Back to results
US 12,614,195 B2Granted
Retention management system
AssigneeToshiba Global Commerce Solutions, Inc.
InventorsPatricia Hogan, Susan Brosnan, Jessica Snead, Daniel Goins, Matthew Slessman
Life of this patent
Estimating the term…
Abstract
Disclosed herein are inventive concepts that facilitate the dynamic management of data retention. Data records associated with point-of-sale transactions can undergo evaluation based on data utilization metrics, which can include frequency of data access and timing of data access. A computational model can process these metrics to quantitatively assess each record based on a composite metric, leading to the generation of a data retention policy. Storage of at least some data can be modified to conform to the data retention policy.
Claims19
A method comprising:
- storing data records in a storage system according to a first data retention policy, the data records forming a part of a plurality of data records stored in the storage system and being associated with point-of-sale transactions across one or more point-of-sale devices in a retail environment;
- retrieving data utilization metrics for a set of data records of the plurality of data records, wherein the data utilization metrics comprise frequency of data access, timing of data access, and storage-utilization parameters indicative of remaining storage capacity;
- processing the data utilization metrics using a trained machine-learning model configured to generate a composite retention-adjustment metric derived from the frequency of data access, the timing of data access, and the storage-utilization parameters;
- determining, with the trained machine-learning model, data-record-specific retention settings based on the composite retention-adjustment metric, the trained machine-learning model having been trained using historical data comprising prior data-access patterns and prior retention outcomes;
- generating a second data retention policy defining storage durations and migration actions for the data records based on the data-record-specific retention settings;
- and
- modifying storage of at least some data records of the set of data records based on the second data retention policy, including modifying metadata associated with the at least some data records to implement deletion, migration, or tiered-storage operations, wherein at least some subsequent data records are stored in the storage system according to the second data retention policy;
- recalculating, during operation of the storage system and on an ongoing or event-driven basis, the composite retention-adjustment metric based on updated data utilization metrics obtained during operation of the storage system, the recalculating forming part of an adaptive feedback loop in which the second data-retention policy is updated, or a new data-retention policy is generated, based on the recalculated composite retention-adjustment metric, such that subsequent data records are stored in accordance with the updated or newly generated data-retention policy.
5 more elements
A system comprising one or more processors configured to:
- store data records in a storage system according to a first data retention policy, the data records forming a part of a plurality of data records stored in the storage system and being associated with point-of-sale transactions across one or more point-of-sale devices in a retail environment;
- retrieve data utilization metrics for a set of data records of the plurality of data records, wherein the data utilization metrics comprise frequency of data access, timing of data access, and storage-utilization parameters indicative of remaining storage capacity;
- process the data utilization metrics using a trained machine-learning configured to generate a composite retention-adjustment metric derived from the frequency of data access, the timing of data access, and the storage-utilization parameters;
- determine, with the trained machine-learning model, data-record-specific retention settings based on the composite retention-adjustment metric, the trained machine-learning model having been trained using historical data comprising prior data-access patterns and prior retention outcomes;
- generate a second data retention policy defining storage durations and migration actions for the data records based on the data-record-specific retention settings;
- modify storage of at least some data records of the set of data records based on the second data retention policy, including modifying metadata associated with the at least some data records to implement deletion, migration, or tiered-storage operations, wherein at least some subsequent data records are stored in the storage system according to the second data retention policy;
- and
- recalculate, during operation of the storage system and on an ongoing or event-driven basis, the composite retention-adjustment metric based on updated data utilization metrics obtained during operation of the storage system, wherein the recalculation forms part of an adaptive feedback loop in which the second data-retention policy is updated, or a new data-retention policy is generated, based on the recalculated composite retention-adjustment metric, such that subsequent data records are stored in accordance with the updated or newly generated data-retention policy.
5 more elements
A non-transitory computer-readable media comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to:
- store data records in a storage system according to a first data retention policy, the data records forming a part of a plurality of data records stored in the storage system and being associated with point-of-sale transactions across one or more point-of-sale devices in a retail environment;
- retrieve data utilization metrics for a set of data records of the plurality of data records, wherein the data utilization metrics comprise frequency of data access, timing of data access, and storage-utilization parameters indicative of remaining storage capacity;
- process the data utilization metrics using a trained machine-learning configured to generate a composite retention-adjustment metric derived from the frequency of data access, the timing of data access, and the storage-utilization parameters;
- determine, with the trained machine-learning model, data-record-specific retention settings based on the composite retention-adjustment metric, the trained machine-learning model having been trained using historical data comprising prior data-access patterns and prior retention outcomes;
- generate a second data retention policy defining storage durations and migration actions for the data records based on the data-record-specific retention settings;
- modify storage of at least some data records of the set of data records based on the second data retention policy, including modifying metadata associated with the at least some data records to implement deletion, migration, or tiered-storage operations, wherein at least some subsequent data records are stored in the storage system according to the second data retention policy;
- and
- recalculate, during operation of the storage system and on an ongoing or event-driven basis, the composite retention-adjustment metric based on updated data utilization metrics obtained during operation of the storage system, wherein the recalculation forms part of an adaptive feedback loop in which the second data-retention policy is updated, or a new data-retention policy is generated, based on the recalculated composite retention-adjustment metric, such that subsequent data records are stored in accordance with the updated or newly generated data-retention policy.
5 more elements
Dependencies are inferred from the claim text.