Next-Generation Multi-Frequency Harmonic Acoustography for Advanced Tissue Analysis

Publication ID: 24-11857373_0010_PTD

Published: October 28, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Generation Multi-Frequency Harmonic Acoustography for Advanced Tissue Analysis,” Published Technical Disclosure No. 24-11857373_0010_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857373_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. 11,857,373.

Summary of the Inventive Concept

A paradigm-shifting approach to multi-frequency harmonic acoustography, enabling unprecedented tissue penetration, real-time analysis, and personalized health insights through the integration of adaptive frequency hopping, machine learning, micro-robotics, and wearable devices.

Background and Problem Solved

The original patent disclosed a method for multi-frequency harmonic acoustography for target identification and border detection. However, it has limitations in terms of tissue penetration, noise reduction, and real-time analysis. The new inventive concept addresses these limitations by introducing advanced technologies that enable enhanced tissue penetration, reduced noise, and real-time tissue classification and anomaly detection.

Detailed Description of the Inventive Concept

The next-generation multi-frequency harmonic acoustography system comprises a transducer array with adaptive frequency hopping, allowing for optimized tissue penetration and reduced noise. A machine learning module enables real-time tissue classification and anomaly detection. Additionally, a swarm of micro-robots, each equipped with a miniaturized transducer, can be used for high-resolution imaging and real-time monitoring of tissue dynamics. The system can also be integrated with wearable devices and cloud-based analytics platforms for personalized health insights.

Novelty and Inventive Step

The new inventive concept introduces several novel and non-obvious features, including adaptive frequency hopping, machine learning-based tissue classification, micro-robotics for high-resolution imaging, and wearable devices for continuous health monitoring. These advancements significantly improve the capabilities of multi-frequency harmonic acoustography and enable new applications in cancer diagnosis, personalized health monitoring, and real-time tissue analysis.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept may include the use of different types of transducers, such as capacitive micromachined ultrasonic transducers (CMUTs) or piezoelectric micromachined ultrasonic transducers (PMUTs). Additionally, the system could be adapted for use in various medical specialties, such as cardiology or neurology, or for industrial applications, such as non-destructive testing.

Potential Commercial Applications and Market

The next-generation multi-frequency harmonic acoustography system has significant commercial potential in the medical device industry, particularly in the areas of cancer diagnosis, personalized health monitoring, and real-time tissue analysis. The market for medical imaging and diagnostics is expected to grow significantly in the coming years, driven by advances in technology and increasing demand for non-invasive and minimally invasive diagnostic tools.

CPC Classifications

Section	Class	Group
A	A61	A61B8/485
A	A61	A61B8/085
A	A61	A61B8/4494
B	B06	B06B1/0622
G	G01	G01S7/52038
G	G01	G01S15/8913
G	G01	G01S15/8922
G	G01	G01S15/8952
A	A61	A61B8/5207
G	G01	G01S7/5203

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical ultrasound imaging and diagnostic technologies, specifically multi-frequency harmonic acoustography with expertise in transducer design, signal processing, and tissue characterization techniques

Person of Ordinary Skill (PHOSITA) Profile

A biomedical engineer or medical imaging specialist with advanced degrees in electrical engineering or biomedical engineering, proficient in ultrasound signal processing, machine learning, and medical device design with 3-5 years of industry/research experience

Obviousness Rationale

A PHOSITA would recognize that the published technical disclosure represents predictable extensions of the source patent's multi-frequency harmonic acoustography framework by applying known machine learning, micro-robotics, and adaptive signal processing techniques to enhance tissue imaging capabilities. The core acoustic imaging principles remain consistent, with the variations representing incremental technological improvements using standard engineering approaches. The disclosed innovations represent logical combinations of existing technologies within the established ultrasound imaging domain.

Obvious Combinations & Variations

Source Patent Element

Confocal transducer with multiple piezoelectric elements for generating different ultrasonic wave frequencies

PTD Variation

Adaptive frequency hopping transducer array with machine learning-based frequency optimization

Obviousness Reasoning

A PHOSITA would find it obvious to incorporate machine learning algorithms to dynamically adjust transducer frequencies, as signal optimization techniques are well-known in signal processing and represent a predictable enhancement to existing ultrasound imaging methods

Source Patent Element

Tissue border detection using ultrasonic wave interactions

PTD Variation

Micro-robot swarm with miniaturized transducers for high-resolution tissue mapping

Obviousness Reasoning

Distributed sensing using multiple small sensors is a known technique in medical imaging, and a PHOSITA would recognize that miniaturizing transducers and coordinating them represents a straightforward technological progression

Source Patent Element

Focused ultrasonic wave detection for tissue characterization

PTD Variation

Deep learning algorithm for automated cancer detection and staging

Obviousness Reasoning

Machine learning classification of medical imaging data is a well-established technique, and applying neural networks to ultrasound signal analysis would be an obvious extension to a skilled practitioner familiar with modern medical imaging technologies

Source Patent Element

Confocal transducer with hydrophone for signal detection

PTD Variation

Wearable device integration with cloud-based analytics platform

Obviousness Reasoning

Continuous health monitoring through wearable sensors is a predictable technological progression, and integrating existing ultrasound imaging principles with IoT and cloud technologies represents a standard engineering design approach

Source Patent Element

Multi-frequency wave interaction for tissue property detection

PTD Variation

GPU-accelerated 3D reconstruction of tissue dynamics

Obviousness Reasoning

High-performance computational techniques for medical imaging are well-known, and using GPU acceleration for signal processing represents a standard optimization approach in medical imaging technologies

35 U.S.C. § 103 Summary: Pursuant to 35 U.S.C. § 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, with a reasonable expectation of success, when considered in light of the teachings of US Patent 11857373. The incremental technological enhancements represent predictable combinations of known techniques in multi-frequency harmonic acoustography, machine learning, and medical imaging technologies, thereby rendering potential patent claims obvious and unpatentable.

Original Patent Information

Patent Number	US 11,857,373
Title	Multi-frequency harmonic acoustography for target identification and border detection
Assignee(s)	The Regents of the University of California