Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/18/2026 has been entered.
Response to Arguments
3. Applicant's arguments filed 09/18/2026 have been fully considered but they are not persuasive.
On page 10 of the amendment, Applicant argued that the limitation “generating, as an output of the neural network, a transform kernel determined based on the prediction block, the neighboring pixels, and the coding context information” is not taught by the cited references.
However, the Examiner respectfully disagrees. ZHAO clearly teaches generating, as an output of the neural network, a transform kernel determined based on the prediction block, the neighboring pixels, and the coding context information (paragraph 0222, a group of transform sets is defined, and a sub-group of transform sets is selected using coded information, such as prediction mode (e.g. intra prediction mode or inter prediction mode), then one transform set of the selected sub-group of transform sets is identified using other code information, such as the prediction block samples of the current block, the neighboring reconstructed samples of the current block. Then, the transform candidate for the current block is selected from the identified transform set using the associated index signaled in the bitstream; paragraph 0225, the neural network is used to identify a transform set that is for secondary transform. Alternatively, the neural network is used to identify a transform set that is used for primary transform. Alternatively, the neural network is used to identify a transform set that is used for specifying a combination of secondary and primary transform; paragraph 0233, the decoding code 810 may cause a neural network to be used in selecting the transform group, the transform sub-group, the transform set, and/or the transform, or otherwise perform at least a part of the decoding, in accordance with embodiments of the present disclosure. According to embodiments, the decoder (800) may further include neural network code (850) that is configured to cause the at least one processor to implement the neural network. Therefore, it is clear from the teachings of paragraphs 0222, 0225 and 0233 that the neural network identifies, broadly interpreted as generating, the transform set, which refers to a group of transform kernel as taught in paragraph 0070, using an index).
4. Applicant’s arguments with respect to the last limitation of 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.
Allowable Subject Matter
5. Claims 5-8 and 12-13 are allowed.
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. 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.
9. Claim(s) 1-3, 9-10, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (US 2022/0078423) hereinafter “ZHAO” in view of Li et al. (US 2023/0007246) hereinafter “Li”.
As per claim 1, ZHAO discloses an artificial intelligence (AI)-based image decoding method comprising:
obtaining a transform block for a current block, from a bitstream (see FIG. 3, output of parser 320; paragraph 0084, The parser (320) may extract from the coded video sequence, a set of subgroup parameters for at least one of the subgroups of pixels in the video decoder, based upon at least one parameters corresponding to the group. Subgroups can include Groups of Pictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units (CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and so forth);
obtaining a residual block of the current block by applying the transform kernel to the transform block (see FIG. 3, output of the scaler/inverse transformation 351; paragraphs 0088 and 0090, The scaler/inverse transform unit (351) may receive quantized transform coefficient as well as control information, including which transform to use, block size, quantization factor, quantization scaling matrices, etc. as symbol(s) (321) from the parser (320). The scaler/inverse transform unit (351) can output blocks including sample values that can be input into the aggregator (355)…the output of the scaler/inverse transform unit (351) (in this case called the residual samples or residual signal)); and
reconstructing the current block by using the residual block and the prediction block (see FIG. 3, output of aggregator 355; see paragraphs 0089 and 0095).
However, ZHAO does not clearly teach in the embodiment related to FIG. 3 inputting a prediction block for the current block, neighboring pixels of the current block, and coding context information to a neural network; and generating, as an output of the neural network, a transform kernel determined based on the prediction block, the neighboring pixels, and the coding context information;
ZHAO in a different embodiment teaches inputting a prediction block for the current block, neighboring pixels of the current block (paragraph 0221, According to embodiments, a neural network-based transform set selection scheme may be provided. The input of the neural network includes, but is not limited to, the prediction block samples of the current block, the neighboring reconstructed samples of the current block), and coding context information to a neural network (paragraph 0228, the parameters used in the neural network depends on coded information, including but not limited to: whether the block is intra coded or not, the block width and/or block height, quantization parameter, whether the current picture is coded as an intra (key) frame or not, and the intra prediction mode); and generating, as an output of the neural network, a transform kernel (see FIG. 14) determined based on the prediction block, the neighboring pixels, and the coding context information (paragraph 0222, a group of transform sets is defined, and a sub-group of transform sets is selected using coded information, such as prediction mode (e.g. intra prediction mode or inter prediction mode), then one transform set of the selected sub-group of transform sets is identified using other code information, such as the prediction block samples of the current block, the neighboring reconstructed samples of the current block. Then, the transform candidate for the current block is selected from the identified transform set using the associated index signaled in the bitstream; paragraph 0225, the neural network is used to identify a transform set that is for secondary transform. Alternatively, the neural network is used to identify a transform set that is used for primary transform. Alternatively, the neural network is used to identify a transform set that is used for specifying a combination of secondary and primary transform; paragraph 0233, the decoding code 810 may cause a neural network to be used in selecting the transform group, the transform sub-group, the transform set, and/or the transform, or otherwise perform at least a part of the decoding, in accordance with embodiments of the present disclosure. According to embodiments, the decoder (800) may further include neural network code (850) that is configured to cause the at least one processor to implement the neural network. Therefore, it is clear from the teachings of paragraphs 0222, 0225 and 0233 that the neural network identifies, broadly interpreted as generating, the transform set, which refers to a group of transform kernel as taught in paragraph 0070, using an index).
Therefore, it would have been obvious for one having skill in the art before the effective filing date of the invention to modify ZHAO embodiment in FIG. 3, by applying neural network-based methods, as taught in paragraphs 0221, 0228, and 0233, for efficient classification of prediction residuals, and therefore provide more efficient transform set selection (see paragraph 0211).
However, ZHAO does not explicitly disclose wherein the transform kernel is directly output from the neural network; wherein the neural network is trained to directly output the transform kernel.
In the same field of endeavor, Li discloses wherein the transform kernel is directly output from the neural network; wherein the neural network is trained to directly output the transform kernel (paragraph 0004, the disclosed aspects/embodiments provide one or more neural network (NN) filter models trained as coding tools to improve the efficiency of video coding. The NN-based coding tools may be used to replace or enhance one or more modules implemented by a video coder/decoder (a.k.a., codec). For example, a NN model can be trained to provide an additional intra prediction mode, an additional inter prediction mode, a transform kernel, and/or a loop filter).
Therefore, it would have been obvious for one having skill in the art before the effective filing date of the invention to modify the teachings of ZHAO, by training a NN model to provide a transform kernel; thus, improving the efficiency of video coding (paragraph 0004 of Li) and improving transform function by achieving the desired output.
As per claim 2, ZHAO discloses the AI-based image decoding method of claim 1, wherein the coding context information comprises at least one of a quantization parameter of the current block, a split tree structure of the current block, a split structure of the neighboring pixels, a split type of the current block, or a split type of the neighboring pixels (quantization parameter as taught in paragraph 0211).
As per claim 3, ZHAO discloses the AI-based image decoding method of claim 1, wherein the transform block is a block transformed by a neural network-based transform kernel or a block transformed by one linear transform kernel from among a plurality of pre-determined linear transform kernels (paragraphs 0221, 0224).
As per claims 9, the claim is directed to an encoding method corresponding to the decoding method of claim 1; therefore, arguments analogous to those applied for claim 1 are applicable for claim 9; in addition, ZHAO teaches an encoding method and apparatus (see FIG. 4 and paragraph 0103).
As per claim 10, arguments analogous to those applied for claim 3 are applicable for claim 10.
As per claims 14-15, arguments analogous to those applied for claim 1-2 are applicable for claims 14-15; in addition, ZHAO discloses decoding apparatus comprising a memory and at least one processor (paragraph 0212).
10. Claim(s) 4 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (US 2022/0078423) in view of Li et al. (US 2023/0007246) in further view of Mukherjee et al. (US 2018/0014031) hereinafter “Mukherjee”.
As per claim 4, ZHAO and Li disclose the AI-based image decoding method of claim 1; however, ZHAO or Li do not explicitly disclose wherein the generated transform kernel comprises a left transform kernel to be applied to a left side of the transform block and a right transform kernel to be applied to a right side of the transform block.
In the same field of endeavor, Mukherjee discloses wherein the generated transform kernel comprises a left transform kernel to be applied to a left side of the transform block and a right transform kernel to be applied to a right side of the transform block (FIG. 7; paragraph 0075, For example, the top partition 710 shown in FIG. 7 is a 4×8 sub-block, and transforming the top partition 710 may include generating a 4×8 prediction block, generating a 4×8 residual block, identifying a transform having a transform size equal to the prediction block size, such as the 4×8 transform 740, or smaller than the prediction block size, such as the left 4×4 transform 750 and the right 4×4 transform 752, and generating transform coefficients using the identified transforms).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by ZHAO and Li, with those of Mukherjee, because all references are drawn to the same field of endeavor, because indeed all references are related to encoding/decoding methods and apparatus using transform coefficients, and because such a combination represents a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of ZHAO, Li and Mukherjee used in this Office Action unless otherwise noted.
As per claim 11, arguments analogous to those applied for claim 4 are applicable for claim 11.
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (US 20230051066)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED JEBARI whose telephone number is (571)270-7945. The examiner can normally be reached M-F: 09:00am-06:00pm.
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/MOHAMMED JEBARI/Primary Examiner, Art Unit 2482
