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公开(公告)号:US11848905B1
公开(公告)日:2023-12-19
申请号:US18228912
申请日:2023-08-01
Applicant: Sandeep Navinchandra Shah
Inventor: Sandeep Navinchandra Shah
IPC: H04L51/216 , H04L51/046 , H04L51/222
CPC classification number: H04L51/216 , H04L51/046 , H04L51/222
Abstract: A system and a method of managing an online communication group. The method includes receiving a list of users and user data. Further, the method includes determining a role of a user from the list of users. Furthermore, a set of users from the list of users is added to the online communication group. Subsequently, the method includes managing the online communication group. The online communication group is managed by adjusting the role of the users based on at least one of a user mode, a user location, and a schedule. Furthermore, the user data is modified based on at least one of the role and an admin recommendation. Subsequently, one or more information databases are linked to the online communication group.
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公开(公告)号:US20250046475A1
公开(公告)日:2025-02-06
申请号:US18228932
申请日:2023-08-01
Applicant: Sandeep Navinchandra SHAH
Inventor: Sandeep Navinchandra SHAH
Abstract: A system and a method for generating a medical knowledge packet in an active conversation session. The system receives a message associated with a conversation thread. The system determines a context of the message using a machine learning model. Further, one or more medical knowledge packets from one or more sources may be generated based on the message and the context. A confidence score to the medical knowledge packet is assigned based on one or more factors comprising relevance, accuracy, the one or more sources, and recency of the medical knowledge packet. The medical knowledge packet with a highest confidence score is modified by formatting, summarizing, highlighting, cross-referencing, and simplifying by using one or more text analysis algorithms.
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公开(公告)号:US12230408B1
公开(公告)日:2025-02-18
申请号:US18228932
申请日:2023-08-01
Applicant: Sandeep Navinchandra Shah
Inventor: Sandeep Navinchandra Shah
Abstract: A system and a method for generating a medical knowledge packet in an active conversation session. The system receives a message associated with a conversation thread. The system determines a context of the message using a machine learning model. Further, one or more medical knowledge packets from one or more sources may be generated based on the message and the context. A confidence score to the medical knowledge packet is assigned based on one or more factors comprising relevance, accuracy, the one or more sources, and recency of the medical knowledge packet. The medical knowledge packet with a highest confidence score is modified by formatting, summarizing, highlighting, cross-referencing, and simplifying by using one or more text analysis algorithms.
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