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 filed 9/1/23 has been considered.
Drawings
The drawings filed 6/1/23 are acceptable to the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-6, 9-10, 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Goh et al.
Re claim 1: Goh et al. teaches a computer-implemented method of audio processing, comprising:
receiving, by at least one processor (166), multiple audio signals from multiple microphones (160, 162, 164), wherein the audio signals are associated with audio emitted from a same source (such as from a user (372), figure 3);
determining an audio quality indicator (characteristics of a user’s voice) of individual ones of the audio signals using a neural network paragraph [0035], page 5 along with paragraph [0065], page 10 voice pattern recognition that recognizes the voice closest to a microphone); and
selecting at least one of the audio signals depending on the audio quality indicators, by the filtering out other voices from other microphones more distantly spaced (figure 3, figure 6, step 625)
Re claim 10: the specifics of this claim are substantially the same as provided in claim 1 and are taught by Goh et al. as discussed above with respect to claim 1. Also the use of a computer readable medium with instructions for operation is taught in paragraph [0043]-[0044]
Re claim 16: the specifics of this claim are substantially the same as provided in claim 1 and are taught by Goh et al. as discussed above with respect to claim 1. Also, the use of a memory to hold the audio signals is satisfied by data base 588), paragraph [0071]
Re claim 5: note in Goh et al. the highest quality indicator (characteristic of the user’s voice) in used in microphone selection (by filtering out other signals picked up by other microphones
Re claim 6: note paragraph [0053] teaching comparing detected voice signals with a threshold value
Re claims 9, 11: note in Goh et al., paragraphs [0036,0074] distance between an audio source and microphone or the speakerphone is determined when determining audio quality
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.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goh et al. in view of Laroche et al. (US 2023/0206936 A1).
Re claims 3-4: The teaching of Goh et al. is discussed above and incorporated herein. Goh et al. does not teach that the neural network is a mean opinion score type (MOS) (claim 2) or deep noise suppression mean opinion score type (DNSMOS) (claim 3). Laroch et al. teaches in a similar environment to use a mean opinion score type of neural network including a deep noise suppression, paragraph [0084] to determine acoustic information of the surroundings of microphone. It would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporate such mean opinion score neural networks as taught by Laroche et al. for the neural network used in Goh et al. to predictably provide specific types of neural networks for the determination of acoustic information surrounding a microphone. Therefor the claimed subject matter would have been obvious before the filing of the invention.
Allowable Subject Matter
Claims 2, 7-8, 12-15, 17-20 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.
The following is a statement of reasons for the indication of allowable subject matter: The claimed method including in combination those features of claim 1, wherein the neural network is trained by at least using a training dataset generated by having one or more people listen to audio samples and rate the audio samples, wherein the audio samples include a single word as set forth in claim 2 is neither taught by nor an obvious variation of the art of record. The claimed method including in combination those features of claim 6/5/1, further comprising setting the threshold at a value to control a frequency of how often one of the audio signals other than a current audio signal being used that is found to have a highest audio quality indicator is to be used as set forth in claim 7 is neither taught by nor an obvious variation of the art of record. The claimed method including in combination those features of claim 1, further comprising placing an identity of a microphone or audio signal and audio quality indicator in an index after the selecting selects a sample of a sampling time and of a selected audio signal as having a highest audio quality to be used, and using an index sampling count so that either: (a) every nth sample is placed into the index for microphone switching, or (b) every nth sample within the index is used for microphone switching, wherein n is greater than 1 as set forth in claim 8 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including those features of claim 10 wherein the instructions cause the computing device to synchronize multiple audio signals from the multiple microphones before providing the audio signals to the neural network as set forth in claim 12 is neither taught by nor an obvious variation of the art of record. The limitations of claim 13 depend upon those features of claim 12/10. The claimed arrangement including those features of claim 10, wherein the instructions cause the computing device to normalize values of a current audio signal being used relative to a selected audio signal as set forth in claim 14 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including those features of claim 10, wherein the instructions cause the computing device to switch from a current microphone to a selected microphone comprising using a fade-in and fade- out switching as set forth in claim 15 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including those features of claim 16, comprising sampling the audio signals at intervals set to start every 1 to 10 seconds, or start every 2 seconds to generate the audio quality indicators at individual sample times as set forth in claim 17 is neither taught by nor an obvious variation of the art of record.
The claimed arrangement including those features of claim 16, wherein the processing circuitry is arranged to deactivate the determining and selecting when multiple sources are talking at the same time as set forth in claim 18 is neither taught by nor an obvious variation of the art of record.
The claimed arrangement including those features of claim 16, wherein the processing circuitry is arranged to perform source separation comprising generating separate audio signals of each a different source of multiple sources associated with a single microphone, performing the determining on each separate audio signal so that each separate audio signal receives a separate audio quality indicator, and combining the separate audio quality indicators to generate a single indicator for a microphone with the multiple sources as set forth in claim 19 is neither taught by nor an obvious variation of the art of record.
The claimed arrangement including those features of claim 16, wherein the neural network comprises an input layer to receive audio signal values without conversion to a Mel-frequency related domain, one or more convolutional layers, and an output layer with one node that outputs an audio quality score as the audio quality indicator for a single audio signal sample as set forth in claim 20 is neither taught by nor an obvious variation of the art of record.
.Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW SNIEZEK whose telephone number is (571)272-7563. The examiner can normally be reached Monday-Friday 7:00 AM-3:30 PM EST.
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/ANDREW SNIEZEK/Primary Examiner, Art Unit 2693
/A.S./Primary Examiner, Art Unit 2693 6/22/26
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