Enhanced System for Respiratory Monitoring of a Subject

Publication ID: 24-11857308_0006_PTD
Published: November 07, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Enhanced System for Respiratory Monitoring of a Subject,” Published Technical Disclosure No. 24-11857308_0006_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857308_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. 11,857,308.

Summary of the Inventive Concept

The present inventive concept relates to an improved system and method for respiratory monitoring of a subject, enhancing the accuracy and efficiency of respiratory effort and airflow estimation through the integration of machine learning algorithms, signal processing techniques, and pattern recognition.

Background and Problem Solved

The original patent, 'System and method for respiratory monitoring of a subject', has limitations in accurately estimating respiratory effort and airflow. The new inventive concept addresses these limitations by introducing advanced algorithms and techniques to improve the estimation process.

Detailed Description of the Inventive Concept

The enhanced system comprises a bioimpedance measurement sensor and a processing unit. The bioimpedance measurement sensor acquires a bioimpedance signal, which is then processed by the processing unit using a machine learning algorithm to determine an estimation of the effort signal component. Alternatively, the processing unit can use a signal processing technique to determine an estimation of the flow signal component. Additionally, a neural network can be employed to estimate the respiratory effort of the subject. The system can also utilize a filter to remove noise from the measured bioimpedance signal, enabling a more accurate estimation of the respiratory airflow. Furthermore, a pattern recognition algorithm can be used to determine the respiratory rate of the subject.

Novelty and Inventive Step

The new claims introduce novel machine learning algorithms, signal processing techniques, and pattern recognition methods that significantly improve the accuracy and efficiency of respiratory effort and airflow estimation, distinguishing the inventive concept from the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of different machine learning algorithms, signal processing techniques, or pattern recognition methods. Additionally, the system could be integrated with other sensors or devices to provide a more comprehensive respiratory monitoring system.

Potential Commercial Applications and Market

The enhanced system and method for respiratory monitoring have significant commercial potential in the healthcare industry, particularly in applications such as remote patient monitoring, critical care, and respiratory therapy.

Field of Art

Biomedical signal processing and respiratory monitoring technologies, with expertise in bioimpedance signal analysis, machine learning, and physiological measurement techniques

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced engineering or biomedical background, proficient in signal processing algorithms, neural networks, and medical sensor technologies, with knowledge of respiratory monitoring techniques

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's machine learning, signal processing, and pattern recognition techniques represent predictable extensions of the source patent's fundamental bioimpedance respiratory monitoring approach. The disclosed variations apply standard computational techniques to enhance the existing bioimpedance monitoring methodology. These modifications represent routine optimization strategies well within the capabilities of a skilled practitioner in biomedical signal processing.

Obvious Combinations & Variations

Source Patent Element
Bioimpedance measurement sensor for acquiring respiratory signals
PTD Variation
Adding machine learning algorithm to process bioimpedance signal for effort estimation
Obviousness Reasoning
Applying machine learning to signal processing is a known technique for improving signal analysis, representing a predictable solution for enhancing measurement accuracy
Source Patent Element
Processing unit for receiving bioimpedance signal
PTD Variation
Implementing neural network to estimate respiratory effort
Obviousness Reasoning
Neural networks are standard computational tools for pattern recognition in physiological signal processing, constituting an obvious design choice for signal interpretation
Source Patent Element
Reference measurement sensor for respiratory monitoring
PTD Variation
Using signal processing filter to remove noise from bioimpedance signal
Obviousness Reasoning
Signal noise reduction through filtering is a well-established technique in signal processing, representing a straightforward optimization approach
Source Patent Element
System for acquiring respiratory signals
PTD Variation
Implementing pattern recognition algorithm to determine respiratory rate
Obviousness Reasoning
Pattern recognition techniques are standard computational methods for extracting physiological parameters, constituting a predictable enhancement to existing monitoring systems
35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. ยง 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 routine computational enhancements to the respiratory monitoring system disclosed in US Patent 11857308, applying standard signal processing and machine learning techniques to bioimpedance measurement technologies.

Original Patent Information

Patent NumberUS 11,857,308
TitleSystem and method for respiratory monitoring of a subject
Assignee(s)Stichting IMEC Nederland