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 .
Introductory Remarks
In response to communications filed on 27 February 2026, no claims are newly amended, canceled or added as new. Therefore, claims 1-7, 9-11, 13, 14, 16-18, 20, 21, 23-28, 30, and 31 were pending in the application, of which, claim(s) 1, 16, 24, and 26 is/are presented in independent form. In response to the restriction requirement, the applicant has elected claims 1-7, 9-11, 13, 14, 23, and 28, withdrawing all other pending claims.
No IDS has been received since the mailing of the last Office action.
Election/Restrictions
Applicant's election with traverse of the restriction requirement mailed 7 January 2026 in the reply filed on 27 February 2026 is acknowledged. The traversal is on the ground(s) that Group I and III do not present an undue search and/or examination burden. This is not found persuasive because the application is a 371 national stage of PCT/US2921/055672. As a national stage application, the standard for restriction is not 1) independent and distinct and 2) undue burden, but rather it is solely the unity of invention standard established under the PCT and there is no requirement to show undue burden. MPEP 823, 1850, and 1893.03(d). Given that the applicant argues a standard that is wholly inapplicable, the applicant’s arguments are not persuasive.
The requirement is still deemed proper and is therefore made FINAL.
Examiner’s Note
The rejections below group claims that may not be identical, but whose language and scope are so substantively similar as to lend themselves to grouping, in the interests of clarity and conciseness. Any citation to the instant specification herein is made to the PGPub version (if applicable). The examiner notes that no statement has been entered regarding the inventorship of individual claims as required under 37 CFR 1.56, and therefore assumes that all claims have the same inventorship or are directed to inventions that were commonly owned as of the effective filing date of the invention.
Claim Objections
Claim 1 is objected to because of the following informalities: it lacks a conjunction between the second to last and last claim limitations; an “and” is presumed. Appropriate correction is required.
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.
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) 1-7, 9-11, 13, 23, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jha et al, DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation, 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), received via IDS (hereinafter Jha) in view of Jansson et al., Singing Voice Separation with Deep U-Net Convolutional Networks, presented at the 18th International Society for Music Information Retrieval Conference, 23-27 October 2017, retrieved on 6 January 2026, retrieved from the Internet <URL: https://openaccess.city.ac.uk/id/eprint/19289/> (hereinafter Jansson).
As per claim 1, Jha teaches a convolutional neural network (CNN) architecture (Fig. 1 and corresponding description), the CNN architecture comprising:
a contracting path of a U-net with a first encoding layer, wherein the contracting path is configured to generate an output representation of the contracting path based on a first signal provided as an input to the contracting path (Fig. 1 and corresponding description - VGG-19),
a multi-scale CNN configured to generate, based on the output representation of the contracting path, an intermediate representation, wherein the multi-scale CNN comprises at least two parallel convolution paths (Fig. 1 and corresponding description - ASPP),
an expansive path of a U-net with a first decoding layer, wherein the expansive path is configured to generate a final representation based on the intermediate representation generated by the multi-scale CNN and to output a second audio signal (Fig. 1 and corresponding description – Decoder blocks).
But Jha does not appear to explicitly disclose:
a convolutional neural network (CNN) architecture for audio processing, the CNN architecture comprising:
a contracting path of a U-net with a first encoding layer, wherein the contracting path is configured to generate an output representation of the contracting path based on a first audio signal provided as an input to the contracting path. (Emphasis added).
Jansson does teach audio processing using a CNN. Jansson at Abstract. It would have been obvious to one of ordinary skill in the art to incorporate the teachings of Jansson into the invention of Jha in order to have the CNN process audio and have the contracting path be configure to generate an output representation of the contracting path based on an audio signal provided as input to the contracting path. This would have been clearly advantageous as it would allow the system of Jha to operate on audio data producing better analysis of main melodic lines and accompaniment (Jansson at Introduction. The combination hereinafter JJ.
Claims 2-5 solely claim features that are inherent to U-nets, and are therefore taught by JJ.
As per claim 6, JJ teaches the CNN architecture according to claim1, wherein the multi-scale CNN is configured to generate an aggregated output based on outputs of the at least two parallel convolution paths (Jha at Fig. 1 and corresponding description).
As per claim 7, JJ teaches the CNN architecture according to claim 6, wherein the multi- scale CNN is configured generate the aggregated output by concatenating or adding the outputs of the at least two parallel convolution paths (Jha at Fig. 1 and corresponding description); and/or
weight the outputs of the at least two parallel convolution path using different weights.
As per claim 9, JJ teaches the CNN architecture according to claim1, wherein each parallel convolution path of the multi-scale CNN includes L convolution layers, wherein L is a natural number >= 1, and wherein an I-th layer among the L layers has N1 filters with I= 1...L (Jha at Fig. 1 and corresponding description).
As per claim 10, JJ teaches the CNN architecture according to claim 9, wherein for each parallel convolution path, the number Ni of filters in the 1-th layer is increasing with increasing layer number 1 (Jha at Fig. 1 and corresponding description).
As per claim 11, JJ teaches the CNN architecture according to claim 9, wherein a filter size of the filters is:
the same within each parallel convolution path; or
different between different parallel convolution paths (the filter size necessarily has to be either the same or different between the different paths).
As per claim 13, JJ teaches the CNN architecture according to claim 9, wherein, for a given parallel convolution path, the filters of at least one of the layers of the parallel convolution path are dilated 2D convolutional filters (Jha at Fig. 1 and corresponding description).
As per claim 23, JJ teaches an apparatus (Jha at section IV(C)) for audio processing, wherein
the apparatus is configured to receive input of an input audio signal and output an output audio signal (see remarks regarding claim 1),
the apparatus comprises the CNN architecture according to claim 1 (see remarks regarding claim 1), and
an input to the contracting path is based on the input audio signal and the output audio signal is based on an output of the expansive path (see remarks regarding claim 1).
As per claim 28, JJ teaches a system (Jha at section IV(C)) for audio processing, comprising:
one or more processors (id.); and
a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (id.) comprising:
receiving an input audio signal (Jansson at Section 3.1);
processing the input audio signal using a CNN architecture according to claim 1, the processing comprising:
providing an input to the contracting path of the CNN architecture based on the input audio signal (Jha at Fig. 1); and
generating an output audio signal based on an output of the expansive path of the CNN architecture (id.).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over JJ as applied to claim 13 above, and further in view of Sun et al. (U.S. PGPub No. 2022/0223144 A1) (hereinafter Sun).
As per claim 14, JJ does not appear to explicitly disclose the claimed limitations. However Sun does teach wherein the dilation operation of the filters of the at least one of the layers of the parallel convolution path is performed on the frequency axis only (0071); or
wherein, for a given parallel convolution path, the filters of two or more of the layers of the parallel convolution path are dilated 2D convolutional filters, and wherein a dilation factor of the dilated 2D convolutional filters increases exponentially with increasing layer number 1 (0072).
It would have been obvious to one of ordinary skill in the art to incorporate the teachings of Sun into the combination of JJ in order to have the dilation operation of the filters of the at least one of the layers of the parallel convolution path be performed on the frequency axis only; or for a given parallel convolution path, the filters of two or more of the layers of the parallel convolution path be dilated 2D convolutional filters, and wherein a dilation factor of the dilated 2D convolutional filters increases exponentially with increasing layer number 1. This would have been clearly advantageous as it would enable the extraction of correlations in features in different receptive fields.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER J TORGRIMSON whose telephone number is (571)270-5550. The examiner can normally be reached Monday - Friday 9 am - 5:30 pm.
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/TYLER J TORGRIMSON/ Primary Examiner, Art Unit 2165
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