# US 2026/0065124 A1 · Data Matching Using a Pipeline of Models

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- Number: US 2026/0065124 A1
- Document ID: US20260065124A1
- Status: Published application
- Kind: A1
- Application number: 18/816,644
- Filed: 2024-08-27
- Published: 2026-03-05
- Earliest claimed: 2024-08-27
- Assignees: Paypal, Inc.
- Applicants: PayPal, Inc.
- Inventors: Avi Simkin; Adva Berta Helman; Matan Marudi
- Web page: https://patentgrep.com/patent/US20260065124A1

## CPC

- G06N 20/00: Machine learning
- G06N 3/045: Combinations of networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Techniques are disclosed relating to training a prediction model using a pipeline of machine learning models. A system embeds values of records of at least two different data sources within a multi-dimensional embedding space. The system may calculate similarity scores for respective pairs of clusters within the multi-dimensional embedding space. Based on the similarity scores, the system identifies correlations between values of records from the two different data sources. Based on the identified correlations, the system generates matching features and inputs the matching features into a matching model. Based on output of the matching model for the matching features, the system combines similar records from the at least two different data sources, where the combining produces an enhanced data source. The system may then input the enhanced data source into the prediction model. The disclosed record matching techniques may advantageously provide customized matching for prediction models.

## Claims

### Claim 1

A method for training a prediction model using a pipeline of machine learning models, the method comprising:
embedding, by a computer system, values of records of at least two different data sources within a multi-dimensional embedding space;
calculating, by the computer system for respective pairs of clusters within the multi-dimensional embedding space, similarity scores;
identifying, by the computer system based on the similarity scores, correlations between values of records from the at least two different data sources;
generating, by the computer system based on the identified correlations, a set of matching features;
inputting, by the computer system, the set of matching features to a matching model;
combining, by the computer system based on output of the matching model for the set of matching features, similar records from the at least two different data sources, wherein the combining produces an enhanced data source; and
inputting, by the computer system, the enhanced data source into the prediction model.

### Claim 2 (depends on 1)

The method of claim 1, wherein the method for training the prediction model further comprises:
executing, by the computer system, a feedback loop including backpropagating a first output of the prediction model into the matching model to update the matching model; and
performing, by the computer system after adjusting the matching model, a second execution of the pipeline of machine learning models, wherein the second execution includes performing the inputting the set of matching features, the combining, and the inputting the enhanced data source to update the matching model.

### Claim 3 (depends on 2)

The method of claim 2, wherein executing the feedback loop further includes:
converting, based on a prediction threshold, the first output of the prediction model to a binary value;
generating, using a loss function, a loss value for the prediction model based on the binary value; and
performing, based on the loss value, backpropagation on the matching model to update weights of the matching model.

### Claim 4 (depends on 2)

The method of claim 2, further comprising:
evaluating, by the computer system after executing the feedback loop and the second execution of the pipeline of machine learning models, performance of the prediction model has improved, wherein the evaluating includes comparing the first output of the prediction model with updated output of the prediction model after the second execution of the pipeline of machine learning models; and
in response to determining, based on the evaluating, that the updated output of the prediction model is within a threshold similarity to the first output of the prediction model, the computer system terminating execution of the pipeline of machine learning models.

### Claim 5 (depends on 1)

The method of claim 1, wherein the embedding includes:
determining a type of content included in the at least two different data sources; and
selecting, based on determining the type of content, at least one of a plurality of different embedding algorithms for embedding the values of the records.

### Claim 6 (depends on 5)

The method of claim 5, wherein the selecting includes:
selecting, based on determining that a first data source includes categorical data, a word-to-vector (Word2Vec) embedding algorithm for embedding values of records in the first data source; and
selecting, based on determining that a second, different data source includes numerical data, a convolutional neural network (CNN) embedding algorithm for embedding values of records in the second, different data source.

### Claim 7 (depends on 1)

The method of claim 1, wherein calculating the similarity scores includes:
executing a similarity measurement algorithm to measure differences between clusters in the multi-dimensional embedding space that correspond to values of different columns of data in the two different data sources.

### Claim 8 (depends on 7)

The method of claim 7, wherein executing the similarity measurement algorithm includes:
determining a similarity between two clusters in the multi-dimensional embedding space by measuring a distance between different pairs of samples in two or more different clusters; and
adding pairs that have short distances between them but belong to different clusters, wherein identifying the correlations includes identifying, based on the adding, which cluster pairs have the greatest number of pairs relative to other cluster pairs.

### Claim 9

A non-transitory computer-readable medium having instructions stored thereon that are executable by a computing device to perform operations comprising:
embedding values of records of a first data source and a second, different data source within a multi-dimensional embedding space;
calculating, for respective pairs of clusters within the multi-dimensional embedding space, similarity scores;
identifying, based on the similarity scores, correlations between values of records from the first data source and values of records from the second, different data source;
generating, based on the identified correlations, a set of matching features;
inputting the set of matching features to a matching model, wherein the matching model is a machine learning model;
combining, based on output of the matching model for the set of matching features, matching records from the first data source and the second, different data source, wherein the combining produces an enhanced data source; and
inputting the enhanced data source into a prediction model, wherein the prediction model is a machine learning classifier, wherein the matching model and the prediction model are included in a pipeline of machine learning models.

### Claim 10 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein the operations further comprise:
executing a feedback loop including backpropagating a first output of the prediction model into the matching model to update the matching model; and
performing, after adjusting the matching model, a second execution of the pipeline of machine learning models, wherein the second execution includes performing the inputting the set of matching features, the combining, and the inputting the enhanced data source to update the matching model.

### Claim 11 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein inputting the enhanced data source into the prediction model includes generating, by the prediction model based on the enhanced data source, one or more predictions, and wherein the operations further comprise:
performing, based on the one or more predictions, one or more actions relative to one or more entities corresponding to the one or more predictions.

### Claim 12 (depends on 10)

The non-transitory computer-readable medium of claim 10, further comprising:
evaluating, after executing the feedback loop and the second execution of the pipeline of machine learning models, performance of the prediction model has improved, wherein the evaluating includes comparing the first output of the prediction model with updated output of the prediction model after the second execution of the pipeline of machine learning models; and
in response to determining, based on the evaluating, that the updated output of the prediction model is within a threshold similarity to the first output of the prediction model, the computer system terminating execution of the pipeline of machine learning models.

### Claim 13 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein the combining includes:
adding new columns to the first data source, wherein the new columns are columns that were previously included in the second, different data source, wherein the combining is performed based on output of the matching model indicating that values of the new columns match values of other columns that are included in the first data source; and
deleting, based on output of the matching model indicating that the first data source and the second, different data source include duplicate columns, one or more duplicate columns from the enhanced data source.

### Claim 14 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein the embedding includes:
determining a type of content included in the first data source and the second, different data source; and
selecting, based on determining that the first data source includes graph data, a node-to-vector (Node2Vec) embedding algorithm for capturing structural and semantic properties of different nodes in the first data source.

### Claim 15 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein calculating the similarity scores includes:
executing a similarity measurement algorithm to measure differences between clusters in the multi-dimensional embedding space that correspond to values of different columns of data in the first data source and the second, different data source, wherein the similarity measurement algorithm calculates an adjusted rand index of a plurality of pairs of two different clusters in the multi-dimensional embedding space.

### Claim 16

A method for automatically generating a prediction using a pipeline of machine learning models that includes a matching model and a prediction model, the method comprising:
executing, by a computer system, the matching model included in the pipeline of machine learning models, wherein the matching model determines whether records from a first data source and records from a second, different data source match;
generating, by the computer system based on output of the matching model for the first data source and the second, different data source, an enhanced data source;
executing, by the computer system based on the enhanced data source, the prediction model, wherein the pipeline of machine learning models is generated by:
embedding values of records of the first data source and the second, different data source within a multi-dimensional embedding space;
identifying, based on similarities between embedded values of the first data source and the second, different data source within the multi-dimensional embedding space, correlations between values of records from the first data source and values of records from the second, different data source;
generating, based on the identified correlations, a set of matching features;
inputting the set of matching features to the matching model, wherein the matching model is a machine learning model;
combining, based on output of the matching model for the set of matching features, similar records from the first data source and the second, different data source, wherein the combining generates the enhanced data source; and
inputting the enhanced data source into the prediction model.

### Claim 17 (depends on 16)

The method of claim 16, wherein the pipeline of machine learning models is further generated by:
executing a feedback loop including backpropagating a first output of the prediction model into the matching model to update the matching model.

### Claim 18 (depends on 17)

The method of claim 17, wherein the pipeline of machine learning models is further generated by:
performing, after adjusting the matching model, a second execution of the pipeline of machine learning models, wherein the second execution includes performing the inputting the set of matching features, the combining, and the inputting the enhanced data source to update the matching model.

### Claim 19 (depends on 16)

The method of claim 16, wherein the embedding includes:
determining a type of content included in the first data source and the second, different data source; and
selecting, based on determining the type of content, at least one of a plurality of different embedding algorithms for embedding the values of the records.

### Claim 20 (depends on 16)

The method of claim 16, wherein identifying includes:
executing a similarity measurement algorithm to measure differences between clusters in the multi-dimensional embedding space that correspond to values of different columns of data in the first data source and the second, different data source, wherein executing the similarity measurement algorithm includes determining a similarity between two clusters in the multi-dimensional embedding space by measuring a distance between different pairs of samples in two or more different clusters.