Synergistic Vascular Disease Diagnosis System

Publication ID: 24-11857292_0008_PTD

Published: November 07, 2025

Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Synergistic Vascular Disease Diagnosis System,” Published Technical Disclosure No. 24-11857292_0008_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857292_0008_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. 11,857,292.

Summary of the Inventive Concept

A comprehensive system integrating AI, IoT, blockchain, and new materials to provide accurate and personalized vascular disease diagnosis and treatment recommendations.

Background and Problem Solved

The original patent disclosed a method for diagnosing vascular disease using a vascular disease diagnosing apparatus. However, the diagnosis method relied solely on the apparatus and did not leverage the potential of emerging technologies. The new inventive concept addresses this limitation by integrating distinct technologies to create a more powerful and accurate system.

Detailed Description of the Inventive Concept

The Synergistic Vascular Disease Diagnosis System comprises an AI-powered processor, a blockchain-based data storage unit, IoT-enabled sensors, and a vascular disease diagnosing apparatus incorporating new material-based sensors. The AI-powered processor integrates data from the vascular disease diagnosing apparatus, IoT-enabled sensors, and blockchain-based data storage unit to provide a comprehensive diagnosis. The system can also utilize a blockchain-based genetic database to store genetic information of patients and integrate it with machine learning algorithms to provide personalized diagnosis and treatment recommendations.

Novelty and Inventive Step

The new inventive concept integrates AI, IoT, blockchain, and new materials in a synergistic manner, which is not obvious from the original patent. The use of blockchain-based data storage and genetic database, IoT-enabled sensors, and new material-based sensors provides a novel and non-obvious solution for vascular disease diagnosis.

Alternative Embodiments and Variations

Alternative embodiments of the Synergistic Vascular Disease Diagnosis System could include the use of different AI algorithms, IoT protocols, or blockchain platforms. Variations of the system could also be designed for specific types of vascular diseases or patient populations.

Potential Commercial Applications and Market

The Synergistic Vascular Disease Diagnosis System has significant commercial potential in the healthcare industry, particularly in the fields of cardiology, vascular surgery, and personalized medicine. The system's ability to provide accurate and personalized diagnosis and treatment recommendations could lead to improved patient outcomes, reduced healthcare costs, and increased market share for healthcare providers and medical device manufacturers.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical diagnostics, specifically vascular disease diagnosis technologies involving machine learning, sensor systems, and computational analysis of medical data

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical technology specialist with expertise in AI-driven diagnostic systems, machine learning algorithms, sensor design, and medical data integration techniques

Obviousness Rationale

A PHOSITA would recognize that integrating blockchain, IoT, and AI technologies into existing vascular disease diagnostic frameworks represents a predictable technological evolution. The source patent's foundational diagnostic method provides a clear technical framework that naturally invites technological augmentation through emerging digital infrastructure. The proposed variations represent straightforward technological extensions using well-established integration techniques in medical diagnostic technologies.

Obvious Combinations & Variations

Source Patent Element

Vascular disease diagnostic method using machine learning models

PTD Variation

Blockchain-based data storage and genetic database integration with machine learning algorithms

Obviousness Reasoning

Blockchain and distributed data storage are known techniques for secure medical data management, and a PHOSITA would find integrating such technologies with existing machine learning diagnostic models a predictable design optimization

Source Patent Element

Fractional flow reserve data calculation and analysis

PTD Variation

IoT-enabled real-time sensor data collection and cloud-based diagnostic processing

Obviousness Reasoning

Extending diagnostic data collection through networked sensors represents a standard technological progression in medical monitoring, with predictable improvements in data granularity and diagnostic accuracy

Source Patent Element

Geometric feature parameter learning for vascular diagnosis

PTD Variation

New material-based sensors with enhanced biomarker detection capabilities

Obviousness Reasoning

Improving sensor technology through advanced materials is a routine engineering approach, and a PHOSITA would recognize this as an incremental technological enhancement with expected performance improvements

Source Patent Element

Machine learning models for vascular disease state determination

PTD Variation

AI-powered processor integrating multiple data sources for comprehensive diagnosis

Obviousness Reasoning

Multimodal data integration using AI is a known technique for enhancing diagnostic precision, representing a logical extension of existing machine learning diagnostic methodologies

Source Patent Element

Vascular disease diagnostic computational methods

PTD Variation

Personalized diagnosis using genetic database and machine learning algorithms

Obviousness Reasoning

Personalization of medical diagnostics through genetic data integration is a foreseeable technological progression in precision medicine, utilizing standard machine learning techniques

35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. § 103, the variations disclosed herein would have been obvious to a person having ordinary skill in the art at the time of invention, as they represent predictable technological extensions of the diagnostic methods disclosed in US Patent 11857292, utilizing standard integration techniques from medical informatics, sensor technologies, and machine learning domains.

Original Patent Information

Patent Number	US 11,857,292
Title	Method for diagnosing vascular disease and apparatus therefor
Assignee(s)	E8IGHT Co., Ltd.