View source for Amazon Technologies, Inc. (20240331821). MEDICAL CONVERSATIONAL INTELLIGENCE simplified abstract

=MEDICAL CONVERSATIONAL INTELLIGENCE=


==Organization Name==

[[:Category:Amazon Technologies, Inc.|Amazon Technologies, Inc.]]


[[Category:Amazon Technologies, Inc.]]

==Inventor(s)==

[[:Category:Vijit Gupta of Mercer Island WA (US)|Vijit Gupta of Mercer Island WA (US)]][[Category:Vijit Gupta of Mercer Island WA (US)]]

[[:Category:Matthew Chih-Hui Chiou of Seattle WA (US)|Matthew Chih-Hui Chiou of Seattle WA (US)]][[Category:Matthew Chih-Hui Chiou of Seattle WA (US)]]

[[:Category:Amiya Kishor Chakraborty of Seattle WA (US)|Amiya Kishor Chakraborty of Seattle WA (US)]][[Category:Amiya Kishor Chakraborty of Seattle WA (US)]]

[[:Category:Anuroop Arora of Seattle WA (US)|Anuroop Arora of Seattle WA (US)]][[Category:Anuroop Arora of Seattle WA (US)]]

[[:Category:Varun Sembium Varadarajan of Redmond WA (US)|Varun Sembium Varadarajan of Redmond WA (US)]][[Category:Varun Sembium Varadarajan of Redmond WA (US)]]

[[:Category:Sarthak Handa of Seattle WA (US)|Sarthak Handa of Seattle WA (US)]][[Category:Sarthak Handa of Seattle WA (US)]]

[[:Category:Amit Vithal Sawant of New Brunswick NJ (US)|Amit Vithal Sawant of New Brunswick NJ (US)]][[Category:Amit Vithal Sawant of New Brunswick NJ (US)]]

[[:Category:Glen Herschel Carpenter of Arvada CO (US)|Glen Herschel Carpenter of Arvada CO (US)]][[Category:Glen Herschel Carpenter of Arvada CO (US)]]

[[:Category:Jesse Deng of Seattle WA (US)|Jesse Deng of Seattle WA (US)]][[Category:Jesse Deng of Seattle WA (US)]]

[[:Category:Mohit Narendra Gupta of Seattle WA (US)|Mohit Narendra Gupta of Seattle WA (US)]][[Category:Mohit Narendra Gupta of Seattle WA (US)]]

[[:Category:Rohil Bhattarai of Seattle WA (US)|Rohil Bhattarai of Seattle WA (US)]][[Category:Rohil Bhattarai of Seattle WA (US)]]

[[:Category:Samuel Benjamin Schiff of New York NY (US)|Samuel Benjamin Schiff of New York NY (US)]][[Category:Samuel Benjamin Schiff of New York NY (US)]]

[[:Category:Shane Michael Mcgookey of Seattle WA (US)|Shane Michael Mcgookey of Seattle WA (US)]][[Category:Shane Michael Mcgookey of Seattle WA (US)]]

[[:Category:Tianze Zhang of Long Island City NY (US)|Tianze Zhang of Long Island City NY (US)]][[Category:Tianze Zhang of Long Island City NY (US)]]

==MEDICAL CONVERSATIONAL INTELLIGENCE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240331821 titled 'MEDICAL CONVERSATIONAL INTELLIGENCE

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.

*Simplified Explanation:
The patent application describes a system and method for summarizing medical audio conversations using machine learning and natural language processing.

*Key Features and Innovation:
- Utilizes a transcription machine learning model to generate a transcript of medical conversations.
- Employs natural language processing to identify medical entities, speaker roles, and extract key phrases for summarization.

*Potential Applications:
- Medical transcription services
- Healthcare documentation automation
- Clinical decision support systems

*Problems Solved:
- Streamlines the process of summarizing medical conversations
- Improves accuracy and efficiency in generating summaries
- Enhances accessibility and usability of medical audio data

*Benefits:
- Saves time for healthcare professionals
- Facilitates better communication and collaboration in medical settings
- Increases the accuracy and quality of medical documentation

*Commercial Applications:
"Medical Audio Summarization System: Revolutionizing Healthcare Documentation and Communication"

*Questions about Medical Audio Summarization:
1. How does the system identify medical entities in the transcript?
- The system uses machine learning models to recognize medical terms and entities in the conversation.

2. What is the role of natural language processing in generating the summary?
- Natural language processing helps extract key information and phrases for summarization.

*Frequently Updated Research:
Stay updated on advancements in machine learning and natural language processing technologies for medical audio summarization. 

Overall, the patent application introduces an innovative approach to summarizing medical conversations using advanced technologies, offering significant benefits for healthcare professionals and improving the efficiency of medical documentation processes.


==Original Abstract Submitted==

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.



[[Category:G16H15/00]]
[[Category:G06F40/40]]
[[Category:G16H10/60]]
[[Category:CPC_G16H15/00]]