Method and System for Diverse Industry Applications using Machine Learning and Biometric Data Analysis

Publication ID: 24-11857292_0002_PTD

Published: November 07, 2025

Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Method and System for Diverse Industry Applications using Machine Learning and Biometric Data Analysis,” Published Technical Disclosure No. 24-11857292_0002_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857292_0002_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

The present inventive concept relates to novel applications of machine learning and biometric data analysis in various industries, including athletic performance monitoring, mental health diagnosis, supply chain logistics, environmental pollution control, and personalized education.

Background and Problem Solved

The original patent disclosed a method for diagnosing vascular disease using machine learning and biometric data analysis. However, the patent's scope was limited to the medical field. The present inventive concept addresses the need for applying the core technology to other industries and fields, thereby expanding its potential impact and commercial value.

Detailed Description of the Inventive Concept

The inventive concept involves the use of machine learning algorithms to analyze biometric data in various industries. For instance, in athletic performance monitoring, the system collects physiological data from wearable devices and provides personalized recommendations for training and recovery. In mental health diagnosis, the system collects biometric data and analyzes it using machine learning models to provide personalized therapeutic interventions. Similarly, in supply chain logistics, the system analyzes real-time data on inventory levels, shipping routes, and weather patterns to optimize delivery times and reduce costs. In environmental pollution control, the system collects data on air and water quality and provides real-time alerts and recommendations for reducing pollution. In personalized education, the system analyzes a student's learning style, aptitude, and performance to provide personalized lesson plans and educational resources.

Novelty and Inventive Step

The inventive concept's novelty lies in its application of machine learning and biometric data analysis to diverse industries beyond the medical field. The inventive step is in the recognition of the potential for this technology to be adapted and applied to various fields, thereby expanding its commercial potential and value.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept may include the use of different types of biometric data, such as genomic data or audio-visual data. Variations may also include the integration of additional sensors or data sources, such as IoT devices or social media platforms, to enhance the accuracy and scope of the analysis.

Potential Commercial Applications and Market

The inventive concept has significant commercial potential in various industries, including sports and fitness, healthcare, logistics, environmental sustainability, and education. The target market includes companies and organizations seeking to leverage machine learning and biometric data analysis to improve performance, reduce costs, and enhance decision-making.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Machine learning-based diagnostic and predictive systems, with expertise in medical data analysis, biometric signal processing, and computational modeling of physiological systems

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced degrees in biomedical engineering, computer science, or medical informatics, possessing expertise in machine learning algorithms, statistical modeling, and interdisciplinary data analysis techniques

Obviousness Rationale

The PTD demonstrates that a PHOSITA would recognize the fundamental transferability of machine learning techniques for biometric data analysis across diverse domains, extending the source patent's core methodology of generating predictive models from physiological data. The variations represent predictable applications of established machine learning principles to different contextual problems. A skilled practitioner would understand that the underlying algorithmic approach of collecting biometric data, generating feature parameters, and creating predictive models remains substantially consistent across different technical domains.

Obvious Combinations & Variations

Source Patent Element

Generating geometric feature parameter learning data based on synthetic models for vascular disease diagnosis

PTD Variation

Applying similar machine learning feature extraction techniques to athletic performance monitoring using physiological data

Obviousness Reasoning

Known technique of transferring machine learning model generation principles across domains with predictable results in feature parameter extraction and analysis

Source Patent Element

Using biometric authentication data in diagnostic learning models

PTD Variation

Extending biometric data analysis to mental health diagnosis and personalized education systems

Obviousness Reasoning

Predictable application of established machine learning techniques for individual-specific data processing and pattern recognition

Source Patent Element

Analyzing stenosis state and determining surgical interventions using machine learning

PTD Variation

Applying similar predictive analytics to supply chain logistics and environmental pollution control

Obviousness Reasoning

Finite set of known machine learning techniques for pattern recognition and predictive decision support are directly transferable across complex systems analysis

Source Patent Element

Computational method for processing complex physiological data using learning models

PTD Variation

Integrating multiple sensor data sources and IoT devices for comprehensive system analysis

Obviousness Reasoning

Obvious extension of existing data integration techniques with predictable improvements in computational modeling capabilities

Source Patent Element

Flow feature information extraction including vorticity analysis

PTD Variation

Collecting and analyzing multi-dimensional biometric and environmental data streams

Obviousness Reasoning

Known technique of expanding feature extraction methodologies to capture more complex systemic interactions

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 the technical modifications represent predictable applications of the machine learning diagnostic methodology disclosed in US Patent 11857292, demonstrating no inventive step beyond the ordinary capabilities of a skilled practitioner in computational biomedicine and machine learning systems.

Original Patent Information

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