公开(公告)号：US20230252353A1

公开(公告)日：2023-08-10

申请号：US18175830

申请日：2023-02-28

Applicant: Samsung Electronics Co., Ltd.

Inventor： Prasanna R ,  Praveen Doreswamy NAIDU ,  Raman Pratap SINGH

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: An on-device training method to train an artificial intelligence (AI) model on a device and a system are provided. The method includes receiving a training request for initiating on-device training from one or more applications based on dataset for the on-device training of the AI model being obtained through the one or more applications. The method further comprises determining whether at least one policy of a plurality of predefined policies regarding state of at least one component included in the device is satisfied, based on the training request. The method also comprises training, based on the at least one policy being satisfied, the AI model using data associated with the one or more applications.

公开(公告)号：US20240135181A1

公开(公告)日：2024-04-25

申请号：US18541972

申请日：2023-12-15

Applicant: Samsung Electronics Co., Ltd.

Inventor： Gokulkrishna M ,  Siva Kailash SACHITHANANDAM ,  Prasanna R ,  Rajath Elias SOANS ,  Alladi Ashok Kumar SENAPATI ,  Praveen Doreswamy NAIDU ,  Pradeep NELAHONNE SHIVAMURTHAPPA

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: A method for validating a trained artificial intelligence (AI) model on a device is provided. The method includes deploying a validation model generated by applying a plurality of anticipated configurational changes associated with the trained AI model requiring validation. Further, the method includes providing input data to each of the validation model and the trained AI model for receiving an output from each of the validation model and the trained AI model, wherein the output of the validation model is further based on one or more actual configurational deviations that occurred during training of the trained AI model since deployment of the trained AI model on the device. Furthermore, the method includes combining the output of each of the validation model and the trained AI model to validate the trained AI model.