公开(公告)号：US11972361B2

公开(公告)日：2024-04-30

申请号：US16817460

申请日：2020-03-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Somnath Saha ,  Jian Liang ,  Ramaraj Pandian

IPC: G06N5/04 ,  G06F16/2458 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06F16/2474 ,  G06N20/00

Abstract: Provided is a method including receiving object IOs for a target device, grouping the object IOs using a first plurality of input parameters, associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, storing a first data entry including the tracking parameter, the first plurality of input parameters, and the performance parameter in a database, extracting a plurality of data entries from the database, the plurality of data entries including the first data entry, training a training model using one or more of the plurality of data entries, cross-validating the training model to determine a degree of error reduction of the training model, performing a model check to compare the training model to an inferencing model, and updating the inferencing model based on the model check.

公开(公告)号：US20210232946A1

公开(公告)日：2021-07-29

申请号：US16817460

申请日：2020-03-12

Applicant: Samsung Electronics Co., Ltd.

Inventor： Somnath Saha ,  Jian Liang ,  Ramaraj Pandian

IPC: G06N5/04 ,  G06F16/2458 ,  G06N20/00

Abstract: Provided is a method including receiving object IOs for a target device, grouping the object IOs using a first plurality of input parameters, associating a tracking parameter with the first plurality of input parameters and a performance parameter corresponding to the first plurality of input parameters, storing a first data entry including the tracking parameter, the first plurality of input parameters, and the performance parameter in a database, extracting a plurality of data entries from the database, the plurality of data entries including the first data entry, training a training model using one or more of the plurality of data entries, cross-validating the training model to determine a degree of error reduction of the training model, performing a model check to compare the training model to an inferencing model, and updating the inferencing model based on the model check.