Fractional Flow Reserve Modeling for Diverse Industrial Applications

Publication ID: 24-11857292_0007_PTD

Published: November 07, 2025

Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Fractional Flow Reserve Modeling for Diverse Industrial Applications,” Published Technical Disclosure No. 24-11857292_0007_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857292_0007_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

This inventive concept leverages the core technology of fractional flow reserve modeling to tackle complex problems in various industries, including renewable energy, agriculture, water quality monitoring, healthcare, and building management.

Background and Problem Solved

The original patent, 'Method for diagnosing vascular disease and apparatus therefor', demonstrated the effectiveness of fractional flow reserve modeling in medical diagnosis. However, its application was limited to vascular disease diagnosis. The new inventive concept addresses this limitation by expanding the technology's scope to address pressing issues in other industries, where flow dynamics and fluid behavior play critical roles.

Detailed Description of the Inventive Concept

The new inventive concept comprises a system and method for generating fractional flow reserve models based on flow feature data from diverse industrial applications. This includes analyzing flow patterns in wind turbines to optimize performance, predicting crop yields by modeling soil moisture levels, monitoring water quality by detecting flow features in water supplies, diagnosing respiratory disease by simulating lung function, and optimizing HVAC system performance by analyzing flow dynamics. The core technology remains the same, but its application is now tailored to the specific needs of each industry.

Novelty and Inventive Step

The new claims introduce a paradigm shift in the application of fractional flow reserve modeling, moving beyond medical diagnosis to tackle complex problems in diverse industries. The inventive step lies in recognizing the potential of this technology to address pressing issues in fields unrelated to its original purpose.

Alternative Embodiments and Variations

Alternative embodiments may include using different types of flow feature data, such as pressure or temperature measurements, or integrating the fractional flow reserve model with other analytical tools, like machine learning algorithms or computational fluid dynamics simulations. Variations may also involve adapting the system and method to address specific challenges in each industry, such as optimizing wind turbine performance in different environmental conditions.

Potential Commercial Applications and Market

The new inventive concept has significant commercial potential in various industries, including renewable energy, agriculture, water quality monitoring, healthcare, and building management. The market demand for innovative solutions in these fields is substantial, and the fractional flow reserve modeling technology can provide a competitive edge for companies seeking to improve efficiency, reduce costs, and enhance performance.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical diagnostics, computational modeling, and flow dynamics analysis, with expertise in generating synthetic models, calculating fractional flow reserve data, and applying machine learning techniques to complex physiological and industrial systems

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced degrees in biomedical engineering, computer science, or related fields, possessing knowledge of computational modeling, machine learning algorithms, and cross-domain flow feature analysis

Obviousness Rationale

A person having ordinary skill in the art would recognize the fundamental similarity between the source patent's vascular disease diagnostic method and the PTD's expanded applications of fractional flow reserve modeling. The core technical approach of generating synthetic models, calculating flow feature data, and applying machine learning techniques remains consistent across different domains. The PTD merely demonstrates the predictable extension of the source patent's core technological framework to alternative industrial and diagnostic contexts.

Obvious Combinations & Variations

Source Patent Element

Generating first geometric feature parameter learning data based on a predetermined synthetic model

PTD Variation

Creating synthetic models for wind turbine performance, soil moisture levels, and HVAC system dynamics

Obviousness Reasoning

A PHOSITA would recognize that synthetic modeling techniques are transferable across domains with predictable results, representing a known technique for system analysis

Source Patent Element

Calculating fractional flow reserve data from geometric feature parameters

PTD Variation

Applying fractional flow reserve calculations to water quality monitoring, crop yield prediction, and respiratory disease diagnosis

Obviousness Reasoning

The mathematical and computational approach remains consistent, demonstrating a predictable application of a known analytical technique to different problem domains

Source Patent Element

Using a learning model to analyze stenosis state and determine medical interventions

PTD Variation

Adapting the learning model to optimize industrial systems like wind turbines and HVAC performance

Obviousness Reasoning

A PHOSITA would recognize that machine learning models for system optimization follow similar computational principles across different technical domains

Source Patent Element

Incorporating flow feature information including vorticity

PTD Variation

Extending flow feature analysis to include pressure, temperature, and moisture measurements in diverse industrial contexts

Obviousness Reasoning

Expanding flow feature parameters represents a predictable design choice for a skilled practitioner seeking to apply computational modeling techniques

Source Patent Element

Biometric authentication and flow feature data integration

PTD Variation

Integrating multiple data sources and analytical tools like machine learning algorithms and computational fluid dynamics simulations

Obviousness Reasoning

Combining analytical techniques and data sources is a known approach for enhancing system modeling and predictive capabilities

35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. § 103, the variations disclosed in this Published Technical Disclosure would have been obvious to a person having ordinary skill in the art at the time of invention, as they represent predictable extensions of the fundamental technological approach disclosed in US Patent 11857292, demonstrating that the claimed innovations would have been obvious combinations of prior art elements yielding predictable results in the field of computational modeling and system analysis.

Original Patent Information

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