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公开(公告)号：US11978445B1
公开(公告)日：2024-05-07
申请号：US17217994
申请日：2021-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Edward Bueche , Amaury Gutierrez Acosta , Francois Mairesse , Yun Suk Paik , Anmol Tiwari , Tao Ye
IPC: G10L15/22
CPC classification number: G10L15/22
Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a model trained to identify a dialog act that is most likely to lead to a desired outcome. Upon receiving an invocation to begin a conversation, a score indicative of a level of confidence that the conversation will have a successful outcome is determined, and a dialog act is selected based on the score. Subsequently, at each turn of the conversation, the score is updated or a new score is calculated, and a dialog act is selected based on the updated or new score. Confidence scores are calculated based on input features that are determined based on the user who uttered the invocation or responses to dialog acts, as well as a context of the conversation, and provided to a linear model or a machine learning model as inputs.
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公开(公告)号：US11776542B1
公开(公告)日：2023-10-03
申请号：US17217943
申请日：2021-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Edward Bueche , Francois Mairesse , Torbjorn Vik , Tao Ye
CPC classification number: G10L15/22 , G06N20/00 , G10L2015/088 , G10L2015/223
Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a machine learning model trained to identify a dialog act that is most likely to lead to a desired outcome. When an invocation is received from a user, a context of the invocation is determined, and a pool of dialog acts is scored based on the context by a machine learning model. Dialog acts are selected from the pool and presented to the user in accordance with a randomization policy. Data regarding the dialog acts and their success in achieving a desired outcome is used to train one or more machine learning models to select dialog acts in response to invocations.
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