AI-driven Respiratory Care Platform

Publication ID: 24-11857726_0010_PTD

Published: October 28, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “AI-driven Respiratory Care Platform,” Published Technical Disclosure No. 24-11857726_0010_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857726_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. 11,857,726.

Summary of the Inventive Concept

A next-generation respiratory care platform leveraging AI, machine learning, and real-time data analysis to optimize patient interface sizes, treatment outcomes, and user experience.

Background and Problem Solved

The original patent for a mask sizing tool using a mobile application, while innovative, has limitations in its reliance on manual input and static image analysis. The new inventive concept addresses these limitations by introducing AI-powered predictive analytics, real-time monitoring, and adaptive adjustments to ensure optimal treatment outcomes and user comfort.

Detailed Description of the Inventive Concept

The AI-driven Respiratory Care Platform consists of a neural network trained to predict optimal patient interface sizes based on real-time analysis of a user's facial features. The platform integrates a mobile application for remote monitoring and adjustment of sizes, a wearable device incorporating a miniaturized RPT device, humidifier, and patient interface, and a cloud-based platform for real-time monitoring and analysis of patient interface size data. The platform utilizes machine learning algorithms to optimize treatment parameters, detect changes in facial features, and adapt to user feedback.

Novelty and Inventive Step

The new inventive concept's use of AI-powered predictive analytics, real-time monitoring, and adaptive adjustments represents a significant departure from the original patent's manual input and static image analysis. The integration of machine learning algorithms, wearable devices, and cloud-based data analysis introduces a new level of sophistication and effectiveness in respiratory care.

Alternative Embodiments and Variations

Alternative embodiments may include variations in the type of AI algorithms used, the design of the wearable device, or the integration of additional sensors or features. The inventive concept could also be adapted for use in other medical fields, such as sleep disorder treatment or anesthesia.

Potential Commercial Applications and Market

The AI-driven Respiratory Care Platform has significant commercial potential in the respiratory care market, with applications in hospitals, clinics, and home care settings. The platform's ability to optimize treatment outcomes, reduce costs, and improve user experience positions it for widespread adoption and market leadership.

CPC Classifications

Section	Class	Group
A	A61	A61M16/06
A	A61	A61B5/097
A	A61	A61B5/1077
A	A61	A61B5/1079
A	A61	A61M16/0051
A	A61	A61M16/021
G	G06	G06V10/7553
G	G06	G06V40/171
G	G16	G16H20/40
G	G16	G16H30/40
G	G16	G16H40/63
A	A61	A61B5/7264
A	A61	A61M2016/0661
A	A61	A61M2205/3306
A	A61	A61M2205/3553
A	A61	A61M2205/3584
A	A61	A61M2205/3592
A	A61	A61M2205/50
A	A61	A61M2205/505
A	A61	A61M2205/52
A	A61	A61M2205/6063
A	A61	A61M2205/6072
A	A61	A61M2230/00
G	G16	G16H10/60

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical device technology, specifically respiratory patient interface sizing and monitoring systems, involving computer vision, machine learning, and medical device optimization

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or computer scientist with expertise in medical device design, machine learning algorithms, image processing, and human-computer interaction, possessing knowledge of medical imaging, anthropometric measurement techniques, and adaptive software systems

Obviousness Rationale

A PHOSITA would recognize that extending the source patent's static image-based patient interface sizing method to an AI-driven, real-time adaptive system represents a predictable technological progression using standard machine learning techniques and known medical device optimization strategies. The core technical problem of accurately determining patient interface size remains consistent, with the PTD merely introducing more sophisticated computational approaches. The integration of cloud-based monitoring, machine learning, and mobile application interfaces would be considered an expected evolutionary step in medical device technology.

Obvious Combinations & Variations

Source Patent Element

Image pixel data processing for facial feature measurement

PTD Variation

Neural network-based real-time facial feature analysis with machine learning adaptation

Obviousness Reasoning

Applying machine learning to existing image processing techniques represents a known approach for improving measurement accuracy and automated feature detection

Source Patent Element

Mobile application for patient interface sizing

PTD Variation

Cloud-based platform with AI-powered predictive analytics and remote monitoring

Obviousness Reasoning

Extending mobile application functionality to include cloud integration and advanced analytics is a predictable technological enhancement in medical software design

Source Patent Element

Static patient interface size selection based on facial measurements

PTD Variation

Dynamic, adaptive patient interface sizing using continuous machine learning feedback

Obviousness Reasoning

Implementing adaptive sizing algorithms is an obvious optimization strategy for improving medical device personalization and treatment effectiveness

Source Patent Element

Manual image-based facial feature measurement

PTD Variation

Automated real-time facial feature tracking with anthropometric correction

Obviousness Reasoning

Automating manual measurement processes using computer vision and AI represents a standard technological progression in medical device design

Source Patent Element

Single-device patient interface sizing method

PTD Variation

Integrated wearable device with miniaturized RPT components and AI-driven sizing

Obviousness Reasoning

Miniaturization and integration of medical devices with intelligent control systems is a predictable technological evolution in medical device engineering

35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. § 103, the variations disclosed in this publication would have been obvious to a person having ordinary skill in the art at the time of invention, with a reasonable expectation of success, based on the teachings of US Patent 11857726 and standard medical device engineering practices. The disclosed AI-driven respiratory care platform represents an incremental technological advancement that combines known techniques in machine learning, medical imaging, and adaptive device design, thereby rendering potential derivative claims obvious and unpatentable.

Original Patent Information

Patent Number	US 11,857,726
Title	Mask sizing tool using a mobile application
Assignee(s)	ResMed Pty Ltd