US 2024/0403603 A1 · REDUCING LATENCY THROUGH PROPENSITY MODELS THAT PREDICT DATA CALLS

Abstract

There are provided systems and methods for reducing latency through propensity models that predict data calls. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users including those for electronic transaction processing. In order to provide sequence-based forecasting of computing events and processing requests for users, accounts, and/or activities associated with the service provider, the service provider may provide a machine learning model, such as a deep neural network, that predicted potential occurrences and likelihoods of computing events occurring at future times. When predicting the events, the service provider's machine learning predictive framework may further predict data calls required to be executed to retrieve data needed for processing during the events. These predicted calls may then be batched together into a batch processing job, which may be executed to retrieve the data prior to the predicted events.

Claims20

Dependent claims

A service provider system comprising:

a non-transitory memory;
and
one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the service provider system to perform operations comprising:
obtaining input feature data for an entity, wherein the input feature data is associated with sequence-based data for neural network features over a past time period;
executing a predictive call execution framework comprising a deep neural network model trained for forecasting of future events associated with the entity, wherein the deep neural network model is trained using training data associated with the neural network features for event sequences from past events associated with the entity;
determining a first predictive forecast of one of the future events for the entity at a future time using the input feature data and the deep neural network model;
and
adding, based on the first predictive forecast, an API call for data from an external computing service to a batch processing event, wherein the data is usable for the one of the future events at the future time.

5 more elements

A method comprising:

determining, using a deep neural network model of a predictive framework configured to enable sequence-based forecasting of future events using external data calls, a plurality of past events for an entity over a time period, wherein the plurality of past events include feature data for features processed by the deep neural network model;
determining, using the deep neural network model, a first predictive forecast of a first future event at a first future time after the time period based on the feature data;
determining a first external data call required for the first future event based on at least one of the plurality of past events;
executing the first external data call to a first external service prior to the first future time, wherein the first external service includes data used during the first future event;
receiving the data from the first external service based on the executing;
and
storing the data for the first future time of the first future event.

4 more elements

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

receiving training data for a recurrent neural network (RNN) model, wherein the training data comprising feature data for model features selected for the RNN model over a time period associated with a plurality of past computing events for an entity;
encoding the plurality of past computing events using the feature data;
training the RNN model using the training data and the encoding of the plurality of past computing events, wherein the RNN model is trained to enable predictive forecasting of a future computing event;
predicting the future computing event using the RNN model;
executing a computing call during a data processing of the future computing event on behalf of the entity;
and
preloading, based on the executing the computing call, data for the data processing of the future computing event prior to an occurrence of the future computing event.

4 more elements

Dependencies are inferred from the claim text.

Reducing latency through propensity models that predict data calls

Life of this patent

Reducing latency through propensity models that predict data calls

Life of this patent

Abstract

Claims20