Prosecution Insights
Last updated: April 19, 2026
Application No. 18/020,375

VIDEO ENCODING AND DECODING USING DEEP LEARNING BASED IN-LOOP FILTER

Final Rejection §103
Filed
Feb 08, 2023
Examiner
ANYIKIRE, CHIKAODILI E
Art Unit
2487
Tech Center
2400 — Computer Networks
Assignee
Ewha University - Industry Collaboration Foundation
OA Round
6 (Final)
75%
Grant Probability
Favorable
7-8
OA Rounds
3y 2m
To Grant
86%
With Interview

Examiner Intelligence

Grants 75% — above average
75%
Career Allow Rate
779 granted / 1042 resolved
+16.8% vs TC avg
Moderate +12% lift
Without
With
+11.5%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
51 currently pending
Career history
1093
Total Applications
across all art units

Statute-Specific Performance

§101
3.7%
-36.3% vs TC avg
§103
46.3%
+6.3% vs TC avg
§102
36.9%
-3.1% vs TC avg
§112
1.5%
-38.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1042 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 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. 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. Claim(s) 1 - 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over NA et al (US 2021/0136367, hereafter NA) in view of Wang et al (US 11,902,561, hereafter Wang). As per claim 1, NA discloses a method performed by a video decoding apparatus for filtering a current frame after reconstruction, the method comprising: reconstructing the current frame to generate a first reconstructed frame for the current frame (Figure 22; current picture and reference picture); acquiring at least one reference frame that has been reconstructed prior to the first reconstructed frame (¶ 186); by providing a deep learning-based detection model with an input data comprising the reference frame and the first reconstructed frame, generating a detection map that is associated with weights for weighted-average between samples in the first reconstructed frame and samples in the reference frame (¶ 186 and 187); and weight-averaging the reference frame with the first reconstructed on the basis of the detection map to generate a second reconstructed frame for the current frame (¶ 186). However, NA does not explicitly teach weighted-sum of samples spatially corresponding to each other within the first reconstructed frame and the reference frame. In the same field of endeavor, Wang teaches weighted-sum of samples spatially corresponding to each other within the first reconstructed frame and the reference frame (column 10 lines 56 – column 11 lines 4). Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of NA in view of Wang. The advantage is optimizing video coding filtering. As per claim 2, NA discloses the method of claim 1, wherein the acquiring of the reference frame includes selecting an Intra frame (I frame) as the reference frame when the intra frame is included in a reference picture list (¶ 260 and 296). As per claim 3, NA discloses the method of claim 1, wherein the acquiring of the reference frame includes selecting, as the reference frame, a frame whose temporal layer is lowest among reference frame candidates included in the reference picture list, selecting, as the reference frame, a frame whose picture order count (POC) is closest to the current frame, or selecting, as the reference frame, a frame encoded with a smallest quantization parameter (¶ 168 – 172 and 296). As per claim 4, NA discloses the method of claim 1, wherein the generating of the detection map includes generating a binary map in which the reference region is marked with a flag 1 and a remaining region not included in the reference region is marked with a flag 0 (¶ 161). As per claim 5, NA discloses the method of claim 4, wherein the generating of the second reconstructed frame includes replacing pixels of the first reconstructed frame with pixels of the reference region when a binary flag of the detection map is 1, and maintaining a pixel value of the first reconstructed frame when the binary flag is not 1 (¶ 161). As per claim 6, NA discloses the method of claim 4, wherein the generating of the second reconstructed frame includes replacing pixels of the first reconstructed frame with pixels of the reference region when a binary flag of the detection map is 1 and applying a preset function to the first reconstructed frame when the binary flag is not 1 (¶ 161). As per claim 7, NA discloses the method of claim 1, wherein the generating of the detection map includes representing pixels of the reference region and remaining regions not included in the reference region with pixel values within a preset range, to generate a detection map on a pixel-by-pixel basis (¶ 161). As per claim 8, NA discloses the method of claim 7, wherein the generating of the enhanced frame includes performing a weighted sum on the current frame and the reference frame on a pixel-by-pixel basis using pixel values on the detection map on a pixel-by-pixel basis to generate the enhanced frame (¶ 143 and 384). As per claim 9, NA discloses the method of claim 7, wherein the generating of the enhanced frame includes performing a weighted sum on the current frame and the reference frame to which a preset function has been applied, respectively, on a pixel-by-pixel basis using pixel values on the detection map on a pixel-by-pixel basis to generate the enhanced frame (¶ 143 and 384). As per claim 10, NA discloses the method of claim 1, wherein the generating of the detection map includes detecting a reference region of each of M (M is a natural number equal to or greater than 2) reference frames using the detection model M times when there are the M reference frames, and generating M corresponding detection maps (¶ 184 - 186). As per claim 11, NA discloses the method of claim 10, wherein the generating of the enhanced frame includes performing a weighted sum on pixel values of reference regions having binary flags of 1 to replace pixels of the current frame when the M detection maps are binary maps, and maintains pixel values of the current frame when all binary flags of the M detection maps are 0 (¶ 163). As per claim 12, NA discloses the method of claim 1, wherein the detection model is implemented as a convolutional neural network (CNN) model, the detection model receiving a concatenation of the current frame and the reference frame as an input and generating the detection map (¶ 147). Regarding claim 13, arguments are analogous to those presented for claim 1 are applicable for claim 13. Regarding claim 14, arguments are analogous to those presented for claim 4 are applicable for claim 14. Regarding claim 15, arguments are analogous to those presented for claim 5 are applicable for claim 15. Regarding claim 16, arguments are analogous to those presented for claim 6 are applicable for claim 16. Regarding claim 17, arguments analogous to those presented for claim 1 are applicable for claim 17. 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 CHIKAODILI E ANYIKIRE whose telephone number is (571)270-1445. The examiner can normally be reached 8 am - 4:30 pm. 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 on 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. /CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
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Prosecution Timeline

Feb 08, 2023
Application Filed
May 06, 2024
Non-Final Rejection — §103
Aug 12, 2024
Response Filed
Aug 22, 2024
Final Rejection — §103
Nov 27, 2024
Request for Continued Examination
Dec 11, 2024
Response after Non-Final Action
Feb 09, 2025
Non-Final Rejection — §103
May 13, 2025
Response Filed
May 21, 2025
Final Rejection — §103
Jul 28, 2025
Interview Requested
Aug 22, 2025
Request for Continued Examination
Sep 06, 2025
Response after Non-Final Action
Nov 10, 2025
Non-Final Rejection — §103
Feb 13, 2026
Response Filed
Mar 04, 2026
Final Rejection — §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

7-8
Expected OA Rounds
75%
Grant Probability
86%
With Interview (+11.5%)
3y 2m
Median Time to Grant
High
PTA Risk
Based on 1042 resolved cases by this examiner. Grant probability derived from career allow rate.

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