REAL-TIME DIABETES MONITORING AND PREDICTIVE MANAGEMENT PLATFORM

Abstract

An integrated diabetes management system combining advanced technologies for comprehensive care. The system analyses meals through photo recognition (101), predicts glucose trends (102), and integrates with food delivery apps for personalized recommendations (103). It provides real-time dietary advice (104), checks medication interactions (105), and facilitates community support (106). The system recommends healthcare professionals (107), implements engagement features (108), and provides medication reminders (109). A user-friendly interface (110) presents information, while ensuring data security (111). Artificial intelligence (112) powers personalized recommendations, integrating with medical devices (113) for comprehensive monitoring. The system delivers tailored educational content (114) and includes an emergency response system (115). The invention empowers users to effectively manage their diabetes, potentially improving health outcomes and quality of life for individuals with this chronic condition.

Specification

Description:The DIABOT system or platform is a comprehensive diabetes management system that integrates various technologies and features to provide personalized, real-time support for individuals with diabetes. The following detailed description outlines the key components and functionalities of the invention:

Meal Analysis System (101): The meal analysis system is a crucial component of the DIABOT system or platform, utilizing advanced image recognition technology to accurately determine the nutritional content of meals. This system operates as follows:
The Image Capture component (101a) employs a high-resolution camera interface supporting a minimum of 12MP resolution. The system implements auto-focus and HDR capabilities to ensure optimal food image capture across various lighting conditions. Built-in lighting adjustment algorithms compensate for varied environmental conditions, while supporting multiple image capture angles for comprehensive food analysis. The system incorporates real-time image quality assessment and automatic blur detection with retake suggestions to ensure optimal image quality for analysis.

The Image Processing component (101b) utilizes a multi-threaded processing pipeline to efficiently handle image data. The system employs adaptive histogram equalization for image enhancement, coupled with bilateral filtering for noise reduction. Colour space conversion is implemented to optimize food detection accuracy, while semantic segmentation enables precise background separation. All images are standardized to 1024x1024 pixels to ensure consistent processing across the platform.

The Food Recognition component (101c) implements a Convolutional Neural Network (CNN) architecture with a ResNet-50 backbone, pre-trained on a dataset of over 500,000 food images spanning 1,000 categories. The system performs real-time classification with confidence scores and supports multi-food item detection within a single image. A hierarchical classification system enables detailed ingredient breakdown, while continuous model updates are maintained through federated learning approaches.

The Nutritional Analysis component (101d) integrates with the USDA Food Database, providing access to over 150,000 food items. The system performs real-time macronutrient calculations and comprehensive micronutrient content analysis. Glycemic index and load calculations are executed for each identified food item, while an integrated allergen identification system flags potential dietary concerns. The system includes a regional cuisine adaptation system to account for local variations in food preparation and ingredients.

The Portion Estimation component (101e) employs 3D depth mapping technology for accurate volume calculation of food items. A reference object scaling system provides size calibration, while machine learning-based weight estimation algorithms convert visual data into precise portion measurements. The system implements portion standardization to common measurements and includes multi-angle portion verification to improve accuracy. A confidence scoring system provides transparency in portion estimation reliability.

The Meal Composition Summary component (101f) generates a detailed nutritional breakdown interface for user review. The system provides comprehensive macro and micronutrient visualizations and generates meal impact prediction graphs based on the analysed composition. Alternative portion suggestions are provided when appropriate, and the system maintains historical meal comparisons for trend analysis. Exportable nutrition reports are generated for healthcare provider review and personal record-keeping.

Predictive Glucose Modelling System (102): The predictive modelling system of DIABOT, forecasting future blood glucose trends based on various inputs. This system includes:
The Data Integration component (102a) implements real-time CGM data processing capabilities while maintaining connections with various wearable device APIs. The system supports manual glucose reading inputs and includes a meal data correlation engine to associate nutritional intake with glucose responses. Activity data synchronization is maintained across multiple devices, while environmental factor integration provides context for glucose variations.

The Physiological Parameter Tracking component (102b) analyzes heart rate variability and assesses sleep quality through integrated wearable devices. The system quantifies stress levels through multiple physiological markers and calculates physical activity intensity based on accelerometer and heart rate data. Body temperature monitoring and hydration level estimation provide additional contextual data for glucose prediction models.

The Machine Learning Model component (102c) utilizes an LSTM neural network architecture for time-series prediction. The system implements multi-variable regression analysis to account for numerous factors affecting glucose levels. The time-series prediction engine generates forward-looking glucose estimates, while an integrated anomaly detection system identifies potential concerns. Confidence interval calculations provide reliability metrics for predictions, and continuous model performance monitoring ensures prediction accuracy.

The Personalization Algorithm component (102d) learns individual response patterns through ongoing data analysis. The system implements adaptive threshold adjustment based on personal glucose variability and calculates individual sensitivity factors for various inputs. Lifestyle pattern recognition algorithms identify recurring behaviours that impact glucose levels, while medication effectiveness tracking provides insights into treatment efficacy. Dynamic parameter adjustment ensures the system remains calibrated to individual user characteristics.

The Short-term and Long-term Predictions component (102e) generates 4-hour glucose trajectory predictions for immediate decision support. The system analyzes 24-hour trends and performs weekly pattern recognition to identify recurring glucose variations. Monthly trend visualization and seasonal variation analysis provide broader context for glucose management, while annual health trajectory projections support long-term care planning.

Third-party Application Integration (103): The seamless integration with food delivery and other relevant applications enhances the practical utility of DIABOT:
The API Integration component (103a) implements a RESTful API architecture with OAuth 2.0 authentication for secure third-party service connections. The system maintains real-time data synchronization capabilities with comprehensive error handling and retry logic for failed connections. Rate limiting management ensures stable service operation, while API version control enables backward compatibility and seamless updates. The integration layer supports multiple concurrent connections and implements automatic failover mechanisms.

The Menu Analysis component (103b) maintains a comprehensive restaurant menu database integration system with real-time nutritional content extraction capabilities. The ingredient identification system analyzes menu descriptions to identify key components and their proportions. Portion size standardization algorithms convert restaurant servings into measurable quantities, while the special diet filtering system flags items based on dietary restrictions. A price-nutrition optimization engine helps users identify the most nutritionally beneficial options within their budget constraints.

The Personalized Recommendations component (103c) generates meal suggestions based on current glucose levels and predicted glucose impact analysis. The system incorporates dietary restrictions and personal preferences into its recommendation algorithm, while implementing preference-based sorting mechanism. Budget optimization features ensure recommendations align with user-defined cost parameters, while time-of-day adaptation adjusts suggestions based on meal timing and daily nutritional goals.

The Portion Size Recommendations component (103d) employs dynamic portion adjustment algorithms that consider current glucose trends and activity levels. The system optimizes meal timing based on predicted glucose responses and historical data. Current activity level consideration enables real-time adjustments to portion recommendations, while metabolic rate calculations provide personalized serving size guidelines. The system maintains historical response integration for continuous improvement of recommendations.

The Order Placement component (103e) provides seamless one-click order integration across multiple food delivery vendors. The system implements secure payment gateway integration with multiple payment method support. Order tracking capabilities provide real-time delivery status updates, while delivery time estimation algorithms consider current traffic and vendor conditions. The order modification handling system enables real-time adjustments to placed orders with automated vendor communication.

The Post-meal Tracking component (103f) implements automated meal logging with integrated glucose response monitoring. The system includes a satisfaction rating system for meal recommendations and automatically adjusts future suggestions based on user feedback. Historical comparison algorithms identify successful meal patterns, while pattern recognition analysis helps predict optimal meal compositions for various times and conditions.

Personalized Diet Planning Tool (104): A personalized diet planning tool (104) delivers dynamic, personalized dietary advice by leveraging real-time data integration. The tool comprises multiple interconnected components that work together to create a comprehensive dietary management system for users with diabetes.

The system implements a nutritional goal-setting interface (104a) that enables users to establish specific dietary objectives aligned with their diabetes management requirements. The interface allows users to input their dietary preferences, restrictions, and medical parameters, which may include carbohydrate limits, protein requirements, and specific nutritional targets prescribed by their healthcare provider. The system stores these parameters and uses them as foundational criteria for all subsequent dietary recommendations.

The system incorporates meal pattern analysis component (104b) that continuously monitors and evaluates the user's eating habits over time. This component employs machine learning algorithms to identify recurring patterns in meal timing, portion sizes, and food choices. The analysis considers factors such as the glycemic impact of different meals, helping to establish correlations between specific food choices and glucose responses.

It has included a real-time adjustment algorithm (104c), assessing real-time data to modify meal recommendations. The algorithm incorporates current readings of connected CGM devices, recent physical activity data from the user's fitness trackers, and future glucose trends with history to adjust the meal suggestions, portion sizes, and timing recommendations to optimize the glucose control in real time.

The system has a module of personalized meal planning, (104d), which will provide individual daily or weekly plans of meals. Plans will be developed incorporating the data from nutritional goals, pattern analysis, and real-time adjustments to provide optimal food combinations and portion sizes. The system is designed to keep every meal schedule in accurate balance with macronutrients while considering the user's preferences as well as dietary restrictions to produce realistic and sustainable meal plans.

The system provides an overall recipe suggestion feature (104e) that is tailored to match the user's customized meal plans. Nutrition information lists of ingredients as well as step-by-step preparation instructions accompany every meal suggested within the recipe database. Due to the filtering and ranking of the recipes according to the user's dietary preferences, the skill level in preparing the meal, and the available preparation time, the suggestions will be suitable and plausible.

The system includes an automated grocery list generation (104f) feature that transforms meal plans and selected recipes into organized shopping lists. The system optimizes these lists by combining ingredients across multiple recipes, suggesting appropriate quantities based on serving sizes, and categorizing items by store department for efficient shopping. In certain implementations, the system integrates with online grocery delivery services, allowing users to automatically transfer their generated lists to shopping carts for convenient ordering and delivery.

Medication Interaction Checker (105): This feature ensures the safety of dietary recommendations in conjunction with prescribed medications:
The Medication Database component (105a) maintains a comprehensive database of prescription and over-the-counter medications, including detailed interaction profiles and contraindications. The system implements regular updates from pharmaceutical databases to maintain current medication information, while supporting custom medication entry for clinical trials or newly approved treatments. The database includes detailed pharmacokinetic profiles for common diabetes medications and their interactions with various food components.

The User Medication Profile component (105b) creates and maintains detailed individual medication records, including dosage schedules, administration routes, and specific timing requirements. The system tracks medication adherence patterns and implements smart alerts for potential missed doses. The profile includes historical medication responses and side effects, while maintaining a comprehensive record of medication changes and their impacts on glucose control.

The Interaction Analysis Algorithm component (105c) performs real-time analysis of potential interactions between medications, foods, and supplements. The system employs machine learning models to predict interaction severity based on user-specific factors and historical data. The algorithm considers timing-based interactions and cumulative effects of multiple medications, while maintaining a risk assessment framework for various interaction types.

The Alert Generation component (105d) implements a multi-level alerting system based on interaction severity and user-specific risk factors. The system generates context-aware notifications with specific timing and detailed recommendations for resolving potential interactions. Alert prioritization algorithms ensure critical warnings are prominently displayed, while maintaining a detailed log of all generated alerts and user responses.

Community Support Features (106): The platform provides a supportive environment for users through various community features:
The Proximity-based Connections component (106a) implements location-aware user matching with privacy-preserving protocols. The system enables optional location sharing with granular privacy controls and supports both permanent and temporary geographic communities. The matching algorithm considers factors such as diabetes type, treatment approaches, and personal interests to facilitate meaningful connections.

The Anonymous Profiles component (106b) provides secure identity masking while maintaining community engagement capabilities. The system implements unique identifier generation for anonymous interactions while supporting optional verified healthcare provider badges. Profile customization options enable users to share specific aspects of their diabetes management journey while maintaining privacy for sensitive information.

The Discussion Forums component (106c) maintains topic-based conversation threads with automated moderation and content organization. The system implements real-time language processing for content classification and keyword tagging. Advanced search capabilities enable users to find relevant discussions and experiences, while maintaining separate spaces for different types of support (emotional, technical, medical).

The Experience Sharing component (106d) facilitates structured sharing of diabetes management successes and challenges through customizable templates. The system supports various media types for sharing experiences, including text, graphs, and anonymized glucose data. Pattern matching algorithms help connect users with similar experiences and management approaches, while maintaining privacy and HIPAA compliance.

Healthcare Professional Recommendation System (107): A healthcare professional recommendation system (107) is provided that facilitates seamless access to professional medical guidance. The system integrates multiple components to ensure comprehensive healthcare support and timely intervention when needed. This feature facilitates access to professional medical advice when needed:
The system includes a symptom tracking (107a) interface that enables users to record and document their health concerns directly within the application. The tracking system allows for detailed documentation of symptom characteristics, duration, and severity.

A Severity Assessment Algorithm (107b) evaluates the severity of reported symptoms by analyzing the user's comprehensive health profile alongside their diabetes management data. The algorithm considers multiple factors including historical health patterns, current glucose levels, and known risk factors.

The system maintains a comprehensive database of healthcare provider (107c) that includes detailed information about their specialties, expertise in diabetes care, locations, and availability. The database is regularly updated to ensure accuracy of provider information.

The system incorporates a matching algorithm (107d) that connects users with appropriate healthcare providers based on multiple criteria including geographical proximity, reported symptoms, medical history, and specific healthcare needs. The algorithm prioritizes providers with relevant expertise in managing the user's particular health concerns.

The system features an integrated appointment scheduling (107e) functionality that allows users to book appointments directly through the application interface. The scheduling system synchronizes with healthcare providers' calendars to show real-time availability.

The system includes telemedicine capabilities (107f) for non-emergency situations, enabling users to conduct virtual consultations with healthcare providers. The telemedicine feature includes secure video conferencing, document sharing, and electronic prescription capabilities.

Engagement and Gamification Features (108): The system implements various gamification elements designed to promote consistent engagement and adherence to management plans. These features work together to create an engaging user experience while encouraging positive health behaviours. To promote consistent engagement and adherence to management plans, the platform incorporates various gamification elements:
The system provides a comprehensive goal-setting module (108a) where users can establish personalized health objectives. These goals may encompass various health metrics including target glucose ranges, weight management goals, and desired activity levels.

The system implements robust progress tracking functionality (108b) that monitors advancement toward established goals. The tracking system generates visual representations of achievements and progress over time.

The system incorporates an achievement badges (108c) system that awards badges to users upon reaching significant health milestones or maintaining consistent adherence to their management plans.

The system includes a streak tracking (108d) mechanism that monitors and rewards consecutive periods of positive health behaviours, such as regular glucose monitoring or consistent medication adherence.

The system generates personalised challenges (108e) based on individual user data and preferences, designed to encourage improvement in specific aspects of diabetes management.

The system implements a point-based reward system (108f) where users earn points for maintaining healthy behaviours. These points can be redeemed for enhanced application features or partner-provided rewards.

Automated Medication Reminder System (109): The system provides comprehensive medication management functionality to support treatment adherence through various integrated features. This feature helps users adhere to their prescribed treatment regimens:
The system allows users to input detailed medication information (109a) including drug names, prescribed dosages, and specific timing requirements. The input interface supports multiple medication regimens with varying schedules.

The system generates intelligent or smart medication reminders (109b) that consider the user's daily routine, preferred notification methods, and optimal timing for medication administration.

The system provides medication Confirmation and Tracking (109c) functionality where users can confirm dose administration, enabling comprehensive adherence monitoring over time.

The system includes intelligent missed dose management or handling (109d) that provides specific guidance based on the medication type, time elapsed, and clinical protocols when doses are missed.

The system monitors medication supplies and generates timely refill reminders (109e) to ensure continuous medication availability.

According to further embodiments, the system generates detailed medication adherence reports (109f) that can be shared with healthcare providers to support treatment optimization.

User-friendly Interface (110): The system implements an intuitive and accessible interface design that ensures ease of use across diverse user populations. The interface is designed to be intuitive and accessible, ensuring ease of use for all users:
The system provides a flexible and customizable dashboard (110a) interface that users can personalize to display their most relevant health information and frequently accessed features.

The system incorporates advanced data visualization (110b) tools that present complex health data through intuitive graphs and charts.

The system supports voice command integration (110c) functionality, enabling hands-free operation for enhanced accessibility.

The system includes comprehensive accessibility features (110d) including text-to-speech capabilities and high-contrast display modes for users with visual impairments.

The system provides multi-language support (110e) functionality to support users from diverse linguistic backgrounds.

The system includes an interactive tutorial system (110f) that guides new users through available features and functionality.

Data Security and Privacy System (111): The system implements robust security measures to protect user data and ensure compliance with privacy regulations. Ensuring the security and privacy of user data is crucial for the platform:
The system employs end-to-end encryption (111a) for all data transmissions, ensuring secure communication between system components.

The system implements data anonymization protocols (111b) that protect user privacy during data analysis processes.

The system provides user consent management (111c) functionality that enables users to control their data sharing preferences.

The system utilizes encrypted secured cloud storage (111d) solutions with regular backup procedures to ensure data security and availability.

The system implements strict access control mechanisms (111e) to protect sensitive user data from unauthorized access.

The system continuously monitors compliance (111f) with relevant data protection regulations and implements necessary updates to maintain compliance.

Artificial Intelligence and Machine Learning Engine (112): The system incorporates advanced AI and ML capabilities to power its analytical and predictive features. The AI and ML engine powers various predictive and analytical features of the platform:
The system employs a deep Neural Network Model (112a) to process complex health data and generate insights.

The system implements Natural Language Processing (112b) capabilities to interpret and process user text inputs effectively.

The system includes Computer Vision Module (112c) for analyzing food images and other visual inputs.

The system utilizes Reinforcement Learning System (112d) to continuously improve recommendations based on user outcomes.

The system implements AI-powered Anomaly Detection (112e) to identify potential health issues or device malfunctions.

The system provides Predictive Maintenance (112f) capabilities for connected medical devices.

Integration with Medical Devices (113): The system enables comprehensive integration with various medical devices to support holistic health monitoring. The platform can integrate with various medical devices for comprehensive health monitoring:
The system provides seamless integration with Continuous Glucose Monitor Integration (CGM) (113a) devices for real-time glucose monitoring.

The system supports connectivity with Insulin Pump Connectivity (113b) to track insulin delivery data.

The system enables integration with Blood Pressure Monitoring (113c) devices for comprehensive health tracking.

The system supports automatic weight data synchronization from connected smart scales (113d).

The system implements Fitness Tracker Synchronization (113e) to incorporate activity data into health analysis.

The system utilizes Standardized Device Protocol (113f) to ensure broad device compatibility.

Personalized Education Module (114): The system provides comprehensive educational content tailored to individual user needs along with knowledge about diabetes management:
The system includes regular knowledge assessment (114a) tools to evaluate user understanding of diabetes management principles.

The system generates Personalized Curriculum (114b) based on assessment results and user data.

The system delivers Micro-learning Content (114c) in concise, digestible formats for optimal learning.

The system provides Interactive Simulations (114d) to enhance understanding of key diabetes management concepts.

The system tracks educational progress (114e) and adjusts content delivery accordingly.

The system maintains a database of Expert-curated (114f) educational materials that are regularly updated.

Emergency Response System (115): The system implements comprehensive emergency response capabilities to handle severe glycemic events. This system is designed to provide rapid assistance in case of severe glycemic events:
The system provides continuous monitoring (115a) of critical health parameters to detect potential emergencies.

The system enables customization of emergency alert thresholds (115b) based on individual needs.

The system includes Automated Emergency Contacts (115c) functionality for designated emergency contacts.

The system provides Location Sharing (115d) capabilities during emergency situations.

The system provides step-by-step guidance for managing diabetes-related emergencies (115e).

The system supports Integration with Emergency Services (115f) when necessary.

Method of Performing the Invention:

The best method of performing this invention involves the following steps:
User Onboarding (116):
a) Download and install the DIABOT application on a smartphone.
b) Create a user profile, including basic health information, diabetes type, and current medication regimen.
c) Connect relevant devices such as CGMs, insulin pumps, and smartwatches.
d) Set initial health goals and preferences.

Daily Use (117):
a) Before meals, take a photo of the food using the meal analysis system (101).
b) Review the nutritional analysis and predicted glucose impact.
c) Make informed decisions about portion sizes or food choices based on the analysis and current glucose levels.
d) After meals, confirm the actual intake if different from the initial analysis.
e) Regularly log physical activities or allow automatic tracking through connected devices.
f) Review glucose predictions and follow recommended actions to maintain optimal levels.

Medication Management (118):
a) Input all current medications into the system, including dosages and schedules.
b) Respond to medication reminders promptly, confirming each dose taken.
c) Use the medication interaction checker before making significant changes to diet or trying new foods.

Regular Health Monitoring (119):
a) Allow continuous data collection from connected devices like CGMs and fitness trackers.
b) Manually log any symptoms or concerns in the app.
c) Review weekly and monthly health reports generated by the system.
d) Adjust health goals based on progress and changing needs.

Community Engagement (120):
a) Join relevant discussion groups within the app's community feature.
b) Share experiences and insights with other users.
c) Participate in group challenges to stay motivated.

Healthcare Provider Interaction (121):
a) Schedule regular check-ups with healthcare providers through the app.
b) Share generated health reports with providers during consultations.
c) Follow up on any recommendations or prescription changes promptly.

Continuous Learning (122):
a) Engage with the personalized education module regularly.
b) Complete knowledge assessments to identify areas for further learning.
c) Apply learned concepts to daily diabetes management.

Emergency Preparedness (123):
a) Set up emergency contacts within the app.
b) Familiarize yourself with the emergency protocol guide.
c) Ensure that location sharing is enabled for emergency situations.

By following these steps and fully utilizing the features of the DIABOT platform, users can achieve optimal diabetes management, leading to improved health outcomes and quality of life.
, Claims:1. A diabetes management system comprising:
- a meal analysis system (101) utilizing image recognition to identify and process meal nutritional data,
- a predictive glucose modelling system (102) that forecasts blood glucose trends based on various inputs,
- third-party application integration (103) for personalized meal recommendations,
- a personalized diet planning tool (104) providing real-time dietary adjustments,
- a medication interaction checker (105) for safe dietary recommendations in conjunction with prescribed medications.

2. The system as claimed in claim 1, wherein the meal analysis system (101) comprises:
- image capture (101a),
- image processing (101b),
- food recognition using deep learning models (101c),
- nutritional analysis (101d), and
- portion size estimation (101e).

3. The system as claimed in claim 1, wherein the predictive glucose modelling system (102) incorporates:
- data integration (102a) from meal data, physical activity, and physiological parameters,
- a machine learning model (102c) for glucose prediction, and
- short-term and long-term glucose trend forecasts (102e).

4. The system as claimed in claim 1, wherein the third-party application integration (103) enables:
- personalized meal recommendations (103c) from food delivery platforms,
- portion size suggestions (103d), and
- direct order placement (103e).

5. The system as claimed in claim 1, wherein the personalized diet planning tool (104) includes:
- real-time meal adjustments (104c),
- customized meal plans (104d),
- recipe suggestions (104e), and
- grocery list generation (104f).

6. The system as claimed in claim 1, wherein the medication interaction checker (105) provides:
- medication database (105a) access,
- user medication profile management (105b), and
- alerts for potential food-medication interactions (105d).

7. A method for managing diabetes, comprising:
- analyzing meals via image recognition (101),
- predicting glucose trends using physiological and lifestyle data (102),
- integrating third-party food applications for tailored meal suggestions (103),
- providing real-time dietary advice (104), and
- ensuring medication compatibility with diet recommendations (105).

8. The method as claimed in claim 7, further comprising:
- providing community support (106) through peer connections, and
- facilitating professional healthcare recommendations (107) based on user symptoms and location.

9. A system for diabetes management, comprising:
- an engagement module (108) with goal-setting, progress tracking, and achievement badges,
- an automated medication reminder system (109) for adherence to treatment schedules,
- and a secure user interface (110) for managing health data.

10. The method as claimed in claim 7, further comprising an emergency response system (115) to detect severe glycemic events, notify emergency contacts, and provide real-time guidance.

Documents