DETAILED ACTION
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 .
This Office Action is in response to correspondence filed 04 February 2025 in reference to application 19/101,189. Claims 1-24 are pending and have been examined.
Response to Amendment
The preliminary amendment filed 04 February 2025 has been accepted and considered in this office action. Claims 4-8, 12-16, and 20-24 have been amended.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dusan et al. (US Patent 10,332,538) in view of Pandey et al. (US PAP 2022/0232342).
Consider claim 1, Dusan teaches a method (abstract) comprising:
receiving a far-field audio signal from a far-field microphone arrangement and a near- field audio signal from a near-field microphone arrangement, the far-field microphone arrangement being at a greater distance from an audio source than the near-field microphone arrangement (figure 1, figure 2, col 3 line 64- col 4 line 18, receiving speech from a remote microphone near a speaker and a local microphone at listening device);
synchronizing the far-field audio signal and the near-field audio signal (col 4 lines 19-31, signals are delayed in order to time align the two signals);
processing the far-field audio signal and the near-field audio signal to remove noise artifacts to output an output audio signal with the noise artifacts removed (col 4 lines 44-56, performing noise suppression processing on the signals in order to generate a noise reduced audio signal).
Dusan does not specifically teach
encoding the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal; and
decoding the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed.
In the same field of multichannel noise suppression, Pandey teaches
encoding the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal (figure 9, 0110-112, encoding multichannel noisy speech signal. 0094, may include signals from microphones far away from other microphones); and
decoding the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed (figure 9, 0110-112, decoding encoded signal into clean speech signal).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use the encoder-decoder archecture as taught by Pandey to perform noise filtering in the system of Dusan in order to improve the performance of the noise reduction filtering (Pandey 0007).
Consider claim 2, Pandey teaches the method of claim 1, wherein encoding the far-field audio signal and the near-field audio signal comprises:
transforming the far-field audio signal and the near-field audio signal into image representations of the far-field audio signal and the near-field audio signal (0111, using STFT transform to generate spectral representations of audio signals); and
processing the image representations through a machine learning module to output encoded audio signals with the noise artifacts removed (0110-11, encoder blocks based on CNN architecture).
Consider claim 3, Pandey teaches the method of claim 2, wherein the machine learning module is a convolutional neural network (0110-11, encoder blocks based on CNN architecture).
Consider claim 4, Pandey teaches the method of claim 2, wherein transforming the far- field audio signal and the near-field audio signal comprises performing a short-time Fourier transform on the far-field audio signal and the near-field audio signal to output the image representations of the far-field audio signal and the near-field audio signal (0111, using STFT transform to generate spectral representations of audio signals).
Consider claim 5, Pandey teaches The method of claim 2, wherein decoding the far-field audio signal and the near-field audio signal comprises:
converting the encoded audio signals to image representations with the noise artifacts removed (0112 decoder generates clean speech representations); and
performing an inverse short-time Fourier transform on the image representations with the noise artifacts removed into the output audio signal with the noise artifacts removed (0112, inverse STFT to generate time domain waveform).
Consider claim 6, Dusan and Pandey teach The method of claim 1, wherein the noise artifacts include reverberation (Dusan col 3 lines 38-45, targeted noise may include reverberation, Pandey 0094-95 removing reverberation).
Consider claim 7, Dusan teaches The method of claim 1, wherein the near-field microphone arrangement includes one or more microphones on at least one of a phone, a tablet, an earbud, or a home assistant device (col 3 lines 45-50, remote microphone i.e. closest to speaker, near-field can be in a smartphone).
Consider claim 8, Dusan and Pandey teach the method of claim 1. Pandey further teaches wherein the far-field microphone arrangement is an array of a plurality of microphones (0041-42, using an array of microphones).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use a microphone array as taught by Pandey in the system of Dusan and Pandey in order to provide better audio source noise filtering (Pandey 0007).
Consider claim 9, Dusan teaches A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device (col 9 lines 42-50, software executable on processors, ), are configured to cause the at least one computing device to:
receive a far-field audio signal from a far-field microphone arrangement and a near- field audio signal from a near-field microphone arrangement, the far-field microphone arrangement being at a greater distance from an audio source than the near-field microphone arrangement (figure 1, figure 2, col 3 line 64- col 4 line 18, receiving speech from a remote microphone near a speaker and a local microphone at listening device);
synchronize the far-field audio signal and the near-field audio signal (col 4 lines 19-31, signals are delayed in order to time align the two signals);
process the far-field audio signal and the near-field audio signal to remove noise artifacts to output an output audio signal with the noise artifacts removed (col 4 lines 44-56, performing noise suppression processing on the signals in order to generate a noise reduced audio signal).
Dusan does not specifically teach
encoding the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal; and
decoding the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed.
In the same field of multichannel noise suppression, Pandey teaches
encoding the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal (figure 9, 0110-112, encoding multichannel noisy speech signal. 0094, may include signals from microphones far away from other microphones); and
decoding the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed (figure 9, 0110-112, decoding encoded signal into clean speech signal).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use the encoder-decoder archecture as taught by Pandey to perform noise filtering in the system of Dusan in order to improve the performance of the noise reduction filtering (Pandey 0007).
Claims 10 contains similar limitations as claim 2 and is therefore rejected for the same reasons.
Claims 11 contains similar limitations as claim 3 and is therefore rejected for the same reasons.
Claims 12 contains similar limitations as claim 4 and is therefore rejected for the same reasons.
Claims 13 contains similar limitations as claim 5 and is therefore rejected for the same reasons.
Claims 14 contains similar limitations as claim 6 and is therefore rejected for the same reasons.
Claims 15 contains similar limitations as claim 7 and is therefore rejected for the same reasons.
Claims 16 contains similar limitations as claim 8 and is therefore rejected for the same reasons.
Consider claim 17, Dusan teaches a system (abstract), comprising:
at least one processor (col 9 lines 42-50, software executable on processors); and
a non-transitory computer-readable medium comprising instructions that, when executed by the at least one processor, cause the system to implement a synchronization module, (col 9 lines 42-50, software executable on processors), wherein:
the synchronization module is configured to:
receive a far-field audio signal from a far-field microphone arrangement and a near- field audio signal from a near-field microphone arrangement, the far-field microphone arrangement being at a greater distance from an audio source than the near-field microphone arrangement (figure 1, figure 2, col 3 line 64- col 4 line 18, receiving speech from a remote microphone near a speaker and a local microphone at listening device);
synchronize the far-field audio signal and the near-field audio signal (col 4 lines 19-31, signals are delayed in order to time align the two signals);
process the far-field audio signal and the near-field audio signal to remove noise artifacts to output an output audio signal with the noise artifacts removed (col 4 lines 44-56, performing noise suppression processing on the signals in order to generate a noise reduced audio signal).
Dusan does not specifically teach an encoder module, a decoder module
the encoder module is configured to encode the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal; and
the decoder module is configured to decode the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed.
In the same field of multichannel noise suppression, Pandey teaches an encoder module, a decoder module (figure 9, 0110-112, encoder and decoder)
the encoder module is configured to encode the far-field audio signal and the near-field audio signal to remove noise artifacts from the far-field audio signal and the near-field audio signal (figure 9, 0110-112, encoding multichannel noisy speech signal. 0094, may include signals from microphones far away from other microphones); and
the decoder module is configured to decode the far-field audio signal and the near-field audio signal to output an output audio signal with the noise artifacts removed (figure 9, 0110-112, decoding encoded signal into clean speech signal).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use the encoder-decoder archecture as taught by Pandey to perform noise filtering in the system of Dusan in order to improve the performance of the noise reduction filtering (Pandey 0007).
Claims 18 contains similar limitations as claim 2 and is therefore rejected for the same reasons.
Claims 19 contains similar limitations as claim 3 and is therefore rejected for the same reasons.
Claims 20 contains similar limitations as claim 4 and is therefore rejected for the same reasons.
Claims 21 contains similar limitations as claim 5 and is therefore rejected for the same reasons.
Claims 22 contains similar limitations as claim 6 and is therefore rejected for the same reasons.
Claims 23 contains similar limitations as claim 7 and is therefore rejected for the same reasons.
Claims 24 contains similar limitations as claim 8 and is therefore rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Borgstrom et al. (US PAP 2023/0162758) teaches a similar encoder-decoder based noise filtering system.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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