Selective data storage based on future event prediction

Patent metadata

Field	Value
Publication number	US-2025190882-A1
Application number	US-202318533762-A
Country	US
Kind code	A1
Filing date	Dec 8, 2023
Priority date	Dec 8, 2023
Publication date	Jun 12, 2025
Grant date	—

Abstract

A method and related system for storing data based on predictions includes obtaining, from a client device, a first set of update data and a time-related prediction generated by a client-side version of a machine learning model. The method further includes determining that the time-related prediction is associated with a first data store of a plurality of data stores comprising the first data store and a second data store. The method further includes updating a record in the first data store based on the first set of update data in response to a determination that the time-related prediction is associated with the first data store. The method further includes updating the record in the first data store based on a second set of update data obtained after obtaining the first set of update data.

First claim

What is claimed is:

1 . A system for selectively determining which data store to use for storing data based on client-provided time-related predictions, the system comprising one or more processors and one or more non-transitory, machine-readable media storing program instructions that, when executed by the one or more processors, perform operations comprising:
providing, to a client device, a machine learning model that outputs time-related predictions indicating a most probable time for a future event based on update data; obtaining, from the client device, initial update data and a time-related prediction that is generated by the client device, wherein the time-related prediction is generated by providing the initial update data to the machine learning model as an input; determining that the time-related prediction satisfies a set of criteria associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; determining that the time-related prediction is associated with the first data store based on a determination that the time-related prediction satisfies the set of criteria; updating a record in the first data store based on the initial update data in response to a determination that the time-related prediction is associated with the first data store; receiving, from the client device, additional update data after obtaining the initial update data, wherein the additional update data shares an identifier with the initial update data; retrieving the record based on an association between the additional update data and the initial update data; and updating the record in the first data store based on the additional update data.

2 . A method comprising:
providing, to a client device, a machine learning model; obtaining, from the client device, a first set of update data and a time-related prediction generated by providing a client-side version of the machine learning model with at least one value of the first set of update data; determining a result indicating that the time-related prediction is associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; updating a record of the first data store based on the first set of update data in response to the result indicating that the time-related prediction is associated with the first data store; and updating the record of the first data store based on a second set of update data by retrieving the record of the first data store based on an association between the second set of update data and the first set of update data.

3 . The method of claim 2 , further comprising:
obtaining, from the client device, a third set of update data, wherein the third set of update data comprises a timestamp indicating a transaction time; determining a result indicating that the transaction time is not within a first time range associated with the first data store; updating training data based on the third set of update data in response to the result indicating that the transaction time is not within the first time range; and updating a server-side version of the machine learning model based on the training data after the training data is updated with the third set of update data.

4 . The method of claim 2 , wherein updating the record in the first data store comprises:
determining an available memory of the first data store; and determining a result indicating that the available memory satisfies a set of memory-related criteria, wherein updating the record comprises updating the record in response to the result indicating that the available memory satisfies the set of memory-related criteria.

5 . The method of claim 2 , wherein the time-related prediction is a first time value, and wherein the record is a first record, further comprising:
obtaining, from the client device, a third set of update data in association with a second time value; determining a second result indicating that the second time value is associated with the second data store; and updating a second record of the second data store based on the third set of update data in response to the second result indicating that the second time value is associated with the second data store.

6 . The method of claim 5 , further comprising storing data from the first record in the second data store based on the second result.

7 . The method of claim 2 , wherein the plurality of data stores comprises a third data store, and wherein the third data store is characterized by a third throughput value that is greater than the first throughput value and less than the second throughput value.

8 . The method of claim 7 , wherein the record is a first record, and wherein the time-related prediction is a first time value, further comprising:
obtaining, from the client device, a second record in association with a second time value, wherein the second time value is different from the first time value; determining, based on the second time value, a result indicating that the second time value is associated with the third data store; and storing the second record in the third data store based on the result indicating that the second time value is associated with the third data store.

9 . The method of claim 2 , further comprising:
obtaining model parameters of the client-side version of the machine learning model; and updating a server-side version of the machine learning model based on the model parameters.

10 . The method of claim 2 , wherein:
the first set of update data comprises location data; and the machine learning model is configured to provide the time-related prediction based on the location data.

11 . One or more non-transitory, machine-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
obtaining, from a client device, a first set of update data and a time-related prediction generated by a client-side version of a machine learning model; determining that the time-related prediction is associated with a first data store of a plurality of data stores comprising the first data store and a second data store, wherein the first data store is characterized by a first throughput value, and wherein the second data store is characterized by a second throughput value; updating a record in the first data store based on the first set of update data in response to a determination that the time-related prediction is associated with the first data store; and updating the record in the first data store based on a second set of update data obtained after obtaining the first set of update data, wherein the second set of update data shares an identifier with the first set of update data.

12 . The one or more non-transitory, machine-readable media of claim 11 , further comprising sending model parameter data to the client device, wherein the client device reconfigures one or more parameters of the client-side version of the machine learning model based on the model parameter data.

13 . The one or more non-transitory, machine-readable media of claim 11 , wherein the time-related prediction comprises a probability value indicating a likelihood that a target future event will occur within a pre-set duration.

Assignees

• Capital One Services Llc

Inventors

• Sharpe Samuel
• Rafferty Galen
• Barr Brian
• Davis Michael
• Turner Taylor
• Bean Kenny

Classifications

• G06N20/00

  Machine learning · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title