公开(公告)号：US12211486B2

公开(公告)日：2025-01-28

申请号：US17647499

申请日：2022-01-10

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/06 ,  G10L15/14 ,  G10L15/22

Abstract: A method includes identifying multiple tokens contained in an input utterance. The method also includes generating slot labels for at least some of the tokens contained in the input utterance using a trained machine learning model. The method further includes determining at least one action to be performed in response to the input utterance based on at least one of the slot labels. The trained machine learning model is trained to use attention distributions generated such that (i) the attention distributions associated with tokens having dissimilar slot labels are forced to be different and (ii) the attention distribution associated with each token is forced to not focus primarily on that token itself.

公开(公告)号：US11875231B2

公开(公告)日：2024-01-16

申请号：US16661827

申请日：2019-10-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Yilin Shen ,  Hongxia Jin

IPC: G06F15/16 ,  G06F9/54 ,  H04L29/06 ,  G06N20/00 ,  G06N5/02

CPC classification number: G06N20/00 ,  G06N5/02

Abstract: An electronic device for complex task machine learning includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive an unknown command for performing a task and generate a prompt regarding the unknown command. The at least one processor is also configured to receive one or more instructions in response to the prompt, where each of the one or more instructions provides information on performing at least a portion of the task. The at least one processor is further configured to determine at least one action for each one of the one or more instructions. In addition, the at least one processor is configured to create a complex action for performing the task based on the at least one action for each one of the one or more instructions.

公开(公告)号：US11790895B2

公开(公告)日：2023-10-17

申请号：US16661581

申请日：2019-10-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Hongxia Jin

IPC: G10L15/18 ,  G06N20/00 ,  G06F40/205 ,  G06F40/284

CPC classification number: G10L15/1815 ,  G06F40/205 ,  G06F40/284 ,  G06N20/00

Abstract: An electronic device for natural language understanding includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to process an utterance using a trained model. The at least one processor is also configured to replace a first portion of the utterance with a first token, where the first token represents a semantic role of the first portion of the utterance based on a slot vocabulary. The at least one processor is further configured to determine a slot value in the utterance based on the first token. In addition, the at least one processor is configured to perform a task corresponding to the utterance based on the determined slot value.

公开(公告)号：US20220375457A1

公开(公告)日：2022-11-24

申请号：US17647499

申请日：2022-01-10

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/06 ,  G10L15/22 ,  G10L15/14

Abstract: A method includes identifying multiple tokens contained in an input utterance. The method also includes generating slot labels for at least some of the tokens contained in the input utterance using a trained machine learning model. The method further includes determining at least one action to be performed in response to the input utterance based on at least one of the slot labels. The trained machine learning model is trained to use attention distributions generated such that (i) the attention distributions associated with tokens having dissimilar slot labels are forced to be different and (ii) the attention distribution associated with each token is forced to not focus primarily on that token itself.

公开(公告)号：US11314940B2

公开(公告)日：2022-04-26

申请号：US15986633

申请日：2018-05-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Yilin Shen ,  Hongxia Jin

IPC: G10L15/22 ,  G06F40/30 ,  G06F9/451 ,  G06N5/02 ,  G06F40/205 ,  G06F40/253 ,  G10L15/07 ,  G10L15/06

Abstract: A method includes determining, by an electronic device, a skill from a first natural language (NL) input. Upon successful determination of the skill, the first NL input is transmitted to a custom skill parser for determination of a skill intent. The custom skill parser is trained based on data including at least a custom training data set. Upon unsuccessful determination of the skill, the first NL input is transmitted to a generic parser for determination of a general intent of the first NL input.

公开(公告)号：US11182565B2

公开(公告)日：2021-11-23

申请号：US15904203

申请日：2018-02-23

Applicant: Samsung Electronics Co., Ltd.

Inventor： Avik Ray ,  Yilin Shen ,  Hongxia Jin

IPC: G06F40/35 ,  G10L15/18 ,  G06F3/16 ,  G06F40/30 ,  G06F40/247 ,  G06F40/295 ,  G10L15/07 ,  G06F40/216

Abstract: A method includes retrieving, at an electronic device, a first natural language (NL) input. An intent of the first NL input is undetermined by both a generic parser and a personal parser. A paraphrase of the first NL input is retrieved at the electronic device. An intent of the paraphrase of the first NL input is determined using at least one of: the generic parser, the personal parser, or a combination thereof. A new personal intent for the first NL input is generated based on the determined intent. The personal parser is trained using existing personal intents and the new personal intent.

公开(公告)号：US20200050934A1

公开(公告)日：2020-02-13

申请号：US16535380

申请日：2019-08-08

Applicant: Samsung Electronics Co., Ltd.

Inventor： Yilin Shen ,  Yue Deng ,  Avik Ray ,  Hongxia Jin

IPC: G06N3/08 ,  G06F3/06 ,  G06N5/04

Abstract: An electronic device including a deep memory model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to receive input data to the deep memory model. The at least one processor is also configured to extract a history state of an external memory coupled to the deep memory model based on the input data. The at least one processor is further configured to update the history state of the external memory based on the input data. In addition, the at least one processor is configured to output a prediction based on the extracted history state of the external memory.