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 8/14/23 has been considered.
Drawings
The drawings filed 8/14/23 are acceptable to the examiner.
Specification
The disclosure is objected to because of the following informalities: Paragraph [0040] of the written specification contains no description.
Appropriate correction is required.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 11-13, 17-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Akotkar et al. (US 2019/0049989 A1).
Re claim 1: Akotkar et al teaches a computer-implemented method (see paragraph [0015]) of audio processing, comprising:
receiving, by processor circuitry (analysis block 110), a mixed audio signal (from plural microphones 108) having a plurality of audio sources (including sounds from a vehicle (102), for example a drone, paragraph [0046]; and surrounding sounds, paragraph [0016]);
separating the mixed audio signal into at least one separate target audio source signal (extracted features from a target audio source, figure 1, such as from a vehicle/drone or from an emergency vehicle); and
determining whether or not the at least one separate target audio source signal is associated with at least one target audio source, and comprising inputting the at least one separate target audio source signal into a classifying neural network (by operation of block (112), paragraph [0017]).
Re claim 11: this claim sets forth similar features as that present in claim 1 that are taught by Akotkar et al. discussed with respect to claim 1. Additionally, the claimed memory is taught in paragraph [0037], memory having program code stored therein.
Re claim 18: this claim sets forth similar features as that present in claim 1 that are taught by Akotkar et al. discussed with respect to claim 1. Additionally having a non-transitory medium comprising instructions as set forth is taught in paragraph [0037]
Re claim 2: note the target audio source can be at least a drone, vehicle or unmanned vehicle as taught in paragraph [0046]
Re claim 3: note operation of block (110), paragraph [0017] which extract audio features from the vehicle and surrounding sounds
Re claim 4: note operation of block (110), paragraph [0017] which extract audio features from the vehicle and surrounding sounds where the surrounding sounds can include different background sounds such as sounds from an emergency vehicle or a message broadcast (paragraph [0016])
Re claim 12: note discussion in paragraph [0027] in which the neural network is trained to identify a target audio source and background audio signal(s); i.e. alarm signals from non-alarm signals along with discussion in paragraph [0020] in which a comparison is made with ground truth signals (such as an emergency alarm signal)
Re claim 13: note paragraph [0025] and [0027] that training is performed on data of audio signal signatures (specific extracted audio features) and that this training can be performed regardless of the existence of repetition of an audio pattern (paragraph [0028] i.e. considering one frame and not necessarily one or more plurality of frames that includes an alarm or non-alarm.
Re claim 17: note discussion in paragraph [0027] in which the neural network is trained to identify a target audio source and background audio signal(s); i.e. alarm signals from non-alarm signals.
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) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. in view of Franklin et al. (US 10,032,464 B2), cited by applicant.
Re claim 5: The teaching of Akotkar et al. is discussed above and incorporated herein. Although the arrangement in Akotkar et al. separates sounds of a drone, the reference does not teach separating audio signals from different drones. Franklin et al. teaches that different drones produce different sound signatures and when detecting the sounds from a specific drone audio signal obtained from a processing circuitry (such as sound card (322) can be used for such a sound signature separation (column 10, lines 38-60). It would have been obvious to one of ordinary skill in the art to incorporate this teaching of Franklin et al. into the arrangement of Akotkar et al. to predictably provide sound separation from audio sources including audio sources from a plurality of different drones. Therefor the claimed subject matter would have been obvious before the filing of the invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akotkar et al. in view of Koshinaka et al. (US 11,900,949 B2).
Re claim 6: The teaching of Akotkar et al. is discussed above and incorporated herein. Although Akotkar et al. teaches the use of a neural network for audio processing the reference does not teach to use a mask estimate in conjunction with the network for audio processing. Koshinaka et al. teaches in a related art a mask estimation unit can be used as a possible way of audio signal extraction. (see figure 8 along with discussion in column 11, lines 31- column 12, line 37). It would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporate this teaching of using a mask estimation unit into the arrangement of Akotkar et al. to predictably provide an alternative means for audio signal separation/extraction. Therefor the claimed subject matter would have been obvious before the filing of the invention.
Allowable Subject Matter
Claim 7-10,14-16, 19-20 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 the combination of those features of claim 6/1, wherein the neural network comprises an encoder, a mask-estimator, and a decoder each having one or more convolutional layers as set forth in claim 7 is neither taught by nor an obvious variation of the art of record. The claimed method including the combination of those features of claim 1, wherein the separating comprises a separator neural network having bidirectional long short-term memory (BLSTM) layers shared between a mask interference branch and a deep clustering branch, and at least one mask interference layer on the mask interference branch separate from at least one deep clustering layer on the deep clustering branch, wherein audio signal data input to the separator neural network is in a time domain as set forth in claim 8 is neither taught by nor an obvious variation of the art of record. The limitations of claim 9 depend upon those features of claim 8/1. The claimed method including the combination of those features of claim 1, wherein the separating comprises using a Chimera type of neural network, and the classifying neural network is a YAMNet type of neural network as set forth in claim 10 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including the combination of those features of claim 11, wherein the processor circuitry is arranged to operate by selecting at least one separating model among multiple separating models to perform the separating, wherein the multiple separating models are individually trained to operate with data of different acoustical environments than others of the multiple separating models as set forth in claim 14 is neither taught by nor an obvious variation of the art of record. The limitations of claim 15 depend upon those features of claim 14/11. The claimed arrangement including the combination of those features of claim 11, wherein the processor circuitry is arranged to operate by selecting at least one separating model among multiple separating models to perform the separating, wherein the multiple separating models are individually trained to operate with a different number of audio sources as set forth in claim 16 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including the combination of those features of claim 18, wherein the instructions cause the computing device to operate by performing the determining by at least two classifier models remote from each other and trained to identify different target audio sources, different background audio sources, or both as set forth in claim 19 is neither taught by nor an obvious variation of the art of record. The claimed arrangement including the combination of those features of claim 18 20. The medium of claim 18, wherein the instructions cause the computing device to operate by performing the separating by at least two separator models remote from each other and trained to separate different target audio sources, different background audio sources, or both as set forth in claim 20 is neither taught by nor an obvious variation of the art of record.
Conclusion
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/ANDREW SNIEZEK/Primary Examiner, Art Unit 2693
/A.S./Primary Examiner, Art Unit 2693 9/21/26
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