Published Technical Disclosure Archive

Next-Generation Insulin Delivery Optimization Platform

Publication ID: 24-11857763_0005_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Generation Insulin Delivery Optimization Platform,” Published Technical Disclosure No. 24-11857763_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857763_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. 11,857,763.

Summary of the Inventive Concept

A futuristic, adaptive insulin delivery system leveraging machine learning, real-time glucose data, and personalized insulin sensitivity profiles to revolutionize diabetes management.

Background and Problem Solved

The original patent addressed adjusting insulin delivery rates based on user input and glucose data. However, it had limitations in terms of adaptability, personalization, and real-time feedback. The new inventive concept overcomes these limitations by integrating machine learning algorithms, predictive analytics, and real-time glucose data feedback to create a more accurate, personalized, and adaptive insulin delivery system.

Detailed Description of the Inventive Concept

The next-generation insulin delivery optimization platform consists of a wearable device, a cloud-based data repository, and a machine learning module. The wearable device tracks glucose levels and sends data to the cloud-based repository. The machine learning module generates personalized insulin sensitivity profiles and predictive models of future glucose values. A recommendation engine selects a basal insulin delivery profile that minimizes differences between predicted future glucose values and a target glucose level. The system adaptively adjusts the selected basal insulin delivery profile based on real-time glucose data feedback and user input.

Novelty and Inventive Step

The new inventive concept introduces a paradigm shift in insulin delivery optimization by integrating machine learning, real-time glucose data, and personalized insulin sensitivity profiles. This approach enables a more accurate, personalized, and adaptive insulin delivery system that surpasses the limitations of the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include variations in the wearable device's design, the type of machine learning algorithms used, or the integration of additional health metrics. These variations could be implemented to cater to different user needs, preferences, or medical conditions.

Potential Commercial Applications and Market

The next-generation insulin delivery optimization platform has vast commercial potential in the diabetes management industry, with potential applications in wearable devices, cloud-based health platforms, and personalized medicine. The target market includes individuals with diabetes, healthcare providers, and pharmaceutical companies.

CPC Classifications

SectionClassGroup
A A61 A61M5/1723
A A61 A61B5/14532
A A61 A61B5/4839
A A61 A61M5/142
A A61 A61M5/145
A A61 A61M5/14244
A A61 A61M5/14248
G G16 G16H20/17
G G16 G16H40/60
G G16 G16H40/67
A A61 A61B2562/0295
A A61 A61M2005/14208
A A61 A61M2205/3303
A A61 A61M2205/3553
A A61 A61M2205/3561
A A61 A61M2205/3569
A A61 A61M2205/3584
A A61 A61M2205/502
A A61 A61M2205/52
A A61 A61M2230/005
A A61 A61M2230/201
G G16 G16H10/60
G G16 G16H40/40
G G16 G16H50/30

Field of Art

Medical Device Technology - Diabetes Management Systems, focusing on insulin delivery optimization, continuous glucose monitoring, and predictive health analytics

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical device specialist with expertise in diabetes management technologies, machine learning applications in healthcare, and algorithmic glucose prediction models, typically holding a Master's or PhD in biomedical engineering, electrical engineering, or computer science with specialized knowledge in medical device design

Obviousness Rationale

A person having ordinary skill in the art would recognize that the PTD's machine learning-based insulin delivery optimization represents a predictable technological evolution of the source patent's glucose management approach. The fundamental concepts of personalized insulin delivery, glucose level prediction, and adaptive profile selection are inherently present in the source patent, with the PTD merely extending these core principles through contemporary machine learning and data analytics techniques. The integration of real-time feedback, predictive modeling, and personalized sensitivity profiles would be considered a natural progression of existing insulin delivery technologies.

Obvious Combinations & Variations

Source Patent Element
Obtaining a fear of hypoglycemia index (FHI) as an input from a user
PTD Variation
Generating personalized insulin sensitivity profiles using machine learning algorithms based on user-specific glucose data
Obviousness Reasoning
Extending user input parameters through advanced machine learning represents a known technique for creating more personalized medical device algorithms with predictable results
Source Patent Element
Calculating probability of achieving glucose levels across multiple insulin delivery profiles
PTD Variation
Implementing a cloud-based recommendation engine that selects optimal basal insulin delivery profiles by minimizing differences between predicted and target glucose values
Obviousness Reasoning
Applying computational optimization techniques to insulin delivery profile selection is a predictable application of known algorithmic approaches in medical device design
Source Patent Element
Method of delivering insulin across different time intervals
PTD Variation
Adaptive closed-loop system automatically adjusting basal insulin delivery rates based on real-time glucose data feedback
Obviousness Reasoning
Implementing dynamic, feedback-driven insulin delivery represents an obvious technological progression using standard control system engineering principles
Source Patent Element
Glucose data collection and processing for diabetes management
PTD Variation
Wearable device with continuous glucose monitoring and integrated predictive analytics module
Obviousness Reasoning
Miniaturizing and integrating glucose monitoring technologies into wearable platforms is a predictable design evolution in medical device engineering
Source Patent Element
Calculating multiple future glucose values across insulin delivery profiles
PTD Variation
Machine learning module generating predictive models of future glucose values using comprehensive data repositories
Obviousness Reasoning
Enhancing glucose prediction through advanced statistical and machine learning techniques represents a logical extension of existing computational medical technologies
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857763 and the comprehensive technical disclosure herein, a person having ordinary skill in the art would find the claimed variations in insulin delivery optimization to be obvious and lacking inventive merit. The disclosed system represents a straightforward technological progression utilizing known machine learning techniques, computational optimization strategies, and predictive modeling approaches within the established domain of diabetes management technologies.

Original Patent Information

Patent NumberUS 11,857,763
TitleAdjusting insulin delivery rates
Assignee(s)INSULET CORPORATION