CTFR 18/756,952 CTFR 89353 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Arguments Applicant’s arguments with respect to claim(s) 1 - 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 1, 3, 6, 7, 9, 10, 12, 15, 16, and 18 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (WO 2024/081872) (hereinafter Zhang) in view of Kim et al. (US 10,282,864) (hereinafter Kim) . Regarding claims 1, 10, and 20 , Zhang teaches a method of decoding encoded video data, a device for decoding encoded video data, the device comprising a memory configured to store the encoded video data and one or more processors implemented in circuitry and configured to perform the method (e.g. Fig. 25, and pars. 183 – 195: depicting and describing a computing device, the computing device comprising a memory and one or more processors configured to perform the decoding method) , and a non-transitory computer readable storage medium storing instructions that when executed by one or more processors cause the one or more processors to perform the method (e.g. pars. 187 and 192 - 195: describing computer readable instructions stored in memory, the instructions causing one or more processors to perform the video decoding method) , the method comprising: determining, from the encoded video data, a block of a picture (e.g. Figs. 2, 3, and 8, and pars. 79 – 80, 85, and par. 91, section 2.3: depicting and describing that the system obtains reconstructed residual data from the encoded video data [ elements 210 and 211 of Fig. 2, element 301 of Fig. 3, and IQ, IT, and REC blocks of Fig. 8], the reconstructed residual data being a block of a picture) ; applying a neural network (NN)-based filter process to the block to generate a filtered block (e.g. par. 91, sections 4 and 5.3 – 5.3.2 and par. 93: describing that the system applies a neural network based filter to generate a filtered block) , wherein applying the NN-based filter process comprises: performing a first feature extraction on pixel data of the block at a first scale to generate a first set of extracted features for the block (e.g. Figs. 16A, 16B, and 23, and par. 91, sections 4 and 5: depicting and describing that the neural network filter performs multi-scale feature extraction, the multi-scale feature extraction including a first feature extraction on input video data at a first scale [Layer 1 of Figs. 16A and B, and C1 of Fig. 23]) ; performing a second feature extraction on the pixel data of the block at a second scale to generate a second set of extracted features for the block, wherein the first scale is different than the second scale block (e.g. Figs. 16A, 16B, and 23, and par. 91, sections 4 and 5: depicting and describing that the neural network filter performs multi-scale feature extraction, the multi-scale feature extraction including a first feature extraction on input video data at a second scale [Layer 2 of Figs. 16A and B, and C2 of Fig. 23], the first and second scales being different from each other [see, e.g. Fig. 23 and par. 91, section 4: depicting and describing that the kernel size of the convolutional layers are different from each other]) ; and generating the filtered block based on the first set of extracted features and the second set of extracted features (e.g. Fig. 23 and par. 91, section 5: depicting and describing that the system generates filtered video data based on the first and second sets of extracted features) ; determining a decoded version of the block based on the filtered block (e.g. Fig. 8 and par. 91, sections 2.3 – 2.9, 4, and 5: depicting and describing that the system reconstructs the block using the filtered block) ; and outputting a decoded version of the picture comprising the decoded version of the block (e.g. Fig. 8 and par. 91, sections 2.3 – 2.9, 4, and 5: depicting and describing that the system outputs the reconstructed block) . Zhang does not explicitly teach: wherein performing the first feature extraction comprises applying a first convolution having a first stride, and wherein performing the second feature extraction comprises applying a second convolution having a second stride, wherein the first stride is different than the second stride. Kim, however, teaches a method of decoding, a device for decoding, and a non-transitory computer readable storage medium storing instructions that when executed by one or more processors cause the one or more processors to perform the decoding method: wherein performing the first feature extraction comprises applying a first convolution having a first stride, and wherein performing the second feature extraction comprises applying a second convolution having a second stride, wherein the first stride is different than the second stride (e.g. Fig. 3 and col 9, line 28 – col 10, line 35: depicting and describing that the system generates a feature map of an input image by performing a first feature extraction [element 330] and second feature extraction [element 340], the first feature extraction performed by applying a first convolution with a first stride [exemplified as having a stride of 2], the second feature extraction performed by applying a second convolution with a second stride [exemplified as a stride of 4], the first and second strides being different from each other) . It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Zhang by adding the teachings of Kim in order to perform the first feature extraction by applying a first convolution having a first stride, and perform the second feature extraction by applying a second convolution having a second stride, wherein the first stride is different than the second stride. One of ordinary skill in the art would have been motivated to make such a modification because the modification extracts features with more diverse characteristics (Kim, e.g. col 2, lines 12 – 20: describing a desire to extract features with more diverse characteristics) . Turning to claims 3 and 12 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang further teaches: wherein: performing the first feature extraction on the block at the first scale comprises applying a first convolution filter with a first support size; and performing the second feature extraction on the block at the second scale comprises applying a second convolution filter with a second support size, wherein the first support size is different than the second support size (e.g. Figs. 16A-B and 23, and par. 91, sections 4 and 5: depicting and describing that the multi-scale feature extraction includes a first convolution filter with a first kernel size [depicting in Fig. 23 as C1 having a kernel size of 1x1] and second convolution filter with a second kernel size [depicted in Fig. 23 as C2 having a kernel size of 3x3], wherein kernel size is the equivalent of the support size) . Regarding claims 6 and 15 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang further teaches: inputting the first set of extracted features for the block into a first parametric rectified linear unit (PReLU) layer; and inputting the second set of extracted features for the block into a second PReLU layer (e.g. Figs. 16A-B, elements Layer3 and Layer4, Fig. 23, and par. 91, sections 4 and 5: depicting and describing that the first set of extracted features are input into a PReLU layer and the second set of extracted features are input into a second PReLU layer) . Turning to claims 7 and 16 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang further teaches: wherein the block comprises a reconstructed block and determining the block of the picture comprises adding a prediction block to a residual block (e.g. Fig. 2, element 212, Fig. 3, element 306, and Fig. 8, and pars. 79 – 80, 90, and 91, section 2.3: depicting and describing that the block is a reconstructed block, the reconstructed block obtained by adding a prediction block to a residual block) . Regarding claim 9 , Zhang and Kim teach all of the limitations of claim 1, as discussed above. Zhang further teaches: wherein the method of decoding is performed as part of a video encoding process (e.g. Figs. 2 and 8, and pars. 79 – 80, and 91, section 2.3: depicting and describing that the decoding is performed as part of a video encoding process) . Turning to claim 18 , Zhang and Kim teach all of the limitations of claim 10, as discussed above. Zhang further teaches: further comprising a display configured to display decoded video data (e.g. Fig. 1, element 122, and Fig. 25 and pars. 57, 191 and 195: depicting and describing that the system further includes a display, the display configured to display the decoded video data) . Regarding claim 19 , Zhang and Kim teach all of the limitations of claim 10, as discussed above. Zhang further teaches: wherein the device comprises one or more of a camera, a computer, a mobile device, a broadcast receiver device, or a set-top box (e.g. Fig. 25 and pars. 185 – 186: depicting and describing that the device includes a camera, a general purpose computer, a mobile device, a broadcast receiver, or a television receiver, wherein the television receiver is the equivalent of the set-top box) . 07-22-aia AIA Claim (s) 2, 4, 5, 8, 11, 13, 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (WO 2024/081872) (hereinafter Zhang) in view of Kim et al. (US 10,282,864) (hereinafter Kim) as applied to claim s 1 and 10, respectively , above, and further in view of Wang et al. (CN 111079825) (hereinafter Wang), as cited by applicant . Regarding claims 2 and 11 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang does not explicitly teach: wherein applying the NN-based filter process comprises: performing a third feature extraction on the pixel data of the block at a third scale to generate a third set of extracted features for the block, wherein the first scale is different than the second scale and the third scale, and the second scale is different than the third scale. Wang, however, teaches a neural network based image filtering method and device: wherein applying the NN-based filter process comprises: performing a third feature extraction on the pixel data of the block at a third scale to generate a third set of extracted features for the block, wherein the first scale is different than the second scale and the third scale, and the second scale is different than the third scale (e.g. Figs. 3 and 4, and pars. 33 – 34: depicting and describing that the multi-scale feature extraction includes a third feature extraction operation, the feature extraction operation having a different kernel size than the other feature extraction operations, wherein having a different kernel size is the equivalent of the third scale being different from the first and second scales) . It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Zhang by adding the teachings of Wang in order to perform a third feature extraction on the pixel data of the block at a third scale to generate a third set of extracted features for the block, the third scale being different from the first scale and the second scale. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves calculation efficiency (Wang, e.g. par. 34: describing a desire to improve calculation efficiency) . Turning to claims 4 and 13 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang does not explicitly teach: wherein: performing the first feature extraction on the block at the first scale comprises applying a first set of cascading convolution filters; and performing the second feature extraction on the block at the second scale comprises applying a second set of cascading convolution filters. Wang, however, teaches a method and device for filtering image data using a neural-network based filter: wherein: performing the first feature extraction on the block at the first scale comprises applying a first set of cascading convolution filters; and performing the second feature extraction on the block at the second scale comprises applying a second set of cascading convolution filters (e.g. Figs. 3 and 4, and pars. 33 and 34: depicting and describing that the multi-scale feature extraction is performed by applying a first series of convolutional filters and a second series of convolutional filters, the series of convolutional filters having different kernel sizes [depicted in Fig. 3 as having a 1x1 kernel size filter set, a 3x3 kernel size filter set and a 5x5 kernel size filter set], wherein a series convolutional filters is the equivalent a set of cascading convolution filters) . It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Zhang by adding the teachings of Wang in order for performing the first feature extraction on the block at the first scale to comprise applying a first set of cascading convolution filters, and performing the second feature extraction on the block at the second scale to comprise applying a second set of cascading convolution filters. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves calculation efficiency (Wang, e.g. par. 34: describing a desire to improve calculation efficiency) . Regarding claims 5 and 14 , Zhang, Kim, and Wang teach all of the limitations of claims 1 and 4, and claims 10 and 13, respectively, as discussed above. Zhang does not explicitly teach: wherein each of the cascading convolution filters of the first set have a first support size and each of the cascading convolution filters of the second set have the first support size. Wang, however, teaches a method and device for neural-network based filtering of image data: wherein each of the cascading convolution filters of the first set have a first support size and each of the cascading convolution filters of the second set have the first support size (e.g. Figs. 3 and 4, and pars. 33 and 34: depicting and describing that the convolution filters in the sets of convolution filters have a same size [exemplifying that convolution filters have a size of 3x3, the number of 3x3 convolution filters in each series changing the overall kernel size of the feature extraction]) . It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Zhang by adding the teachings of Wang in order for each of the cascading convolution filters of the first set have a first support size and each of the cascading convolution filters of the second set have the first support size. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves calculation efficiency (Wang, e.g. par. 34: describing a desire to improve calculation efficiency) . Turning to claims 8 and 17 , Zhang and Kim teach all of the limitations of claims 1 and 10, respectively, as discussed above. Zhang further teaches: wherein the first scale and second scale are different from each other, the first scale and second scale being one of 1x1, 3x3, and 5x5 (e.g. par. 91, section 4: describing that the kernel sizes for the multi-scale feature extraction are different from each other and are selected from kernel sizes of 1x1, 3x3, and 5x5, wherein kernel size is the equivalent of scale) . Zhang does not explicitly teach: wherein the first scale is 3x3 and the second scale is 5x5. Wang, however, teaches a method and device for neural-network based filtering of image data: wherein the first scale is 3x3 and the second scale is 5x5 (e.g. pars. 33-34: describing that a first feature extraction of the multi-scale feature extraction has a 3x3 kernel size, and a second feature extraction of the multi-scale feature extraction has a 5x5 kernel size, wherein kernel size is the equivalent of the scale) . It therefore would have been obvious to one of ordinary skill in the art to modify the teachings of Zhang by adding the teachings of Wang in order for the first scale to be 3x3 and the second scale to be 5x5. One of ordinary skill in the art would have been motivated to make such a modification because the modification improves calculation efficiency (Wang, e.g. par. 34: describing a desire to improve calculation efficiency) . Conclusion 07-40 AIA 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANIKA M BRUMFIELD whose telephone number is (571)270-3700. The examiner can normally be reached M-F 8:30 - 5 PM AWS. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Czekaj can be reached at 571-272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. SHANIKA M. BRUMFIELD Examiner Art Unit 2487 /SHANIKA M BRUMFIELD/Examiner, Art Unit 2487 /Dave Czekaj/Supervisory Patent Examiner, Art Unit 2487 Application/Control Number: 18/756,952 Page 2 Art Unit: 2487 Application/Control Number: 18/756,952 Page 3 Art Unit: 2487 Application/Control Number: 18/756,952 Page 4 Art Unit: 2487 Application/Control Number: 18/756,952 Page 5 Art Unit: 2487 Application/Control Number: 18/756,952 Page 6 Art Unit: 2487 Application/Control Number: 18/756,952 Page 7 Art Unit: 2487 Application/Control Number: 18/756,952 Page 8 Art Unit: 2487 Application/Control Number: 18/756,952 Page 9 Art Unit: 2487 Application/Control Number: 18/756,952 Page 10 Art Unit: 2487 Application/Control Number: 18/756,952 Page 11 Art Unit: 2487 Application/Control Number: 18/756,952 Page 12 Art Unit: 2487 Application/Control Number: 18/756,952 Page 13 Art Unit: 2487 Application/Control Number: 18/756,952 Page 14 Art Unit: 2487
Prosecution Insights