A DEVICE AND METHOD FOR REAL-TIME DIABETES MANAGEMENT USING AI-BASED PREDICTIVE INSULIN DOSAGE CONTROL

Abstract

ABSTRACT The present invention relates to a device and method to enhance diabetes management through real-time monitoring and predictive insulin dosing. The present invention integrates a Continuous Glucose Monitor (CGM) with an automated insulin pump, utilizing advanced AI modules and machine learning to analyze glucose data and predict future fluctuations. By dynamically adjusting insulin delivery based on individual health patterns, the device aims to maintain optimal blood glucose levels, thereby reducing the risks of hypo- and hyperglycemia. Additionally, the present invention features IoT connectivity for remote monitoring by healthcare providers, facilitating timely interventions and personalized treatment plans. With its user-friendly interface and proactive approach, the invention empowers diabetic patients to manage their condition more effectively and improve their overall quality of life. FIG. 3

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to a device and method for managing diabetes, and more particularly to a smart self-care device that utilizes artificial intelligence (AI) and machine learning (ML) modals to predict and control insulin dosage in real time. Further, the present invention discloses a continuous glucose monitoring system integrated with IoT connectivity for remote patient monitoring and data sharing with healthcare professionals.
BACKGROUND
[0002] In the rapidly evolving landscape of diabetes management, the need for advanced, automated solutions has become increasingly evident. Diabetes affects millions of individuals globally, with significant health implications arising from poor glycemic control. Traditional monitoring techniques often rely on manual input and frequent blood tests, which can be inconvenient and uncomfortable for patients. As technology advances, integrating artificial intelligence and machine learning into diabetes care offers the potential for real-time monitoring and more effective management.
[0003] Conventional methods available for diabetes management primarily include continuous glucose monitors (CGMs) and insulin pumps. While CGMs provide real-time glucose readings, they often lack predictive capabilities, leaving patients to respond reactively to fluctuations. Insulin pumps, although useful, typically require manual adjustments based on the user's current glucose levels and carbohydrate intake, making it challenging to maintain optimal glycemic control. These methods do not sufficiently address the complexities of individual metabolic responses, leading to potential risks of hypoglycemia or hyperglycemia.
[0004] Furthermore, the existing systems merely focus on reactive management without anticipating future glucose trends, resulting in inadequate support for patients. Current technologies often fail to consider various influencing factors, such as physical activity, stress levels, and dietary habits, which can significantly impact blood glucose levels. As a result, there is a need for a system and method that not only automates insulin delivery but also intelligently predicts and adjusts insulin dosages based on comprehensive data analysis. Further, the system aims to empower patients, enhance compliance, and ultimately improve health outcomes.
[0005] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through the comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.
SUMMARY
[0006] In an embodiment, a method for managing diabetes in real-time using an AI-powered device is disclosed. In one example, the method includes continuously monitoring glucose levels through a Continuous Glucose Monitor (CGM) sensor, analyzing the collected data with machine learning modals to predict future glucose fluctuations, and automatically adjusting insulin dosage via an insulin pump based on these predictions. Furthermore, the method allows for remote monitoring by transmitting data to healthcare providers for timely adjustments and includes a notification system to alert users of critical glucose levels, ensuring proactive management of diabetes and enhancing patient safety.
[0007] In an embodiment, a device for real-time diabetes management using AI-based predictive analytics is disclosed. In one example, the device comprises a Continuous Glucose Monitor (CGM) sensor to measure blood glucose levels, an insulin pump to deliver controlled doses of insulin, and a microprocessor unit with machine learning modals to analyze glucose trends and predict future fluctuations. Furthermore, the device includes an IoT module for transmitting data to healthcare providers for remote monitoring, along with a notification system to alert the user of critical glucose levels, enabling proactive, adaptive diabetes management.
BRIEF DESCRIPTION OF DRAWINGS
[0008] The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Further, the elements may not be drawn to scale.
[0009] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate and not to limit the scope in any manner, wherein similar designations denote similar elements, and in which:
[0010] FIG. 1 is a block diagram illustrating the system environment 100 in which various embodiments of the present invention may be implemented.
[0011] FIG. 2 is a block diagram illustrating the architecture of the processing unit 104 configured for the diabetes management device, in accordance with an embodiment of the present invention.
[0012] FIG. 3 is a flowchart that illustrates a method for real-time diabetes management and predictive insulin delivery, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION
[0013] The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.
[0014] References to "one embodiment," "at least one embodiment," "an embodiment," "one example," "an example," "for example," and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase "in an embodiment" does not necessarily refer to the same embodiment.
[0015] The present invention addresses the limitations of conventional diabetes management systems, which often lack predictive capabilities and require frequent manual intervention. The present device integrates a Continuous Glucose Monitor (CGM), an insulin pump, and a microprocessor unit powered by AI modals, enabling it to monitor blood glucose levels in real-time and predict fluctuations based on individual health patterns. The device dynamically adjusts insulin dosage through the pump, reducing the risk of hypo- or hyperglycemic episodes. Additionally, the device features an IoT connectivity module for remote monitoring, allowing healthcare providers to review patient data and make timely adjustments. With its notification system, the device alerts users to critical glucose levels, ensuring proactive and personalized diabetes management, and significantly enhancing patient safety and convenience.
[0016] The primary objective of the present invention is to provide a comprehensive, real-time diabetes management solution that minimizes manual intervention and enhances patient safety. To achieve this, the present subject matter aims to utilize AI-powered predictive analytics to anticipate blood glucose fluctuations and dynamically adjust insulin dosage through an automated insulin pump. The present invention's objective is to offer personalized, data-driven insights that empower patients to manage their condition proactively. Additionally, the present subject matter seeks to enable seamless remote monitoring, allowing healthcare providers to review glucose trends and make timely adjustments, ultimately reducing the risk of complications associated with diabetes and improving the overall quality of life for patients.
[0017] The present invention is an advanced AI-based diabetes management device that continuously monitors and predicts blood glucose levels, offering a solution to the limitations of traditional glucose monitoring systems. The device integrates a Continuous Glucose Monitor (CGM) with an automated insulin pump, controlled by a microprocessor unit running machine learning modals. Unlike conventional systems, which only react to current glucose levels, this device uses AI to analyze historical data, meal intake, physical activity, and health parameters to predict future glucose fluctuations, allowing for proactive insulin adjustments. Its adaptive, self-learning model customizes insulin dosage in real-time, reducing hypo- or hyperglycemic risks. Furthermore, it includes IoT connectivity for remote data transmission, enabling healthcare providers to monitor patient data and adjust treatment remotely, enhancing patient safety and compliance. With its predictive, adaptive approach and seamless integration of AI, IoT, and medical technology, the present invention offers a transformative solution for managing diabetes, setting it apart from existing products in the market.
[0018] FIG. 1 is a block diagram illustrating the system environment 100 in which various embodiments of the present invention may be implemented. The system environment 100 generally comprises a diabetes management device 102, a processing unit 104, a communication network 106, an insulin pump 108, and a user interface 110. The components within the system environment 100 are interconnected, enabling seamless data exchange and coordinated operation to ensure real-time diabetes management and proactive insulin regulation.
[0019] As according to the present invention, the diabetes management device 102 is an advanced AI-driven system designed to monitor and predict glucose levels for diabetic patients. The device integrated with the processing unit 104 continuously collects glucose data from the patient's bloodstream via various sensors. The collected data is then relayed to processing unit 104, where AI and machine learning modals analyze the glucose trends and predict future fluctuations. Based on these predictions, the device autonomously adjusts insulin dosage via the connected insulin pump 110, aiming to maintain optimal glucose levels and reduce the risk of hypoglycemic or hyperglycemic events.
[0020] As according to the present invention, the processing unit 104 is responsible for real-time data processing and predictive analytics. It incorporates advanced AI and machine learning models that evaluate glucose data, historical health patterns, and contextual factors such as meal intake and physical activity. The processing unit 104 continuously calibrates insulin dosage recommendations, factoring in the patient's individualized glucose response patterns, stress levels, and other health indicators. By dynamically adjusting the insulin dosage, processing unit 104 ensures adaptive, precise glucose management tailored to the patient's unique needs.
[0021] As according to the present invention, the communication network 106 provides the infrastructure for secure, real-time data transmission between the device components and external systems, including cloud servers and healthcare provider portals. This network supports various communication protocols such as Bluetooth, Wi-Fi, and cellular connections, enabling seamless data transfer and remote monitoring. Through this network, healthcare providers can remotely review the patient's glucose history, and dosage adjustments, and receive alerts in case of critical glucose levels, facilitating proactive medical intervention when necessary.
[0022] As according to the present invention, the insulin pump 108, delivers insulin in a controlled manner based on the AI-driven recommendations from the processing unit 104. The said pump autonomously adjusts the insulin dosage as needed, responding to predicted glucose level changes. In cases of anticipated hypoglycemia, the insulin pump 108 may reduce or pause insulin delivery, while it can increase dosage for predicted hyperglycemic episodes, thereby achieving real-time insulin regulation.
[0023] As according to the present invention, the user interface 110 is designed to facilitate easy interaction between the patient and the device. It includes various components such as a touchscreen display, tactile buttons, and auditory feedback mechanisms. Through this interface, patients can view real-time glucose readings, receive alerts, and adjust device settings. Additionally, the user interface 110 supports voice commands, making it accessible for visually impaired users, and provides a user-friendly experience for interacting with the device's various functionalities and customization options.
[0024] FIG. 2 is a block diagram illustrating the architecture of the processing unit 104 configured for the diabetes management device, in accordance with an embodiment of the present invention. The processing unit 104, as depicted in conjunction with elements from FIG. 1, includes a processor 202, memory 204, transceiver 206, and input/output unit 208. The processing unit 104 is interconnected to various components such as the diabetes management device 102, the communication network 106, the insulin pump 108, the user interface 110, and additional health and environmental sensors. Key components of the processing unit include a Continuous Glucose Monitor (CGM) sensor 210, a data analysis module 212, a predictive analytics unit 214, and an insulin adjustment control module 216. The processor 202 is communicatively coupled with memory 204, transceiver 206, input/output unit 208, and all connected sensors to facilitate seamless data processing and adaptive insulin management.
[0025] The processor 202 is equipped with logic, circuitry, interfaces, and software code necessary to execute instructions stored in memory 204, enabling real-time analysis of glucose data and management of insulin dosage. It operates in conjunction with memory 204, transceiver 206, and input/output unit 208 to coordinate data collection and processing across the CGM sensor 210, insulin pump 108, and other health sensors. The Processor 202 also integrates with specialized modules such as the data analysis module 212 and predictive analytics unit 214, enabling proactive glucose monitoring and automated insulin adjustments tailored to the patient's glucose trends and lifestyle factors.
[0026] The memory 204 includes logic, circuitry, and interfaces configured to store executable instructions for the processor 202, as well as patient data and historical glucose readings. Preferably, memory 204 includes programs and algorithms for machine learning models that facilitate predictive analytics. The Memory 204 may utilize technologies such as RAM, ROM, solid-state drives, or cloud storage, ensuring secure data retention and access for real-time and historical data analysis.
[0027] The transceiver 206 is designed with advanced logic, circuitry, and interfaces for reliable data transmission between the diabetes management device 102 and external systems, including healthcare provider platforms. The transceiver 206 supports various communication protocols such as Bluetooth, Wi-Fi, and cellular networks, enabling real-time updates on glucose trends, insulin doses, and other patient data. By leveraging these capabilities, the transceiver 206 ensures secure and continuous connectivity for remote monitoring and control, facilitating proactive healthcare interventions.
[0028] The input/output unit 208 comprises a range of input devices (e.g., tactile buttons, voice activation) and output devices (e.g., LED indicators, auditory feedback) to enable patient interaction with the device. This unit allows patients to view glucose levels, adjust settings, and receive critical alerts regarding glucose or insulin dosage changes. The input/output unit 208 is essential for user accessibility, providing options for visual, auditory, and tactile feedback to support ease of use across diverse user needs.
[0029] The CGM sensor 210, as part of the present invention, is a minimally invasive sensor that continuously monitors glucose levels, transmitting real-time data to the processing unit 104. It is designed with biocompatible materials for prolonged wear and incorporates long-lasting, low-power technology, reducing the need for frequent replacements. The CGM sensor 210 allows for continuous, precise glucose tracking, ensuring the device can respond to rapid glucose fluctuations effectively.
[0030] The data analysis module 212 within the processing unit 104 processes real-time glucose data and correlates it with patient lifestyle inputs, such as physical activity or diet. This module enhances the device's ability to detect patterns in glucose fluctuations, ensuring that insulin adjustments are aligned with the patient's unique metabolic profile and daily routine.
[0031] The predictive analytics unit 214 leverages AI algorithms and machine learning models to forecast future glucose levels based on historical data, meal intake, activity, and other contextual factors. This unit enables the device to anticipate glucose changes and proactively adjust insulin doses, minimizing risks associated with sudden glucose spikes or drops.
[0032] The insulin adjustment control module 216 is responsible for managing the insulin pump 110 based on predictive insights from the predictive analytics unit 214. This module adjusts insulin dosage dynamically, ensuring real-time regulation of glucose levels and enhancing patient safety. Through these adjustments, the insulin adjustment control module 216 effectively supports adaptive and personalized diabetes management, reducing the burden on patients and improving overall treatment outcomes.
[0033] In an exemplary operation, a system to manage diabetes through real-time monitoring and predictive insulin adjustment is disclosed. The system comprises a Continuous Glucose Monitor (CGM) sensor for continuous glucose level tracking and an insulin pump for automated insulin delivery. The system comprises a processing unit that utilizes machine learning algorithms to analyze glucose data, predict fluctuations, and adjust insulin doses accordingly. In an embodiment, the system includes a data analysis module that correlates glucose readings with lifestyle inputs like meal intake and physical activity. In an embodiment, the system has a predictive analytics unit that forecasts future glucose trends, allowing proactive insulin adjustments to prevent hypo- or hyperglycemia. In an embodiment, the system includes an IoT module that transmits data to healthcare providers for remote monitoring and adjustments, enhancing patient safety and providing seamless, adaptive diabetes management.
[0034] In an embodiment, the processor is configured to receive real-time glucose data from the Continuous Glucose Monitor (CGM) sensor and analyze it using embedded machine learning algorithms. In an embodiment, the processor is configured to predict future glucose levels based on historical patterns, meal intake, and physical activity, enabling proactive insulin adjustments. In an embodiment, the processor is configured to control the insulin pump, automatically adjusting dosage in response to predicted glucose fluctuations to maintain optimal levels. In an embodiment, the processor is configured to transmit glucose and insulin data to a secure cloud server via an IoT module, allowing remote monitoring by healthcare providers. In an embodiment, the processor is configured to generate alerts for critical glucose levels, notifying the user and providing actionable insights to prevent hypo- or hyperglycemic events.
[0035] In another embodiment, the present invention provides an AI-powered diabetes management device with enhanced user interaction and accessibility features. This embodiment includes a voice-activated interface that allows users to check glucose levels, adjust device settings, and receive real-time health updates through spoken commands, offering hands-free operation ideal for visually impaired patients. The device also integrates with wearable health sensors to monitor additional physiological parameters such as heart rate and stress levels, enriching the data used for insulin dosing predictions. The processing unit leverages this multi-source data to adapt insulin delivery more precisely, accounting for factors that could influence glucose fluctuations. Additionally, the device incorporates a feedback system that provides haptic and auditory alerts for immediate response in critical situations, ensuring users receive timely notifications without needing to view the display. The embodiment enables a comprehensive, user-friendly solution for diabetes management that adapts to each patient's lifestyle and needs.
[0036] In another embodiment, the present invention enables the storage of glucose readings, insulin dosage adjustments, and other patient data on a secure blockchain network, ensuring that medical records are immutable and protected against unauthorized access. The device's IoT module facilitates real-time data transfer to a blockchain ledger accessible only to authorized healthcare providers, promoting transparency and trust. Additionally, this embodiment includes smart contract functionality to trigger automatic notifications to healthcare providers in cases of critical glucose levels, ensuring rapid intervention when necessary. By integrating blockchain, this embodiment not only safeguards sensitive patient data but also supports regulatory compliance for data privacy and security, providing an advanced, secure solution for continuous diabetes management.
[0037] FIG. 3 is a flowchart that illustrates a method for real-time diabetes management and predictive insulin delivery, in accordance with an embodiment of the present invention. The method begins in a Start step 302 and proceeds to step 304. At step 304, the system continuously collects glucose data from the Continuous Glucose Monitor (CGM) sensor. Step 306 involves analyzing the collected glucose data using machine learning modals to identify trends and patterns. At step 308, the system predicts future glucose fluctuations based on the analysis, taking into account variables such as meal intake and physical activity. Step 310 entails determining the appropriate insulin dosage required to maintain optimal glucose levels. At step 312, the insulin pump is automatically adjusted to deliver the calculated insulin dosage. At step 314, the system generates alerts for the user and healthcare providers if critical glucose levels are detected, ensuring timely intervention and enhancing overall diabetes management. The method concludes at the End of step 316.
[0038] The present invention offers several technical advantages over conventional diabetes management systems. Its integration of multiple sensors, including a Continuous Glucose Monitor (CGM) and additional health metrics such as heart rate and activity level, enables comprehensive and real-time monitoring of the patient's physiological state. As such, the enhanced data collection allows for more accurate predictions of glucose fluctuations using advanced machine learning modals. Additionally, the use of AI-driven predictive analytics enables proactive insulin dosage adjustments, significantly reducing the risk of hypo- or hyperglycemic events. The incorporation of an IoT connectivity module facilitates seamless data transmission to healthcare providers for remote monitoring, ensuring timely interventions when necessary. Furthermore, the user-friendly interface, equipped with voice activation and haptic feedback, enhances accessibility, particularly for visually impaired users, making diabetes management more intuitive and responsive. Overall, these technical advancements create a more effective, personalized, and user-centered approach to diabetes care, addressing the limitations of traditional systems.
[0039] The present invention provides a significant technical problem in the field of diabetes management, specifically addressing the challenges of real-time glucose monitoring and automated insulin delivery. The present invention offers specific technical features and functionalities, such as an integrated Continuous Glucose Monitor (CGM) that continuously tracks blood glucose levels and a sophisticated insulin pump that automatically adjusts insulin dosages based on predictive analytics. Further, the present invention employs advanced machine learning models within the processing unit to analyze historical and real-time glucose data, enabling it to forecast glucose fluctuations with high accuracy. Such a predictive capability allows for timely adjustments to insulin delivery, minimizing the risks of hypo- or hyperglycemia. Additionally, the invention includes an IoT connectivity module for remote monitoring, enabling healthcare providers to access patient data in real-time, thus facilitating proactive management and ensuring improved patient outcomes. Overall, the present disclosure represents a significant advancement in the integration of AI, IoT, and medical technology to enhance the management of diabetes.
[0040] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above-disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
[0041] Those skilled in the art will appreciate that any of the aforementioned steps and/or system modules may be suitably replaced, reordered, or removed, and additional steps and/or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software or a combination thereof.
[0042] While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
, Claims:We Claim:
1. A device for managing diabetes, the device comprising:
a Continuous Glucose Monitor (CGM) sensor configured to continuously measure blood glucose levels;
an insulin pump configured to deliver insulin to a patient;
a processing unit comprising machine learning modals configured to analyze the glucose data from the CGM sensor and predict future glucose fluctuations;
an IoT module configured to transmit glucose data to healthcare providers for remote monitoring; and
a user interface configured to provide real-time feedback to the patient, including alerts for critical glucose levels.

2. The device of claim 1, wherein the processing unit is further configured to:
dynamically adjust insulin dosage delivered by the insulin pump based on predicted glucose fluctuations, utilizing the analysis of historical glucose data and lifestyle inputs.

3. The device of claim 1, wherein the user interface includes a voice-activated system enabling hands-free operation for the patient.

4. The device of claim 1, wherein the CGM sensor is minimally invasive and incorporates biocompatible materials for prolonged wear, and the IoT module supports multiple communication protocols, including Bluetooth, Wi-Fi, and cellular networks for seamless data transmission.

5. The device of claim 1, further comprising a feedback system that provides haptic and auditory alerts for critical glucose levels to ensure timely intervention by the patient.

6. A method for managing diabetes in a patient, the method comprising:
continuously monitoring blood glucose levels using a Continuous Glucose Monitor (CGM) sensor;
analyzing the glucose data with a processing unit that employs machine learning algorithms to predict future glucose fluctuations;
automatically adjusting insulin dosage via an insulin pump based on the predicted glucose levels;
transmitting glucose data and insulin dosage information to healthcare providers through an IoT module for remote monitoring; and
notifying the patient of critical glucose levels through a user interface that includes auditory and haptic alerts.

7. The method of claim 6, wherein the predicting of future glucose fluctuations further comprises:
correlating current glucose data with historical data, meal intake, and physical activity levels to refine insulin dosage adjustments.

8. The method of claim 6, wherein the patient is provided with the ability to interact with the device using voice commands to request information and any settings change.

9. The method of claim 6, further comprising:
generating a report summarizing the patient's glucose trends and insulin adjustments over a specified period and transmitting said report to the healthcare provider for evaluation.

10. The method of claim 6, wherein the alerts for critical glucose levels include user-customizable thresholds based on the patient's health parameters.

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