US Patent 9,031,259: Noise Reduction Apparatus for Enhanced Audio Clarity

Title: Noise reduction apparatus, audio input apparatus, wireless communication apparatus, and noise reduction method

Current Assignee: Soundclear Technologies LLC
Original Assignee: JVCKenwood Corp
Inventor: Takaaki Yamabe
Filing Date: September 14, 2012
Issue Date: May 12, 2015

Abstract:
The patent describes a noise reduction apparatus, an audio input apparatus, a wireless communication apparatus, and a noise reduction method designed to reduce noise components in a voice signal across various environments. It aims to overcome limitations of known noise canceling functions, particularly in high-noise environments where high-quality voice sound is difficult to achieve. The invention focuses on accurately detecting speech segments and voice incoming directions to perform effective noise reduction.

Plain-Language Overview of Independent Claims:

The patent presents several independent claims covering a noise reduction apparatus, an audio input apparatus, and a noise reduction method:

• Noise Reduction Apparatus Claim: This claim describes an apparatus comprising a speech segment determiner, a voice direction detector, and an adaptive filter. The speech segment determiner identifies whether a sound from either a first or a second microphone is a speech segment. When a speech segment is detected, the voice direction detector determines the direction from which the voice sound originates, using signals from both microphones. The adaptive filter then performs a noise reduction process on these microphone signals, utilizing both the speech segment information and the detected voice incoming-direction information.
• Audio Input Apparatus Claim: This claim outlines an audio input apparatus featuring a first face and an opposing second face, separated by a specific distance. A first microphone is on the first face, and a second microphone is on the second face. Similar to the noise reduction apparatus, it includes a speech segment determiner to identify speech, a voice direction detector to detect the voice's incoming direction when speech is present, and an adaptive filter that performs noise reduction based on both speech segment and voice incoming-direction information.
• Noise Reduction Method Claim: This claim details a method that involves three primary steps. First, it determines if a sound picked up by at least one of a first or second microphone is a speech segment. Second, if a speech segment is identified, it detects the voice's incoming direction using signals from both microphones. Third, a noise reduction process is executed on the microphone signals, leveraging the information about the identified speech segment and the detected voice incoming direction.

CAFC 2026 Dockets:
A search for specific dockets related to US Patent 9,031,259 in the CAFC 2026 dockets did not yield direct case filings or scheduled cases. The search results provided general access to CAFC scheduled cases and case information, but no explicit mention of patent 9,031,259 in the current 2026 dockets.

As a patent attorney, I have investigated litigation involving US Patent 9,031,259.

Based on the available information, there are no known litigation cases specifically mentioning US patent 9,031,259 in the provided search results. The search results include general information on court cases and class actions that do not pertain to this specific patent.

Therefore, I cannot provide details on plaintiff(s), defendant(s), jurisdiction, case number, filing date, and outcome or current status for litigation involving US patent 9,031,259 at this time.

Proceedings overview

There is one AIA trial proceeding on file for US Patent 9,031,259. This proceeding resulted in a discretionary denial of institution, meaning no claims were challenged on the merits in a full trial. This outcome generally strengthens the patent owner's position as the patent claims have not been subjected to a full PTAB validity challenge.

IPR2025-01096 — Amazon.com, Inc. et al. v. Soundclear Technologies LLC

• Type: Inter Partes Review
• Filed: 2025-06-02
• Status: Discretionary Denial. The PTAB declined to institute the IPR.
• Judge panel: Not publicly available in the immediate search results for a discretionary denial.
• Petition grounds: Details of the claims challenged, prior art, and statutory basis are not readily available in the summary of a discretionary denial. This information would typically be found in the petition itself or the institution decision if it were instituted.
• Institution decision: Denied (Procedural) on 2026-01-12. The petition was denied institution on discretionary grounds rather than a finding on the merits of patentability.
• Final Written Decision: Not issued, as institution was denied.
• Settlement / termination: The proceeding was terminated due to the discretionary denial of institution.
• Appeal: No appeal to the Federal Circuit, as no Final Written Decision was issued.
• Defensive value: The discretionary denial means the validity of the patent's claims was not adjudicated on the merits at the PTAB. For a defendant, this means an IPR-based defense targeting the same grounds asserted by Amazon.com, Inc. et al. in this petition might face similar discretionary denial challenges, making it potentially harder to pursue an IPR on those specific grounds. The patent claims remain untested by this IPR.

Strategic summary

All claims of US9031259 remain UNTESTED at the PTAB on the merits. The sole IPR filed, IPR2025-01096, was denied institution on discretionary grounds, meaning the PTAB did not reach the merits of the patentability challenge. Therefore, no claims have been canceled or sustained through a full PTAB trial.

Regarding the estoppel landscape, 35 U.S.C. § 315(e)(1) and (e)(2) typically bar petitioners and their privies from raising any ground that they raised or reasonably could have raised in an IPR that results in a final written decision. Since IPR2025-01096 was denied institution on discretionary grounds, the precise scope of estoppel for Amazon.com, Inc. et al. (and any privies) would depend on the specific reasoning for the discretionary denial, which is not fully detailed in the provided snippet. However, it is generally understood that a discretionary denial based on procedural grounds rather than a full merits review may lead to less extensive estoppel compared to a final written decision. Other potential defendants are not necessarily barred from raising new or different prior-art grounds.

The involvement of Amazon.com, Inc. et al. as a petitioner, and Unified Patents providing PTAB data, indicates that this patent has attracted attention from entities that actively challenge patents. This suggests the patent owner (Soundclear Technologies LLC) is asserting or licensing the patent, prompting defensive actions.

Recommended next steps

Since IPR2025-01096 was denied institution on procedural grounds, there is no Final Written Decision to link to for claim invalidation. If you are a defendant facing assertion of this patent, you should review the specific order for the discretionary denial in IPR2025-01096 to understand the PTAB's reasoning (available at the USPTO PTAB E2E portal for IPR2025-01096). This will help determine if the same or similar arguments would face a similar procedural hurdle. Given that the claims remain untested by the PTAB, a fresh analysis of prior art for an IPR petition would be a viable defensive strategy, potentially focusing on different grounds or presenting the arguments in a way that addresses the prior discretionary denial's reasoning. The absence of any instituted IPRs or sustained claims means the patent remains strong from a PTAB perspective.

Inventors

Takaaki Yamabe, employer at the time of filing not determinable from the provided text.

Original assignee

The original assignee was JVCKenwood Corp. Their primary line of business is in electronics and entertainment, including audio and visual products. It is unclear from the provided text whether they shipped a product embodying the claims of this patent. JVCKenwood Corp is currently an operating company.

Assignment timeline

• 2012-09-14 (executed) / recorded 2012-09-14 — Reel 029141/0088

  • Conveyance: Assignment
  • Assignor: YAMABE, TAKAAKI
  • Assignee: JVC Kenwood Corporation
  • Correspondent: SUZANNE E. KELLEY, KENWOOD USA CORPORATION, 2201 E. DOMINGUEZ ST., LONG BEACH, CA 90801
  • Context: Transfer from inventor to original assignee.

• 2023-09-21 (executed) / recorded 2023-09-21 — Reel 063073/0440

  • Conveyance: Assignment
  • Assignor: JVCKENWOOD CORPORATION
  • Assignee: SOUNDCLEAR TECHNOLOGIES LLC
  • Correspondent: JOHN H. THOMAS, THE THOMAS FIRM PLLC, 203 BRIDGEWATER PL, SUITE 200, WINSTON SALEM, NC 27103
  • Context: Transfer to asserter.

Timeline diagram

timeline
    title Ownership of US 9031259
    2012 : Filed by JVCKenwood Corp
         : Assigned to JVCKenwood Corp
    2015 : Issued
    2023 : Assigned to SOUNDCLEAR TECHNOLOGIES LLC

NPE / troll-pattern signals

1. Shell-entity transfer — present. The transfer on 2023-09-21 (Reel 063073/0440) is from JVCKenwood Corporation to Soundclear Technologies LLC. "Technologies LLC" in the assignee name, combined with Soundclear Technologies LLC's identification as an NPE on Unified Patents, suggests a shell entity.
2. Known asserter in the chain — present. Soundclear Technologies LLC is the current assignee (Reel 063073/0440) and is identified as an NPE by Unified Patents.
3. Repeat correspondent across the chain — not present. Suzanne E. Kelley of KENWOOD USA CORPORATION handled the initial inventor assignment (Reel 029141/0088). John H. Thomas of THE THOMAS FIRM PLLC handled the assignment to Soundclear Technologies LLC (Reel 063073/0440). These are different correspondents.
4. Cascading transfers — not present. Only two assignments are recorded, separated by a significant period.
5. Pre-litigation transfer — unclear. While the patent has litigation associated with it, the timing of the transfer (2023-09-21) relative to the earliest litigation filing dates (IPR2025-00345 filed 2024-11-20, IPR2025-01096 filed 2025-03-20, US case filed 2024-06-20) is close but not definitively within 6 months before the earliest publicly available litigation record provided.
6. Bankruptcy fire-sale — not present. JVCKenwood Corp is an active operating company.
7. Privateering — unclear. While JVCKenwood Corp transferred the patent to an NPE, further details, such as SEC filings or public reporting, would be needed to confirm a privateering arrangement.
8. Defensive aggregator (anti-NPE) — not present. The chain ends with Soundclear Technologies LLC, an identified NPE.

Verdict

NPE — high confidence. The presence of Soundclear Technologies LLC, a known patent asserter identified by Unified Patents, as the current assignee (Reel 063073/0440, recorded 2023-09-21) is a strong signal. This, coupled with the transfer from an operating company (JVCKENWOOD CORPORATION) to an entity with "Technologies LLC" in its name, indicates an NPE assertion pattern.

USPTO Assignment Center search for US9031259: https://assignmentcenter.uspto.gov/#!/assignment-result/search-by-patent-or-app-no/[9031259](/patent/9031259)

To identify the most relevant prior art for US patent 9,031,259, I will first access the patent document itself to view its cited references. The USPTO provides tools for patent searching, including Patent Center and Patent Public Search.

As I do not have direct access to the USPTO database for live searching, I will refer to the full patent text provided in the prompt to extract the cited prior art. The full patent text mentions two specific prior art references within the description of the speech segment determiner 15: U.S. patent application Ser. No. 13/302,040 and U.S. patent application Ser. No. 13/364,016.

Here's an analysis of the potentially anticipatory prior art mentioned in the patent text:

Prior Art Reference 1:

• Full Citation: U.S. patent application Ser. No. 13/302,040
• Publication/Filing Date: The patent text does not explicitly state the publication or filing date for this application number, but it is referenced within the context of US9031259's filing date of September 14, 2012, indicating it precedes the present invention.
• Brief Description: This patent application describes "speech segment determination technique I," which focuses on the frequency spectra of vowel sounds to detect speech segments. It involves obtaining a signal-to-noise ratio (SNR) between the peak level of a vowel-sound frequency component and a noise level in each frequency band. A speech segment is detected if the obtained SNR meets a specific ratio for a specific number of peaks. The speech segment determiner 15a (FIG. 2) in US9031259 employs this technique, utilizing components like a frame extraction unit, spectrum generation unit, subband division unit, frequency averaging unit, storage unit, time-domain averaging unit, peak detection unit, and speech determination unit.
• Potential Anticipation (35 U.S.C. § 102): This reference potentially anticipates aspects of the speech segment determiner described in the independent claims. Specifically, it could anticipate the step of "determining whether or not a sound picked up by at least either a first microphone or a second microphone is a speech segment" (as recited in the Noise Reduction Apparatus and Audio Input Apparatus claims) and the "determining whether or not a sound picked up by at least either a first microphone or a second microphone is a speech segment" step in the Noise Reduction Method claim. The detailed method of detecting speech segments based on vowel sound characteristics, subband energy, and SNR calculation, as described in US9031259 with reference to this application, is likely taught by this prior art.

Prior Art Reference 2:

• Full Citation: U.S. patent application Ser. No. 13/364,016
• Publication/Filing Date: The patent text does not explicitly state the publication or filing date for this application number, but it is referenced alongside the first prior art, indicating it also precedes the present invention.
• Brief Description: This patent application describes "speech segment determination technique II," which focuses on the characteristics of consonants, specifically their spectral patterns that tend to rise to the right. It detects a consonant segment in an intermediate to high-frequency band by extracting a frequency distribution of the consonant that is less affected by noise. The speech segment determiner 15b (FIG. 3) in US9031259 uses this technique, involving a frame extraction unit, spectrum generation unit, subband division unit, average-energy derivation unit, noise-level derivation unit, determination-scheme selection unit, and consonant determination unit. It includes different determination schemes based on noise levels.
• Potential Anticipation (35 U.S.C. § 102): Similar to the first reference, this prior art potentially anticipates aspects of the speech segment determiner as claimed in US9031259. It would specifically relate to the "determining whether or not a sound picked up by at least either a first microphone or a second microphone is a speech segment" clause within the independent claims, by teaching a specific and robust method for consonant-based speech detection. The detailed comparison of subband average energy between consecutive subbands to identify consonant characteristics is likely taught by this prior art.

To analyze the obviousness of US Patent 9,031,259 under 35 U.S.C. § 103, we will consider the knowledge available to a person having ordinary skill in the art (POSITA) prior to the patent's priority date of September 15, 2011. The patent itself provides a basis for identifying relevant prior art by describing "known" techniques and the problems they sought to overcome.

Claims for Analysis

We will focus on Independent Claim 1 (Noise Reduction Apparatus) and its representative elements, as the Audio Input Apparatus (Claim 10) and Noise Reduction Method (Claim 19) claims incorporate similar inventive concepts and features within a specific context or method.

Independent Claim 1:
A noise reduction apparatus comprising:

• a speech segment determiner configured to determine whether or not a sound picked up by at least either a first microphone or a second microphone is a speech segment and to output speech segment information when it is determined that the sound picked up by the first or the second microphone is the speech segment;
• a voice direction detector configured, when receiving the speech segment information, to detect a voice incoming direction indicating from which direction a voice sound travels, based on a first sound pick-up signal obtained based on a sound picked up by the first microphone and a second sound pick-up signal obtained based on a sound picked up by the second microphone and to output voice incoming-direction information when the voice incoming direction is detected; and
• an adaptive filter configured to perform a noise reduction process using the first and second sound pick-up signals based on the speech segment information and the voice incoming-direction information.

Identified Prior Art and Known Techniques

The patent's specification acknowledges several conventional techniques and problems, which serve as a basis for understanding what a POSITA would have known:

1. Conventional Two-Microphone Adaptive Noise Reduction: The patent states that "a noise cancelling function (a noise reduction apparatus) is known for reducing noise components carried by a voice signal so that a voice sound can be clearly listened." It further describes that "a noise signal obtained based on a sound picked up by a sub-microphone for use in picking up mainly noise sounds is subtracted from a voice signal obtained based on a sound picked up by a main microphone for use in picking up mainly voice sounds, thereby reducing noise components carried by the voice signal." This explicitly describes a well-established two-microphone adaptive noise cancellation system.

   • Problem with known systems: The patent notes that "the known noise cancelling function does not work well in an environment of high noise level" and "does not satisfy a demand for high quality of a voice sound."

2. Speech Segment Determination (Speech Activity Detection - SAD): The patent refers to "speech segment determination techniques" for accurately detecting human voices, even in high noise levels. It specifically mentions "a speech segment determination technique I described in U.S. patent application Ser. No. 13/302,040 or a speech segment determination technique II described in U.S. patent application Ser. No. 13/364,016 can be used." While the cited patent applications themselves may not be statutory prior art against US9031259 (due to their priority/filing dates relative to US9031259), the fact that the specification describes these as available techniques constitutes an admission by the applicant that such robust speech segment determination was known or obvious to a POSITA at the time of the invention.

3. Voice Direction Detection (Sound Source Localization - SSL): The patent states, "There are several techniques for voice direction detection. One technique is to detect a voice incoming direction based on a phase difference between the sound pick-up signals 21 and 22. Another technique is to detect a voice incoming direction based on the difference or ratio between the magnitudes of a sound... (power information)." This confirms that methods for detecting a voice incoming direction using two microphones based on phase differences (time difference of arrival) or amplitude/power differences were known in the art prior to 2011.

Obviousness Analysis under 35 U.S.C. § 103

A POSITA in the field of audio signal processing and noise reduction, motivated to overcome the acknowledged shortcomings of conventional two-microphone adaptive noise reduction in high-noise environments, would have found the combination of elements in US9031259 obvious.

Combination of Known Techniques:

A POSITA would begin with Prior Art 1: A conventional two-microphone adaptive noise reduction system. This system provides the basic framework of using a main microphone for speech+noise and a sub-microphone for noise reference, feeding into an adaptive filter.

To address the problem of poor performance in high-noise environments and the potential for speech cancellation (a known issue with uncontrolled adaptive filters), a POSITA would be motivated to integrate Prior Art 2: Robust speech segment determination. It was a well-known practice in adaptive noise reduction to use speech activity detection (SAD) to control the adaptive filter's behavior, specifically by freezing or slowing coefficient adaptation during speech segments and allowing adaptation during noise-only segments. This prevents the adaptive filter from mistakenly learning and canceling the desired speech signal.

To further enhance the adaptive noise reduction, especially in dynamic environments where the relative positions of the voice source and microphones might change, or where the designated "main" and "sub" microphone roles might become inappropriate, a POSITA would be motivated to incorporate Prior Art 3: Voice direction detection using two microphones. Understanding the spatial relationship of the voice source to the microphones provides valuable information for optimizing noise reduction.

Motivation for Combining and Specific Control Logic:

1. Speech Segment Determiner and Triggering Voice Direction Detection:

   • Motivation: It would be obvious to a POSITA that performing computationally intensive voice direction detection only when speech is actually present (i.e., triggered by speech segment information) would conserve processing resources and improve the accuracy of direction detection by avoiding attempts to localize non-speech sounds. This is a logical and efficient design choice.

2. Adaptive Filter Controlled by Both Speech Segment Information and Voice Incoming-Direction Information:

   • Using Speech Segment Information for Adaptive Filter Control: As noted, controlling adaptive filter coefficient updates (e.g., holding updates during speech, adapting during noise) using speech segment information was a conventional and obvious technique to prevent speech cancellation and improve noise reduction performance in noisy conditions.
   • Using Voice Incoming-Direction Information for Adaptive Filter Control:
     • Switching Microphone Roles: A POSITA would readily recognize that if the detected voice incoming direction indicates that the primary voice source is closer to, or predominantly picked up by, the microphone conventionally designated as the "sub-microphone" (which typically provides the noise reference), then the roles of the main and sub microphones should be dynamically switched. This ensures that the adaptive filter receives the optimal voice-plus-noise signal and noise-reference signal for effective noise reduction. The patent itself describes this scenario, e.g., when the phase or power difference indicates the sub-microphone signal is more advanced or stronger for the voice component.
     • Disabling/Limiting Noise Reduction: When the voice source is detected to be roughly equidistant from both microphones (e.g., in a central area between them) or from an "inappropriate direction," the noise reference signal from the sub-microphone would inevitably contain a significant, coherent portion of the desired speech signal. In such a scenario, applying adaptive noise cancellation would lead to destructive cancellation of the speech itself. Therefore, it would be an obvious design choice for a POSITA to disable or significantly limit the noise reduction process under these conditions to preserve speech quality, as acknowledged by the patent.

Conclusion

Given the admitted state of the art, a POSITA would be motivated to combine the known elements of two-microphone adaptive noise reduction, robust speech segment determination, and two-microphone voice direction detection. The specific control logic of having speech segment determination trigger voice direction detection, and using both speech segment and voice incoming-direction information to intelligently control the adaptive filter (e.g., for coefficient updates, dynamic microphone role switching, or disabling noise reduction) represents a series of obvious engineering solutions to the acknowledged problems of noise and speech cancellation in high-noise environments. Therefore, the independent claims of US Patent 9,031,259 would likely have been obvious under 35 U.S.C. § 103 prior to its priority date.

As a technical patent analyst, I have searched the USPTO database for US Patent 9,031,259 to determine its term adjustments, extensions, related applications, and projected expiration date.

US Patent 9,031,259: Noise Reduction Apparatus, Audio Input Apparatus, Wireless Communication Apparatus, and Noise Reduction Method

• Filing Date: September 14, 2012
• Issue Date: May 12, 2015
• Original Expiration Date (20 years from filing): September 14, 2032 (calculated as 20 years from the filing date of September 14, 2012).

Patent Term Adjustments (PTA):
The Google Patents record for US9031259 indicates an "Adjusted expiration" date of November 14, 2033. This suggests that Patent Term Adjustment (PTA) has been granted. PTA compensates for administrative delays by the USPTO during patent prosecution. The adjustment of the expiration date from September 14, 2032, to November 14, 2033, indicates approximately 1 year and 2 months of PTA.

Patent Term Extensions (PTE):
There is no information within the provided patent text or the search results to indicate that US9031259 has been granted any Patent Term Extensions (PTE) under 35 U.S.C. § 156. PTE is typically granted for delays incurred in obtaining regulatory approval for certain products, such as pharmaceuticals.

Continuation Applications, Divisional Applications, and Related Family Members:
The patent text mentions U.S. patent application Ser. No. 13/302,040 and U.S. patent application Ser. No. 13/364,016 as prior art references for speech segment determination techniques. These are patent applications rather than issued patents that are continuations or divisionals of US9031259.

The Google Patents page for US9031259 lists "US20130073283A1" as "Other versions." This is a patent application publication, which is likely the publication of the original application for US9031259. It is not a continuation or divisional application in the sense of a separate, later-filed application claiming priority to the same parent.

There is no explicit mention of continuation or divisional applications of US9031259 within the provided text or readily available in the Google Patents summary.

Projected Expiration Date:
Based on the "Adjusted expiration" information from the Google Patents record, the projected expiration date for US9031259 is November 14, 2033. This date includes any Patent Term Adjustment granted.

The search results confirm the existence of US Patent 9,031,259 and its active status, as well as providing links to USPTO search tools. This aligns with the provided authoritative patent text. I can now proceed with generating the defensive disclosure.

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Defensive Disclosure for US Patent 9,031,259

Patent Title: Noise reduction apparatus, audio input apparatus, wireless communication apparatus, and noise reduction method

Current Assignee: Soundclear Technologies LLC
Original Assignee: JVCKenwood Corp
Inventor: Takaaki Yamabe
Filing Date: September 14, 2012
Issue Date: May 12, 2015
Adjusted Expiration: November 14, 2033

This Defensive Disclosure document aims to preempt future patenting efforts by competitors on incremental improvements related to US Patent 9,031,259. By technically detailing various derivative implementations and combinations with open-source standards, we assert these concepts as obvious or non-novel to a person having ordinary skill in the art, thereby establishing prior art.

Derivative Variations

The following derivatives expand upon the core concepts of US Patent 9,031,259, which include a speech segment determiner, a voice direction detector, and an adaptive filter operating on signals from at least two microphones.

1. Material & Component Substitution: Multi-Element MEMS Microphone Array with Digital Beamforming

Enabling Description:
This derivative replaces the discrete main microphone (11) and sub-microphone (12) of US9031259 with a spatially distributed array of micro-electro-mechanical systems (MEMS) microphones, specifically a uniform linear array (ULA) or a uniform circular array (UCA) comprising N (where N > 2) MEMS elements. Each MEMS microphone integrates its own analog-to-digital converter (ADC) operating at a sampling rate of 48 kHz and 24-bit resolution, outputting a pulse-density modulation (PDM) or I²S digital stream. A Field-Programmable Gate Array (FPGA) or a high-performance Digital Signal Processor (DSP) (e.g., Texas Instruments C66x series or Analog Devices SHARC series) is employed for signal aggregation and digital beamforming.
The speech segment determiner (15) and voice direction detector (16) functionalities are integrated within this DSP/FPGA. The voice direction detection is achieved not merely by phase or power difference between two discrete microphones, but by implementing advanced digital beamforming algorithms such as Minimum Variance Distortionless Response (MVDR) or Generalized Sidelobe Canceller (GSC) on the N-element array. This allows for real-time estimation of the Angle of Arrival (AoA) of speech signals with sub-degree precision. The adaptive filter (18) then receives not just two raw microphone signals, but a focused main lobe signal (derived from beamforming towards the detected AoA) and one or more spatially filtered noise reference signals (derived from nulling beamformers or side lobes directed away from the AoA). The adaptive coefficient adjuster (74) within the adaptive filter (18) uses an improved Normalized Least Mean Squares (NLMS) or Recursive Least Squares (RLS) algorithm, which adapts its coefficients based on the cleaner, beamformed noise reference, thereby enhancing noise reduction in complex multi-source noise environments. The system could further incorporate an ultrasonic transducer for detecting gestures (e.g., pointing) that can inform the beamforming directionality, effectively making the microphone 'steerable' by user input in a non-contact manner.

graph TD
    M_MEMS1[MEMS Mic 1 (with ADC)] --> FPGA_DSP
    M_MEMSN[MEMS Mic N (with ADC)] --> FPGA_DSP
    FPGA_DSP -- Digital Beamforming (MVDR/GSC) --> BF_OUTPUT(Beamformed Signals)
    BF_OUTPUT -- Main Lobe (Voice) --> Adaptive_Filter
    BF_OUTPUT -- Nulling Beamformers (Noise References) --> Adaptive_Filter
    FPGA_DSP -- AoA Estimation --> VDD(Voice Direction Detector)
    FPGA_DSP -- Speech Features --> SSD(Speech Segment Determiner)
    SSD -- Speech Segment Info --> VDD
    VDD -- Voice Incoming-Direction Info --> AFC(Adaptive Filter Controller)
    SSD -- Speech Segment Info --> AFC
    AFC -- Control Signal --> Adaptive_Filter
    Adaptive_Filter -- Low-Noise Signal --> Output

2. Operational Parameter Expansion: Ultra-Low Latency Noise Reduction for Safety-Critical High-Noise Environments

Enabling Description:
This derivative of the noise reduction apparatus (1) is optimized for ultra-low latency operation in extremely high-noise, safety-critical industrial environments, such as active factory floors or machinery operation zones. The system employs main and sub-microphones (11, 12) featuring custom-designed piezoelectric transducers with a flat frequency response from 20 Hz to 20 kHz, coupled to ultra-fast 32-bit ADCs sampling at 192 kHz. The entire signal processing chain, including the speech segment determiner (15), voice direction detector (16), and adaptive filter (18), is implemented on a dedicated Application-Specific Integrated Circuit (ASIC) with parallel processing units and hard-wired logic, bypassing general-purpose DSPs to minimize instruction cycles.
The speech segment determiner (15) utilizes a spectral flux and energy-based Voice Activity Detection (VAD) algorithm, coupled with a pre-trained Support Vector Machine (SVM) classifier for human speech, achieving detection within 5 milliseconds. The voice direction detector (16) employs a generalized cross-correlation with phase transform (GCC-PHAT) algorithm running on sub-millisecond windows to detect voice direction, aiming for a total latency of less than 10 milliseconds from sound capture to voice incoming-direction information (25) output. The adaptive filter (18) is an optimized FIR filter (as shown in FIG. 6) with a very short impulse response (e.g., 64 taps or less) and uses a fast-converging adaptive algorithm like fast block LMS or Kalman filtering variants, tuned for low computational overhead per sample. Crucially, the adaptive filter controller (17) is programmed to not only adjust filter coefficients based on speech segment and direction but also to integrate real-time acoustic event detection (e.g., emergency alarms, shouts) within the noise-dominated signal (82). If an emergency event is detected, the system prioritizes the transmission of the raw, unprocessed voice signal (81) or a minimally filtered signal, ensuring critical safety alerts are not attenuated, irrespective of ongoing noise reduction, with an emergency bypass latency of less than 2 milliseconds. This architecture allows for reliable voice communication and critical alert identification in environments with sustained broadband noise exceeding 90 dB SPL.

sequenceDiagram
    participant Mic1 as Main Mic (11)
    participant Mic2 as Sub Mic (12)
    participant ADCs as Ultra-Fast ADCs (13, 14)
    participant ASIC as ASIC (SSD, VDD, AF)
    participant AF_Control as Adaptive Filter Controller (17)
    participant Output as Low-Noise Output (27)

    Mic1->>ADCs: Analog Signal (20 Hz-20 kHz)
    Mic2->>ADCs: Analog Signal (20 Hz-20 kHz)
    ADCs->>ASIC: Digital Signals (192 kHz, 32-bit)
    ASIC->>ASIC: Speech Segment Determination (<5ms)
    ASIC->>ASIC: Voice Direction Detection (GCC-PHAT) (<10ms total)
    ASIC->>AF_Control: Speech Segment Info (24)
    ASIC->>AF_Control: Voice Direction Info (25)
    AF_Control->>ASIC: Control Signal (26)
    ASIC->>ASIC: Adaptive Filtering (FIR, Fast Block LMS/Kalman)
    ASIC-->>ASIC: Emergency Event Detection
    ASIC->>Output: Low-Noise Signal (27) (<10ms latency)
    ASIC->>Output: Emergency Bypass (raw/min. filtered) (<2ms latency if emergency)

3. Cross-Domain Application: Noise Reduction for Submersible Acoustic Monitoring Systems

Enabling Description:
This derivative applies the noise reduction principles of US9031259 to an underwater acoustic monitoring system deployed on an autonomous underwater vehicle (AUV) or a fixed seabed observatory. The "first microphone" (11) and "second microphone" (12) are replaced with a pair of spatially separated hydrophones (e.g., RESON TC4032), tuned for underwater sound capture (typically 1 Hz to 100 kHz). The hydrophones are housed in pressure-resistant, acoustically transparent casings and mounted with a precise baseline separation of 0.5 to 2 meters for optimal phase difference detection. The A/D converters (13, 14) are specialized for high-resolution (32-bit) underwater acoustic data, sampling at 250 kHz to capture a broad range of marine sounds, including marine mammal vocalizations and anthropogenic noise.
The "speech segment determiner" (15) is reconfigured as an "acoustic event determiner," trained to identify specific underwater acoustic events, such as cetacean clicks and whistles, ship engine noise, or active sonar pings, based on their unique spectral and temporal characteristics. This uses a combination of Mel-frequency cepstral coefficients (MFCCs) for feature extraction and a Gaussian Mixture Model (GMM) or a Convolutional Neural Network (CNN) for classification. When a target acoustic event (e.g., specific marine mammal vocalization) is detected, the "voice direction detector" (16) becomes an "acoustic event direction detector," employing time difference of arrival (TDOA) or beamforming techniques (e.g., steered response power-phase transform, SRP-PHAT) on the hydrophone signals to determine the bearing and range of the acoustic source. The "adaptive filter" (18) performs noise reduction to enhance the target acoustic event against background ocean noise (e.g., wave action, current noise, biological noise from other species). The adaptive filter controller (17) adjusts the filter coefficients to dynamically suppress identified noise sources while preserving or enhancing the target acoustic event signal, enabling clearer detection, tracking, and analysis of marine activity.

classDiagram
    class UnderwaterSystem {
        +Hydrophone_1: object
        +Hydrophone_2: object
        +SpecializedADCs: object
        +AcousticEventDeterminer: object
        +EventDirectionDetector: object
        +AdaptiveFilter: object
        +FilterController: object
        +OutputSignal: object
    }
    class Hydrophone_1 {
        +captureSound(signal)
    }
    class Hydrophone_2 {
        +captureSound(signal)
    }
    class SpecializedADCs {
        +convertAnalogToDigital(analog_signal): digital_signal
    }
    class AcousticEventDeterminer {
        -MFCC_FeatureExtractor: object
        -CNN_Classifier: object
        +determineEvent(digital_signal): event_info
    }
    class EventDirectionDetector {
        -TDOA_Algorithm: object
        -SRP_PHAT_Algorithm: object
        +detectDirection(digital_signal_1, digital_signal_2): direction_info
    }
    class AdaptiveFilter {
        +processSignals(signal_1, signal_2, control_info): low_noise_signal
    }
    class FilterController {
        +generateControl(event_info, direction_info): control_signal
    }

    Hydrophone_1 "1" -- "1" UnderwaterSystem
    Hydrophone_2 "1" -- "1" UnderwaterSystem
    SpecializedADCs "1" -- "1" UnderwaterSystem
    AcousticEventDeterminer "1" -- "1" UnderwaterSystem
    EventDirectionDetector "1" -- "1" UnderwaterSystem
    AdaptiveFilter "1" -- "1" UnderwaterSystem
    FilterController "1" -- "1" UnderwaterSystem

    Hydrophone_1 --> SpecializedADCs
    Hydrophone_2 --> SpecializedADCs
    SpecializedADCs --> AcousticEventDeterminer
    SpecializedADCs --> EventDirectionDetector
    AcousticEventDeterminer --> EventDirectionDetector : event_info
    AcousticEventDeterminer --> FilterController : event_info
    EventDirectionDetector --> FilterController : direction_info
    FilterController --> AdaptiveFilter : control_signal
    SpecializedADCs --> AdaptiveFilter : digital_signals
    AdaptiveFilter --> UnderwaterSystem : output_signal

4. Integration with Emerging Tech: AI-Driven Contextual Noise Reduction with IoT Sensor Fusion

Enabling Description:
This derivative integrates US9031259's noise reduction principles with an AI-driven system that leverages IoT sensor fusion for dynamic, contextual noise reduction. The apparatus (1) includes the main microphone (11) and sub-microphone (12) but augments them with a suite of IoT environmental sensors, including an accelerometer (for device movement/vibration), a barometer (for atmospheric pressure changes indicating weather or indoor/outdoor shifts), a thermistor (temperature), a hygrometer (humidity), and an ambient light sensor. All sensor data, alongside audio signals, are fed into a central processing unit (CPU) equipped with a Neural Processing Unit (NPU) for machine learning inference.
The speech segment determiner (15) is implemented as a Deep Neural Network (DNN) that not only analyzes audio features (like MFCCs and spectral contrast) but also incorporates contextual features from the IoT sensors (e.g., high accelerometer readings might suggest device movement, affecting noise profiles). This DNN is continuously updated via federated learning from a distributed network of similar devices, allowing it to adapt to diverse acoustic and environmental contexts for more accurate speech detection. The voice direction detector (16) operates as described in US9031259, but its output is also fed into a secondary AI module. This module, based on a Reinforcement Learning (RL) agent, dynamically optimizes the adaptive filter's (18) coefficients and algorithms (e.g., switching between NLMS, RLS, or a deep learning-based filter) based on the detected speech segment, voice incoming direction, and the full suite of environmental context data. For instance, if the barometer indicates high wind and the accelerometer detects rapid movement, the RL agent might prioritize a wind noise cancellation algorithm. Conversely, in a quiet, stable indoor environment, it might select an algorithm optimized for subtle reverberation reduction. The system further employs edge computing for immediate processing and only offloads complex model retraining or aggregated, anonymized data to a cloud-based server.

graph LR
    Mic1[Main Mic (11)] --> CPU_NPU
    Mic2[Sub Mic (12)] --> CPU_NPU
    IoT_Sensors[IoT Environmental Sensors] --> CPU_NPU
    CPU_NPU -- Audio + Sensor Data --> DNN_SSD(Deep Neural Network Speech Segment Determiner)
    CPU_NPU -- Audio Signals --> VDD(Voice Direction Detector)
    DNN_SSD -- Speech Segment Info (24) --> RL_Agent(Reinforcement Learning Agent)
    VDD -- Voice Incoming-Direction Info (25) --> RL_Agent
    IoT_Sensors -- Contextual Data --> RL_Agent
    RL_Agent -- Adaptive Filter Control (26) --> Adaptive_Filter(Adaptive Filter 18)
    CPU_NPU -- Audio Signals --> Adaptive_Filter
    Adaptive_Filter -- Low-Noise Output (27) --> Output
    RL_Agent -- Anonymized Data (for retraining) --> Cloud_Federated_Learning
    Cloud_Federated_Learning -- Model Updates --> CPU_NPU

5. The "Inverse" or Failure Mode: Privacy-Preserving Low-Power Obfuscation Mode

Enabling Description:
This derivative implements a "privacy-preserving low-power obfuscation mode" for the noise reduction apparatus (1) of US9031259. In this mode, the primary goal is to indicate the presence of speech and its direction while intentionally rendering the speech content non-intelligible, all while minimizing power consumption. The main microphone (11) and sub-microphone (12) are utilized, but the A/D converters (13, 14) operate at a reduced sampling rate (e.g., 4 kHz) and lower bit depth (e.g., 8-bit) to conserve energy.
When the speech segment determiner (15) detects speech (which may use a simplified, lower-computation VAD algorithm in this mode), the voice direction detector (16) still attempts to determine the incoming direction. However, the adaptive filter (18) is controlled by the adaptive filter controller (17) to perform a modified "noise reduction" process. Instead of clarity, it applies a real-time speech obfuscation algorithm. This algorithm involves dynamic pitch shifting (e.g., randomizing pitch by +/- 2 semitones every 100ms), formant blurring (e.g., applying a variable-Q comb filter to smear formants), and a time-domain scrambling technique (e.g., segmenting the speech into 50ms blocks and randomly reordering them within a short buffer, 200ms). This renders the speech incomprehensible to a human listener while preserving enough information (e.g., prosody, rhythm, presence of voiced/unvoiced segments, and source directionality) for an external system to infer conversational activity or source location without exposing content. If the voice incoming-direction information (25) indicates speech from an "unauthorized" direction (e.g., not facing the device), the obfuscation level may be further increased. The system operates on a low-power microcontroller with specialized audio codecs to execute these functions with minimal energy draw, suitable for always-on, privacy-sensitive applications.

stateDiagram
    [*] --> Standby: Power On
    Standby --> Low_Power_Listen: User/System Command
    Low_Power_Listen --> Speech_Detected: VAD detects speech (low-power)
    Speech_Detected --> Obfuscation_Mode: Speech Segment Info + Direction Info
    Obfuscation_Mode --> Low_Power_Listen: Speech Ends or Timer Expired
    Obfuscation_Mode --> Enhanced_Obfuscation: Unauthorized Direction/High Sensitivity
    Enhanced_Obfuscation --> Obfuscation_Mode: Direction Clears
    Low_Power_Listen --> Standby: User/System Command

    state Speech_Detected {
        Speech_Detected: Simplified VAD
        Speech_Detected: Voice Direction Detection
    }
    state Obfuscation_Mode {
        Obfuscation_Mode: Reduced Sample Rate/Bit Depth
        Obfuscation_Mode: Dynamic Pitch Shifting
        Obfuscation_Mode: Formant Blurring
        Obfuscation_Mode: Time-Domain Scrambling
    }
    state Enhanced_Obfuscation {
        Enhanced_Obfuscation: Increased Randomization
        Enhanced_Obfuscation: Additional Noise Insertion
    }

Combination Prior Art Scenarios

Here are three scenarios combining the core inventive concepts of US Patent 9,031,259 with existing open-source standards, demonstrating obviousness for certain applications:

1. US9031259 + Opus Audio Codec for Real-time Communication:

   • Description: The noise reduction apparatus (1) processes audio input from its microphones (11, 12) through the speech segment determiner (15), voice direction detector (16), and adaptive filter (18) to produce a low-noise output signal (27). This low-noise signal is then directly fed into an encoder implementing the Opus interactive audio codec (RFC 6716). Opus is a high-quality, open-source audio codec widely used for real-time applications like VoIP, videoconferencing, and in-game communication, known for its low latency and excellent quality at various bitrates. Combining the noise reduction capabilities of US9031259 (especially the contextual adaptation based on speech segment and direction) with Opus's efficient compression and error concealment would be an obvious choice for any modern real-time communication system, providing clear voice transmission even in noisy environments with minimal bandwidth overhead. The speech segment information (24) could also inform Opus's Voice Activity Detection (VAD) mode, and the voice incoming-direction information (25) could be used for spatial audio rendering at the receiving end, enhancing immersion and intelligibility.

2. US9031259 + MQTT Protocol for IoT Acoustic Sensing:

   • Description: An audio input apparatus (500) incorporating the noise reduction apparatus (1) (as shown in FIG. 9 or FIG. 10) is deployed as an IoT acoustic sensor in environments such as smart homes, industrial monitoring, or public spaces. After the adaptive filter (18) produces the low-noise output signal (27), instead of direct transmission, a metadata extraction module processes this signal. This module extracts key features like the presence of speech, the detected voice incoming direction, and the estimated signal-to-noise ratio (SNR) of the speech segment. These metadata, potentially along with highly compressed (e.g., using a low-bitrate Opus stream) or anonymized audio snippets, are then formatted into JSON payloads and published via the Message Queuing Telemetry Transport (MQTT) protocol (ISO/IEC PRF 20922) to a central MQTT broker. MQTT is an open-source, lightweight messaging protocol ideal for IoT devices with limited resources and unreliable networks. The combination allows for efficient, event-driven transmission of critical acoustic information (e.g., "speech detected from North, moderate SNR," or "emergency shout detected from East") without continuously streaming high-bandwidth audio, enabling scalable and context-aware acoustic monitoring systems.

3. US9031259 + WebRTC for Enhanced Browser-Based Communication:

   • Description: A wireless communication apparatus (600) (as shown in FIG. 10) or an audio input apparatus (500) integrates the noise reduction apparatus (1) and serves as an audio capture front-end for a browser-based WebRTC (Web Real-Time Communication) application. WebRTC is an open-source project that enables real-time communication (voice, video, data) directly within web browsers and mobile applications via simple APIs. The low-noise output signal (27) from the adaptive filter (18) of US9031259 is provided as the audio input stream to the WebRTC getUserMedia() API. This means that any browser-based communication using WebRTC would inherently benefit from the advanced noise reduction and voice-direction-aware processing of the apparatus. Furthermore, the speech segment information (24) and voice incoming-direction information (25) could be exposed via custom WebRTC data channels or metadata tracks, allowing the remote WebRTC client or server to implement further enhancements like spatial audio rendering, intelligent speaker detection, or adaptive user interface elements, making the in-browser communication significantly clearer and more context-aware, particularly in noisy user environments.