Published Technical Disclosure Archive

AI-Driven Patient Ventilation and Aspiration System

Publication ID: 24-11857725_0005_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “AI-Driven Patient Ventilation and Aspiration System,” Published Technical Disclosure No. 24-11857725_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857725_0005_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. 11,857,725.

Summary of the Inventive Concept

A wearable, AI-powered respiratory monitoring device integrated with a tracheostomy or endotracheal connector, predicting and preventing ventilator-associated pneumonia (VAP) through real-time monitoring and adaptive suction control.

Background and Problem Solved

The original patent relates to a catheter tube connector for a suction system used with a tracheostomy or endotracheal patient ventilation system. However, the existing system lacks real-time monitoring and adaptive control, leading to a high risk of VAP. The new inventive concept addresses this limitation by incorporating AI-powered respiratory monitoring and adaptive suction control to prevent VAP.

Detailed Description of the Inventive Concept

The AI-driven patient ventilation and aspiration system consists of a wearable, AI-powered respiratory monitoring device integrated with a tracheostomy or endotracheal connector. The device predicts and prevents VAP through real-time monitoring of patient data and adaptive suction control. The system includes a neural network trained to detect early signs of VAP and automatically adjusts suction pressure, frequency, and duration to prevent VAP onset. Additionally, the system enables remote monitoring and control of patient ventilation and aspiration through a cloud-based platform, integrating real-time patient data from wearable devices, ventilators, and suction systems.

Novelty and Inventive Step

The new inventive concept introduces the use of AI-powered respiratory monitoring and adaptive suction control to prevent VAP, which is a novel and non-obvious improvement over the original patent. The incorporation of machine learning algorithms, neural networks, and cloud-based remote monitoring and control enables real-time adaptation to patient needs, reducing the risk of VAP and improving patient outcomes.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of different AI algorithms, sensor types, or communication protocols. Variations could include the integration of additional features, such as real-time patient data analytics or personalized treatment planning.

Potential Commercial Applications and Market

The AI-driven patient ventilation and aspiration system has significant commercial potential in the healthcare industry, particularly in critical care and respiratory therapy. The system could be marketed as a premium product for hospitals and healthcare facilities, offering improved patient outcomes and reduced healthcare costs.

CPC Classifications

SectionClassGroup
A A61 A61M16/0463
A A61 A61M16/0465
A A61 A61M16/08
A A61 A61M16/0816
A A61 A61M16/0833
A A61 A61M16/0875
A A61 A61M16/109

Field of Art

Medical respiratory support systems, specifically tracheostomy and endotracheal tube connectors with integrated suction and ventilation technologies, requiring advanced knowledge of medical device engineering, pneumatic systems, and biomedical instrumentation

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical device designer with expertise in respiratory support technologies, familiar with ventilation system design, infection prevention strategies, and emerging digital health monitoring techniques

Obviousness Rationale

A PHOSITA would recognize that integrating AI-driven monitoring and adaptive control into existing respiratory support systems represents a predictable technological evolution, leveraging known machine learning techniques to enhance patient care and reduce complications. The core mechanical and functional elements of the source patent provide a clear foundation for implementing intelligent monitoring and control features. The proposed variations represent incremental improvements using standard engineering approaches to address known clinical challenges.

Obvious Combinations & Variations

Source Patent Element
Catheter mount with multiple passages for patient connection and gas transport
PTD Variation
Adding integrated sensor arrays and AI-powered monitoring capabilities to the existing connector design
Obviousness Reasoning
Incorporating electronic monitoring into medical connectors is a known technique, with predictable results of enhanced diagnostic capabilities
Source Patent Element
Suction tube passage and piercing member design
PTD Variation
Implementing adaptive suction control algorithms that dynamically adjust pressure and frequency based on real-time patient data
Obviousness Reasoning
Applying machine learning to medical device control represents a standard engineering approach to improving device performance and patient outcomes
Source Patent Element
Connector locking mechanisms
PTD Variation
Cloud-based remote monitoring platform enabling healthcare professionals to adjust device parameters
Obviousness Reasoning
Integrating medical devices with networked communication systems is a predictable technological progression with well-understood implementation strategies
Source Patent Element
Tracheostomy and endotracheal tube connection system
PTD Variation
3D-printed modular connector design allowing customization and rapid prototyping of medical interface components
Obviousness Reasoning
Applying advanced manufacturing techniques to medical device design represents a logical extension of existing engineering practices
Source Patent Element
Patient respiratory support system
PTD Variation
Neural network-based early detection of ventilator-associated pneumonia (VAP) risk
Obviousness Reasoning
Applying machine learning to clinical risk prediction is a known technique with established methodological approaches in medical technology
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857725 and the disclosed technical variations, a person having ordinary skill in the art would find the proposed AI-driven respiratory monitoring and adaptive control system to be an obvious combination of known medical device technologies, rendering potential patent claims in this domain obvious and unpatentable as a matter of standard engineering practice.

Original Patent Information

Patent NumberUS 11,857,725
TitlePatient ventilating and aspirating system
Assignee(s)Fisher & Paykel Healthcare Limited