Next-Generation Hearing Health Platform

Publication ID: 24-11857312_0010_PTD

Published: November 07, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Generation Hearing Health Platform,” Published Technical Disclosure No. 24-11857312_0010_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857312_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. 11,857,312.

Summary of the Inventive Concept

A novel, non-invasive hearing health platform leveraging fNIRS, cardiac information, and AI-driven analytics to predict hearing loss propensity, provide real-time hearing assessments, and offer personalized audio signal processing and hearing health recommendations.

Background and Problem Solved

The original hearing assessment system and method, while innovative, had limitations in terms of predicting hearing loss propensity, providing real-time assessments, and offering personalized recommendations. The new inventive concept addresses these limitations by integrating fNIRS, cardiac information, and AI-driven analytics to create a comprehensive hearing health platform.

Detailed Description of the Inventive Concept

The Next-Generation Hearing Health Platform consists of a neural network trained on fNIRS data and cardiac information signals to identify patterns indicative of hearing loss risk. The platform includes a user interface for presenting personalized hearing health recommendations, a wearable device for continuous hearing monitoring, and a system for personalized audio signal processing. The platform's real-time hearing assessment capability is enabled by machine learning algorithms that process fNIRS data and cardiac information signals to identify patterns indicative of hearing loss.

Novelty and Inventive Step

The new inventive concept introduces the use of AI-driven analytics, neural networks, and machine learning algorithms to predict hearing loss propensity, provide real-time hearing assessments, and offer personalized audio signal processing and hearing health recommendations. These advancements represent a significant departure from the original patent's limitations, providing a more comprehensive and proactive approach to hearing health.

Alternative Embodiments and Variations

Alternative embodiments of the Next-Generation Hearing Health Platform could include variations in the type of AI-driven analytics used, the design of the wearable device, or the integration of additional sensors or data sources. These variations could enable the platform to be tailored to specific industries or applications, such as healthcare, education, or entertainment.

Potential Commercial Applications and Market

The Next-Generation Hearing Health Platform has significant commercial potential in various industries, including healthcare, education, and entertainment. The platform's ability to predict hearing loss propensity, provide real-time hearing assessments, and offer personalized audio signal processing and hearing health recommendations could revolutionize the way hearing health is managed and maintained. The market for this technology is substantial, with potential applications in hearing aid development, audio signal processing, and hearing health monitoring.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical diagnostics and neurological assessment technologies, specifically hearing health monitoring using functional near-infrared spectroscopy (fNIRS) and physiological signal processing

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or neurotechnology researcher with expertise in medical signal processing, machine learning, and physiological monitoring techniques, familiar with non-invasive neuroimaging and diagnostic technologies

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's AI-driven approach represents a predictable extension of the source patent's fundamental fNIRS hearing assessment methodology. The core technical principles of using physiological signals for hearing assessment remain consistent, with the PTD merely applying advanced machine learning techniques to enhance signal processing and diagnostic capabilities. The integration of neural networks and continuous monitoring represents an incremental technological advancement rather than a non-obvious innovation.

Obvious Combinations & Variations

Source Patent Element

Method of assessing hearing using fNIRS with response signal processing

PTD Variation

AI-driven neural network for processing fNIRS and cardiac signals to predict hearing loss

Obviousness Reasoning

Applying machine learning to signal processing is a known technique in medical diagnostics, representing a predictable optimization of existing signal analysis methods

Source Patent Element

Removing unwanted signal elements from physiological measurements

PTD Variation

Continuous real-time hearing monitoring with advanced noise reduction algorithms

Obviousness Reasoning

Extending signal cleaning techniques to create continuous monitoring is an obvious design improvement using known signal processing techniques

Source Patent Element

Aural stimulation with variable parameters for hearing assessment

PTD Variation

Personalized audio signal processing based on individual hearing profiles

Obviousness Reasoning

Customizing audio signals based on diagnostic data represents a logical and predictable application of the source patent's measurement techniques

Source Patent Element

Functional near-infrared spectroscopy for patient response measurement

PTD Variation

Wearable device with miniaturized fNIRS sensor for continuous monitoring

Obviousness Reasoning

Miniaturization of medical sensing technologies is a standard engineering approach with predictable technological progression

Source Patent Element

Measuring brain and cardiac activity during hearing assessment

PTD Variation

Neural network trained to identify hearing loss risk patterns from combined physiological signals

Obviousness Reasoning

Applying machine learning pattern recognition to multi-signal physiological data represents a known technique in medical diagnostics

35 U.S.C. § 103 Summary: Based on US Patent 11857312's fundamental teachings of fNIRS-based hearing assessment, the present publication demonstrates that a person of ordinary skill in the art would find the disclosed AI-driven hearing health monitoring techniques obvious and predictable extensions of existing technological approaches. The variations represent incremental advancements utilizing standard signal processing, machine learning, and diagnostic methodologies known in the field of medical technology.

Original Patent Information

Patent Number	US 11,857,312
Title	Hearing assessment system and method
Assignee(s)	THE BIONICS INSTITUTE OF AUSTRALIA