Published Technical Disclosure Archive

Next-Generation Personalized Diabetes Management System

Publication ID: 24-11857765_0005_PTD
Published: November 07, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Generation Personalized Diabetes Management System,” Published Technical Disclosure No. 24-11857765_0005_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857765_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. 11,857,765.

Summary of the Inventive Concept

A futuristic, AI-driven diabetes management system that integrates wearable devices, cloud-based analytics, and autonomous insulin delivery to provide real-time, personalized glucose control and insulin dosing recommendations.

Background and Problem Solved

Current diabetes management systems rely on manual input and calibration, leading to inaccurate glucose readings and suboptimal insulin dosing. The original patent's personalized parameter modeling methods, while innovative, are limited by their reliance on historical data and lack of real-time adaptability. The new inventive concept addresses these limitations by leveraging machine learning, deep learning, and IoT connectivity to create a proactive, autonomous, and highly personalized diabetes management system.

Detailed Description of the Inventive Concept

The next-generation system comprises a wearable device with a non-invasive glucose sensor, a machine learning module, and a communication module. The wearable device transmits real-time glucose data to a cloud-based platform, which analyzes the data using deep learning algorithms to generate personalized glucose prediction models and recommended insulin dosing regimens. The cloud-based platform also integrates data from multiple wearable devices and insulin pumps to provide a comprehensive view of patient glucose trends and insulin delivery patterns. The system's autonomous insulin pump adjusts insulin delivery rates in real-time based on the personalized glucose prediction models and recommended insulin dosing regimens.

Novelty and Inventive Step

The new inventive concept's use of machine learning, deep learning, and IoT connectivity to enable real-time, autonomous, and highly personalized diabetes management represents a significant departure from the original patent's methods. The integration of wearable devices, cloud-based analytics, and autonomous insulin delivery creates a novel and non-obvious system that addresses the limitations of current diabetes management systems.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of different machine learning algorithms, integration with other health monitoring devices, or the development of specialized algorithms for specific patient populations. Variations of the system could also include different wearable device form factors, cloud-based platform architectures, or autonomous insulin delivery protocols.

Potential Commercial Applications and Market

The next-generation personalized diabetes management system has significant commercial potential in the diabetes management market, which is projected to reach $12.2 billion by 2025. The system's ability to provide real-time, personalized glucose control and insulin dosing recommendations could improve patient outcomes, reduce healthcare costs, and increase patient adherence to treatment regimens. Target industries include pharmaceutical companies, medical device manufacturers, and healthcare providers.

CPC Classifications

SectionClassGroup
A A61 A61M5/1723
A A61 A61B5/14532
A A61 A61B5/4839
G G16 G16H10/60
G G16 G16H20/17
G G16 G16H40/40
G G16 G16H40/63
G G16 G16H50/20
G G16 G16H50/50
A A61 A61B5/0022
A A61 A61B5/02438
A A61 A61B5/7239
A A61 A61B5/7242
A A61 A61B2560/0242
A A61 A61B2562/0219
A A61 A61M5/14244
A A61 A61M2205/3569
A A61 A61M2205/502
A A61 A61M2205/52
A A61 A61M2205/70
A A61 A61M2230/005
A A61 A61M2230/06
A A61 A61M2230/201
G G16 G16H15/00

Field of Art

Medical Device Informatics and Personalized Healthcare Technology, focusing on diabetes management systems, sensor calibration, and adaptive medical device control using machine learning and data analytics

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical device researcher with expertise in sensor technologies, machine learning algorithms, data processing, and medical device integration, holding advanced degrees in bioengineering, computer science, or related fields

Obviousness Rationale

A PHOSITA would recognize that the PTD's machine learning-enhanced personalized diabetes management system represents a predictable technological evolution of the source patent's patient-specific parameter modeling methods. The integration of cloud-based analytics, wearable sensors, and autonomous insulin delivery follows established technological trajectories in medical device innovation. The core technical problem of personalized glucose management remains consistent, with the PTD offering incremental improvements through advanced computational techniques.

Obvious Combinations & Variations

Source Patent Element
Obtaining current operational context information and expected calibration factor parameter models for patient-specific sensing devices
PTD Variation
Implementing machine learning and deep learning algorithms to generate more sophisticated personalized glucose prediction models using multi-source data inputs
Obviousness Reasoning
Applying advanced machine learning techniques to improve sensor calibration and predictive modeling represents a known and predictable approach in medical device informatics, with a reasonable expectation of enhanced performance
Source Patent Element
Calculating calibration factor values based on historical operational context information
PTD Variation
Developing cloud-based platforms that integrate data from multiple wearable devices and insulin pumps to generate comprehensive patient glucose trend analyses
Obviousness Reasoning
Expanding data collection and analysis methods through cloud computing and IoT integration is a natural technological progression for improving medical device performance and personalization
Source Patent Element
Autonomous operation of infusion devices based on calibrated measurement values
PTD Variation
Implementing real-time, AI-driven autonomous insulin delivery systems that dynamically adjust insulin rates based on personalized prediction models
Obviousness Reasoning
Enhancing autonomous medical device control through more sophisticated algorithmic decision-making is a logical and foreseeable technological advancement in medical device design
Source Patent Element
Patient-specific parameter modeling methods for therapy management
PTD Variation
Non-invasive glucose sensing technologies integrated with machine learning modules for continuous health monitoring and predictive interventions
Obviousness Reasoning
Transitioning from invasive to non-invasive sensing technologies with enhanced computational intelligence represents a standard evolutionary path in medical device innovation
Source Patent Element
Obtaining measurement offset models associated with patient data
PTD Variation
Developing specialized machine learning algorithms for specific patient populations and personalized health management strategies
Obviousness Reasoning
Creating targeted algorithmic approaches for diverse patient groups is a predictable application of machine learning techniques in personalized healthcare technology
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857765 and the disclosed technological variations, a person of ordinary skill in the art would find the claimed innovations in personalized diabetes management systems to be obvious variations that do not meet the non-obviousness requirements of 35 U.S.C. ยง 103. The incremental technological advancements represented in the published technical disclosure demonstrate a predictable combination of known techniques in medical device informatics and machine learning-driven healthcare solutions.

Original Patent Information

Patent NumberUS 11,857,765
TitlePersonalized parameter modeling methods and related devices and systems
Assignee(s)MEDTRONIC MINIMED, INC.