公开(公告)号：US11798538B1

公开(公告)日：2023-10-24

申请号：US17027162

申请日：2020-09-21

Applicant: Amazon Technologies, Inc.

Inventor： Christopher Geiger Parker ,  Piyush Bhargava ,  Aparna Nandyal ,  Rajagopalan Ranganathan ,  Mugunthan Govindaraju ,  Vidya Narasimhan

IPC: G10L15/18 ,  G06F16/9032 ,  G10L15/26 ,  G10L15/183 ,  G10L15/22

CPC classification number: G10L15/1822 ,  G06F16/90332 ,  G10L15/183 ,  G10L15/26 ,  G10L2015/228

Abstract: This disclosure relates to answer prediction in a speech processing system. The system may disambiguate entities spoken or implied in a request to initiate an action with respect to a target user. To initiate the action, the system may determine one or more parameters; for example, the target (e.g., a contact/recipient), a source (e.g., a caller/requesting user), and a network (voice over internet protocol (VOIP), cellular, video chat, etc.). Due to the privacy implications of initiating actions involving data transfers between parties, the system may apply a high threshold for a confidence associated with each parameter. Rather than ask multiple follow-up questions, which may frustrate the requesting user, the system may attempt to disambiguate or determine a parameter, and skip a question regarding the parameter if it can predict an answer with high confidence. The system can improve the customer experience while maintaining security for actions involving, for example, communications.

公开(公告)号：US11195522B1

公开(公告)日：2021-12-07

申请号：US16443345

申请日：2019-06-17

Applicant: Amazon Technologies, Inc.

Inventor： Sumit Makashir ,  Adrien Carre ,  Jack FitzGerald ,  Cong Zhang ,  Piyush Bhargava ,  Chandrashekar Nagaraju ,  Md Moshiur Rahman ,  Xin Liang

IPC: G10L15/00 ,  G10L15/18 ,  G10L15/22 ,  G10L15/02 ,  G10L15/30 ,  G10L15/06 ,  G06F40/30 ,  G06F40/295 ,  G10L13/00

Abstract: Devices and techniques are generally described for rejecting false invocations of speech processing skills. In various examples, utterance data comprising automatic speech recognition (ASR) data and natural language understanding (NLU) data may be received. In some examples, ASR confidence data indicating a confidence level of the ASR data may be received. In further examples, NLU confidence data indicating a confidence level of the NLU data may be received. A machine learning model may determine, based at least in part on the ASR confidence data and the NLU confidence data, first false invocation data indicating a likelihood of false invocation of a speech processing skill. In some examples, a first directive may be sent to the speech processing system based at least in part on the first false invocation data. The first directive may be effective to cause the speech processing system to end a current dialog session.