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, see Remarks, filed May 6, 2026, with respect to claim 1 have been fully considered and are persuasive.
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 Kim (US 2019/0230354.
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, Kim teaches weighted-sum of samples spatially corresponding to each other within the first reconstructed frame and the reference frame (¶ 108, 139, and 239).
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 Kim. 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
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.
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
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