公开(公告)号：US11954090B1

公开(公告)日：2024-04-09

申请号：US17546891

申请日：2021-12-09

Applicant: Amazon Technologies, Inc.

Inventor： Venkata Harish Mandala ,  Andygibb Halim ,  Amiya Kishor Chakraborty ,  Sayali Subhash Degaonkar ,  Shahinaz S Azazy ,  Ajay Avinash Kulkarni

IPC: G06F16/00 ,  G06F16/23 ,  G06F16/25

CPC classification number: G06F16/2365 ,  G06F16/254

Abstract: Techniques and systems can process data of a dataset to determine when a portion of data is comprised in the data of the dataset. An output generated from processing the data of the dataset can be evaluated, where the output can signify that processing the data of the dataset was unable to locate the portion of data in the data of the dataset. Based on evaluating the output, the data of the dataset can be automatically reprocessed to determine the portion of data is in the data of the dataset. A result can then be generated from the portion of data determined to be in the data of the dataset.

公开(公告)号：US20240331821A1

公开(公告)日：2024-10-03

申请号：US18194350

申请日：2023-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Vijit Gupta ,  Matthew Chih-Hui Chiou ,  Amiya Kishor Chakraborty ,  Anuroop Arora ,  Varun Sembium Varadarajan ,  Sarthak Handa ,  Amit Vithal Sawant ,  Glen Herschel Carpenter ,  Jesse Deng ,  Mohit Narendra Gupta ,  Rohil Bhattarai ,  Samuel Benjamin Schiff ,  Shane Michael McGookey ,  Tianze Zhang

IPC: G16H15/00 ,  G06F40/40 ,  G16H10/60

CPC classification number: G16H15/00 ,  G06F40/40 ,  G16H10/60

Abstract: Systems and methods for performing medical audio summarizing for medical conversations are disclosed. An audio file and meta data for a medical conversation are provided to a medical audio summarization system. A transcription machine learning model is used by the medical audio summarization system to generate a transcript and a natural language processing service of the medical audio summarization system is used to generate a summary of the transcript. The natural language processing service may include at least four machine learning models that identify medical entities in the transcript, identify speaker roles in the transcript, determine sections of the transcript corresponding to the summary, and extract or abstract phrases for the summary. The identified medical entities and speaker roles, determined sections, and extracted or abstracted phrases may then be used to generate the summary.