US 2025/0156728 A1Published application

Minimizing usage of sources of records with machine learning

AssigneeCapital One Services, LLC

InventorSunil Pradhan SHARMA

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Abstract

Methods and systems are described herein for minimizing data source usage. A data access system may receive an access request that includes a request for a multitude of parameters and generate a vector representation that includes those parameters. The data access system may then input the vector representation into a machine learning model to obtain a plurality of data sources that have access to the parameters within the request. Once the required data sources are obtained, the data access system may generate a message to each data source to retrieve the parameter data requested by the access request and transmit the messages to the appropriate data sources. Once the parameter data is received, the data access system may transmit a response to the request such that the response may include the parameter data.

Claims20

Dependent claims

A system for minimizing data source usage with machine learning, the system comprising:

one or more processors;
and
a non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to:
receive, from a computing device, a data request for a plurality of parameters, wherein the plurality of parameters is a subset of parameters accessed through a plurality of sources of records;
generate a vector representation of the plurality of parameters;
input the vector representation into a machine learning model to obtain a subset of the plurality of sources of records for retrieving the plurality of parameters, wherein the machine learning model has been trained to output sets of sources of records that minimize a number of sources of records for retrieving requested parameters;
generate a corresponding message for each source of records within the subset of the plurality of sources of records, wherein each message comprises a corresponding request for a corresponding subset of parameters of the plurality of parameters;
transmit each corresponding message to a matching source of records of the subset of the plurality of sources of records;
receive a plurality of parameter values for the plurality of parameters from the subset of the plurality of sources of records;
and
transmit a response to the data request to the computing device, wherein the response comprises the plurality of parameter values.

8 more elements

A method comprising:

receiving, from a computing device, a request for a plurality of parameters, wherein the plurality of parameters is a subset of parameters accessed through a plurality of data sources;
generating a vector representation of the plurality of parameters;
inputting the vector representation into a machine learning model to obtain a subset of the plurality of data sources for retrieving the plurality of parameters, wherein the machine learning model has been trained to generate subsets of data sources that minimize a number of data sources to be accessed for retrieving requested parameters;
generating a corresponding message for each data source within the subset of the plurality of data sources;
receiving a plurality of parameter values for the plurality of parameters from the subset of the plurality of data sources;
and
transmitting a response to the request to the computing device, wherein the response comprises the plurality of parameter values.

4 more elements

One or more non-transitory computer-readable storage media storing instructions that, when executed by one or more processors, cause operations comprising:

receiving, from a computing device, a request for a plurality of parameters, wherein the plurality of parameters is a subset of parameters accessed through a plurality of data sources;
generating a vector representation of the plurality of parameters;
inputting the vector representation into a machine learning model to obtain a subset of the plurality of data sources for retrieving the plurality of parameters, wherein the machine learning model has been trained to generate subsets of data sources that minimize a number of data sources to be accessed for retrieving requested parameters;
generating a corresponding message for each data source within the subset of the plurality of data sources;
receiving the plurality of parameters from the subset of the plurality of data sources;
and
transmitting a response to the request to the computing device, wherein the response comprises the plurality of parameters.

4 more elements

Dependencies are inferred from the claim text.