Detailed Action
This communication is in response to the Application filed on 11/21/2024.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected.
Apparent priority: 3/29/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2024, 11/29/2024 have been 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.
Regarding Independent Claim 1, Claim 1 recites,
“1. An own voice detection apparatus, comprising: a first sound receiver; a second sound receiver; and
a signal processor communicatively connected to the first sound receiver and the second sound receiver, [This relates a human using the auditory system to listen and interpret audio.]
wherein the signal processor is configured to perform an own voice detection method including a training phase and a detection phase, [This relates a human using the auditory system to listen and interpret audio.]
wherein during the training phase, the signal processor is configured to:
receive a first audio signal from the first sound receiver and receive a second audio signal from the second sound receiver; [This relates a human using the auditory system to listen and interpret audio.]
perform a voice activity detection based on the first audio signal or the second audio signal, thereby determining whether a voice activity is present; and [This relates a human using the auditory system to listen and interpret speech and detect voice in the human mind.]
train a filter based on the first audio signal and the second audio signal when the voice activity is present, thereby finding optimal filter coefficients for optimizing the filter, [This relates to a mathematical process a human can perform using pen and paper.]
wherein the optimal filter coefficients reflect a frequency response difference between two acoustic paths from a wearer’s mouth to the first and second sound receivers respectively; [This relates to a mathematical process a human can perform using pen and paper.]
wherein during the detection phase, the signal processor is configured to: receive the first audio signal from the first sound receiver and receive the second audio signal from the second sound receiver; [This relates a human using the auditory system to listen and interpret audio.]
filter the first audio signal by the filter with the optimal filter coefficients to obtain a third audio signal; [This relates a human using the auditory system to listen and interpret audio and a mathematical process a human can perform using pen and paper.]
compare the third audio signal and the second audio signal to obtain a similarity index between the third audio signal and the second audio signal; and [This relates a human using the auditory system to listen and interpret audio and a mathematical process a human can perform using pen and paper.]
determine that the first audio signal and the second audio signal contain own voice when the similarity index is greater than a threshold. [This relates a human using the auditory system to listen and interpret own voice.]
Regarding Independent Claim 12, claim 12 is a Method claim with limitations similar to that of claim 1 and is rejected under the same rationale.
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.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements of “processors” For example, in [0015] there is the description of
the signal processor 100 is an electronic component with computing capabilities such as a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and an application specific integrated circuit (ASIC).
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 a signal processor 100 is an electronic component with computing capabilities such as a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and an application specific integrated circuit (ASIC)
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 own voice detection apparatus of claim 1, wherein when the own voice detection apparatus is worn on a head of a wearer, the first sound receiver and the second sound receiver are located at the same ear of the wearer. Sound receivers are noted as additional limitations.
Dependent claim 3 recites,
“3. The own voice detection apparatus of claim 2, wherein when the own voice detection apparatus is worn on the head of the wearer, the first sound receiver is closer to the wearer’s mouth than the second sound receiver. Sound receivers are noted as additional limitations.
Dependent claim 4 recites,
“4. The own voice detection apparatus of claim 1, wherein the training phase is performed in a silent environment, and a sound pressure level of the silent environment does not exceed 50 decibels. [This relates to a human training in a silent environment No additional limitations present.
Dependent claim 5 recites,
“5. The own voice detection apparatus of claim 1, wherein the similarity index is a cosine similarity between the third audio signal and the second audio signal. [this relates to a mathematical process a human can perform.] No additional limitations present.
Dependent claim 6 recites,
“6. The own voice detection apparatus of claim 1, wherein the similarity index is a correlation coefficient between the third audio signal and the second audio signal. [this relates to a mathematical process a human can perform.] No additional limitations present.
Dependent claim 7 recites,
“7. The own voice detection apparatus of claim 1, wherein the signal processor optimizes the filter according to an objective function:
minhEMic2-h*Mic1, wherein h is a vector of filter coefficients, Mic1 is the first audio signal, Mic2 is the second audio signal, and E is a mathematical expectation, and [this relates to a mathematical process a human can perform.] No additional limitations present.
wherein the optimal filter coefficients are the filter coefficients that makes the objective function attains a minimum value. [this relates to a mathematical process a human can perform.] No additional limitations present.
Dependent claim 8 recites,
“8. The own voice detection apparatus of claim 1, wherein the signal processor finds the optimal filter coefficients by utilizing a least mean square error algorithm, a normalized least mean square error algorithm, or an adaptive least mean square error algorithm. [this relates to a mathematical process a human can perform.] No additional limitations present.
Dependent claim 9 recites,
“9. The own voice detection apparatus of claim 1, wherein the signal processor performs a mathematical operation on the first audio signal to obtain the third audio signal, and then the optimal filter coefficients are calculated by performing an optimization process with a goal to maximize the similarity index between the third audio signal and the second audio signal. [this relates to a mathematical and auditory processes a human can perform.] No additional limitations present.
Dependent claim 10 recites,
10. The own voice detection apparatus of claim 9, wherein when the filter is optimized in time domain, the mathematical operation is convolution. [this relates to a mathematical process a human can perform.] No additional limitations present.
Dependent claim 11 recites,
“11. The own voice detection apparatus of claim 9, wherein when the filter is optimized in frequency domain, the mathematical operation is multiplication. [this relates to a mathematical process a human can perform.] No additional limitations present.
As to dependent claim 13, Claim 13 is a method claim with limitations similar to that of claim 4 and is rejected under the same rationale.
As to dependent claim 14, Claim 14 is a method claim with limitations similar to that of claim 5 and is rejected under the same rationale.
As to dependent claim 15, Claim 25 is a method claim with limitations similar to that of claim 6 and is rejected under the same rationale.
As to dependent claim 16, Claim 26 is a method claim with limitations similar to that of claim 7 and is rejected under the same rationale.
As to dependent claim 17, Claim 17 is a method claim with limitations similar to that of claim 8 and is rejected under the same rationale.
As to dependent claim 18, Claim 18 is a method claim with limitations similar to that of claim 9 and is rejected under the same rationale.
As to dependent claim 19, Claim 19 is a method claim with limitations similar to that of claim 10 and is rejected under the same rationale.
As to dependent claim 20, Claim 20 is a method claim with limitations similar to that of claim 11 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 1-3, 5-7, 12, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Merks (U.S. Patent Number US 10171922 B2) in view of PEDERSEN (U.S. Patent Number US 20180054683 A1) and further in view of Gao (U.S. Patent Number US 20230046518 A1).
Regarding independent Claim 1, Merks teaches
1. An own voice detection apparatus, comprising: a first sound receiver; (see Merks (2:10-11) “(6) …includes a microphone that receives sound”)
a second sound receiver; and (see Merks (2:14) “(6) …second microphone”)
a signal processor communicatively connected to the first sound receiver and the second sound receiver, (see Merks Figure 3, Elements 305, 308, “SOUND PROCESSOR”, MIC 1, MIC 2, (2:14) “(5)…a sound processor” (3:7-11) “(10) Various embodiments disclosed herein provide a self-correcting voice detector, capable of reliably detecting the presence of the user's own voice through automatic adjustments that accommodate changes in the user's voice and environment.”)
wherein the signal processor is configured to perform an own voice detection method including a training phase and a detection phase, (see Merks (2:4-6) “(5)… The voice detector includes an adaptive filter to receive signals from the first microphone and the second microphone”)(Examiner interprets training phase as “adaptive filter”.)
wherein the optimal filter coefficients reflect a frequency response difference between two acoustic paths from a wearer’s mouth to the first and second sound receivers respectively; (see Merks (5:8) “(17)… response of the filter” (3:50-57) “(13)…the sound vectors representing travel of the user's voice from the user's mouth to the microphones are different. The first microphone (MIC 1) is further away from the mouth than the second microphone (MIC 2). Sound received by MIC 2 will be relatively high amplitude and will be received slightly sooner than sound detected by MIC 1.”)
wherein during the detection phase, the signal processor is configured to:
receive the first audio signal from the first sound receiver and receive the second audio signal from the second sound receiver; (see Merks (5:8) “(17)… response of the filter” (3:50-57) “(13)…the sound vectors representing travel of the user's voice from the user's mouth to the microphones are different. The first microphone (MIC 1) is further away from the mouth than the second microphone (MIC 2). Sound received by MIC 2 will be relatively high amplitude and will be received slightly sooner than sound detected by MIC 1.”)
filter the first audio signal by the filter with the optimal filter coefficients to obtain a third audio signal; (see Merks Fig. 4, Element 411, Fig. 6, (2:15-16) “(6)… coefficients of the adaptive filter and an error signal from the adaptive filter.” )
Merks teaches comparing audio signals to third audio for voice detection (see Merks (2:15-16) but does not specifically teach compare the third audio signal and the second audio signal to obtain a similarity index between the third audio signal and the second audio signal; and (However, PEDERSEN does teach this limitation (see PEDERSEN [0055] “…the detection unit is configured to determine a similarity, e.g. a correlation, such as a cross correlation, between sound from the user's mouth received at the hearing device and at the microphone unit. [0056] In an embodiment, the detection unit is configured to determine a cross correlation between sound from the user's mouth received at a microphone of the hearing device and sound received at one of the multitude M of microphones of the microphone unit...”)
determine that the first audio signal and the second audio signal contain own voice when the similarity index is greater than a threshold. (see PEDERSEN [0056] “…provides an optimal value of the cross-correlation…”)(examiner interprets threshold as “optimal value”) [0055] “…the detection unit is configured to determine a similarity, e.g. a correlation, such as a cross correlation, between sound from the user's mouth received at the hearing device and at the microphone unit. [0056] In an embodiment, the detection unit is configured to determine a cross correlation between sound from the user's mouth received at a microphone of the hearing device and sound received at one of the multitude M of microphones of the microphone unit...”)
train a filter based on the first audio signal and the second audio signal when the voice activity is present, (see PEDERSEN [0037] “The voice model is a model that is trained in advance according to a general optimization reference using a voice signal prepared for training (voice signal for training).”)
Merks and PEDERSEN 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 Merks to include compare the third audio signal and the second audio signal to obtain a similarity index between the third audio signal and the second audio signal; and determine that the first audio signal and the second audio signal contain own voice when the similarity index is greater than a threshold. train a filter based on the first audio signal and the second audio signal when the voice activity is present, of PEDERSEN. This allows for enhanced accuracy of speaker recognition as recognized by PEDERSEN [0013].
Merks in view of PEDERSEN teaches adaptive filtering for voice detection (see Merks (2:21-26) but does not specifically teach wherein during the training phase, the signal processor is configured to:
receive a first audio signal from the first sound receiver and receive a second audio signal from the second sound receiver; (However, Gao does teach this limitation (see Gao [0029] terminal 102 or terminal 104 “obtains a current audio signal.”)
perform a voice activity detection based on the first audio signal or the second audio signal, thereby determining whether a voice activity is present; and (see Gao [0040] “The voice activity detection (VAD)”)
Merks in view of PEDERSEN and Gao 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 Merks and PEDERSEN to include wherein during the training phase, the signal processor is configured to: receive a first audio signal from the first sound receiver and receive a second audio signal from the second sound receiver; perform a voice activity detection based on the first audio signal or the second audio signal, thereby determining whether a voice activity is present; and train a filter based on the first audio signal and the second audio signal when the voice activity is present, of Gao. This allows for improved quality of signal as recognized by Gao [0055].
Regarding Independent Claim 12, claim 12 is a Method claim with limitations similar to that of claim 1 and is rejected under the same rationale. Furthermore, PEDERSEN teaches 12. An own voice detection method, comprising: performing a training phase, and the training phase comprises steps of: (see PEDERSEN [0037] “The voice model is a model that is trained in advance according to a general optimization reference using a voice signal prepared for training (voice signal for training).”)
Merks and PEDERSEN 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 Merks to include performing a training phase, and the training phase comprises steps of PEDERSEN. This allows for enhanced accuracy of speaker recognition as recognized by PEDERSEN [0013].
As to Claim 2, Merks in view of PEDERSEN and further in view of Gao teaches. 2. The own voice detection apparatus of claim 1,
Furthermore, Merks teaches wherein when the own voice detection apparatus is worn on a head of a wearer, (see Merks (2:7-9) “(6)…a housing configured to be worn behind the ear or over the ear”)
the first sound receiver and the second sound receiver are located at the same ear of the wearer. (see Merks (2:7-10) “(6) Another example of an apparatus includes a housing configured to be worn behind the ear or over the ear, a first microphone in the housing, and an ear piece configured to be positioned in the ear canal,”)
As to Claim 3, Merks in view of PEDERSEN and further in view of Gao teaches 3. The own voice detection apparatus of claim 2,
Furthermore, Merks teaches wherein when the own voice detection apparatus is worn on the head of the wearer, the first sound receiver is closer to the wearer’s mouth than the second sound receiver. (see Merks (2:7-10) “(6) Another example of an apparatus includes a housing configured to be worn behind the ear or over the ear, a first microphone in the housing, and an ear piece configured to be positioned in the ear canal,”)
As to Claim 5, Merks in view of PEDERSEN and further in view of Gao teaches 5. The own voice detection apparatus of claim 1,
Furthermore, PEDERSEN teaches wherein the similarity index is a cosine similarity between the third audio signal and the second audio signal. (see PEDERSEN [0056] “…provides an optimal value of the cross-correlation…”)(examiner interprets threshold as “optimal value”) [0055] “…the detection unit is configured to determine a similarity, e.g. a correlation, such as a cross correlation, between sound from the user's mouth received at the hearing device and at the microphone unit. [0056] In an embodiment, the detection unit is configured to determine a cross correlation between sound from the user's mouth received at a microphone of the hearing device and sound received at one of the multitude M of microphones of the microphone unit...”)(see PEDERSEN [0134] “….FIG. 4 (L′12=L12.Math.cos θ)…”)
Merks in view of PEDERSEN and further in view of Gao 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 Merks PEDERSEN and Gao to include wherein the similarity index is a cosine similarity between the third audio signal and the second audio signal of PEDERSEN. This allows for enhanced accuracy of speaker recognition as recognized by PEDERSEN [0013].
As to Claim 6, Merks in view of PEDERSEN and further in view of Gao teaches 6. The own voice detection apparatus of claim 1,
Furthermore, PEDERSEN teaches wherein the similarity index is a correlation coefficient between the third audio signal and the second audio signal. (see PEDERSEN “[0133] When the distance (D1 in FIG. 2, 3) from the user's mouth to the body worn microphone unit MICU is known, we can choose a set of directional coefficients (e.g. frequency dependent beamformer weights w(k), where k is a frequency band index), e.g. stored in a dictionary located in a memory of the microphone unit together with other sets of beamformer weights representing other distances)…”)
Merks in view of PEDERSEN and further in view of Gao 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 Merks PEDERSEN and Gao to include wherein the similarity index is a correlation coefficient between the third audio signal and the second audio signal of PEDERSEN. This allows for enhanced accuracy of speaker recognition as recognized by PEDERSEN [0013].
As to Claim 7, Merks in view of PEDERSEN and further in view of Gao teaches 7. The own voice detection apparatus of claim 1,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein the signal processor optimizes the filter according to an objective function: minhEMic2-h*Mic1, wherein h is a vector of filter coefficients, Mic1 is the first audio signal, Mic2 is the second audio signal, and E is a mathematical expectation, and wherein the optimal filter coefficients are the filter coefficients that makes the objective function attains a minimum value. (see PEDERSEN [0055] “In an embodiment, the detection unit is configured to determine a similarity, e.g. a correlation, such as a cross correlation, between sound from the user's mouth received at the hearing device and at the microphone unit. [0056] In an embodiment, the detection unit is configured to determine a cross correlation between sound from the user's mouth received at a microphone of the hearing device and sound received at one of the multitude M of microphones of the microphone unit. The cross-correlation is used to determine a difference in time of arrival (t.sub.a) of acoustic signals from the user's mouth to the respective microphones (thereby identifying the time difference Δt(HD-MICU)=t.sub.a(HD)−t.sub.a(MICU) that provides an optimal value of the cross-correlation. Knowing the distance D.sub.R (and/or D.sub.L) between the user's mouth and the hearing device (HD.sub.R, HD.sub.L) see FIG. 3),..”)
Merks in view of PEDERSEN and further in view of Gao 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 Merks PEDERSEN and Gao to include wherein the signal processor optimizes the filter according to an objective function: minhEMic2-h*Mic1, wherein h is a vector of filter coefficients, Mic1 is the first audio signal, Mic2 is the second audio signal, and E is a mathematical expectation, and wherein the optimal filter coefficients are the filter coefficients that makes the objective function attains a minimum value of PEDERSEN. This allows for enhanced accuracy of speaker recognition as recognized by PEDERSEN [0013].
As to dependent claim 14, Claim 14 is a method claim with limitations similar to that of claim 5 and is rejected under the same rationale.
As to dependent claim 15, Claim 15 is a method claim with limitations similar to that of claim 6 and is rejected under the same rationale.
As to dependent claim 16, Claim 26 is a method claim with limitations similar to that of claim 7 and is rejected under the same rationale.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Merks (U.S. Patent Number US 10171922 B2) in view of PEDERSEN (U.S. Patent Number US 20180054683 A1), and further in view of Gao (U.S. Patent Number US 20230046518 A1), and further in view of Claussen (U.S. Patent Number US 20110261983 A1),
As to dependent Claim 4, Merks in view of PEDERSEN and further in view of Gao teaches 4. The own voice detection apparatus of claim 1,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein the training phase is performed in a silent environment, and a sound pressure level of the silent environment does not exceed 50 decibels. However, Claussen does teach this limitation (see Claussen [0106] FIGS. 15a, 15b and 15c show the score and standard deviation for 3 different noise scenarios at the two extreme SNR levels under three different training scenarios (no noise ("SAT"), 20 dB noise ("W"), and 30 dB noise ("X")). Overall, the results indicate that using noise in the training process reduces the standard deviation of missed detections (and hence correct detections), and it also reduces false alarms during silence periods. These effects only seem to be prominent under test scenarios with high SNR (e.g. 10 dB), and they steadily diminish as the test SNR approaches -5 dB.”)
Merks in view of PEDERSEN and further in view of Gao and Claussen 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 method combination of Merks PEDERSEN and of Gao to include wherein the training phase is performed in a silent environment, and a sound pressure level of the silent environment does not exceed 50 decibels of Claussen This allows for reduced standard deviation of missed detections and also reduces false alarms during silence periods as recognized by Claussen [0106].
As to dependent claim 13, Claim 13 is a method claim with limitations similar to that of claim 4 and is rejected under the same rationale.
Claims 8-11 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Merks (U.S. Patent Number US 10171922 B2) in view of PEDERSEN (U.S. Patent Number US 20180054683 A1), and further in view of Gao (U.S. Patent Number US 20230046518 A1), and further in view of Lai (U.S. Patent Number US 20220078561 A1),
As to Claim 8, Merks in view of PEDERSEN and further in view of Gao teaches 8. The own voice detection apparatus of claim 1,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein the signal processor finds the optimal filter coefficients by utilizing a least mean square error algorithm, a normalized least mean square error algorithm, or an adaptive least mean square error algorithm. However, Lai does teach this limitation (see Lai [0044] “FIG. 3C shows a relationship between a vibration complex-valued samples X.sub.k and a speech complex-valued sample Z.sub.k for the same frequency bin k. Referring to FIG. 3C, two vectors {right arrow over (X.sub.k)} and {right arrow over (Z.sub.k)} respectively representing two complex-valued samples X.sub.k and Z.sub.k for the same frequency bin k point to different directions. A vector τ.sub.k{right arrow over (Z.sub.k)}, the projection of {right arrow over (X.sub.k)} on {right arrow over (Z.sub.k)}, represents an own voice component on {right arrow over (Z.sub.k)}. According to the definition of linear minimum mean square error (MMSE) estimator (please go to the web site: https://en.wikipedia.org/wiki/Minimum_mean_square_error), we deduce the suppression mask α.sub.k for frequency bin k as follows. Since the two vectors ({right arrow over (X.sub.k)}−τ.sub.k{right arrow over (Z.sub.k)}) and {right arrow over (Z.sub.k)} are orthogonal,”)
Merks in view of PEDERSEN and further in view of Gao and Lai 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 method combination of Merks PEDERSEN and of Gao to include wherein the signal processor finds the optimal filter coefficients by utilizing a least mean square error algorithm, a normalized least mean square error algorithm, or an adaptive least mean square error algorithm of Lai. This allows for improve speech intelligibility as recognized by Lai [0032].
As to Claim 9, Merks in view of PEDERSEN and further in view of Gao teaches 9. The own voice detection apparatus of claim 1,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein the signal processor performs a mathematical operation on the first audio signal to obtain the third audio signal, and then the optimal filter coefficients are calculated by performing an optimization process with a goal to maximize the similarity index between the third audio signal and the second audio signal. However, Lai does teach this limitation (see Lai [0072] “15. The apparatus according to claim 14, wherein the second computing unit comprises: a second suppression mask calculation unit for generating L second suppression masks for L first sub-band signals with L passbands adjacent to a passband j according to L matching scores and L average speech power values for the L first sub-band signals and L average vibration power values for L second sub-band signals with the L passbands and for computing an average of the L second suppression masks to generate a second suppression mask α for a first sub-band signal with the passband j, where 0<=α<=1, L>=1 and 0<=j<=(Q−1); wherein when L=1, the second suppression mask and a matching score for the first sub-band signal with the passband j are inversely proportional and the second suppression mask and a magnitude of a second sub-band signal with the passband j are inversely proportional. 16. The apparatus according to claim 1, wherein the own voice indication module comprises: a voice identification module for receiving the audio signal to generate Q matching scores for Q signal components as the indication signal, where Q>=1.”)(examiner interprets filter coefficient as “suppression mask α”)
Merks in view of PEDERSEN and further in view of Gao and Lai 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 method combination of Merks PEDERSEN and of Gao to include wherein the signal processor performs a mathematical operation on the first audio signal to obtain the third audio signal, and then the optimal filter coefficients are calculated by performing an optimization process with a goal to maximize the similarity index between the third audio signal and the second audio signal of Lai. This allows for improve speech intelligibility as recognized by Lai [0032].
As to Claim 10, Merks in view of PEDERSEN and further in view of Gao teaches 10. The own voice detection apparatus of claim 9,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein when the filter is optimized in time domain, the mathematical operation is convolution. However, Lai does teach this limitation (see Lai [0036] “The own voice indication module 130A includes a bone conduction sensor 231 and an own voice reconstruction module 232. The bone conduction sensor 231 may be implemented by a MEMS voice accelerometer. As well known in the art, a voice accelerometer is configured to measure vibrations caused by speech/voice/mouth movement of the user, particularly at low frequencies, to output a vibration signal S2. The audio signal S1 and the vibration signal S2 may be analog or digital. If the signals S1 and S2 are analog, they may be digitized using techniques well known in the art. It is assumed that the amplified signal Z[n] and the reconstructed signal X[n] need to be digitized before being fed to the suppression module 150A. In general, the human voice/speech spans a range from about 125 Hz to 20 kHz. However, the bandwidth of the vibration signal S2 is normally restricted to a range from 0 to 3 kHz depending on the specification of the bone conduction sensor 231, and thus the vibration signal S2 usually sounds muffled. To solve this problem, the own voice reconstruction module 232 is provided to reconstruct the lost high-frequency components from the vibration signal S2 with a frequency range below 3 kHz by any existing or yet-to-be developed audio bandwidth extension approaches or high frequency reconstruction algorithms to generate a reconstructed signal X[n] with a frequency range extended up to 20 KHz. In an embodiment, the own voice reconstruction module 232 includes a deep neural network (not shown) that extracts feature values from the vibration signal S2 and then reconstructs its high-frequency components to generate a reconstructed signal X[n]. The deep neural network may be one or a combination of a recurrent neural network (RNN) and a convolutional neural network (CNN).”)
Merks in view of PEDERSEN and further in view of Gao and Lai 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 method combination of Merks PEDERSEN and of Gao to include wherein when the filter is optimized in time domain, the mathematical operation is convolution of Lai. This allows for improve speech intelligibility as recognized by Lai [0032].
As to Claim 11, Merks in view of Gao teaches 11. The own voice detection apparatus of claim 9,
Merks in view of PEDERSEN and further in view of Gao do not specifically teach wherein when the filter is optimized in frequency domain, the mathematical operation is multiplication. However, Lai does teach this limitation (see Lai [0036] “The own voice indication module 130A includes a bone conduction sensor 231 and an own voice reconstruction module 232. The bone conduction sensor 231 may be implemented by a MEMS voice accelerometer. As well known in the art, a voice accelerometer is configured to measure vibrations caused by speech/voice/mouth movement of the user, particularly at low frequencies, to output a vibration signal S2. The audio signal S1 and the vibration signal S2 may be analog or digital. If the signals S1 and S2 are analog, they may be digitized using techniques well known in the art. It is assumed that the amplified signal Z[n] and the reconstructed signal X[n] need to be digitized before being fed to the suppression module 150A. In general, the human voice/speech spans a range from about 125 Hz to 20 kHz. However, the bandwidth of the vibration signal S2 is normally restricted to a range from 0 to 3 kHz depending on the specification of the bone conduction sensor 231, and thus the vibration signal S2 usually sounds muffled. To solve this problem, the own voice reconstruction module 232 is provided to reconstruct the lost high-frequency components from the vibration signal S2 with a frequency range below 3 kHz by any existing or yet-to-be developed audio bandwidth extension approaches or high frequency reconstruction algorithms to generate a reconstructed signal X[n] with a frequency range extended up to 20 KHz. In an embodiment, the own voice reconstruction module 232 includes a deep neural network (not shown) that extracts feature values from the vibration signal S2 and then reconstructs its high-frequency components to generate a reconstructed signal X[n]. The deep neural network may be one or a combination of a recurrent neural network (RNN) and a convolutional neural network (CNN).”)
Merks in view of PEDERSEN and further in view of Gao and Lai 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 method combination of Merks PEDERSEN and of Gao to include wherein when the filter is optimized in frequency domain, the mathematical operation is multiplication of Lai. This allows for improve speech intelligibility as recognized by Lai [0032].
As to dependent claim 17, Claim 17 is a method claim with limitations similar to that of claim 8 and is rejected under the same rationale.
As to dependent claim 18, Claim 18 is a method claim with limitations similar to that of claim 9 and is rejected under the same rationale.
As to dependent claim 19, Claim 19 is a method claim with limitations similar to that of claim 10 and is rejected under the same rationale.
As to dependent claim 20, Claim 20 is a method claim with limitations similar to that of claim 11 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
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