Machine Learning Entity Resolution for Real-Time Data Linkage

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Solution Overview

Problem

The challenge of efficiently and accurately extracting relevant data from a vast array of data sources for personalized and timely applications is hindered by the need for extensive domain expertise and resource-intensive processes, leading to generalized guidance that is often ineffective in specific circumstances.

Innovation Solution

A system utilizing a machine learning model to convert data into a canonical representation, clean and group records, generate a versioned data store, and optimize it for real-time lookup, enabling efficient data retrieval and linkage based on entity identifiers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional manual data evaluation processes are used, then data accuracy can be ensured through expert analysis, but the process becomes extremely time-consuming and resource-intensive

Engineering Contradiction:

Improvedata accuracyVSAvoidevaluation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual expert analysis (mechanical human process) with automated machine learning models and computational algorithms. The system uses entity resolution technology, natural language processing, and automated data matching algorithms to perform data evaluation tasks that previously required human experts, thereby maintaining accuracy while dramatically reducing time consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated processing layer between raw data and final analysis results. This intermediary system includes data cleaning modules, entity resolution components, and machine learning models that preprocess and structure data before it reaches the analysis stage, enabling both high accuracy and efficient processing by bridging the gap between raw data and expert-level insights.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If generalized analytical processes are applied, then broad data coverage is achieved, but the processes cannot be effectively repurposed for specific individual circumstances

Engineering Contradiction:

Improveprocess reusabilityVSAvoiddata relevance

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent implements dynamic, configurable analytical processes that can adapt to different domains and specific circumstances. The system allows users to configure data sources, entity types, resolution criteria, and analysis parameters according to specific needs, transforming a static generalized process into a dynamic adaptable framework that maintains both reusability and precision.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent applies local quality by allowing different data processing rules, entity resolution strategies, and analysis methods to be applied to different data sources and entity types within the same system. Each data source can have customized processing parameters, and entity resolution can use domain-specific criteria, enabling the system to maintain high relevance for specific circumstances while remaining broadly applicable.

Inventive Principle:

Principle #3Local quality

3Quantity of substance

If extensive data sources are utilized, then comprehensive analysis coverage is achieved, but the complexity of determining optimal values and sources increases significantly

Engineering Contradiction:

Improvedata coverageVSAvoidprocess complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent segments the complex task of evaluating multiple data sources into distinct modular components: data collection modules, data cleaning modules, entity resolution modules, and analysis modules. Each module handles a specific aspect of the process independently, reducing overall complexity while maintaining comprehensive data coverage. The modular architecture allows each segment to be optimized separately and combined systematically.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal multi-functional platform that can handle diverse data sources and analysis requirements through a common framework. The system uses standardized data interfaces, universal entity resolution algorithms, and configurable analysis templates that work across different domains and data types, reducing the complexity of managing extensive data sources by providing a unified approach rather than requiring separate processes for each source.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12443628B2Systems and methods for machine learning models for entity resolution

Publication Date: 2025.10.14 GRAND ROUNDS INC

AI summary

Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.