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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 110, 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]).
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.
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 9, Bouchard teaches a first plurality of noise reduction gains, a second plurality of noise gains (reads on gains determined in stage 2 and 4) and a mathematical function (equation in stage 5), but 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 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).
Allowable Subject Matter
Claims 11 and 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PING LEE/Primary Examiner, Art Unit 2695
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