公开(公告)号：US10452992B2

公开(公告)日：2019-10-22

申请号：US14538723

申请日：2014-11-11

Applicant: Amazon Technologies, Inc.

Inventor： Polly Po Yee Lee ,  Nicolle M. Correa ,  Leo Parker Dirac ,  Aleksandr Mikhaylovich Ingerman

IPC: G06N5/04 ,  G06N20/00 ,  G09B5/00

Abstract: A first data set corresponding to an evaluation run of a model is generated at a machine learning service for display via an interactive interface. The data set includes a prediction quality metric. A target value of an interpretation threshold associated with the model is determined based on a detection of a particular client's interaction with the interface. An indication of a change to the prediction quality metric that results from the selection of the target value may be initiated.

公开(公告)号：US20200050968A1

公开(公告)日：2020-02-13

申请号：US16657886

申请日：2019-10-18

Applicant: Amazon Technologies, Inc.

Inventor： Polly Po Yee Lee ,  Nicolle M. Correa ,  Leo Parker Dirac ,  Aleksandr Mikhaylovich Ingerman

IPC: G06N20/00 ,  G09B5/00

Abstract: A first data set corresponding to an evaluation run of a model is generated at a machine learning service for display via an interactive interface. The data set includes a prediction quality metric. A target value of an interpretation threshold associated with the model is determined based on a detection of a particular client's interaction with the interface. An indication of a change to the prediction quality metric that results from the selection of the target value may be initiated.

公开(公告)号：US10013416B1

公开(公告)日：2018-07-03

申请号：US14974501

申请日：2015-12-18

Applicant: Amazon Technologies, Inc.

Inventor： Vikas Seshagiri Rao Bhardwaj ,  Jared Kane Kramer ,  Hung Chiu Phillip Keung ,  Kapil Chahal ,  Tarun Agarwal ,  Jason Sun ,  Gunashekar Jayaraman ,  Dhvani Yogesh Shah ,  Ariel Grob Dos Santos ,  Sheng Bi ,  Polly Po Yee Lee ,  Anirban Chakraborty

IPC: G06F17/27 ,  G06F17/28 ,  H04L29/06 ,  H04L29/08 ,  G06N99/00 ,  G10L15/18

CPC classification number: G10L15/18 ,  G06F17/2785 ,  G06N5/022 ,  G06N20/00 ,  G10L15/26

Abstract: Disclosed are various embodiments for an automated service agent. Natural language inputs are obtained from a client. The natural language inputs and other data are input to machine learning models to identify intents reflected in the natural language inputs. A state in a workflow is updated to reflect the identified intents. Responses may be communicated to the client to further progress in the workflow.