Derivative works — US Patent 11900016

Defensive Disclosure: Multi-frequency Sensing Method and Apparatus Using Mobile-Clusters (US11900016)

This document describes derivative variations of the core inventive concepts disclosed in US Patent 11900016, aimed at establishing prior art for future incremental advancements in distributed multi-frequency sensing, analysis, and autonomous output control systems. The intention is to demonstrate the obviousness or lack of novelty of such variations to a Person Having Ordinary Skill in the Art (PHOSITA) as of the earliest priority date of US11900016 (September 23, 2014), thereby limiting the patentability landscape for competitors.

Derivatives of Independent System Claim 1

Core Claim 1 (Summary): A system comprising an audio control source; at least one cluster of at least one computing device (with a sound sensing mechanism and wireless transceiver); at least one output device (with a power source, speaker, and communication mechanism); the audio control source in electronic communication, having memory, processor, and computer-executable instructions for identifying, isolating, determining frequency outside a predetermined threshold, altering, and outputting.

1. Material & Component Substitution

Derivative 1.1: Optical Acoustic Sensing with Piezoelectric Actuation

Enabling Description: This derivative system employs optical acoustic sensors, specifically fiber optic interferometric microphones, for sound sensing within each computing device cluster. These sensors utilize changes in light intensity or phase, modulated by acoustic vibrations impacting a diaphragm or fiber, to detect noise across a wide frequency spectrum (e.g., 1 Hz to 1 MHz). The raw optical data is converted to electrical signals via photodetectors and processed by a local ASIC for initial signal conditioning before wireless transmission to the audio control source. For sound output, the system integrates piezoelectric speakers and/or actuators within the output devices. These solid-state components leverage the inverse piezoelectric effect to generate sound waves through mechanical deformation, offering compact size, high efficiency, and precise frequency response up to ultrasonic ranges, particularly useful for targeted acoustic manipulation or localized sound field creation. The audio control source's instructions identify and isolate sounds, determine if any frequency falls outside a predetermined optical-to-electrical signal threshold, and then generate specific control voltages to drive the piezoelectric actuators to alter the sound output.

graph TD
    SUBGRAPH Cluster [Cluster of Computing Devices]
        FOIS[Fiber Optic Interferometric Sensor] --> PD[Photodetector & ADC]
        PD --> LP[Local Processor (ASIC)]
        LP --> WTR[Wireless Transceiver]
    END
    WTR --> AWS[Audio Control Source]
    AWS --> WTC[Wireless Transceiver]
    SUBGRAPH Output Device [Output Device]
        WTC --> PVA[Piezoelectric Actuator Driver]
        PVA --> PS[Piezoelectric Speaker]
    END
    AWS -- Instructions: Identify, Isolate, Threshold, Alter --> PVA

Derivative 1.2: Quantum Dot Light Sensing with Magnetostrictive Sound Emitters

Enabling Description: In this embodiment, specialized computing devices within clusters incorporate quantum dot (QD) based electro-optical transducers for broadband light sensing, particularly focused on specific visible and near-infrared (NIR) spectra (e.g., harmful blue light between 400-490 nm). The QDs are tuned to specific absorption/emission wavelengths, providing highly sensitive and spectrally selective light detection. The signal from the QD sensor is digitized and transmitted wirelessly. Simultaneously, for multi-frequency sound sensing, micro-electromechanical systems (MEMS) acoustic sensors fabricated from advanced ceramic composites (e.g., lead zirconate titanate, PZT) are utilized. The audio control source processes both light and sound data. For sound output, output devices integrate magnetostrictive sound emitters. These emitters use ferromagnetic materials (e.g., Terfenol-D) that change shape in response to magnetic fields, converting electrical signals into mechanical vibrations to produce sound. This allows for robust, high-power acoustic output with high fidelity and durability in harsh environments. The ACS's algorithms detect abnormal light spectra (e.g., excessive blue light) or hazardous acoustic frequencies, and autonomously alters output via the magnetostrictive emitters (e.g., sound masking, warning tones) or integrated display backlights (e.g., adjusting blue light intensity).

graph TD
    SUBGRAPH Cluster [Cluster Device]
        QDL[Quantum Dot Light Sensor] --> LD[Light Data Processing]
        MEMS[MEMS Acoustic Sensor (Ceramic Composite)] --> AD[Audio Data Processing]
        LD --> WT1[Wireless Transceiver]
        AD --> WT1
    END
    WT1 --> ACS[Audio Control Source]
    ACS -- Instructions: Multi-Spectral Analysis & Thresholding --> ACS_P[ACS Processor]
    ACS_P --> WT2[Wireless Transceiver]
    SUBGRAPH Output Device [Output Device]
        WT2 --> MSD[Magnetostrictive Driver]
        MSD --> MSE[Magnetostrictive Sound Emitter]
        ACS_P --> BLD[Blue Light Display Driver]
        BLD --> D[Display with Blue Light Emitter]
    END
    ACS_P -- Control Signals for Light & Sound --> MSE

2. Operational Parameter Expansion

Derivative 2.1: Ultra-Local Micro-Acoustic Field Management

Enabling Description: This system focuses on managing acoustic environments at an ultra-localized, micro-scale, such as within individual headphone earcups, hearing aid canals, or smart personal space zones (e.g., 10 cm radius around a user's head). Clusters consist of highly miniaturized computing devices, each integrating multiple MEMS microphone arrays (e.g., 64-element arrays on a 5x5mm footprint) capable of spatial soundfield capture, and micro-actuators (e.g., balanced armature drivers, bone conduction transducers) for highly directional or personalized sound output. The system processes sound data at sampling rates up to 192 kHz for high-fidelity acoustic analysis, detecting subtle frequency anomalies (e.g., resonance peaks, phase distortions) at specific points of reception. The audio control source (which can be integrated into a wearable device) implements advanced beamforming and active noise cancellation (ANC) algorithms to identify and isolate undesired acoustic energy, apply specific inverse phase signals or frequency attenuation to precisely alter the sound, and then output a perceptually optimized soundfield to the user, ensuring specific predetermined frequency thresholds are maintained within the ultra-localized zone.

graph TD
    SUBGRAPH Micro-Cluster [Micro-Cluster (e.g., in a headphone)]
        MMA[MEMS Microphone Array] --> µP[Micro-Processor (DSP)]
        µP --> WT[Wireless Transceiver]
        WT --> ACS[Audio Control Source (Wearable)]
    END
    ACS -- High-Speed Acoustic Data Stream --> FA[Fast Algorithm Processor (FPGA)]
    FA -- ANC & Beamforming Algorithms --> MA[Micro-Actuator Driver]
    MA --> MAT[Micro-Actuator Transducer (e.g., Balanced Armature)]
    ACS -- Instructions: Spatial Analyze, Isolate, Threshold, Alter --> FA

Derivative 2.2: Extreme-Frequency Multi-Modal Environmental Sensing

Enabling Description: This system operates across an unprecedented breadth of the electromagnetic and acoustic spectrum, extending far beyond human audibility. Computing device clusters integrate an array of transducers: a sub-Hertz infrasonic transducer (e.g., microbarometer), a broadband ultrasonic transducer (e.g., PZT array up to 2 MHz), and a millimeter-wave (mmWave) radar module (e.g., 60-90 GHz FMCW radar). The sound sensing mechanism is interpreted broadly as "energy sensing." Each computing device employs a software-defined radio (SDR) architecture for configurable reception and transmission across these diverse bands, enabling real-time analysis of environmental phenomena from seismic vibrations to atmospheric pressure waves and electromagnetic radiation. The audio control source processes the raw, high-bandwidth data streams (e.g., 10 GSPS for mmWave, 10 MSPS for ultrasound). Predetermined thresholds are established for various phenomena: specific infrasonic resonance frequencies (e.g., 7 Hz for psychological effects), ultrasonic cavitation patterns, and mmWave power density levels (e.g., regulatory exposure limits). If a threshold is exceeded, the system autonomously alters an environmental output, which could range from emitting a counter-frequency acoustic wave, activating an electromagnetic shielding device, or triggering a visible/haptic warning via a local output device.

graph TD
    SUBGRAPH Cluster [Cluster Device]
        IST[Infrasonic Transducer] --> ADC1[ADC]
        UST[Ultrasonic Transducer] --> ADC2[ADC]
        MMW[mmWave Radar Module] --> SDR[Software Defined Radio]
        ADC1 --> SDR
        ADC2 --> SDR
        SDR --> WT[Wireless Transceiver]
    END
    WT --> ACS[Audio Control Source (High-Performance Compute)]
    ACS -- Real-time Multi-Modal Data Stream --> SP[Spectrum Processor (FPGA/GPU)]
    SP -- Threat Analysis & Thresholding --> CDM[Control Decision Module]
    CDM --> OD[Output Device (e.g., Active Shielding, Haptic/Visual Alert, Counter-Emitter)]
    ACS -- Instructions: Multi-modal Sense, Process, Threshold, Alter --> CDM

3. Cross-Domain Application

Derivative 3.1: Precision Agricultural Acoustic Pest Deterrence (AgTech)

Enabling Description: This system is deployed in agricultural fields or greenhouses for precision pest deterrence. Mobile clusters, consisting of ruggedized computing devices with omnidirectional acoustic transducers and embedded GNSS receivers, are distributed across crop areas. These clusters continuously sense ambient audio, identifying specific frequencies and patterns associated with known agricultural pests (e.g., insect wingbeats, rodent vocalizations, bird calls harmful to crops). The audio control source, residing on a central farm management server, receives acoustic fingerprints from the clusters. If pest-specific frequencies exceed a predetermined threshold of presence or activity, the ACS activates localized output devices. These output devices comprise directional ultrasonic emitters capable of generating high-intensity ultrasonic deterrents (e.g., 20-60 kHz) or infrasonic discomfort frequencies, subtly altered to prevent habituation. The system dynamically alters the deterrent frequency, intensity, and direction based on real-time pest location and density data from the clusters, minimizing impact on beneficial organisms and preventing crop damage without chemical intervention.

graph TD
    SUBGRAPH Field Cluster [Agricultural Field Cluster]
        AT[Acoustic Transducer] --> P1[Processor (Edge AI)]
        GNSS[GNSS Receiver] --> P1
        P1 --> WT[Wireless Transceiver]
    END
    WT -- Acoustic Fingerprints & Location --> ACS[Farm Management Server (Audio Control Source)]
    ACS -- Pest ID & Density Analysis --> SP[Strategic Planner (AI)]
    SP -- Deterrent Commands --> WT2[Wireless Transceiver]
    SUBGRAPH Output Device [Localized Deterrent Unit]
        WT2 --> DUE[Directional Ultrasonic Emitter]
        WT2 --> IDE[Infrasonic Discomfort Emitter]
    END
    SP -- Instructions: Sense, Identify Pest, Threshold, Generate Deterrent --> DUE

Derivative 3.2: Underwater Acoustic Anomaly Detection and Mitigation (Marine Exploration/Defense)

Enabling Description: This system operates in subsea environments for monitoring marine activity, infrastructure integrity, or defense applications. Clusters of autonomous underwater vehicles (AUVs) or fixed seafloor nodes function as computing devices, each equipped with hydrophone arrays (sound sensing mechanisms) capable of detecting acoustic energy from seismic activity, marine life, vessel movements, or specific sonar signatures (1 Hz to 1 MHz). These devices use secure acoustic modems or tethered fiber optic connections (wireless transceivers in a broader sense) to transmit data to a surface or shore-based audio control source. The ACS employs advanced signal processing to identify and isolate specific acoustic signatures. Predetermined thresholds are set for anomalous events, such as unusual seismic patterns, unauthorized vessel noise profiles, or equipment cavitation sounds. If a threshold is exceeded, the system autonomously alters the acoustic environment. Output devices, such as localized underwater acoustic projectors, can emit counter-signals for stealth applications, generate deterrent frequencies for marine mammal protection, or broadcast targeted informational signals, dynamically adjusting output parameters based on real-time acoustic analysis to maintain specific subsea acoustic conditions.

graph TD
    SUBGRAPH Subsea Cluster [AUV / Seafloor Node]
        HA[Hydrophone Array] --> DSP[Digital Signal Processor]
        DSP --> AM[Acoustic Modem / Fiber Optic Link]
    END
    AM -- Encrypted Acoustic Data --> ACS[Surface/Shore Control Station]
    ACS -- Anomaly Detection & Signature Matching --> CP[Command Processor]
    CP -- Mitigation Commands --> AM2[Acoustic Modem / Fiber Optic Link]
    SUBGRAPH Output Device [Underwater Acoustic Projector]
        AM2 --> APA[Acoustic Projector Array]
    END
    CP -- Instructions: Sense, Analyze, Threshold, Generate Counter-Signal --> APA

Derivative 3.3: Urban Environmental Noise Characterization and Adaptive Response (Smart Cities)

Enabling Description: This system is integrated into urban infrastructure for comprehensive environmental noise characterization and adaptive response. Clusters of computing devices are embedded in streetlights, public transport hubs, and building facades throughout a city. Each device includes a networked MEMS microphone array (sound sensing mechanism) to capture ambient sound (e.g., traffic, construction, public address systems, emergency sirens) and an environmental sensor suite (e.g., particulate matter, NOx, ozone for broader "noise" sensing). Data is wirelessly transmitted via municipal IoT networks (e.g., LoRaWAN, 5G-IoT) to a central cloud-based audio control source. The ACS performs real-time spatio-temporal analysis to identify dominant noise sources, isolate specific problematic frequencies (e.g., excessively loud vehicle exhausts, disruptive low-frequency hums), and determine if they exceed dynamically adjusted, location-specific predetermined noise thresholds (e.g., residential vs. commercial zones). If a threshold is breached, the system autonomously alters aspects of the urban environment. Output devices, such as adaptive sound masking systems in public spaces, dynamic traffic signal controllers, or variable message signs, are adjusted. For instance, in response to high construction noise, nearby public address systems could automatically adjust their broadcast frequency and volume for intelligibility, or traffic flow could be rerouted.

graph TD
    SUBGRAPH Urban Cluster [Smart Infrastructure Node]
        MMA[MEMS Microphone Array] --> ED[Edge Processor]
        ESS[Environmental Sensor Suite] --> ED
        ED --> WT[IoT Wireless Transceiver]
    END
    WT -- Geo-tagged Noise & Env Data --> ACS[Cloud-based Audio Control Source]
    ACS -- Spatio-Temporal Noise Analysis & Policy Engine --> DM[Decision Module]
    DM --> WT2[IoT Wireless Transceiver]
    SUBGRAPH Output Device [Urban Response Mechanism]
        WT2 --> ASM[Adaptive Sound Masking System]
        WT2 --> TSC[Traffic Signal Controller]
        WT2 --> VMS[Variable Message Sign]
    END
    DM -- Instructions: Sense Urban Noise, Isolate, Threshold, Alter Urban Output --> ASM

4. Integration with Emerging Tech

Derivative 4.1: AI-Driven Predictive Acoustic Management with IoT Context

Enabling Description: This system integrates AI-driven predictive modeling and a comprehensive network of IoT environmental sensors to achieve highly optimized and proactive acoustic management. Clusters of computing devices, comprising broadband MEMS microphones and dedicated edge AI processors (e.g., custom ASICs with neural network accelerators), continuously sense sound. Simultaneously, a dense overlay of IoT sensors monitors environmental parameters such as air temperature, humidity, wind velocity, atmospheric pressure, and even crowd density via anonymized video analytics or LiDAR. All data is streamed to a central audio control source, which hosts a sophisticated AI model (e.g., a deep learning recurrent neural network trained on vast acoustic and environmental datasets). This AI model performs real-time sound identification and isolation, but critically, it predicts future acoustic conditions (e.g., impending echoes, wind-steered sound, sudden crowd surges leading to dangerous SPLs) based on current and historical environmental context. When a predicted acoustic parameter (e.g., SPL, specific frequency energy) is projected to exceed a predetermined threshold, the AI autonomously generates and applies proactive alterations to the audio output devices (e.g., multi-zone loudspeakers, personal sound zones). These alterations could include predictive equalization, dynamic range compression, phase alignment, or targeted sound masking, optimizing output before the issue manifests.

graph TD
    SUBGRAPH Cluster [Cluster of Devices]
        MM[MEMS Microphone] --> EDAI[Edge AI Processor]
        IOT[IoT Environmental Sensor] --> EDAI
        EDAI --> WTR[Wireless Transceiver]
    END
    WTR -- Sensed Data + Local Pred. --> ACS[Audio Control Source (Cloud/Central AI)]
    ACS -- Global Predictive Acoustic Model (DL RNN) --> APA[Adaptive Control Policy Engine]
    APA -- Proactive Control Commands --> OD[Output Device Array]
    OD -- Optimized Sound Output --> Listener[Listeners]
    ACS -- Instructions: Sense, Contextualize, Predict, Proactively Alter --> APA

Derivative 4.2: Blockchain-Secured Acoustic Data Integrity and Auditable Control

Enabling Description: This system focuses on ensuring the tamper-proof integrity of sensed acoustic data and providing auditable control over audio output alterations, particularly critical in regulated environments (e.g., industrial safety, public health compliance, forensic acoustics). Each computing device within a cluster incorporates a hardware security module (HSM) that cryptographically signs all raw and processed sound data (e.g., frequency spectrum, SPL measurements) before transmission. These signed data blocks are then submitted as transactions to a distributed ledger (blockchain, e.g., an enterprise Ethereum or Hyperledger Fabric network) managed by the audio control source and authorized network participants. When the audio control source's processor executes instructions to identify, isolate, and determine if a frequency is outside a predetermined threshold, this decision-making process and any subsequent alteration commands sent to output devices are also recorded as immutable transactions on the blockchain. Smart contracts govern the permissioned access to acoustic data and the authorized parameters for output alterations. This architecture provides verifiable proof of environmental conditions, algorithm execution, and system responses, enabling comprehensive audits for compliance, liability, and post-event analysis.

graph TD
    SUBGRAPH Cluster [Cluster Device]
        SSM[Sound Sensing Mechanism] --> AFE[Analog Front End]
        AFE --> DSP[Digital Signal Processor]
        DSP --> HSM[Hardware Security Module]
        HSM -- Cryptographic Signature --> BD[Blockchain Data Packet]
        BD --> WT[Wireless Transceiver]
    END
    WT --> ACS[Audio Control Source]
    ACS -- Validate & Add to Ledger --> BCN[Blockchain Network]
    BCN -- Verifiable Data & Commands --> APE[Auditable Policy Engine]
    APE -- Signed Alteration Commands --> OD[Output Device]
    ACS -- Instructions: Sense, Identify, Isolate, Threshold, Signed Alteration --> APE

5. The "Inverse" or Failure Mode

Derivative 5.1: Adaptive Low-Power Emergency Monitoring Mode

Enabling Description: This system is designed for prolonged operation in remote or power-constrained environments, with an intelligent adaptive low-power emergency monitoring mode. Each computing device within a cluster includes an energy harvesting module (e.g., solar, vibration) and a multi-tiered power management unit. The sound sensing mechanism is a low-power, always-on acoustic event detector (e.g., a specialized ASIC that monitors for a few pre-configured trigger frequencies or amplitude transients). In normal operation, the system provides full multi-frequency sensing and output alteration. However, upon detecting a power threshold breach (e.g., battery level dropping below 20%) or an extended period of communication loss with the audio control source, the clusters automatically transition to an "Emergency Monitoring Mode." In this mode, sensing frequency is drastically reduced (e.g., from 48 kHz to 8 kHz, or periodic duty cycling), and processing is limited to only critical hazardous frequency bands (e.g., infrasonic earthquake precursors, specific emergency siren frequencies, distress signals). Complex alteration of sound output is disabled. Instead, output devices (which also have low-power modes) activate highly energy-efficient visual alerts (e.g., blinking ultra-low-power LEDs) or transmit compressed, urgent notification bursts via a dedicated low-power wide-area network (LPWAN) transceiver, ensuring essential safety alerts are prioritized over full fidelity.

graph TD
    SUBGRAPH Cluster [Power-Constrained Cluster]
        EHM[Energy Harvesting Module] --> PMU[Power Management Unit]
        PMU --> LPDM[Low-Power Detector Module]
        LPDM --> LPDM_SSM[Low-Power Sound Sensing (ASIC)]
        LPDM_SSM --> µC[Microcontroller]
        µC --> LPWAN[LPWAN Transceiver]
    END
    PMU -- Power Threshold Breach --> µC
    µC -- Mode Switch Trigger --> LPDM
    µC -- Critical Event Data --> LPWAN
    LPWAN -- Emergency Notification --> ACS[Audio Control Source / Emergency Services]
    SUBGRAPH Output Device [Low-Power Alert Device]
        LPWAN -- Alert Command --> LED[Ultra-Low-Power LED Array]
        LPWAN -- Alert Command --> HVD[Haptic Vibration Device]
    END
    µC -- Instructions: Power Monitor, Mode Switch, Critical Sense, Limited Alert --> LED

Derivative 5.2: Self-Healing System with Redundant Fail-Safe Broadcast

Enabling Description: This system is designed for high-reliability applications where continuous, albeit basic, audio output is paramount even during partial system failures. Each computing device cluster incorporates redundant sound sensing mechanisms (e.g., primary MEMS mic, secondary electret condenser mic) and multiple wireless transceivers operating on diverse protocols (e.g., Wi-Fi mesh, redundant cellular IoT). The audio control source is implemented as a distributed, fault-tolerant architecture (e.g., Kubernetes cluster with multiple instances across different data centers). It continuously monitors the health of all clusters and output devices. Upon detection of a critical system failure (e.g., a cluster processing unit crash, loss of primary ACS instance, or corrupted alteration algorithm), the system initiates a "Fail-Safe Broadcast Mode." In this mode, complex real-time frequency analysis and alteration are temporarily suspended. Instead, the output devices, each equipped with a local pre-recorded emergency audio buffer and a direct-broadcast fallback communication channel (ee.g., a simple analog RF transmitter), are commanded to broadcast a universal, pre-defined safe audio signal (e.g., standard white noise, evacuation instructions, or a specific "all-clear" tone) at a moderate, non-hazardous volume. Concurrently, diagnostic data from the failing components is logged to an immutable, off-grid storage medium (e.g., write-once optical media, segregated flash memory) for post-mortem analysis. Once the fault is isolated or a redundant component takes over, the system gracefully transitions back to full operational mode.

graph TD
    SUBGRAPH Cluster [Redundant Cluster Device]
        PSM[Primary Sound Sensor] --> PC[Primary Processor]
        RSM[Redundant Sound Sensor] --> RC[Redundant Processor]
        PC -- Health Check --> FAULT_DETECTOR[Fault Detector]
        RC -- Health Check --> FAULT_DETECTOR
        FAULT_DETECTOR -- Diagnostic Data --> LDM[Local Diagnostic Memory (Immutable)]
        PC --> WT1[Wireless Transceiver 1]
        RC --> WT2[Wireless Transceiver 2]
    END
    WT1 & WT2 --> ACS[Distributed Audio Control Source (Fault Tolerant)]
    ACS -- Fault Monitoring & Health Check --> ACS_HEALTH[ACS Health Monitor]
    ACS_HEALTH -- Critical Failure Detected --> FSB_CMD[Fail-Safe Broadcast Command]
    FSB_CMD --> OD[Output Device with Fallback]
    SUBGRAPH OD [Output Device with Fallback]
        OD_WT[Multi-Protocol Rx] --> FSBM[Fail-Safe Broadcast Module]
        FSBM -- Pre-recorded Audio --> SP[Speaker]
        FSBM --> DB_RF[Direct Broadcast RF Tx]
    END
    ACS -- Instructions: Monitor Health, Detect Fault, Initiate Fail-Safe --> FSBM

Combination Prior Art Scenarios with Open-Source Standards

These scenarios illustrate how the core concepts of US11900016 could be implemented or extended using widely available open-source standards, thereby establishing prior art for incremental innovations built upon these foundations.

1. US11900016 + MQTT over Wi-Fi (IEEE 802.11) for Smart Home Acoustics

Description: A smart home acoustic management system is implemented using the principles of US11900016. Clusters of computing devices are realized as low-cost, open-source hardware nodes (e.g., ESP32 microcontrollers) each equipped with an I2S digital microphone (sound sensing mechanism) and a Wi-Fi (IEEE 802.11) module (wireless transceiver). These nodes are flashed with custom firmware (e.g., ESPHome, Tasmota) that publishes real-time sound data (e.g., Fast Fourier Transform analysis results, A-weighted SPL) as MQTT messages to a central MQTT broker (e.g., Mosquitto, an open-source message broker) running on a local server (e.g., Raspberry Pi). The Raspberry Pi also hosts the audio control source, a Python application that subscribes to the MQTT topics, identifies and isolates problematic sounds (e.g., infant crying, breaking glass, excessively loud music), determines if specific frequency bands or SPLs exceed predetermined thresholds (configured via a web interface), and then generates control commands. These commands are published back as MQTT messages to smart speakers (e.g., DIY speakers running OpenHAB or custom ESP32 firmware that can receive MQTT commands), acting as output devices, instructing them to alter their output (e.g., lower volume in specific zones, play calming sounds, equalize a room's acoustics).
Relevance: This combination makes obvious any distributed acoustic sensing and control system in a smart home environment that leverages standard IoT communication protocols and readily available open-source hardware/software for real-time sound analysis and adaptive audio output.

2. US11900016 + ROS (Robot Operating System) for Mobile Acoustic Sensing in Robotics

Description: A mobile robot platform (e.g., an autonomous ground vehicle running ROS, such as a modified TurtleBot) acts as a mobile cluster. It integrates a multi-microphone array (sound sensing mechanism) connected to an onboard computing device (e.g., a NVIDIA Jetson Nano running Ubuntu with ROS). The robot's existing Wi-Fi or Ethernet interface serves as the wireless transceiver. The onboard computer, running ROS nodes, acts as the local audio control source. One ROS node processes the microphone array data, performing sound source localization and real-time spectral analysis (identifying and isolating sounds). Another ROS node determines if any detected frequency or sound event (e.g., high-frequency motor whine, human speech in a restricted area) exceeds a predetermined threshold. If a breach occurs, a control node alters the robot's acoustic output via its integrated speaker (output device) – for instance, emitting a warning tone, muting onboard sounds to reduce noise pollution, or dynamically adjusting speech synthesis output to be more intelligible in noisy environments. The robot could also communicate these acoustic findings to a central ROS master or cloud system for broader environmental awareness.
Relevance: This renders obvious the application of distributed multi-frequency sensing, thresholding, and adaptive audio output control within mobile robotic platforms, utilizing the established open-source ROS framework for data communication, processing, and system integration.

3. US11900016 + WebRTC (Web Real-Time Communication) for Collaborative Spatial Audio

Description: A collaborative spatial audio system for virtual meetings or mixed-reality environments is implemented using WebRTC. Each user's personal computing device (laptop, smartphone) functions as a cluster device, leveraging its built-in microphone (sound sensing mechanism) and speaker (output device). WebRTC, an open-source standard, provides the wireless communication mechanism for real-time, peer-to-peer or SFU-mediated audio streaming between participants. A central server, running a WebRTC Selective Forwarding Unit (SFU) and an associated audio control source (e.g., Node.js with Web Audio API processing), receives all participant audio streams. The ACS dynamically analyzes the aggregated audio, identifies and isolates problematic frequencies (e.g., feedback loops, excessive background noise, specific speech frequencies causing intelligibility issues), and determines if these exceed predetermined thresholds for audio quality or participant safety. If a threshold is breached, the ACS applies real-time alterations to the individual audio streams (e.g., noise suppression, dynamic equalization, gain adjustments) and sends the processed streams back to the respective output devices via WebRTC, thereby altering the sound output perceived by each participant to improve the overall collaborative experience.
Relevance: This makes obvious real-time, distributed acoustic sensing and adaptive audio output control in interactive, web-based, or virtual environments, leveraging the open and widely adopted WebRTC standard for communication and audio processing.