview

Problem

Existing methods for processing entity information are inefficient and lack the ability to accurately determine entity attributes, leading to suboptimal service provision and search capabilities.

Innovation Solution

A computer-implemented method and system using multi-task machine learning models, including recurrent neural networks and entity identifier validation, to generate numerical representations of entities based on gathered information, enabling prediction of attributes such as location, category, and employee information, and facilitating efficient search and clustering.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to process entity information, then the system is simpler to implement, but the accuracy of determining entity attributes deteriorates

Engineering Contradiction:

Improveaccuracy of determining entity attributesVSAvoidcomplexity of processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments entity information processing into distinct functional modules: entity identifier validation module validates entity identifiers, entity server address determination module determines server addresses, information extraction module extracts relevant information, and machine learning model generates numerical representations. Each module handles a specific aspect of the processing pipeline, improving attribute determination accuracy through specialized processing while maintaining manageable system complexity through modular design.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary components including validation modules that verify entity identifiers and server addresses before processing, and a machine learning model that acts as an intermediary between raw entity information and final attribute representations. These intermediaries enhance processing accuracy by ensuring data quality and transforming unstructured information into structured numerical representations suitable for attribute prediction.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If comprehensive entity information is gathered from various repositories, then the quality of service provision improves, but the time and resources required for processing increase

Engineering Contradiction:

Improvequality of service provisionVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary validation of entity identifiers and server addresses before initiating full information gathering and processing. The entity identifier validation module verifies identifiers in advance, and the entity server address determination module resolves addresses beforehand, ensuring that subsequent information extraction from multiple repositories proceeds efficiently without rework, thus maintaining service quality while reducing overall processing time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical information processing methods with machine learning-based numerical representation generation. The machine learning model automatically transforms extracted entity information into structured representations and predicts attributes, substituting manual or rule-based processing with intelligent algorithms that handle comprehensive data more efficiently and accurately.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If entity information is processed without validation, then the processing speed increases, but the reliability of the results deteriorates

Engineering Contradiction:

Improveprocessing speedVSAvoidreliability of entity attributes

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies preliminary anti-action by implementing validation modules that proactively identify and correct potential errors in entity identifiers and server addresses before they propagate through the processing pipeline. The entity identifier validation module checks identifier validity, and the entity server address determination module verifies address correctness, preventing unreliable data from reaching the information extraction and machine learning stages, thus maintaining both processing speed and result reliability.

Inventive Principle:

Principle #9Preliminary anti-action

Data Source

PatentUS12591598B2Systems and methods for determining entity attribute representations

Publication Date: 2026.03.31 XERO

AI summary

A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information.