Published Technical Disclosure Archive

Enhanced Method and Device for Assessing Exercise Fatigue

Publication ID: 24-11857838_0006_PTD
Published: October 28, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Enhanced Method and Device for Assessing Exercise Fatigue,” Published Technical Disclosure No. 24-11857838_0006_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857838_0006_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

An improved system and method for assessing exercise fatigue that incorporates machine learning, real-time weather data, and location-specific exercise load computation to provide more accurate and personalized fatigue level determination.

Background and Problem Solved

The original patent, 'Method and device for assessing exercise fatigue', has limitations in its exercise load computation model, which can lead to inaccurate fatigue level determination. The new inventive concept addresses these limitations by introducing adaptive adjustments to the exercise load computation model based on historical exercise data and environmental factors.

Detailed Description of the Inventive Concept

The enhanced method and device for assessing exercise fatigue utilize machine learning algorithms to adaptively adjust the exercise load computation model based on the user's historical exercise data and environmental factors. The system incorporates real-time weather data to adjust the exercise load computation model, accounting for the impact of environmental conditions on the user's exercise performance. Additionally, the device includes a built-in GPS module and altimeter to provide location-specific exercise load computation, enabling more accurate fatigue level assessment in varying terrain and altitude conditions. The system integrates with wearable devices and mobile applications to provide real-time fatigue level feedback and personalized exercise recommendations, enhancing the user's exercise experience and overall well-being.

Novelty and Inventive Step

The new inventive concept introduces novel features such as adaptive adjustments to the exercise load computation model, real-time weather data integration, location-specific exercise load computation, and personalized exercise recommendations. These features provide a non-obvious improvement over the original patent, enabling more accurate and personalized fatigue level determination.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include using different machine learning algorithms, incorporating additional environmental factors such as air quality or humidity, or integrating with other wearable devices or health metrics. Variations could include adapting the system for use in specific sports or fitness activities, such as cycling or running.

Potential Commercial Applications and Market

The enhanced method and device for assessing exercise fatigue have significant commercial potential in the fitness and sports industries, particularly in the growing market for wearable devices and mobile health applications. The system's ability to provide personalized exercise recommendations and real-time fatigue level feedback can enhance the user's exercise experience, improve overall well-being, and reduce the risk of injury or burnout.

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, wearable technology, and biometric monitoring systems, with expertise in heart rate analysis, exercise load computation, and fatigue assessment techniques

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or computer scientist with advanced knowledge of physiological monitoring, machine learning algorithms, sensor integration, and exercise performance tracking technologies

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's proposed enhancements represent predictable variations of the source patent's core exercise fatigue assessment methodology. The additional features of machine learning, real-time environmental data integration, and location-specific computation are logical extensions of the existing exercise load computation framework. These modifications represent standard engineering approaches to improving measurement precision and personalization in physiological monitoring systems.

Obvious Combinations & Variations

Source Patent Element
Exercise load computation model TR=Σ1TB*C*TK*GK with altitude coefficient
PTD Variation
Incorporating machine learning algorithms to dynamically adjust exercise load computation model parameters
Obviousness Reasoning
Adaptive parameter tuning is a known technique in predictive modeling, and a PHOSITA would find it obvious to apply machine learning to refine existing computational approaches
Source Patent Element
Heart rate-based fatigue assessment method
PTD Variation
Adding real-time weather and environmental data as additional input parameters
Obviousness Reasoning
Integrating environmental factors is a predictable enhancement to improve measurement accuracy, representing a standard design optimization approach
Source Patent Element
Basic exercise heart rate monitoring
PTD Variation
GPS and altimeter integration for location-specific exercise load computation
Obviousness Reasoning
Incorporating geospatial data represents a straightforward extension of existing physiological monitoring techniques, providing more granular performance assessment
Source Patent Element
Static fatigue level determination based on Training Stress Balance (TSB)
PTD Variation
Neural network-based predictive fatigue level analysis with proactive recommendations
Obviousness Reasoning
Applying advanced machine learning to existing physiological metrics is an obvious improvement using known computational techniques
Source Patent Element
Exercise performance tracking method
PTD Variation
Integration with wearable devices and mobile applications for real-time feedback
Obviousness Reasoning
Expanding data collection and user interface through connected technologies represents a standard evolutionary approach in health monitoring systems
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857838 and the disclosed technical variations, a person having ordinary skill in the art would find the proposed exercise fatigue assessment enhancements obvious and lacking inventive step. The combination of known physiological monitoring techniques with standard machine learning and sensor integration approaches fails to demonstrate non-obvious subject matter, thereby rendering potential derivative claims anticipated and obvious under 35 U.S.C. § 103.

Original Patent Information

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