US 2023/0401186 A1Published application

Generating datastore checkpoints

AssigneeMaplebear, Inc.(dba Instacart)

InventorJacob Jensen

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Abstract

The present disclosure is directed to generating datastore checkpoints. In particular, the methods and systems of the present disclosure may generate, within a datastore, data representing multiple checkpoints. Each checkpoint of the checkpoints may correspond to a respective record of the datastore and may represent a common shared value for a field based at least in part on which the datastore is ordered. Based at least in part on the checkpoints, the datastore may be queried to produce one or more responsive records to one or more criteria of the query. Based at least in part on the responsive record(s), training data may be generated. The training data may be utilized for training one or more machine learning (ML) models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered.

Claims20

Dependent claims

A method comprising:

generating, by one or more computing devices and within a datastore, data representing a plurality of checkpoints, each checkpoint of the plurality of checkpoints corresponding to a respective record of the datastore and representing a common shared value for a field based at least in part on which the datastore is ordered;
querying, by the one or more computing devices and based at least in part on the data representing the plurality of checkpoints, the datastore to produce one or more responsive records to one or more criteria of the query;
generating, by the one or more computing devices and based at least in part on the one or more responsive records, training data for training one or more machine learning (ML) models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered;
and
storing, by the one or more computing devices, the training data for training the one or more ML models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered.

2 more elements

A system comprising:

one or more processors;
and
a memory storing instructions that when executed by the one or more processors cause the system to perform operations comprising:
generating, within a datastore, data representing a plurality of checkpoints, each checkpoint of the plurality of checkpoints corresponding to a respective record of the datastore and representing a common shared value for a field based at least in part on which the datastore is ordered;
and
generating, based at least in part of the data representing the plurality of checkpoints, training data for one or more machine learning (ML) models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered.

3 more elements

One or more non-transitory computer-readable media comprising instructions that when executed by one or more computing devices cause the one or more computing devices to perform operations comprising generating, within a datastore, data representing a plurality of checkpoints, each checkpoint of the plurality of checkpoints corresponding to a respective record of the datastore and representing a common shared value for a field based at least in part on which the datastore is ordered.

Dependencies are inferred from the claim text.