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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/11/2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-8, 10, 12, 13, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bouchard et al. (US20110305345 A1; hereafter Bouchard) in view of Sun et al. (US 20220417647 A1; hereafter Sun).
Regarding claim 1, Bouchard discloses a method of audio processing, the method comprising:
capturing, by a plurality of audio capture devices (hearing aid microphone on each ear, see Fig. 1A, [0008], [0016]), an audio signal having at least two channels including a left channel and a right channel;
calculating, by a system (determining gains in stage 2 and/or stage 4 in Fig. 1A), a plurality of noise reduction gains (gains for stage 3 bottom and/or stage 5) for each channel of the at least two channels (equations 11 and 12 and equations 30 and 31);
calculating a plurality of shared noise reduction gains (gain determined at stage 3 bottom and/or stage 5, equation 32) based on the plurality of noise reduction gains (determined in stage 2 and/or stage 4) for each channel; and
generating a modified audio signal (output of stage 5 in Fig. 1B) by applying (multiplication with YR and YL) the plurality of shared noise reduction gains to each channel of the at least two channels ([0054]),
wherein the plurality of audio capture devices comprises a first device that captures the left channel and a second device that captures the right channel (see Fig. 1A,
wherein the plurality of noise reduction gains includes a first plurality of noise reduction gains and a second plurality of noise reduction gains (reads on gains determined in stage 2 and 4), wherein the first plurality of noise reduction gains corresponds to a first gain vector for a plurality of bands of the left channel (gain per frequency), and the second plurality of noise reduction gains corresponds to a second gain vector for a plurality of bands of the right channel (gain per frequency),
wherein calculating the plurality of shared noise reduction gains comprises combining the first plurality of noise reduction gains and the second plurality of noise reduction gains according to a mathematical function (equation in stage 5), and
wherein combining the first plurality of noise reduction gains and the second plurality of noise reduction gains comprises applying the mathematical function to each band of the plurality of bands (shared gain per frequency).
Bouchard fails to show a computer-implemented method and a machine learning system. However, one skilled in the art would have recognized the complex calculation and determination involved for the noise reduction scheme taught in Bouchard. Sun teaches a system for adjusting gain of an audio signal for a person with hearing loss in a noisy environment ([0002]). The gain could be determined using complex calculation, such as least mean square algorithm or machine learning model ([0027], e.g.). The machine learning model is being trained to determine the appropriate gain based on the input features and user feedback ([0034]). Sun further teaches that the method is implemented by a computer ([0007], [0019]). Thus, it would have been obvious to one of ordinary skill in the art to modify Bouchard in view of Sun by utilizing a computer and a machine learning model in order to accurately and efficiently determine the appropriate gain based on the detected input audio features.
Bouchard fails to show first and second ear buds. However, Bouchard teaches that the user could wear headsets, headphones or hearing aids ([0003]). One skilled in the art would have recognized that an ear bud is a functionally equivalent device to those suggested by Bouchard. Examiner takes Official Notice that earbud with a microphone is notoriously well known in the art. Thus, it would have been obvious to one of ordinary skill in the art to modify the combination of Bouchard and Sun by wearing specific device, such as well known left and right ear buds with corresponding microphones, because it is considered as a matter of user preference.
Regarding claim 2, Bouchard teaches transforming the audio signal from a first signal domain to a second signal domain, wherein the first signal domain is a time domain, and wherein the plurality of noise reduction gains is calculated based on the audio signal having been transformed to the second signal domain ([0018]); and
transforming the modified audio signal from the second signal domain to the first
signal domain (IFFT in stage 5 of Fig. 1B).
Regarding claim 3, Bouchard teaches calculating the plurality of noise reduction gains, calculating the plurality of shared noise reduction gains, and generating the modified audio signal are performed contemporaneously with capturing the audio
signal (as illustrated in Figs. 1A and 1B).
Regarding claim 4, the combination of Bouchard and Sun teaches storing the audio signal having been captured as the computer stores captured audio signal in the working memory, wherein calculating the plurality of noise reduction gains, calculating the shared noise reduction gains, and generating the modified audio signal are performed on the audio signal having been stored.
Regarding claim 5, Bouchard fails to show feature extraction. Sun teaches how to train a machine learning model offline by inputting test signal with specific input features, such as background noise, and providing appropriate gain based on the specific input feature ([0034]). Once the model is established, the model could be used for actual input with various background noise level. The claimed feature extraction is a well known technique using a machine learning model for determining an output based on an input. Examiner takes Official Notice that this feature is notoriously well known in the art. Thus, it would have been obvious to one of ordinary skill in the art to modify the combination of Bouchard and Sun by using well known feature extraction technique in order to enable the machine learning model classifying the current noise condition and providing the appropriate gain based on the detected noise condition.
Regarding claims 6 and 8, the combination of Bouchard and Sun as discussed above teaches the monaural model (the model taught in Sun for stage 2 of Bouchard).
Regarding claims 7 and 8, the combination of Bouchard and Sun as discussed above teaches the monaural model (the model taught in Sun for stage 3 top of Bouchard).
Regarding claim 10, Bouchard teaches that the mathematical function includes a maximum (equations 10, 15 and 32, stage 5).
Regarding claim 12, Bouchard teaches a joint plurality of noise reduction gains (gains at the output of stage 4, e.g.) and the plurality of shared noise reduction gains (gains at stage 5).
Regarding claim 13, Bouchard fails to show a mobile phone with a front camera and a rear camera. Bouchard teaches general headphones, headsets and earphones ([0003]). One skilled in the art would have expected that such devices could couple to other electronic device. Sun teaches a smartphone coupled to headsets ([0013], [0016], [0053]). Although not explicitly shown, Examiner takes Official Notice that a smartphone with both front and back cameras that is capable of taking video contemporaneously with captured audio is notoriously well known in the art. Thus, it would have been obvious to one of ordinary skill in the art to further modify the combination of Bouchard and Sun by utilizing noise reduction for captured audio signal as taught in Bouchard for improving audio quality captured by a smartphone with both front and back camera while recording both video and audio.
Regarding claim 19, the combination of Bouchard and Sun teaches the claimed non-transitory computer readable medium ([0007] in Sun).
Regarding claim 20, Bouchard teaches the apparatus (see title, e.g., Figs. 1A and 1B).
Claim(s) 21 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bouchard and Sun as applied to claim 1 above, and further in view of Wung et al. (US 20180040333A1; hereafter Wung).
Regarding claims 21 and 22, Bouchard fails to teaches offline training in a training phase and calculating gains in an operational phase. Sun teaches a general a machine learning system. A machine learning system allows a processor learning from training data and generating desired output. Using Offline training was well known in the art. One skilled in the art would have expected that well known design for the machine learning system, including an offline training stage of a model and an operational stage, could be used without generating any unexpected result. Wung is cited to show a machine learning system with a machine learning model that has been trained offline using audio training data (such as a target training signal, see [0022]). The machine learning model generates training gain function to be applied to the noise speech signal. The offline training would train the machine model in a controlled environment (a controlled audio signal) to a provide a predict outcome. Referring back to Bouchard, the plurality of gains for each channel (see Figs. 1A and 1B) are calculated through several stages. By using an offline-trained model, several stage could be eliminated based on the trained model which would generate predicted gains based on the detected inputs. Thus, it would have been obvious to one of ordinary skill in the art to modify the combination of Bouchard and Sun in view of Wung by training a model offline using audio data and generating plurality of gains for each channel in order to calculate desired gain based on a trained model which would provide reliable gain value based on the previously trained model.
The claimed ”a monaural model” reads on the model used for left or right channel, the claimed “copy of the monaural model” reads on the model used for the other of left or right channel.
Allowable Subject Matter
Claims 14-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 3/11/2026 have been fully considered but they are not persuasive.
Applicant argued, on p. 7, the amended limitation “mathematical function” includes the one or more of the function specified in the specification. It is noted that the features upon which applicant relies (i.e., the specific functions disclosed in the specification) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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/PING LEE/Primary Examiner, Art Unit 2695
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