公开(公告)号：US11929070B1

公开(公告)日：2024-03-12

申请号：US17461124

申请日：2021-08-30

Applicant: Amazon Technologies, Inc.

Inventor： Ruhi Sarikaya ,  Zheng Du ,  Xiaohu Liu ,  Kai Liu ,  Sriharsha Venkata Chintalapati ,  Chenlei Guo ,  Hung Tuan Pham ,  Joe Pemberton ,  Zhenyu Yao ,  Bigyan Rajbhandari

IPC: G10L15/22 ,  G06N20/20 ,  G10L15/02 ,  G10L15/06

CPC classification number: G10L15/22 ,  G06N20/20 ,  G10L15/02 ,  G10L15/063 ,  G10L2015/225

Abstract: Techniques for performing centralized unsuperivised learning in a multi-domain system are described. A user may request labeled data for an ML task, where the request includes a prompt for obtaining relevant explicit user feedback. The system may use the prompt to collect explicit user feedback for relevant runtime user inputs. After a duration of time (in the user's request for labeled data) has elapsed, the system determines whether collected user feedback indicates processing of the user input was defective and, if so, determines a cause of the defective processing. The system then uses one or more label generators to generate labeled data using the collected user feedback, whether the processing was defective, and the determined defect cause.

公开(公告)号：US12148417B1

公开(公告)日：2024-11-19

申请号：US17354215

申请日：2021-06-22

Applicant: Amazon Technologies, Inc.

Inventor： Aidan Thomas Cardella ,  Anand Victor ,  Vipin Gupta ,  Zheng Du ,  John Rajiv Malik ,  Li Erran Li ,  Jarrett Alegre Bato ,  Peng Yang ,  Alejandro Ricardo Mottini D'Oliveira

IPC: G10L15/01 ,  G10L15/16 ,  G10L15/18 ,  G10L15/26

Abstract: Devices and techniques are generally described for confidence score generation for label generation. In some examples, first data may be received from a first computing device. In various further examples, first label data classifying at least one aspect of the first data may be received. First metadata associated with how the first label data was generated may be received. In some cases, the first label data may be generated by a first user. In various examples, a first machine learning model may generate a first confidence score associated with the first label data based at least in part on the first data and second data related to label generation by the first person. In various examples, output data comprising the first confidence score may be sent to the first computing device.