Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
1. This in response to an amendment filed 06/22/2026. No claims have been amended. Claims 14-15 have been added. No claims have been canceled. Claims 1-15 are now pending in this application.
Allowable Subject Matter
2. Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of record fails to teach or fairly suggest the claimed “circuitry is configured to stop the adaptive operation of the adaptive filter and the output of the noise cancellation sound while performing the noise separation process” as recited in claim 15. Accordingly, claim 15 contains allowable subject matter.
Claim Rejections - 35 USC § 103
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US PAT # 9,578,415 B1) in view of Tackett (Pub.No.: 2025/0078798 A1).
Regarding claim 1, Zhou teaches an active noise control system (reads on noise cancellation system for cancelling ambient noise in an audio device, see col. lines 25-30) comprising:
a microphone configured to output a collected sound as a monitoring signal (reads on reference and error microphones receiving ambient sound signals used in ANC processing, see col. 2, lines 10-18);
a speaker configured to output a noise cancellation sound (reads on transducer generating anti-noise to cancel detected ambient noise, see col. 3, lines 1-8);
an adaptive filter (reads on adaptive filter 32 receiving a reference signal and producing an anti-noise signal, see col. 6, lines 45-55);
a storage (see col.6, lines 45-44 and col. 7, lines 5-20); and
perform an adaptive operation to adapt the monitoring signal as an error (reads on updating Adaptive filter coefficients using an error microphone signal to minimize noise, see col. 7, lines 5-20).
Zhou features already y addressed in the rejection of claim 1. Zhou does not teach explicitly “circuitry configured to perform a noise separation process at a predetermined timing, the noise separation process including separating a noise component from the monitoring signal, and
storing, in the storage, a pseudo noise signal representing the noise component, wherein the adaptive filter is configured to receive an input of the pseudo noise signal stored in the storage, and generate the noise cancellation sound to be output from the speaker based on the pseudo noise signal”.
However, Tackett teaches deriving noise-related signals from microphone measurements, selecting stored signal models representing nowise characteristics, and filtering reference signals using such stored models in adaptive noise cancellation processing (see [0025], [0028] and [0101]-[0105]). Note that discloses that the anti-noise signal y[n], after being filtered by the estimated secondary -path characteristic, is subtracted from the error signal e[n], resulting in an estimated noise signal dp[n] at the error microphone (see Fig. 2 and [0028]). Thus, deriving or separating noise-related component from a microphone-derived signal. In addition, Tackett further teaches storing ANC-related signal information for subsequent use in adaptive noise-control processing, including a bank of pre-stored impulse responses stored in memory and loading selected response coefficients into an adaptation unit for controlling the adaptive filter (see [0053], [0060-0062] and [0103-105]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the ANC system of Zhou to incorporate Tackett’s noise-estimation and stored ANC-information techniques so that a noise-related component derived from a monitored signal may be retrained and used in the adaptive noise-control operation, in order to improve the accuracy and stability of the adaptive noise-cancellation processing.
Independent claim 2 is rejected for the same reasons as set forth for claim 1. Note that claim 2 recites the ANC system of claim 1 with signal processing performed in the frequency domain (Tackett already teaches processing microphone-derived signals using signal-domain transformation within ANCY systems, see [0028] and [0101]. Thus, it would have been obvious to implement the ANC processing of Zhou using the frequency domain signal processing technique taught by Tackett).
Regarding claim 3, the combination of Zhou and Tackett teaches wherein the circuitry is configured to convert the output of the microphone to the frequency domain signal by a fast Fourier transform, and output the frequency domain signal as the monitoring signal (Tackett teaches transforming signals between domains for ANC processing, see [0101]), and
convert the output of the adaptive filter to the time domain signal by an inverse fast Fourier transform (Tackett teaches converting transformed signals back to time domain for output, see [0101]).
Regarding claim 4, Zhou teaches an active noise control system (reads on ANC system for cancelling ambient noise, see col. lines 25-30) comprising:
a microphone Zhou teaches reference and error microphones used in in ANC (see col. 2, lines 10-18);
a speaker configured to output a noise cancellation sound (reads on transducer generating anti-noise signal, see col. 3, lines 1-8);
an adaptive filter (reads on adaptive filter receiving reference signal, see col. 6, lines 45-55); and
a storage (see col.6, lines 45-44 and col. 7, lines 5-20), and
perform an adaptive operation to adapt the monitoring signal as an error (reads on updating filter coefficients to minimize noise, see col. 7, lines 5-20).
Zhou features already addressed in the rejection of claim 1. Zho does not teach explicitly “circuitry configured to convert an output of the microphone to a weighting factor of a predetermined transformation function that generates a time domain signal by weighting an orthonormal basis, and output the weighting factor as a monitoring signal; wherein the circuitry is configured to perform a noise separation process at a predetermined timing, the noise separation process including separating a noise component from the monitoring signal and storing the noise component in the storage as a pseudo noise signal, wherein the adaptive filter is configured to receive an input of the pseudo noise signal stored in the storage, and wherein the circuitry is configured to convert an output of the adaptive filter to the time domain signal by the transformation function, and generate the noise cancellation sound to be output from the speaker”.
However, Tackett teaches processing microphone-derived signals using signal transformations and model-based representations for adaptive noise cancellation processing (see [0028] and [0101]-[0103]). Tackett further teaches selecting stored signal models representing noise characteristics and using such models during adaptive filtering (see [0103]-[0105]).
Thus, it would have been obvious to a person of an ordinary skill in the art before the effective filing date of the claimed invention to modify the ANC system of Zhou to incorporate the transformation-based signal representation techniques taught by Tackett in order to improve signal processing accuracy and adaptive noise cancellation performance, since transform-domain signal processing is a known-technique for improving adaptive filtering efficiency.
Regarding claim 5, the combination of Zhou and Tackett teaches wherein the circuitry is configured to
convert the output of the microphone to a frequency domain signal by a fast Fourier transform, and output the frequency domain signal as the monitoring signal (Tackett teaches transformation of microphone signals using signal-domain conversion, see [0101]), and
convert an output of the adaptive filter to the time domain signal by an inverse fast Fourier transform (Tackett teaches converting transformed signals back to time-domain signals, see [0101]).
Regarding claim 6, the combination of Zhou and Tackett teaches wherein the circuitry is configured to
convert the output of the microphone to the weighting factor by a wavelet transform, and output the weighting factor as the monitoring signal (Tackett teaches signal transformation and representation for processing noise signals, see [0101]-[0103]), and
convert the output of the adaptive filter to the time domain signal by an inverse wavelet transform (Tackett teaches conversion of transformed signals back for output generation, see [0101]).
Regarding claim 7, the combination of Zhou and Tackett teaches wherein the circuitry is configured to perform the noise separation process at the predetermined timing at which the output of the microphone becomes greater than a predetermined level (reads on updating ANC filter operation based on microphone-derived error signal levels, see col. 7, lines 5-20).
Regarding claim 8, the combination of Zhou and Tackett teaches wherein the circuitry is configured to separate the noise component from the monitoring signal by independent component analysis (Tackett teaches analyzing microphone-derived signals to estimate noise components, see [0025] and [0028]).
Regarding claim 9, the combination of Zhou and Tackett teaches wherein the circuitry is configured to infer and separate the noise component from the monitoring signal by a neural network that is preliminarily trained to infer the noise component from the monitoring signal (reads on Tackett estimating noise-related signals derived from microphone inputs, see [0101]-[0103]).
Regarding claim 10, the combination of Zhou and Tackett teaches wherein the circuitry is configured to calculate a principal component having a predetermined order of the monitoring signal by performing principal component analysis, and separate the principal component as the noise component (reads on Tackett signal estimation and analysis to derive noise-related signal components, see [0028]).
Independent claim 11 is rejected for the same reasons as set forth for claim 4. Note that claim 11 recites the ANC system of claim 4 in which a pseudo noise signal is preliminarily stored and used by the adaptive filter. Tackett teaches selecting a pre-stored impulse response from a plurality of stored impulse responses used for ANC filtering (see [0103]-[0105]).Therefore, it would have been obvious to store the pseudo-noise signal representation used by the adaptive filter in the ASNC system of Zhou.
Regarding claim 12, the combination of Zhou and Tackett teaches wherein the circuitry is configured to
convert the output of the microphone to the weighting factor by a fast Fourier transform, and output the weighting factor as the monitoring signal (Tackett teaches transform domain processing of microphone signals, see [0101]), and
convert the output of the adaptive filter to the time domain signal by an inverse fast Fourier transform (Tackett teaches converting processed signals back to time-domain signals).
Regarding claim 13, the combination of Zhou and Tackett teaches wherein the circuitry is configured to
convert the output of the microphone to the weighting factor by a wavelet transform, and output the weighting factor as the monitoring signal (reads on representing signals in transformed domain, see Tackett, [0101]), and
convert the output of the adaptive filter to the time domain signal by an inverse wavelet transform (reads on generating output signals after inverse transformation see Tackett [0101]).
Regarding claim 14, the combination of Zhou and Tackett teaches wherein the adaptive filter is configured to generate the noise cancellation sound by filtering the pseudo noise signal (Zhou teaches adaptive filter 323 filtering the reference microphone signal to generate a feedforward anti-noise component, see Fig. 3A and corresponding discussion at col. 6. Also, Tackett similarly teaches adaptive filter W(z)) filtering reference signal x[n] to generate anti-noise signal y[n], see Fig. 1 and [0025]).
Response to Arguments
4. Applicant's arguments filed 06/22/2026 have been fully considered but they are not persuasive.
Applicant argues that Tackett merely stores secondary-path impulse responses and tuning parameters and therefore does not teach the claimed separation and storage of pseudo noise signal. However, Applicant’s characterization does not account for Tackett’s complete disclosure, because Tackett expressly teaches deriving an estimated noise signal dp[n] from a microphone-derived error signal by subtracting an estimated anti-0noise contribution from the error signal (see Fig. 2 and [0028] of Tackett). Thus, Tackett not limited to merely storing tuning parameters, but expressly performs signal processing that isolates an estimated noise components form a microphone-derived signal.
Tackett further teaches retaining ANC-related signal information for subsequent use in adaptive noise-control processing, including a bank of pre-stored impulse responses stored in memory and loading selected response coefficients into an adaptation unit for controlling the adaptive filter (see [0053], [0060-0062] and [0103-105]).
The rejection does mot require that Tackett’s stored impulse response itself be constructed as an audio waveform. Rather, Zhou supplies the underlying adaptive ANC architecture, while Tackett teaches both derivation of an estimated noise component and storage/reuse of ANC information in the adaptive-filter operation.
Conclusion
5. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth
In 37 CFR 1.136(a).
shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.
In the event a first reply is filed within TWO MONTHS of the mailing date of this final. action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rasha S. AL-Aubaidi whose telephone number is (571) 272-7481. The examiner can normally be reached on Monday-Friday from 8:30 am to 5:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ahmad Matar, can be reached on (571) 272-7488.
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/RASHA S AL AUBAIDI/Primary Examiner, Art Unit 2693
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