Next-Generation Exercise Fatigue Assessment and Prevention System

Publication ID: 24-11857838_0010_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Generation Exercise Fatigue Assessment and Prevention System,” Published Technical Disclosure No. 24-11857838_0010_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857838_0010_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. 11,857,838.

Summary of the Inventive Concept

A cutting-edge system for predicting and preventing exercise fatigue, leveraging machine learning, wearable devices, and environmental sensors to provide personalized recommendations for optimal exercise routines and intensity adjustments.

Background and Problem Solved

The original patent disclosed a method and device for assessing exercise fatigue based on heart rate data. However, this approach has limitations in terms of accuracy and personalization. The present inventive concept addresses these limitations by incorporating machine learning algorithms, real-time environmental data, and wearable devices to provide a more comprehensive and accurate assessment of exercise fatigue.

Detailed Description of the Inventive Concept

The next-generation system comprises a wearable device that collects real-time exercise heart rates, GPS data, and environmental sensors. The data is transmitted to a cloud-based server, where machine learning algorithms analyze the user's historical exercise data, environmental factors, and physiological parameters to calculate a personalized fatigue threshold. The system provides users with real-time feedback and recommendations for adjusting exercise intensity to prevent fatigue. The system can also optimize exercise routines based on fatigue levels, incorporating a database of exercise routines and adapting to the user's changing fitness levels.

Novelty and Inventive Step

The new claims introduce a paradigm shift in exercise fatigue assessment by incorporating machine learning, wearable devices, and environmental sensors. The inventive concept's novelty lies in its ability to provide personalized and accurate fatigue assessments, enabling users to optimize their exercise routines and prevent fatigue.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include integrating additional physiological parameters, such as sleep quality and heart rate variability, or incorporating social media and community features to enhance user engagement. Variations could also include developing specialized systems for specific sports or exercise disciplines.

Potential Commercial Applications and Market

The next-generation exercise fatigue assessment and prevention system has vast commercial potential in the fitness, sports, and healthcare industries. The system could be marketed as a premium service for professional athletes, fitness enthusiasts, and individuals seeking to optimize their exercise routines and prevent fatigue. The system's ability to provide personalized recommendations and real-time feedback could also be leveraged in the development of customized exercise programs and fitness products.

CPC Classifications

SectionClassGroup
A A63 A63B24/0062
A A61 A61B5/02438
A A61 A61B5/1112
A A61 A61B5/1118
G G16 G16H50/30
A A61 A61B2503/10
A A63 A63B2024/0065
A A63 A63B2220/62
A A63 A63B2230/062

Field of Art

Exercise physiology and performance monitoring technologies, specifically focused on fatigue assessment systems involving biometric data collection, computational modeling, and personalized exercise analysis

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in biomedical engineering, exercise science, wearable technology, and machine learning algorithms for physiological data processing, typically holding a master's or doctoral degree with practical experience in sensor design and health monitoring systems

Obviousness Rationale

A person having ordinary skill would recognize that extending the source patent's exercise fatigue assessment method with machine learning, environmental sensors, and real-time adaptive recommendations represents a predictable technological progression. The fundamental computational approach of analyzing heart rate data and establishing exercise load models provides a clear technical foundation for incorporating more sophisticated data processing techniques. The PTD's variations represent natural technological evolution by integrating emerging sensor technologies and computational approaches into an existing exercise monitoring framework.

Obvious Combinations & Variations

Source Patent Element
Exercise load computation model using heart rate parameters
PTD Variation
Machine learning algorithms analyzing expanded physiological and environmental data
Obviousness Reasoning
Predictable extension of existing computational modeling techniques using known machine learning approaches to enhance data interpretation
Source Patent Element
Heart rate-based fatigue assessment method
PTD Variation
Incorporating GPS, environmental sensors, and real-time feedback mechanisms
Obviousness Reasoning
Logical technological progression using standard sensor integration techniques to provide more comprehensive exercise monitoring
Source Patent Element
Computational method for determining exercise load and fatigue levels
PTD Variation
Cloud-based server processing with personalized recommendation generation
Obviousness Reasoning
Obvious implementation of distributed computing techniques to enhance computational capabilities of existing exercise monitoring systems
Source Patent Element
Exercise fatigue assessment using heart rate parameters
PTD Variation
Wearable device with integrated physiological and environmental data collection
Obviousness Reasoning
Predictable design evolution utilizing miniaturized sensor technologies and standard embedded computing approaches
Source Patent Element
Exercise load computation including altitude parameters
PTD Variation
Expanded environmental factor integration including temperature, humidity, and geographic data
Obviousness Reasoning
Logical extension of existing environmental parameter modeling using known scientific approaches to comprehensive exercise analysis
35 U.S.C. § 103 Summary: Based on US Patent 11857838's disclosed exercise fatigue assessment method, the present publication demonstrates that a person having ordinary skill in the art would find the claimed variations obvious through standard technological progression, incorporating machine learning, expanded sensor integration, and computational modeling techniques that represent predictable extensions of the prior art's fundamental technical approach.

Original Patent Information

Patent NumberUS 11,857,838
TitleMethod and device for assessing exercise fatigue
Assignee(s)Guangdong COROS Sports Technology Joint Stock Company