US 12,608,365 B2Granted

System and method for setting and monitoring checkpoints in structured data

AssigneeWorkStarr, Inc.

InventorJoseph D. Rando

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Abstract

Systems for setting and monitoring checkpoints in structured data and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to retrieve event data, wherein the event data includes an objective, generate, using a first machine learning model, a checkpoint as a function of the event data, validate the checkpoint against the event data and the objective, monitor, using a second machine learning model, a progress as a function of the validated checkpoint, wherein monitoring the progress includes receiving supplemental event data, comparing the supplemental event data against the checkpoint, and generating a progress indication as a function of the comparison, and generate a recommended course of action as a function of the progress indication.

Claims18

Dependent claims

A system for setting and monitoring checkpoints in structured data, the system comprising:

a processor;
and
a memory communicatively connected to the processor, wherein the memory comprises instructions configuring the processor to:
retrieve event data, wherein the event data comprises at least an objective, wherein retrieving event data comprises:
identifying a first timestamp and a second timestamp from the event data;
determining a temporal span as a function of the first timestamp and the second timestamp;
and
determining the at least an objective as a function of the first timestamp, the second timestamp, and the temporal span;
identify, using a first machine learning model trained on checkpoint identification training data, at least a checkpoint as a function of the event data, wherein the checkpoint training data comprises one or more validated event data from previous sessions,
wherein using the first machine learning model comprises:
retrieving from a server-side data storage a cross-session state variable comprising prior session datum based on device identifier;
and
inputting the cross-session state variable and event data into the first machine learning model;
validate the at least a checkpoint against the event data and the at least an objective;
monitor, using a second machine learning model trained on progress indication training data, a progress as a function of the at least a checkpoint, wherein monitoring the progress comprises:
receiving supplemental event data;
comparing the supplemental event data against the at least a checkpoint;
and
generating a progress indication as a function of the comparison;
and
generate a recommended course of action comprising a corrective action including suggestions for an entity to modify its work habits as a function of the progress indication.

18 more elements

A method for setting and monitoring checkpoints in structured data, the method comprising:

retrieving, by a processor, event data, wherein the event data comprises at least an objective, wherein retrieving event data comprises:
identifying a first timestamp and a second timestamp from the event data;
determining a temporal span as a function of the first timestamp and the second timestamp;
and
determining the at least an objective as a function of the first timestamp, the second timestamp, and the temporal span;
identifying, by the processor using a first machine learning model trained on checkpoint identification training data, at least a checkpoint as a function of the event data, wherein the checkpoint training data comprises one or more validated event data from previous sessions, wherein using the first machine learning model comprises:
retrieving from a server-side data storage a cross-session state variable comprising prior session datum based on device identifier;
and
inputting the cross-session state variable and event data into the first machine learning model;
validating, by the processor, the at least a checkpoint against the event data and the at least an objective;
monitoring, by the processor using a second machine learning model trained on progress indication training data, a progress as a function of the at least a checkpoint,
wherein monitoring the progress comprises:
receiving supplemental event data;
comparing the supplemental event data against the at least a checkpoint;
and
generating a progress indication as a function of the comparison;
and
generating, by the processor, a recommended course of action comprising a corrective action including suggestions for an entity to modify its work habits as a function of the progress indication.

15 more elements

Dependencies are inferred from the claim text.