Published Technical Disclosure Archive

Personalized Glucose Management System with Predictive Analytics and Real-Time Feedback

Publication ID: 24-11857350_0005_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Personalized Glucose Management System with Predictive Analytics and Real-Time Feedback,” Published Technical Disclosure No. 24-11857350_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857350_0005_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,350.

Summary of the Inventive Concept

A wearable glucose monitoring system that integrates machine learning, predictive analytics, and real-time feedback to provide personalized glucose management and alert users of potential hypoglycemic events.

Background and Problem Solved

The original patent's limitation lies in its reliance on acceleration data to generate alerts, which may not accurately reflect glucose levels. This new inventive concept addresses this limitation by incorporating machine learning algorithms, user behavior patterns, and environmental factors to provide more accurate and personalized glucose management.

Detailed Description of the Inventive Concept

The system comprises a wearable glucose sensor, a machine learning module, a personalized alert module, and a neural interface. The machine learning module predicts hypoglycemic events based on glucose data, user behavior patterns, and environmental factors. The personalized alert module generates customized alerts for the user based on the predicted events. The neural interface provides real-time feedback to the user, enabling proactive glucose management. The system can be integrated with virtual assistants for voice-based notifications, and social sharing features can be added for sharing glucose data with healthcare professionals.

Novelty and Inventive Step

The new claims introduce the novel concept of integrating machine learning, predictive analytics, and real-time feedback to provide personalized glucose management. This inventive step lies in the use of machine learning algorithms to analyze user behavior patterns and environmental factors, enabling more accurate predictions of hypoglycemic events.

Alternative Embodiments and Variations

Alternative embodiments may include using non-invasive glucose sensors, cloud-based analytics platforms, or blockchain-based data storage systems. Variations may include integrating the system with wearable devices, such as smartwatches, or developing mobile applications for personalized glucose insights and alerts.

Potential Commercial Applications and Market

This inventive concept has significant commercial potential in the healthcare industry, particularly in the diabetes management market. The system can be marketed as a premium product for individuals with diabetes, offering a more accurate and personalized glucose management solution.

CPC Classifications

SectionClassGroup
A A61 A61B5/746
A A61 A61B5/0002
A A61 A61B5/0004
A A61 A61B5/01
A A61 A61B5/1112
A A61 A61B5/1118
A A61 A61B5/145
A A61 A61B5/1459
A A61 A61B5/14532
A A61 A61B5/7282
A A61 A61B5/742
A A61 A61B5/7405
A A61 A61B5/7455
A A61 A61B5/0533
A A61 A61B5/14503
A A61 A61B5/6898
A A61 A61B5/7203
A A61 A61B2562/0219

Field of Art

Medical device technology, specifically glucose monitoring systems and wearable health tracking devices with a focus on sensor integration, data analysis, and personalized health alerts

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical device designer with expertise in sensor technologies, machine learning, wireless communication, and health monitoring systems, typically holding a master's or doctoral degree with 3-5 years of industry experience

Obviousness Rationale

A person of ordinary skill would recognize that the PTD represents a predictable extension of the source patent's core concepts of analyte sensing and movement-based data integration. The machine learning and predictive analytics approach naturally follows from the source patent's foundation of using acceleration and sensor data for health monitoring. The additional personalization and real-time feedback features would be considered obvious improvements using standard techniques in medical device design and data analytics.

Obvious Combinations & Variations

Source Patent Element
Analyte sensor system with accelerometer for generating acceleration data
PTD Variation
Machine learning module predicting hypoglycemic events using glucose data and user behavior patterns
Obviousness Reasoning
Predictable application of machine learning to existing sensor data collection methods, representing a known technique for enhancing medical device functionality
Source Patent Element
Wearable transceiver with wireless communication interface
PTD Variation
Neural interface and virtual assistant integration for real-time glucose management alerts
Obviousness Reasoning
Logical extension of existing wireless communication technologies to provide more sophisticated user interaction and feedback
Source Patent Element
Display device configured to generate activity information
PTD Variation
Cloud-based analytics platform and mobile application providing personalized glucose insights
Obviousness Reasoning
Obvious implementation of cloud computing and mobile technologies to expand data processing and user engagement capabilities
Source Patent Element
Sensor system for generating analyte concentration data
PTD Variation
Non-invasive glucose sensor using optical spectroscopy with blockchain-based data storage
Obviousness Reasoning
Predictable exploration of alternative sensing technologies and data management approaches within the established medical device domain
Source Patent Element
Wearable transceiver with memory and controller
PTD Variation
Power harvesting module generating energy from user movements integrated with low-power wireless communication
Obviousness Reasoning
Known design optimization technique for improving wearable device energy efficiency using inherent user movement
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857350 and the disclosed technical variations, a person of ordinary skill in the art would find the claimed innovations of the published technical disclosure to be obvious and non-patentable. The proposed system represents a predictable combination of known medical device technologies, sensor integration techniques, and data analytics methodologies that would be readily apparent to a skilled practitioner in the field of wearable health monitoring systems.

Original Patent Information

Patent NumberUS 11,857,350
TitleAnalyte concentration alert function for analyte sensor system
Assignee(s)Senseonics, Incorporated