Synergistic Insulin Management Systems

Publication ID: 24-11857314_0003_PTD
Published: November 07, 2025
Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Synergistic Insulin Management Systems,” Published Technical Disclosure No. 24-11857314_0003_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857314_0003_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. 11,857,314.

Summary of the Inventive Concept

The present invention integrates insulin management with cutting-edge technologies such as blockchain, AI, IoT, and new materials to create more powerful and personalized systems for managing insulin administration.

Background and Problem Solved

The original patent addressed the need for automated insulin management, but it relied on manual calculations and limited data sources. The present invention solves this problem by incorporating advanced technologies to provide more accurate, secure, and personalized insulin dosing recommendations.

Detailed Description of the Inventive Concept

The synergistic insulin management system comprises a blockchain-based data storage module for securely storing patient glucose data, an AI-powered insulin dosing algorithm for analyzing the stored data and generating personalized insulin dosing recommendations, and a communication module for transmitting the recommendations to a healthcare provider. Additionally, the system can integrate with IoT-enabled glucose monitoring devices, leveraging machine learning models to predict glucose levels and generate alerts for healthcare providers. Furthermore, the system can utilize natural language processing to extract relevant patient information from electronic medical records, and machine learning to analyze the extracted information and generate personalized insulin dosing recommendations. The system can also incorporate wearable glucose monitoring devices with graphene-based sensors, and AI-powered clinical decision support systems to provide more accurate and personalized insulin dosing recommendations.

Novelty and Inventive Step

The novelty of the present invention lies in the integration of advanced technologies such as blockchain, AI, IoT, and new materials with insulin management, providing a more comprehensive and personalized approach to insulin administration. The inventive step is the synergistic combination of these technologies to create a more powerful and accurate system for managing insulin administration.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of different blockchain platforms, AI algorithms, or IoT devices. Variations could also include the integration of other advanced technologies such as augmented reality or 5G networks to enhance the system's capabilities.

Potential Commercial Applications and Market

The synergistic insulin management system has significant commercial potential in the healthcare industry, particularly in the fields of diabetes management and personalized medicine. The system's ability to provide more accurate and personalized insulin dosing recommendations could improve patient outcomes and reduce healthcare costs.

Field of Art

Medical Informatics and Healthcare Technology, specifically insulin management systems and digital health technologies. A PHOSITA would have expertise in medical device software, data processing for healthcare, and clinical decision support systems

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced degrees in biomedical engineering, computer science, or medical informatics, possessing knowledge of healthcare data systems, machine learning applications in clinical settings, and medical device integration

Obviousness Rationale

A PHOSITA would recognize that integrating advanced digital technologies like AI, blockchain, and IoT into existing insulin management systems represents a predictable evolution of the source patent's core methodology. The fundamental insulin dosing and data processing principles established in the source patent provide a clear technical foundation for incorporating emerging technologies. These technological extensions would be seen as logical improvements to existing medical data management and treatment recommendation systems.

Obvious Combinations & Variations

Source Patent Element
Computer-implemented method for receiving sequential glucose measurements and determining insulin infusion rates
PTD Variation
Adding AI-powered machine learning algorithms to analyze glucose data and generate personalized insulin dosing recommendations
Obviousness Reasoning
Applying machine learning to medical data processing is a known technique in clinical decision support, representing a predictable technological enhancement to existing measurement and dosing systems
Source Patent Element
Electronic medical record (EMR) system integration for glucose measurement collection
PTD Variation
Implementing natural language processing to extract patient information from EMRs and generate insulin recommendations
Obviousness Reasoning
Expanding data extraction capabilities using NLP is an obvious technological progression for improving clinical data utilization and decision-making
Source Patent Element
Continuous glucose monitoring system for sequential measurements
PTD Variation
Integrating IoT-enabled wearable devices with graphene-based sensors for advanced glucose monitoring
Obviousness Reasoning
Utilizing emerging sensor technologies to improve medical monitoring represents a predictable technological evolution with finite, identifiable implementation strategies
Source Patent Element
Computer-implemented method for calculating insulin dosing rates
PTD Variation
Blockchain-based secure data storage and transmission of patient glucose and insulin dosing information
Obviousness Reasoning
Implementing blockchain for secure medical data management is a known technique for enhancing data privacy and integrity in healthcare information systems
Source Patent Element
Electronic system for tracking and recommending insulin administration
PTD Variation
AI-powered clinical decision support system with deep learning models for generating personalized insulin recommendations
Obviousness Reasoning
Applying advanced machine learning techniques to clinical decision support represents a logical and predictable technological progression in medical informatics
35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. ยง 103, the variations disclosed in this publication would be considered obvious to a Person Having Ordinary Skill In The Art in medical informatics, given the teachings of US Patent 11857314. The incremental technological enhancements involving AI, blockchain, IoT, and advanced sensor technologies represent predictable extensions of the foundational insulin management methodology, thereby rendering potential derivative claims non-patentable as obvious variations.

Original Patent Information

Patent NumberUS 11,857,314
TitleInsulin management
Assignee(s)Aseko, Inc.