Entity Resolution Models With Versioned Real-Time Data Lookup
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Solution Overview
Problem
The abundance of data sources and information presents challenges in accurately and efficiently identifying relevant data points for personalized scenarios, requiring extensive domain expertise and resources, and existing methods often provide only generalized guidance that is not adaptable to specific circumstances.
Innovation Solution
A system and method utilizing machine learning models to collect, convert, and clean data into a canonical representation, generate a versioned data store, and optimize it for real-time lookup, enabling efficient data retrieval and linkage between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual evaluation methods are used to identify relevant data points, then accuracy can be maintained through expert analysis, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual expert evaluation (mechanical human analysis) with an automated machine learning system that uses trained models to identify and evaluate relevant data points. The system ingests training data, trains ML models to perform entity resolution and data relevance assessment, and automatically applies these models to identify pertinent information without requiring continuous human expert intervention, thereby maintaining accuracy while dramatically reducing time requirements.
Solution Approach 2:
The system enables self-service by automatically training machine learning models using provided training data, then using these trained models to autonomously identify and evaluate relevant data points. The ML models perform entity resolution, data matching, and relevance assessment independently without requiring ongoing manual expert analysis, allowing the system to serve itself in maintaining accurate data identification across multiple scenarios.
2Adaptability or versatility
If generalized analytical processes are developed for data evaluation, then broad applicability is achieved, but the processes become insufficient for personalized and specific circumstances
Solution Approach 1:
The patent implements dynamics by making the analytical process adaptive rather than static. The system accepts training data specific to individual scenarios and uses this to dynamically train and customize machine learning models for each specific application. This allows the same general framework to adapt its behavior and parameters based on the particular characteristics of each scenario, maintaining both broad applicability of the overall system and precision for specific circumstances through scenario-specific model training.
Solution Approach 2:
The system changes parameters by using scenario-specific training data to adjust the parameters and characteristics of machine learning models for different applications. Rather than using fixed generalized rules, the system modifies model parameters based on the specific characteristics of each scenario's training data, enabling the same core system to achieve high precision across diverse personalized circumstances through parameter adaptation.
3Reliability
If extensive domain expertise and multiple individuals are involved in data evaluation, then comprehensive analysis is achieved, but the process complexity and resource requirements increase significantly
Solution Approach 1:
The patent merges the capabilities of multiple domain experts into a single machine learning system. By collecting training data that represents expert knowledge and analysis patterns, the system consolidates what would require multiple individuals into one automated model that can perform comprehensive analysis independently. This merging maintains the comprehensiveness of expert analysis while eliminating the complexity and resource requirements of coordinating multiple people.
Solution Approach 2:
The system creates a copy of expert analytical capabilities by training machine learning models on data that captures expert knowledge and decision-making patterns. Rather than requiring actual experts to perform each analysis, the system creates a computational copy of their expertise that can be replicated and applied consistently across multiple scenarios without the complexity of human coordination and resource management.
4Measurement precision
If manual processes are customized for individual scenarios, then accuracy for specific circumstances improves, but valuable time and resources are spent modifying the process
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using scenario-specific training data before actual data evaluation begins. Rather than manually customizing processes at the time of use, the system performs the customization work in advance by training models on representative data for each scenario. This preliminary training enables the system to achieve high accuracy for personalized scenarios while eliminating the need for time-consuming modifications during actual operation, as the models are already optimized for their specific applications.
Data Source
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.