Detailed Action
This communication is in response to the Application filed on 6/17/2024.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected
Claims 1, and 11 are independent are apparatus, method claims, respectively.
Apparent priority: 7/18/2023.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/17/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent Claims are directed to statutory categories:
Claim 1 is an apparatus claim and directed to the machine or manufacture category of patentable subject matter.
Claim 9 is a apparatus claim and directed to the process category of patentable subject matter.
Claim 11 is a method claim and is directed to the machine or manufacture category of patentable subject matter.
Regarding Independent Claim 1, Claim 1 recites,
1. An electronic apparatus comprising: a memory configured to store at least one instruction; and at least one processor connected to the memory to control the electronic apparatus, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
obtain a first audio signal including a voice signal and a noise signal; [This relates to a human receiving audio in the auditory system.]
convert the first audio signal in a time domain to a second audio signal in a frequency domain; [This relates to a human converting an audio signal to a frequency domain using pen and paper.]
obtain a first gain value representing a Signal-to-Noise Ratio (SNR) from the second audio signal; [This relates to a human obtaining a gain value using pen and paper.]
obtain a second gain value with a first dynamic range by filtering the first gain value; [This relates to a human obtaining a gain value using pen and paper.]
obtain a third gain value by inputting the second gain value to a neural network model trained to output a signal from which noise is removed; and [This relates to a human obtaining a gain value using pen and paper.]
convert the second audio signal to a third audio signal from which at least a portion of the noise signal is removed, using the third gain value. [This relates to a human converting an audio signal using pen and paper.]
The Dependent Claim does not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
Regarding Independent Claim 11, Claim 11 is a Method claim with limitations similar to that of claim 1 and is rejected under the same rationale.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements of “processor” and “memory” For example, in [0069] of the as filed specification, there is description of using the processor and memory as part of a CPU… computer machine. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using an apparatus processor and memory is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claim 2 recites,
“2. The electronic apparatus as claimed in claim 1, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction,
to back-convert the third audio signal in a frequency domain to a fourth audio signal in a time domain. [This relates to a human back-converting an audio signal using pen and paper.] No additional limitations present.
Dependent claim 3 recites,
“3. The electronic apparatus as claimed in claim 1, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction,
to convert the first audio signal to the second audio signal using Short-Time Fourier Transform (STFT).” [This relates to a human converting a audio signal using STFT using pen and paper.] No additional limitations present.
Dependent claim 4 recites,
“4. The electronic apparatus as claimed in claim 1, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
obtain at least one of a first noise value, a first posteriori SNR, or a first priori SNR based on a second audio signal; and [This relates to a human obtaining a noise value using pen and paper.]
obtain the first gain value based on at least one of the first noise value, the first posteriori SNR or the first priori SNR. [This relates to a human obtaining a gain value using pen and paper.] No additional limitations present.
Dependent claim 5 recites,
“5. The electronic apparatus as claimed in claim 4, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
obtain the first noise value from the second audio signal based on a first parameter stored in the memory; [This relates to a human obtaining a noise value using pen and paper.]
obtain the first posteriori SNR from the second audio signal and the first noise value based on a second parameter stored in the memory; [This relates to a human obtaining a SNR using pen and paper]
obtain the first priori SNR from second audio signal and the first posteriori SNR based on a third parameter stored in the memory; [This relates to a human obtaining a SNR using pen and paper]
and obtain the first gain value from the second audio signal and the first priori SNR based on a fourth parameter stored in the memory. [This relates to a human obtaining a gain value using pen and paper] No additional limitations present.
Dependent claim 6 recites,
“6. The electronic apparatus as claimed in claim 4, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
obtain a second noise value with a second dynamic range by filtering the first noise value; [This relates to a human obtaining a noise value using pen and paper]
obtain a second posterior SNR with a third dynamic range by filtering the first posteriori SNR; and [This relates to a human obtaining a SNR using pen and paper]
obtain a second priori SNR with a fourth dynamic range by filtering the first priori SNR. [This relates to a human obtaining a SNR using pen and paper] No additional limitations present.
Dependent claim 7 recites,
“7. The electronic apparatus as claimed in claim 6, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction,
to obtain the third gain value by inputting the second gain value, the second noise value, the second posterior SNR, and the second priori SNR to the trained neural network model. [This relates to a human obtaining a SNR using pen and paper.] No additional limitations present.
Dependent claim 8 recites,
“8. The electronic apparatus as claimed in claim 1, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
identify a noise component corresponding to the second audio signal based on the third gain value; and convert the second audio signal to the third audio signal by removing the noise component from the second audio signal. [This relates to a human identifying a noise component using vision or auditory processes] No additional limitations present.
Dependent claim 9 recites,
“9. The electronic apparatus as claimed in claim 1, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to:
identify the noise signal based on the first audio signal; [This relates to a human identifying a noise signal using vision or auditory processes]
generate a reverse noise signal based on the noise signal; [This relates to a human generating a reverse noise signal using pen and paper]
obtain a first filtering signal by combining the first audio signal and the reverse noise signal; and [This relates to a human obtaining a filtering signal using pen and paper]
convert the second audio signal to the third audio signal based on the first filtering signal and the third gain value. [This relates to a human converting an audio using pen and paper] No additional limitations present.
Dependent claim 10 recites,
“10. The electronic apparatus as claimed in claim 1, wherein the electronic apparatus further comprises a communication interface connected to an external device; and wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction,
to obtain the first audio signal from the external device through the communication interface. A communication interface device is an additional limitation.
As to Claim 12, Claim 12 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 13, Claim 13 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 14, Claim 14 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 15, Claim 15 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 16, Claim 16 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 17, Claim 17 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 18, Claim 18 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 19, Claim 19 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 20, Claim 20 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 4-10, 11, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shin (U.S. Patent Number US 20240304186 A1), in view of Ding (U.S. Patent Number US 20230154483 A1), and further in view of Shuang (U.S. Patent Number US 20230267947 A1).
Regarding Claim 1, Shin teaches 1. An electronic apparatus comprising: a memory configured to store at least one instruction; and at least one processor connected to the memory to control the electronic apparatus, wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: (see Shin [0025] “Various implementations can include a non-transitory computer readable storage medium storing instructions executable by a processor to perform a method such as one or more of the methods described herein. Yet other various implementations can include a system including memory and one or more hardware processors operable to execute instructions, stored in the memory, to perform a method such as one or more of the methods described herein.”) obtain a first audio signal including a voice signal and a noise signal; (see Shin [0003] “… the plurality of audio signals can include at least: a first audio signal collected by one or more microphones of a first client device (e.g., a smart phone), and a second audio signal collected by one or more microphones of a second client device”) convert the first audio signal in a time domain to a second audio signal in a frequency domain; (see Shin [0005] “In various implementations, the first audio signal can be processed to determine a first digital representation for the first audio signal, such as a spectrogram or other digital representation defining variation of a frequency of the first audio signal over time. The second audio signal can be processed to determine a second digital representation for the second audio signal, such as a spectrogram or other digital representation showing/defining variation of a frequency of the second audio signal over time. The first spectrogram can be processed using a trained neural network model as input, to generate a first output that indicates/reflects a predicted SNR for the first audio signal (may be referred to as “first SNR”). The predicted SNR for the first audio signal (“first SNR”) can be a ratio or numeric value predicting a strength of the first speech component relative to a strength of the first background noise component. Optionally, the first SNR can be determined based on the first output.”)
Shin does not specifically teach obtain a first gain value representing a Signal-to-Noise Ratio (SNR) from the second audio signal; obtain a second gain value with a first dynamic range by filtering the first gain value; However Ding does teach this limitation (see Ding [0179] “In some possible implementations, in addition to directly using the first initial Wiener gain factor and the second initial Wiener gain factor as the left channel Wiener gain factor and the right channel Wiener gain factor to construct the first improved frequency domain weighting function, a corresponding binary masking function may alternatively be constructed based on the first initial Wiener gain factor and the second initial Wiener gain factor, to obtain the first improved Wiener gain factor and the second improved Wiener gain factor. A frequency bin slightly affected by noise can be screened out by using the first improved frequency domain weighting function constructed by using the first improved Wiener gain factor and the second improved Wiener gain factor, improving ITD estimation precision of the stereo audio signal.”)
Shin and Ding are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus Shin to incorporate obtain a first gain value representing a Signal-to-Noise Ratio (SNR) from the second audio signal; obtain a second gain value with a first dynamic range by filtering the first gain value; of Ding. This allows improves sound quality as recognized by Ding [0006].
Shin in view of Ding does not specifically teach obtain a third gain value by inputting the second gain value to a neural network model trained to output a signal from which noise is removed; and convert the second audio signal to a third audio signal from which at least a portion of the noise signal is removed, using the third gain value. However, Shuang does teach this limitation (see Shuang [0017] FIG. 1 is a block diagram of a noise reduction system 100. The noise reduction system 100 may be implemented in a mobile device (e.g., see FIG. 2), such as a mobile telephone, a video camera with a microphone, etc. The components of the noise reduction system 100 may be implemented by a processor, for example as controlled according to one or more computer programs. The noise reduction system 100 includes a windowing block 102, a transform block 104, a band features analysis block 106, a neural network 108, a Wiener filter 110, a gain combination block 112, a band gains to bin gains block 114, a signal modification block 116, an inverse transform block 118, and an inverse windowing block 120. The noise reduction system 100 may include other components that (for brevity) are not described in detail.”)
Shin in view of Ding and Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified apparatus of combination of Shin and Ding to incorporate obtain a third gain value by inputting the second gain value to a neural network model trained to output a signal from which noise is removed; and convert the second audio signal to a third audio signal from which at least a portion of the noise signal is removed, using the third gain value of Shuang. This allows for improved estimation of the background noise as recognized by Shuang [0014-0033].
Regarding Claim 4, Shin in view of Ding and further in view of Shuang teaches 4. The electronic apparatus as claimed in claim 1,
Furthermore, Ding teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain at least one of a first noise value, a first posteriori SNR, or a first priori SNR based on a second audio signal; and obtain the first gain value based on at least one of the first noise value, the first posteriori SNR or the first priori SNR. (see Ding [0179] “In some possible implementations, in addition to directly using the first initial Wiener gain factor and the second initial Wiener gain factor as the left channel Wiener gain factor and the right channel Wiener gain factor to construct the first improved frequency domain weighting function, a corresponding binary masking function may alternatively be constructed based on the first initial Wiener gain factor and the second initial Wiener gain factor, to obtain the first improved Wiener gain factor and the second improved Wiener gain factor. A frequency bin slightly affected by noise can be screened out by using the first improved frequency domain weighting function constructed by using the first improved Wiener gain factor and the second improved Wiener gain factor, improving ITD estimation precision of the stereo audio signal.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Shin, ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain at least one of a first noise value, a first posteriori SNR, or a first priori SNR based on a second audio signal; and obtain the first gain value based on at least one of the first noise value, the first posteriori SNR or the first priori SNR of Ding. This allows improves sound quality as recognized by Ding [0006].
Regarding Claim 5, Shin in view of Ding and further in view of Shuang teaches 5. The electronic apparatus as claimed in claim 4,
Furthermore, Ding teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain the first noise value from the second audio signal based on a first parameter stored in the memory; obtain the first posteriori SNR from the second audio signal and the first noise value based on a second parameter stored in the memory; obtain the first priori SNR from second audio signal and the first posteriori SNR based on a third parameter stored in the memory; and obtain the first gain value from the second audio signal and the first priori SNR based on a fourth parameter stored in the memory. (See Ding [0044] “Optionally, the construction factor of the first weighting function includes: a Wiener gain factor corresponding to the first channel frequency domain signal, a Wiener gain corresponding to the second channel frequency domain signal, an amplitude weighting parameter, and a squared coherence value of the current frame. The construction factor of the second weighting function includes: an amplitude weighting parameter and a squared coherence value of the current frame.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Shin, ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain the first noise value from the second audio signal based on a first parameter stored in the memory; obtain the first posteriori SNR from the second audio signal and the first noise value based on a second parameter stored in the memory; obtain the first priori SNR from second audio signal and the first posteriori SNR based on a third parameter stored in the memory; and obtain the first gain value from the second audio signal and the first priori SNR based on a fourth parameter stored in the memory of Ding. This allows improves sound quality as recognized by Ding [0006].
Regarding Claim 6, Shin in view of Ding and further in view of Shuang teaches 6. The electronic apparatus as claimed in claim 4,
Furthermore, Ding teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain a second noise value with a second dynamic range by filtering the first noise value; obtain a second posterior SNR with a third dynamic range by filtering the first posteriori SNR; and obtain a second priori SNR with a fourth dynamic range by filtering the first priori SNR. (see Ding, [0202] In some possible implementations, after S301, the audio coding apparatus may further preprocess the current frame, for example, perform high-pass filtering processing on x.sub.1(n) and x.sub.2(n) to obtain a preprocessed left channel time domain signal and a preprocessed right channel time domain signal, where the preprocessed left channel time domain signal is denoted as x.sub.1.sup.hp(n), and the preprocessed right channel time domain signal is denoted as x.sub.2.sup.hp(n). Optionally, the high-pass filtering processing may be an infinite impulse response (IIR) filter with a cut-off frequency of 20 Hz, or may be another type of filter. This is not specifically limited in this embodiment of this application.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Shin, ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: obtain a second noise value with a second dynamic range by filtering the first noise value; obtain a second posterior SNR with a third dynamic range by filtering the first posteriori SNR; and obtain a second priori SNR with a fourth dynamic range by filtering the first priori SNR of Ding. This allows improves sound quality as recognized by Ding [0006].
Regarding Claim 7, Shin in view of Ding and further in view of Shuang teaches 7. The electronic apparatus as claimed in claim 6,
Furthermore, Shuang teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to obtain the third gain value by inputting the second gain value, the second noise value, the second posterior SNR, and the second priori SNR to the trained neural network model. (see Shuang, [0051] At 302, first band gains and a voice activity detection value of an audio signal are generated using a machine learning model. For example, the CPU 201 may implement the neural network 108 to generate the gains 158 and the VAD 160 (see FIG. 1) by processing the band features 156 according to a model.”) (see Shuang [0053-0054] At 306, second band gains are generated by processing the audio signal using a Wiener filter controlled by the background noise estimate. For example, the CPU 201 may implement the Wiener filter 110 to generate the gains 162 by processing the band features 156 as controlled by the background noise estimate (see 304). For example, when the number of noise frames exceeds a threshold (e.g., 50 noise frames) for a particular band, the Wiener filter generates the second band gains for that particular band. [0054] At 308, combined gains are generated by combining the first band gains and the second band gains. For example, the CPU 201 may implement the gain combination block 112 to generate the gains 164 by combining the gains 158 (from the neural network 108) and the gains 162 (from the Wiener filter 110). The first band gains and the second band gains may be combined by multiplication. The first band gains and the second band gains may be combined by selecting a maximum of the first band gains and the second band gains for each band. Limiting may be applied to the combined gains. The first band gains and the second band gains may be combined by multiplication or by selecting a maximum for each band, and limiting may be applied to the combined gains.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified apparatus of combination of Shin, Ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to obtain the third gain value by inputting the second gain value, the second noise value, the second posterior SNR, and the second priori SNR to the trained neural network model of Shuang. This allows for improved estimation of the background noise as recognized by Shuang [0014-0033].
Regarding Claim 8, Shin in view of Ding and further in view of Shuang teaches 8. The electronic apparatus as claimed in claim 1,
Furthermore, Shuang teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: identify a noise component corresponding to the second audio signal based on the third gain value; and convert the second audio signal to the third audio signal by removing the noise component from the second audio signal. (see Shuang, [0017] FIG. 1 is a block diagram of a noise reduction system 100. The noise reduction system 100 may be implemented in a mobile device (e.g., see FIG. 2), such as a mobile telephone, a video camera with a microphone, etc. The components of the noise reduction system 100 may be implemented by a processor, for example as controlled according to one or more computer programs. The noise reduction system 100 includes a windowing block 102, a transform block 104, a band features analysis block 106, a neural network 108, a Wiener filter 110, a gain combination block 112, a band gains to bin gains block 114, a signal modification block 116, an inverse transform block 118, and an inverse windowing block 120. The noise reduction system 100 may include other components that (for brevity) are not described in detail.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified apparatus of combination of Shin, Ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: identify a noise component corresponding to the second audio signal based on the third gain value; and convert the second audio signal to the third audio signal by removing the noise component from the second audio signal of Shuang. This allows for improved estimation of the background noise as recognized by Shuang [0014-0033].
Regarding Claim 9, Shin in view of Ding and further in view of Shuang teaches 9. The electronic apparatus as claimed in claim 1,
Furthermore, Shuang teaches wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: identify the noise signal based on the first audio signal; generate a reverse noise signal based on the noise signal; obtain a first filtering signal by combining the first audio signal and the reverse noise signal; and convert the second audio signal to the third audio signal based on the first filtering signal and the third gain value. (See Shuang [0040] “The inverse transform block 118 receives the modified transform features 168, performs an inverse transform on the modified transform features 168, and generates audio frames 170. In general, the inverse transform performed is an inverse of the transform performed by the transform block 104. For example, the inverse transform block 118 may implement an inverse Fourier transform (e.g., an inverse FFT), an inverse QMF transform, etc.”) (See Shuang [0053] “At 306, second band gains are generated by processing the audio signal using a Wiener filter controlled by the background noise estimate. For example, the CPU 201 may implement the Wiener filter 110 to generate the gains 162 by processing the band features 156 as controlled by the background noise estimate (see 304). For example, when the number of noise frames exceeds a threshold (e.g., 50 noise frames) for a particular band, the Wiener filter generates the second band gains for that particular band.”)
Shin in view of Ding and further in view of Shuang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified apparatus of combination of Shin, Ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to: identify the noise signal based on the first audio signal; generate a reverse noise signal based on the noise signal; obtain a first filtering signal by combining the first audio signal and the reverse noise signal; and convert the second audio signal to the third audio signal based on the first filtering signal and the third gain value of Shuang. This allows for improved estimation of the background noise as recognized by Shuang [0014-0033].
Regarding Claim 10, Shin in view of Ding and further in view of Shuang teaches 10. The electronic apparatus as claimed in claim 1,
Furthermore, Shin teaches wherein the electronic apparatus further comprises a communication interface connected to an external device; and wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to obtain the first audio signal from the external device through the communication interface. (see Shin [0098] At block 507A, the system can process, using a trained neural network model, the first digital representation of the first audio data as input, to generate a first output that reflects a first signal-to-noise ratio (SNR) predicted for the first audio data. At block 507B, the system can process, using the trained neural network model, the second digital representation of the second audio data as input, to generate a second output that reflects a second SNR predicted for the second audio data.”) (see Shin [0103] “Computing device 610 typically includes at least one processor 614 which communicates with a number of peripheral devices via bus subsystem 612. These peripheral devices may include a storage subsystem 624, including, for example, a memory subsystem 625 and a file storage subsystem 626, user interface output devices 620, user interface input devices 622, and a network interface subsystem 616. The input and output devices allow user interaction with computing device 610. Network interface subsystem 616 provides an interface to outside networks and is coupled to corresponding interface devices in other computing devices.”)
As to Independent Claim 11, Claim 11 is a Method claim with limitations similar to that of claim 1 and is rejected under the same rationale.
As to Claim 14, Claim 14 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 15, Claim 15 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 16, Claim 16 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 17, Claim 17 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 18, Claim 18 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 19, Claim 19 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 20, Claim 20 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Shin (U.S. Patent Number US 20240304186 A1), in view of Ding (U.S. Patent Number US 20230154483 A1), and further in view of Shuang (U.S. Patent Number US 20230267947 A1) and further in view of Tang (U.S. Patent Number US 11146607 B1)
As to Claim 2, Shin in view of Ding and further in view of Shuang teaches 2. The electronic apparatus as claimed in claim 1,
Shin in view of Ding and further in view of Shuang do not specifically teach wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to back-convert the third audio signal in a frequency domain to a fourth audio signal in a time domain. However, Tang does teach this limitation (see Tang, (9:8-13) “(44) The IFFT module 309 may convert the frequency domain signal back to time domain by using the Inverse Fast Fourier Transform. The acoustic echo cancellation module 313 and smart level control module 315 may operate in the time domain to cancel acoustic echo and control audio volume levels, respectively.”)
Shin in view of Ding and further in view of Shuang and Tang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Shin, Ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to back-convert the third audio signal in a frequency domain to a fourth audio signal in a time domain of Tang. This allows for improved voice quality and intelligibility of noise-contaminated speech as recognized by Tang (1:23-24).
As to Claim 12, Claim 12 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shin (U.S. Patent Number US 20240304186 A1), in view of Ding (U.S. Patent Number US 20230154483 A1), and further in view of Shuang (U.S. Patent Number US 20230267947 A1) and further in view of Mesgarani (U.S. Patent Number US 11875813 B2)
As to Claim 3, Shin in view of Ding and further in view of Shuang teaches 3. The electronic apparatus as claimed in claim 1,
Shin in view of Ding and further in view of Shuang do not specifically teach wherein the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to convert the first audio signal to the second audio signal using Short-Time Fourier Transform (STFT). However, Mesgarani does teach this limitation (see Mesgarani, (9:40-67) “(37) In some example implementations, audio signals processing may include capturing (through a single microphone, or through multiple microphones) audio segments (e.g., 4 seconds segments) that are transformed to the frequency domain with a STFT using a window size of 512 and a step size of 125. The choice of the length in time (4 seconds) is arbitrary and different segment lengths may be used instead. The choice of 125 samples is appropriate for some applications because the audio sampling rate is 8 kHz and an output rate of 64 Hz, that matches the envelope sampling rate, may be desired. Because of the Hermitian property of the Fourier transform on real data, only the positive frequencies of the transformed signal can be kept, thus obtaining as input a 3D tensor of size 2×257×257. For the output mask, a complex-valued mask may be used instead of a real-valued magnitude mask. Using a real-valued magnitude mask forces the use of the noisy phase when inverting the estimated separated spectrogram to the time domain, and it has been shown that using the compressed complex mask gives better results. Because, in some embodiments, a complex STFT with overlapping windows is used, there exists an ideal complex mask that perfectly isolates the desired source from the mixture. Unfortunately, the mask values can be arbitrarily high and unbounded, and this poses a problem for the training process. For this reason, a hyperbolic tangent compression may be used that limits the output mask values to the range [−1, 1]. In such situations, only an approximation of the ideal mask can be computed.”) (12:4-19) “(46) When estimating the frequency-domain masks for speech separation, the mean squared error (MSE) is generally used as the cost function. However, the estimated masks are usually smeared, limiting the separation quality. In the approaches described herein, a time-domain optimization method is proposed for use with a frequency domain solution by embedding both the STFT and iSTFT procedure into the training pipeline. Because these operations are differentiable, the normal backpropagation algorithm can be used to train the model. An example of a cost function used to optimize the model is SI-SDR. Optimizing the SI-SDR has shown very good results in time domain separation due to the fact that the model directly optimizes the measure which is used to evaluate its performance. The SI-SDR metric (SDR for simplicity) can be calculated directly from the time domain signals as follows:”)
Shin in view of Ding and further in view of Shuang and Mesgarani are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Shin, Ding and Shuang to incorporate the at least one processor is configured, individually and/or collectively, to control the electronic apparatus, by executing the at least one instruction, to convert the first audio signal to the second audio signal using Short-Time Fourier Transform (STFT) of Mesgarani. This allows for improved speech separation, even in scenes with background noise as recognized by Mesgarani (6:7-10).
As to Claim 13, Claim 13 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale.
Conclusion
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/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659