公开(公告)号：US11410646B1

公开(公告)日：2022-08-09

申请号：US16368399

申请日：2019-03-28

Applicant: Amazon Technologies, Inc.

Inventor： Cengiz Erbas ,  Thomas Kollar ,  Avnish Sikka ,  Spyridon Matsoukas ,  Simon Peter Reavely

IPC: G10L15/22 ,  G10L15/06 ,  G10L15/02 ,  G10L15/18

Abstract: A system capable of performing natural language understanding (NLU) on utterances including complex command structures such as sequential commands (e.g., multiple commands in a single utterance), conditional commands (e.g., commands that are only executed if a condition is satisfied), and/or repetitive commands (e.g., commands that are executed until a condition is satisfied). Audio data may be processed using automatic speech recognition (ASR) techniques to obtain text. The text may then be processed using machine learning models that are trained to parse text of incoming utterances. The models may identify complex utterance structures and may identify what command portions of an utterance go with what conditional statements. Machine learning models may also identify what data is needed to determine when the conditionals are true so the system may cause the commands to be executed (and stopped) at the appropriate times.

公开(公告)号：US20170278514A1

公开(公告)日：2017-09-28

申请号：US15196540

申请日：2016-06-29

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Lambert Mathias ,  Thomas Kollar ,  Arindam Mandal ,  Angeliki Metallinou

IPC: G10L15/22 ,  G06F17/30 ,  G10L15/18 ,  G10L15/14 ,  G10L15/26 ,  G10L15/02

CPC classification number: G10L15/22 ,  G06F17/277 ,  G06F17/279 ,  G06F17/30637 ,  G06F17/30654 ,  G06F17/30705 ,  G10L15/02 ,  G10L15/142 ,  G10L15/1815 ,  G10L15/26 ,  G10L2015/223

Abstract: A system capable of performing natural language understanding (NLU) without the concept of a domain that influences NLU results. The present system uses a hierarchical organizations of intents/commands and entity types, and trained models associated with those hierarchies, so that commands and entity types may be determined for incoming text queries without necessarily determining a domain for the incoming text. The system thus operates in a domain agnostic manner, in a departure from multi-domain architecture NLU processing where a system determines NLU results for multiple domains simultaneously and then ranks them to determine which to select as the result.

公开(公告)号：US20230032575A1

公开(公告)日：2023-02-02

申请号：US17882874

申请日：2022-08-08

Applicant: Amazon Technologies, Inc.

Inventor： Cengiz Erbas ,  Thomas Kollar ,  Avnish Sikka ,  Spyridon Matsoukas ,  Simon Peter Reavely

IPC: G10L15/22 ,  G10L15/06 ,  G10L15/02 ,  G10L15/18

Abstract: A system capable of performing natural language understanding (NLU) on utterances including complex command structures such as sequential commands (e.g., multiple commands in a single utterance), conditional commands (e.g., commands that are only executed if a condition is satisfied), and/or repetitive commands (e.g., commands that are executed until a condition is satisfied). Audio data may be processed using automatic speech recognition (ASR) techniques to obtain text. The text may then be processed using machine learning models that are trained to parse text of incoming utterances. The models may identify complex utterance structures and may identify what command portions of an utterance go with what conditional statements. Machine learning models may also identify what data is needed to determine when the conditionals are true so the system may cause the commands to be executed (and stopped) at the appropriate times.

公开(公告)号：US10304444B2

公开(公告)日：2019-05-28

申请号：US15196540

申请日：2016-06-29

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Lambert Mathias ,  Thomas Kollar ,  Arindam Mandal ,  Angeliki Metallinou

IPC: G06F17/20 ,  G10L15/22 ,  G10L15/26 ,  G10L15/02 ,  G10L15/18 ,  G10L15/14 ,  G06F16/35 ,  G06F16/332 ,  G06F17/27

Abstract: A system capable of performing natural language understanding (NLU) without the concept of a domain that influences NLU results. The present system uses a hierarchical organizations of intents/commands and entity types, and trained models associated with those hierarchies, so that commands and entity types may be determined for incoming text queries without necessarily determining a domain for the incoming text. The system thus operates in a domain agnostic manner, in a departure from multi-domain architecture NLU processing where a system determines NLU results for multiple domains simultaneously and then ranks them to determine which to select as the result.