US 2024/0256947 A1 · CIRCULAR-BUFFER FOR GENERATING MACHINE LEARNING ESTIMATES OF STREAMING OBSERVATIONS IN REAL TIME

Abstract

Systems, methods, and other embodiments associated with generating a stream of ML estimates from a stream of observations in real-time using a circular double buffer are described. In an example method, observations are received from the stream of observations. The observations are loaded in real time into a circular buffer. The circular buffer includes a first buffer and a second buffer that are configured together in a circular configuration. Estimates of what the observations are expected to be are generated by a machine learning model from the observations that are in the circular buffer. The generation of estimates alternates between generating the estimates from observations in the first buffer in parallel with loading the second buffer, and generating the estimates from observations in the second buffer in parallel with loading the first buffer. The estimates are written to the stream of estimates in real time upon generation.

Claims20

Dependent claims

A non-transitory computer-readable medium that includes stored thereon computer-executable instructions for generating a stream of estimates from a stream of observations in real time that when executed by at least a processor of a computer cause the computer to:

receive observations from the stream of observations;
load the observations in real time into a circular buffer, wherein the circular buffer includes a first buffer and a second buffer that are configured together in a circular configuration;
generate estimates by a machine learning model of what the observations are expected to be from the observations that are in the circular buffer, wherein the generation of estimates alternates between generating the estimates from the observations that are in the first buffer in parallel while the second buffer is being loaded, and generating the estimates from the observations that are in the second buffer in parallel while the first buffer is being loaded;
and
write the estimates to the stream of estimates in real time as the estimates are generated.

2 more elements

A computer-implemented method for generating a stream of estimates from a stream of observations in real time, the method comprising:

receiving observations from the stream of observations;
loading the observations in real time into a circular buffer, wherein the circular buffer includes a first buffer and a second buffer that are configured together in a circular configuration;
generating estimates by a machine learning model of what the observations are expected to be from the observations that are in the circular buffer, wherein the generation of estimates alternates between generating the estimates from the observations that are in the first buffer in parallel while the second buffer is being loaded, and generating the estimates from the observations that are in the second buffer in parallel while the first buffer is being loaded;
and
writing the estimates to the stream of estimates in real time as the estimates are generated.

2 more elements

A computing system, comprising:

at least one processor connected to at least one memory;
a non-transitory computer-readable medium including instructions stored thereon for generating a stream of estimates from a stream of observations in real time that when executed by at least the processor cause the computing system to:
receive observations from the stream of observations;
load the observations in real time into a circular buffer, wherein the circular buffer includes a first buffer and a second buffer that are configured together in a circular configuration;
generate estimates by a machine learning model of what the observations are expected to be from the observations that are in the circular buffer, wherein the generation of estimates alternates between generating the estimates from the observations that are in the first buffer in parallel while the second buffer is being loaded, and generating the estimates from the observations that are in the second buffer in parallel while the first buffer is being loaded;
and
write the estimates to the stream of estimates in real time as the estimates are generated.

4 more elements

Dependencies are inferred from the claim text.

Circular-buffer for generating machine learning estimates of streaming observations in real time

Life of this patent

Circular-buffer for generating machine learning estimates of streaming observations in real time

Life of this patent

Abstract

Claims20