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A Continuous Glucose Monitoring System Integrated With Ai Algorithms For Real Time Insulin Dose Suggestions In Diabetic Care Managed By Nurses

Abstract: The invention provides a wearable CGM system integrated with AI algorithms for real-time insulin dose recommendations in diabetic care. It comprises a glucose sensor, AI engine, nurse dashboard, and cloud storage. The system empowers nurses to make accurate insulin-related decisions, enhances glycemic control, and reduces complications in diabetic patients. This AI-integrated solution is ideal for modern healthcare environments requiring continuous and intelligent monitoring.

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Notices, Deadlines & Correspondence

Patent Information

Application #
Filing Date
03 May 2025
Publication Number
21/2025
Publication Type
INA
Invention Field
BIO-MEDICAL ENGINEERING
Status
Email
Parent Application

Applicants

IMRAN KHAN
Sharda University Private university in Greater Noida, Uttar Pradesh PIN 201306.
Dr Rizu
Associate Professor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
Ms Ritu Verma
Associate Professor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
Ms Christa Mathew
Associate Professor, Department of Medical Surgical Nursing, Sharda School of Nursing Science and Research, Sharda University
Dr.Priyadarshani Moon
Associate Professor, Department of Child Health Nursing, Godavari college of Nursing Jalgaon.Maharashtra
Dr Sonal Chand
Assistant Professor, Department of Menatal Health Nursing, Sharda School of Nursing Science and Research, Sharda University
Dr Nitika Thakur
Associate Professor, Department of Child Health Nursing, Sharda School of Nursing Science and Research, Sharda University
Ms Madhvi Sharma
Senior Tutor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
Dr Dinesh Selvam S
Principal, Amity College of Nursing, Amity University Haryan Manesar Panchgaon.
Ms. Sharmila
Principal, Vidyawati college of nursing, Mahendergarh, Haryana
Ms Supreet Rupam
Assistant Professorl, Amity College of Nursing, Amity University Haryan manesar panchgaon.

Inventors

1. IMRAN KHAN
Sharda University Private university in Greater Noida, Uttar Pradesh PIN 201306.
2. Dr Rizu
Associate Professor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
3. Ms Ritu Verma
Associate Professor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
4. Ms Christa Mathew
Associate Professor, Department of Medical Surgical Nursing, Sharda School of Nursing Science and Research, Sharda University
5. Dr.Priyadarshani Moon
Associate Professor, Department of Child Health Nursing, Godavari college of Nursing Jalgaon.Maharashtra
6. Dr Sonal Chand
Assistant Professor, Department of Menatal Health Nursing, Sharda School of Nursing Science and Research, Sharda University
7. Dr Nitika Thakur
Associate Professor, Department of Child Health Nursing, Sharda School of Nursing Science and Research, Sharda University
8. Ms Madhvi Sharma
Senior Tutor, Department of OBG, Sharda School of Nursing Science and Research, Sharda University
9. Dr Dinesh Selvam S
Principal, Amity College of Nursing, Amity University Haryan Manesar Panchgaon.
10. Ms. Sharmila
Principal, Vidyawati college of nursing, Mahendergarh, Haryana
11. Ms Supreet Rupam
Assistant Professorl, Amity College of Nursing, Amity University Haryan manesar panchgaon.

Specification

Description:Diabetes mellitus is a chronic metabolic disorder that requires precise monitoring
and insulin management. Traditional methods of insulin administration often
depend on manual blood glucose monitoring and physician intervention. These
processes can delay treatment adjustments and are prone to human error. Nurses
are increasingly playing a vital role in managing diabetic patients, particularly in
hospital and home care settings. There is a growing need for an intelligent system
that continuously monitors glucose levels and provides real-time, nurse-assisted
insulin dose recommendations. , Claims:1. A continuous glucose monitoring system comprising a wearable biosensor, an AI
powered processing unit, a nurse-accessible dashboard, and a dose
recommendation engine, wherein the AI engine processes real-time and historical
glucose data to suggest optimal insulin dosages.
2. The system of claim 1, wherein the AI engine is trained using supervised learning
models including patient-specific features and clinical guidelines.
3. The system of claim 1, further comprising a cloud-based storage module for
archiving glucose readings, insulin dosages, and nurse interventions.
4. The system of claim 1, wherein the nurse dashboard provides real-time alerts,
allows dose override, and supports remote monitoring.

Documents

Application Documents

# Name Date
1 202511043031-Sequence Listing in PDF [03-05-2025(online)].pdf 2025-05-03
2 202511043031-REQUEST FOR EARLY PUBLICATION(FORM-9) [03-05-2025(online)].pdf 2025-05-03
3 202511043031-FORM-9 [03-05-2025(online)].pdf 2025-05-03
4 202511043031-FORM 1 [03-05-2025(online)].pdf 2025-05-03
5 202511043031-DRAWINGS [03-05-2025(online)].pdf 2025-05-03
6 202511043031-COMPLETE SPECIFICATION [03-05-2025(online)].pdf 2025-05-03