Prosecution Insights
Last updated: April 19, 2026
Application No. 18/202,772

VIDEO CODEC USING DEEP LEARNING MODEL BASED ON BLOCK

Final Rejection §103
Filed
May 26, 2023
Examiner
HOLDER, ANNER N
Art Unit
2483
Tech Center
2400 — Computer Networks
Assignee
Ewha University - Industry Collaboration Foundation
OA Round
4 (Final)
78%
Grant Probability
Favorable
5-6
OA Rounds
3y 1m
To Grant
92%
With Interview

Examiner Intelligence

Grants 78% — above average
78%
Career Allow Rate
575 granted / 734 resolved
+20.3% vs TC avg
Moderate +14% lift
Without
With
+14.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
33 currently pending
Career history
767
Total Applications
across all art units

Statute-Specific Performance

§101
7.4%
-32.6% vs TC avg
§103
50.4%
+10.4% vs TC avg
§102
25.9%
-14.1% vs TC avg
§112
3.3%
-36.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 734 resolved cases

Office Action

§103
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 . Response to Arguments Applicant’s arguments with respect to claim(s) 1-11, 15 and 18-20 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 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. Claim(s) 1-2, 8, 10-11, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al US 2021/0409783 in view of Tsai et al US 2013/0128986. As to claim 1, Wan teaches a method performed by a video decoding apparatus for inloop-filtering a video block based on a deep learning technology, [abstract; figs. 1-2; ¶ 0024-0028] the method comprising: decoding parameters of at least one deep learning model; [abstract; fig. 2; ¶ 0024-0028; ¶ 0074; ¶ 0084; ¶ 0109] obtaining a video input block that includes a luma block, a first chroma block, and a second chroma block, which respectively have a luminance component signal, a first chrominance component signal, and a second chrominance component signal in a 4:2:0 or 4:4:4 format of sampling rate; [abstract; fig. 2; ¶ 0026-0028; ¶ 0035-0038] generating an input block by stacking or combining the luma block, the first chroma block, and the second chroma block; [figs. 2-3; figs. 7-8; ¶ 0030-0036] converting quantization parameters used for decoding of the video input block into an encoded map; [fig. 2; ¶ 0026-0028; ¶ 0034; ¶ 0065-0072] stacking the encoded map with the input block; [fig. 3; figs. 7-8; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] inputting a stack of the encoded map and the input block to the at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] generating an output block from the stack of the encoded map and the input block by performing a convolutional operation based on the at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0053-0054; ¶ 0082-0085; ¶ 0109] and generating a video output block from the output block. [fig. 2; figs. 7-8; ¶ 0027-0028; ¶ 0109] Wan does not explicitly teach padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block. Tsai teaches padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block; [figs. 7-8; ¶ 0025-0026] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Tsai with the teachings of Wan allowing for improved image quality. As to claim 2, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches wherein generating the input block comprises: when the luminance component signal, the first chrominance component signal, and the second chrominance component signal are in a 4:2:0 format, enlarging the first chroma block and the second chroma block to match in size the luma block; [¶ 0037-0038] and stacking the luma block, an enlarged first chroma block, and an enlarged second chroma block. [figs. 7-8; fig. 3; figs. 7-8; ¶ 0034-0038; ¶ 0053-0054; ¶ 0082-0085] As to claim 8, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches wherein generating the input block comprises: when the luminance component signal, the first chrominance component signal, and the second chrominance component signal are in a 4:2:0 format, generating a super block that is equal in size to the luma block by using the first chroma block and the second chroma block; [¶ 0037-0038] and stacking the super block and the luma block. [fig. 3; figs. 7-8; ¶ 0034-0038; ¶ 0053-0054; ¶ 0082-0085] As to claim 10, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches wherein generating the input block comprises: when the luminance component signal, the first chrominance component signal, and the second chrominance component signal are in a 4:2:0 format, [¶ 0037-0038] generating a super block by combining the luma block, the first chroma block, and the second chroma block. [fig. 3; figs. 7-8; ¶ 0034-0038; ¶ 0053-0054; ¶ 0082-0085] As to claim 11, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches wherein generating the input block comprises: when the luminance component signal, the first chrominance component signal, and the second chrominance component signal are in a 4:4:4 format, generating the input block by stacking the luma block, the first chroma block, and the second chroma block. [fig. 3; figs. 7-8; ¶ 0034-0038; ¶ 0053-0054; ¶ 0082-0085] As to claim 19, Wan teaches a method performed by a video encoding apparatus for inloop-filtering a video block based on a deep learning technology, [abstract; figs. 1-2; ¶ 0024-0028] the method comprising: obtaining a video input block that includes a luma block, a first chroma block, and a second chroma block, which respectively have a luminance component signal, a first chrominance component signal, and a second chrominance component signal in a 4:2:0 or 4:4:4 format of sampling rate; [abstract; fig. 2; ¶ 0026-0028; ¶ 0035-0038] generating an input block by stacking or combining the luma block, the first chroma block, and the second chroma block; [figs. 2-3; figs. 7-8; ¶ 0030-0036] converting quantization parameters used for decoding of the video input block into an encoded map stacking the encoded map with the input block; [fig. 2; ¶ 0026-0028; ¶ 0034; ¶ 0065-0072] stacking the encoded map with the input block; [fig. 3; figs. 7-8; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] inputting a stack of the encoded map and the input block to at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] generating an output block from the stack of the encoded map and the input block by performing a convolutional operation based on the at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0053-0054; ¶ 0082-0085; ¶ 0109] generating a video output block from the output block; [fig. 2; figs. 7-8; ¶ 0027-0028; ¶ 0109] and encoding parameters of the at least one deep learning model. [abstract; fig. 2; ¶ 0024-0028; ¶ 0074; ¶ 0084] Wan does not explicitly teach padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block; Tsai teaches padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block; [figs. 7-8; ¶ 0025-0026] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Tsai with the teachings of Wan allowing for improved image quality. As to claim 20, Wan teaches a method for providing a video decoding device with video data, the method comprising: encoding the video data into a bitstream; [figs. 1-2; ¶ 0024-0027] and transmitting the bitstream to the video decoding device, [figs. 1-2; ¶ 0024-0028; ¶ 0109] wherein the encoding of the video data comprises: obtaining a video input block to be inloop-filtered that includes a luma block, a first chroma block, and a second chroma block, which respectively have a luminance component signal, a first chrominance component signal, and a second chrominance component signal in a 4:2:0 or 4:4:4 format of sampling rate; [abstract; figs. 1-2; ¶ 0024-0028; ¶ 0035-0038] generating an input block by stacking or combining the luma block, the first chroma block, and the second chroma block; [figs. 2-3; figs. 7-8; ¶ 0030-0036] converting quantization parameters used for decoding of the video input block into an encoded map; stacking the encoded map with the input block; [fig. 2; ¶ 0026-0028; ¶ 0034; ¶ 0065-0072] stacking the encoded map with the input block; [fig. 3; figs. 7-8; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] inputting a stack of the encoded map and the input block to at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] generating an output block from the stack of the encoded map and the input block by performing a convolutional operation based on the at least one deep learning model; [fig. 3; figs. 7-8; ¶ 0027-0028; ¶ 0053-0054; ¶ 0082-0085; ¶ 0109] generating a video output block from the output block; [fig. 2; figs. 7-8; ¶ 0027-0028; ¶ 0109] and encoding parameters of the at least one deep learning model. [abstract; fig. 2; ¶ 0024-0028; ¶ 0074; ¶ 0084] Wan does not explicitly teach padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block; Tsai teaches padding a periphery of the input block with previously reconstructed samples adjacent to the input block to generate a padded input block; [figs. 7-8; ¶ 0025-0026] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Tsai with the teachings of Wan allowing for improved image quality. Claim(s) 3, 9, 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al US 2021/0409783 in view of Tsai et al US 2013/0128986 further in view of Gao et al US 2023/0336776. As to claim 3, Wan (modified by Tsai) teaches the limitations of claim 2. Wan teaches wherein enlarging comprises: enlarging the first chroma block to match in size the luma block. [¶ 0037-0038; ¶ 0083] Wan does not explicitly teach wherein enlarging comprises: enlarging the first chroma block to match in size the luma block by repeating the first chroma block 4 times through mirroring the first chroma block up and down or left to right and combining four first chroma blocks resulting from the mirroring. Gao, analogous art in the same field of endeavor, teaches wherein enlarging comprises: enlarging the first chroma block to match in size the luma block by repeating the first chroma block 4 times through mirroring the first chroma block up and down or left to right and combining four first chroma blocks resulting from the mirroring. [fig. 23; ¶ 0243-0244] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Gao with the teachings of Wan allowing for improved coding efficiency. As to claim 9, Wan (modified by Tsai) teaches the limitations of claim 8. Wan (modified by Tsai) does not explicitly teach wherein generating the super block comprises: combining the first chroma block and the second chroma block up and down and then upsampling the first chroma block and the second chroma block in a horizontal direction, or combining the first chroma block and the second chroma block from side to side and then upsampling the first chroma block and the second chroma block in a vertical direction. Gao teaches wherein generating the super block comprises: combining the first chroma block and the second chroma block up and down and then upsampling the first chroma block and the second chroma block in a horizontal direction, or combining the first chroma block and the second chroma block from side to side and then upsampling the first chroma block and the second chroma block in a vertical direction. [abstract; figs. 1-2; figs. 13-14; ¶ 0094-0096; ¶ 0141-0147; ¶ 0152-0160; ¶ 0181-0182; ¶ 0186-0189] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Gao with the teachings of Wan (modified by Tsai) allowing for improved coding efficiency. As to claim 15, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches stack of the encoded map [Wan - fig. 3; figs. 7-8; ¶ 0034-0036; ¶ 0053-0054; ¶ 0082-0085] and the padded input block. [Tsai - figs. 7-8; ¶ 0025-0026] Wan (modified by Tsai) does not explicitly teach wherein performing the convolutional operation: setting a stride value for the sack of encoded map and the input block; and filtering the stack of the encoded map and the input block by using a preset kernel. Gao teaches wherein performing the convolutional operation: setting a stride value for the sack of encoded map and the input block; [figs. 2a-c; ¶ 0102-0113] and filtering the stack of the encoded map and the input block by using a preset kernel. [figs. 2a-c; ¶ 0102-0113] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Gao with the teachings of Wan (modified by Tsai) allowing for improved processing efficiency. As to claim 18, Wan (modified by Tsai) teaches the limitations of claim 15. Gao teaches wherein the stride value is set based on a size of the input block. [figs. 2a-c; ¶ 0102-0113] Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al US 2021/0409783 in view of Tsai et al US 2013/0128986 in view of Gao et al US 2023/0336776 in view of Lim et al. US 2022/0166998. As to claim 4, Wan (modified by Tsai) teaches the limitations of claim 2. Wan (modified by Tsai) does not explicitly teach wherein enlarging comprises: centering the first chroma block and then enlarging the first chroma block to match in size the luma block by padding a periphery of the first chroma block, while filling the periphery of the first chroma block with values of the luma block co-located with the first chroma block. Gao teaches wherein enlarging comprises: and then enlarging the first chroma block to match in size the luma block by padding a periphery of the first chroma block, while filling the periphery of the first chroma block with values of the luma block co-located with the first chroma block. [fig. 23; ¶ 0102-0110; ¶ 0243-0244] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Gao with the teachings of Wan allowing for improved coding efficiency. Wan (modified by Tsai and Gao) does not explicitly teach centering the first chroma block. Lim teaches centering the first chroma block. [¶ 0332] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Lim with the teachings of Wan (modified by Tsai and Gao) allowing for improved coding efficiency. Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al US 2021/0409783 in view of Tsai et al US 2013/0128986 in view of Ko et al. US 2020/0322601. As to claim 5, Wan (modified by Tsai) teaches the limitations of claim 2. Wan (modified by Tsai) does not explicitly teach wherein enlarging comprises: positioning the first chroma block in one quadrant of the enlarged first chroma block and then filling remaining quadrants of the enlarged first chroma block with values of the luma block co-located with the first chroma block. Ko teaches wherein enlarging comprises: positioning the first chroma block in one quadrant of the enlarged first chroma block and then filling remaining quadrants of the enlarged first chroma block with values of the luma block co-located with the first chroma block. [¶ 0077; ¶ 0107-0111; ¶ 0127-0128; ¶ 0131-0132] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Ko with the teachings of Wan (modified by Tsai) allowing for improved coding efficiency. Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wan et al US 2021/0409783 in view of Tsai et al US 2013/0128986 in view of Yin et al. US 2019/0273948. As to claim 6, Wan (modified by Tsai) teaches the limitations of claim 1. Wan teaches wherein generating the input block comprises: when the luminance component signal, the first chrominance component signal, and the second chrominance component signal are in a 4:2:0 format. [abstract; figs. 1-2; ¶ 0024-0028; ¶ 0035-0038] Wan (modified by Tsai) does not explicitly teach quadrisecting the luma block to match in size the first chroma block; and stacking the quadrisected luma blocks, the first chroma block, and the second chroma block. Yin teaches quadrisecting the luma block to match in size the first chroma block; [fig. 4; ¶ 0067] and stacking the quadrisected luma blocks, the first chroma block, and the second chroma block. [fig. 4; ¶ 0067] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Yin with the teachings of Wan (modified by Tsai) allowing for improved processing efficiency. As to claim 7, Wan (modified by Tsai) teaches the limitations of claim 6. Wan (modified by Tsai) does not teach wherein quadrisecting comprises: decimating samples that constitute the luma block in horizontal and vertical directions to generate the quadrisected luma blocks. Yin teaches wherein quadrisecting comprises: decimating samples that constitute the luma block in horizontal and vertical directions to generate the quadrisected luma blocks. [fig. 4; ¶ 0067] It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the techniques of Yin with the teachings of Wan (modified by Tsai) allowing for improved processing efficiency. 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNER HOLDER whose telephone number is (571)270-1549. The examiner can normally be reached M-F 7:30-4. 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, Joseph Ustaris can be reached on 571.272.7383. 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. /ANNER HOLDER/Primary Examiner, Art Unit 2483
Read full office action

Prosecution Timeline

May 26, 2023
Application Filed
Sep 19, 2024
Non-Final Rejection — §103
Dec 20, 2024
Response Filed
Apr 09, 2025
Final Rejection — §103
Jul 15, 2025
Request for Continued Examination
Jul 22, 2025
Response after Non-Final Action
Sep 18, 2025
Non-Final Rejection — §103
Dec 22, 2025
Response Filed
Apr 03, 2026
Final Rejection — §103 (current)

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Prosecution Projections

5-6
Expected OA Rounds
78%
Grant Probability
92%
With Interview (+14.0%)
3y 1m
Median Time to Grant
High
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