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 .
Information Disclosure Statement
The IDS(s) received on date 01/12/2026 is in compliance with the provisions of 37 CFR 1.97, being reviewed and considered by the Examiner.
Response to Amendment
The Amendment filed 04/01/2026 has been entered. Claims 1-20 are pending in this application.
Response to Arguments
Applicant's arguments filed 04/01/2026 have been fully considered but they are not persuasive.
First Argument (page 8- 9) under Remarks:
MPEP § 2142 states that "[t]he key to supporting any rejection under 35 U.S.C. § 103 is the clear articulation of the reason(s) why the claimed invention would have been obvious ... [R]ejections on obviousness cannot be sustained with mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness." In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir 2006). Here, no prima facie case of obviousness has been established at least because the Office has failed to provide any articulate reasoning to support the legal conclusion of obviousness.
Reply: The Examiner respectfully disagrees.
The office action set forth articulated reasoning with rational underpinning the conclusion of obviousness, as required by MPEP 2141 and in re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006). The office action stated “it would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invitation to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051])”. This reasoning identifies the base reference Chen, the secondary reaching incorporated (feeding of the post-processed image to a machine vision module (Gao [0082])), and the purpose served (improving the encoding and decoding of video data). That purpose is supported directly by Gao as it teaches “the VCM encoder 310 and the VCM decoder 320 may be optimized for machine tasks… the enhanced module 402 may be designed to further improve the decoded images/video fir machine vision tasks” (Gao 0051). Under KSR and MPEP 2143, the motivation to combine may be found in the prior art, in the nature of the problem, or in ordinary design inventive, all of which Gao supplies by identifying the problem of coding video for machine consumption and the solution of providing the enhanced image to a machine vision module (Gao [0005]; [0051, [0082]]).
Second Argument (pages 9- 11) under Remarks:
Applicant respectfully submits that Chen and Gao are not compatible in the manner alleged by the Office because neither reference teaches or suggests feeding a post-processed visual signal produced by Chen's conventional codec pipeline into a machine task network, as required by claim 1, without fundamentally altering the purpose and function of Chen's post-processing step.
Reply: The Examiner respectfully disagrees.
In response to applicant’s argument that the Chen and Gao cannot be combined without fundamentally altering the purpose and function of Chen’s post processing step because “Chen's decoded picture, obtained by a conventional codec, and Gao' s decoded picture, obtained by a VCM codec”; however, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
The combination does not alter Chen’s structure, rather it uses Chen’s post processed reconstructed picture (Chen [0171]) to a machine task network as taught by Gao (Gao [0082]). Furthermore, Chen is not limited to a “conventional codec pipeline” whose post-processed output is a terminal output. Chen teaches post-processing including “any other processing” (Chen [0171]), and state that “encoding and decoding are not the only application for the invention” and that “a stand-alone image modification deployment is possible” (Chen [0091]), and employs a structure “originally developed for biomedical image segmentation” (Chen [0071]), confirming applicability to machine tasks. Gao further teaches that “these methods can be applied to any VCM codec” (Gao [0047]) and teaches feeding the plain decoded output to the machine vision module (Gao [0045]). Therefor the reference are compatible, without altering the purpose and function of Chen.
Third Argument (pages 11- 12) under Remarks:
A person of ordinary skill in the art would understand that Chen's decoded picture, obtained by a conventional codec, and Gao' s decoded picture, obtained by a VCM codec, are architecturally and functionally different and are generated for different purposes.
Gao's VCM framework emphasizes task-specific optimization and pipeline design for machine inference, while Chen does not address such considerations and instead produces a post-processed visual signal as the terminal output of a conventional codec pipeline. This divergence reflects different design priorities rather than complementary teachings. A person of ordinary skill following Chen's approach would have no reason to introduce the additional complexity and task-specific constraints associated with Gao's machine-vision pipeline, particularly where Chen does not identify machine-task performance as a limitation. This divergence at least discourages, and therefore teaches away from, the proposed modification.
Reply: The Examiner respectfully disagrees.
Chen “does not criticize, discredit, or otherwise discourage the solution claimed” (MPEP 2145 ). Chen contemplates uses beyond display (Chen [0091]; [0071]). The combination does not change Chen’s principle of operation; feeding Chen’s post processed picture to a machine task network of Gao does not alter how Chen generates that picture and requires no substantial redesign for a person skilled in the art. Rather it would have been obvious to a person skilled in the art to modify the method and apparatus for generation a post processed visual signal as disclosed by Chen to Gao’s machine task network to improve the encoding and decoding of video data (Gao [0051]) with a reasonable expectation of success.
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.
Claims 1- 3, and 5- 6, 10, 15- 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hu Chen (US 20230069953 A1) (hereinafter Chen) in view of Wen Gao (US 20230269378 A1) (hereinafter Gao):
Regarding Claim 1, Chen teaches a video processing method ([0006] teaches the video processing method), comprising:
compressing and reconstructing an original visual signal to obtain a reconstructed visual signal ([0054], and [0122] teaches compressing and reconstructing a visual signal to generate a reconstructed visual signal);
processing the reconstructed visual signal to obtain a post-processed visual signal ([0171] teaches processing the reconstructed signal to obtain the post processing signal).
Chen does not explicitly teach the following limitations; however, in an analogous art, Gao teaches feeding the post-processed visual signal to a machine task network ([0082] teaches providing the post processed image to a machine vision module that performs the machine vision task).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Regarding Claim 2, Chen in view of Gao teach the method according to claim 1. Chen further teaches wherein processing the reconstructed visual signal to obtain the post-processed visual signal further comprises: processing the reconstructed visual signal based on compression ratio ([0096- 0097] teaches parameterizing and selecting the neural network behavior based on the compression level and compression ratio).
Regarding Claim 3, Chen in view of Gao teach the method according to claim 2. Chen further teaches wherein processing the reconstructed visual signal based on compression ratio further comprises:
obtaining an intermediate feature map based on the reconstructed visual signal and the compression ratio ([0012], and [0097] teach the NN processing is conditioned on the compression parameter while processing the decoded image);
performing feature down-sampling on the intermediate feature map to obtain a down-sampled feature map ([0073] teaches downsampling to create down-sampled feature maps);
transforming the down-sampled feature map to obtain an enhanced feature map ([0073] teaches convolutions that transform the downsampled feature for the enhanced feature map); and
performing feature up-sampling on the enhanced feature map to obtain an up-sampled feature map ([0074] teaches feature map upsampling producing an upsampled feature map).
Regarding Claim 5, Chen in view of Gao teach the method according to claim 3. Chen further teaches wherein performing feature down-sampling on the intermediate feature map to obtain the down-sampled feature map further comprises:
transforming the intermediate feature map to an enhanced intermediate feature map ([0072] teaches repeated convolutional blocks that transform intermediate feature maps, and produces enhanced intermediate representation); and
performing the feature down-sampling on the enhanced intermediate feature map to obtain the down-sampled feature map ([0074] teaches the convolution blocks precede pooling downsampling at each level); and
wherein performing feature up-sampling on the enhanced feature map to obtain the up-sampled feature map comprises: transforming the up-sampled feature map to an enhanced up-sampled feature map ([0074] teaches the expansive path that up samples then applies convolution blocks to the upsampled and combined feature map giving the enhanced upsampled features) .
Regarding Claim 6, Chen in view of Gao teach the method according to claim 3. Chen further teaches wherein performing feature up-sampling on the enhanced feature map to obtain the up-sampled feature map further comprises:
performing feature up-sampling on the enhanced feature map to obtain an intermediate up-sampled feature map ([0074] teaches upsampling operation that produces the upsampled feature map); and
reducing a channel size of the combination of the enhanced intermediate feature map and the intermediate up-sampled feature map to obtain the up-sampled feature map ([0074] teaches combining the skip features with upsampled features to reduce the channel count).
Regarding Claim 10, Chen teaches A video procession system, comprising:
a codec configured to compress and reconstruct an original visual signal to obtain a reconstructed visual signal ([0054], and [0122] teaches compressing and reconstructing a visual signal to generate a reconstructed visual signal);
a post-processing network configured to process the reconstructed visual signal to obtain a post-processed visual signal ([0171] teaches processing the reconstructed signal to obtain the post processing signal).
Chen does not explicitly teach the following limitations; however, in an analogous art, Gao teaches
a machine task network configured to process the post-processed visual signal ([0082] teaches providing the post processed image to a machine vision module that performs the machine vision task).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Regarding Claim 15, Chen teaches a non-transitory computer readable medium that stores a set of instructions that is executable by one or more processors of an apparatus to cause the apparatus to perform operations ([0025] teaches a non-transitory medium that includes instructions that are executed on one or more processors) comprising
compressing and reconstructing an original visual signal to obtain a reconstructed visual signal ([0054], and [0122] teaches compressing and reconstructing a visual signal to generate a reconstructed visual signal);
processing the reconstructed visual signal to obtain a post-processed visual signal ([0171] teaches processing the reconstructed signal to obtain the post processing signal).
Chen does not explicitly teach the following limitations; however, in an analogous art, Gao teaches feeding the post-processed visual signal to a machine task network ([0082] teaches providing the post processed image to a machine vision module that performs the machine vision task).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Regarding Claim 16, Chen in view of Gao teach the non-transitory computer readable medium according to claim 15. Chen further teaches wherein processing the reconstructed visual signal to obtain the post-processed visual signal further comprises: processing the reconstructed visual signal based on compression ratio ([0096- 0097] teaches parameterizing and selecting the neural network behavior based on the compression level and compression ratio).
Regarding Claim 17, Chen in view of Gao teach the non-transitory computer readable medium according to claim 16. Chen further teaches wherein processing the reconstructed visual signal based on compression ratio further comprises:
obtaining an intermediate feature map based on the reconstructed visual signal and the compression ratio ([0012], and [0097] teach the NN processing is conditioned on the compression parameter while processing the decoded image);
performing feature down-sampling on the intermediate feature map to obtain a down-sampled feature map ([0073] teaches downsampling to create down-sampled feature maps);
transforming the down-sampled feature map to obtain an enhanced feature map ([0073] teaches convolutions that transform the downsampled feature for the enhanced feature map); and
performing feature up-sampling on the enhanced feature map to obtain an up-sampled feature map ([0074] teaches feature map upsampling producing an upsampled feature map).
Regarding Claim 19, Chen in view of Gao teach non-transitory computer readable medium according to claim 17. Chen further teaches wherein performing feature down-sampling on the intermediate feature map to obtain the down-sampled feature map further comprises:
transforming the intermediate feature map to an enhanced intermediate feature map ([0072] teaches repeated convolutional blocks that transform intermediate feature maps, and produces enhanced intermediate representation); and
performing the feature down-sampling on the enhanced intermediate feature map to obtain the down-sampled feature map ([0074] teaches the convolution blocks precede pooling downsampling at each level); and
wherein performing feature up-sampling on the enhanced feature map to obtain the up-sampled feature map comprises: transforming the up-sampled feature map to an enhanced up-sampled feature map ([0074] teaches the expansive path that up samples then applies convolution blocks to the upsampled and combined feature map giving the enhanced upsampled features) .
Claims 4, and 18 rejected under 35 U.S.C. 103 as being unpatentable over Hu Chen (US 20230069953 A1) (hereinafter Chen) in view of Wen Gao (US 20230269378 A1) (hereinafter Gao) further in view of Chaoyi Lin (US 20240137518 A1) (hereinafter Lin):
Regarding Claim 4, Chen in view of Gao teach the method according to claim 3; however, do not explicitly teach expanding the compression ratio to a same size of the reconstructed visual signal; and
obtaining the intermediate feature map based on a combination of the expanded compression ratio and the reconstructed visual signal.
However, in an analogous art, Lin teaches expanding the compression ratio to a same size of the reconstructed visual signal ([0443] teaches expanding the QP into a QP map of the same spatial resolution as the reconstructed frame input); and
obtaining the intermediate feature map based on a combination of the expanded compression ratio and the reconstructed visual signal ([0365] teaches combining the reconstructed sample data with QP map as network input, resulting in intermediate features based on both inputs).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Lin as disclosed above to improve the coding efficiency of video data (Lin [0041]).
Regarding Claim 18, Chen in view of Gao teach the non-transitory computer readable medium according to claim 17; however, do not explicitly teach expanding the compression ratio to a same size of the reconstructed visual signal; and
obtaining the intermediate feature map based on a combination of the expanded compression ratio and the reconstructed visual signal.
However, in an analogous art, Lin teaches expanding the compression ratio to a same size of the reconstructed visual signal ([0443] teaches expanding the QP into a QP map of the same spatial resolution as the reconstructed frame input); and
obtaining the intermediate feature map based on a combination of the expanded compression ratio and the reconstructed visual signal ([0365] teaches combining the reconstructed sample data with QP map as network input, resulting in intermediate features based on both inputs).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Lin as disclosed above to improve the coding efficiency of video data (Lin [0041]).
Claims 7- 9, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hu Chen (US 20230069953 A1) (hereinafter Chen) in view of Wen Gao (US 20230269378 A1) (hereinafter Gao) further in view of Fabien Racape (US 20230370622 A1) (hereinafter Racape):
Regarding Claim 7, Chen in view of Gao teach the method according to claim 3. Chen further teaches a hyper-parameter of a weight ([0076] teaches the weights as hyper parameter).
a hyper-parameter of a weight ([0076] teaches the weights as hyper parameter).
Chen does not explicitly teach the following limitations; however, in an analogous art, Gao teaches where
L
D
is a visual signal distortion loss ([0054] teaches visual distortion metric),
L
F
is a feature distortion loss ([0059] teaches the feature distortion ),
λ
D
is a weight for the visual signal distortion loss, and
λ
D
is a weight for the feature distortion loss ([0055] teaches the weight of the distortion).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Gao does not explicitly teach the following limitations; however, in an analogous art, Racape teaches obtaining a loss function by:
L
a
l
l
=
λ
D
L
D
+
λ
F
L
F
, ([0127], [0072- 0073] teaches the loss function as the sum of the weight and the distortion).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Racape as disclosed above to reduce the coding complexity of video data (Racape [0127]).
Regarding Claim 8, Chen in view of Gao and Racape teach the method according to claim 7. Gao further teaches wherein the visual signal distortion loss is calculated based on a loss of the original visual signal and the post-processed visual signal ([0054] teaches visual distortion metric which is based on the original and processed signal).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Regarding Claim 9, Chen in view of Gao and Racape teach the method according to claim 7. Gao further teaches wherein the feature distortion loss is generated based on the machine task network and a feature map of the machine task network ([0059- 0061] teaches the feature distortion loss based on machine task feature maps).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Regarding Claim 20, Chen in view of Gao teach the non-transitory computer readable medium according to claim 15. Chen further teaches a hyper-parameter of a weight ([0076] teaches the weights as hyper parameter).
a hyper-parameter of a weight ([0076] teaches the weights as hyper parameter).
Chen does not explicitly teach the following limitations; however, in an analogous art, Gao teaches where
L
D
is a visual signal distortion loss ([0054] teaches visual distortion metric),
L
F
is a feature distortion loss ([0059] teaches the feature distortion ),
λ
D
is a weight for the visual signal distortion loss, and
λ
D
is a weight for the feature distortion loss ([0055] teaches the weight of the distortion).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen to add the teachings of Gao as disclosed above to improve the encoding and decoding of video data (Gao [0051]).
Gao does not explicitly teach the following limitations; however, in an analogous art, Racape teaches obtaining a loss function by:
L
a
l
l
=
λ
D
L
D
+
λ
F
L
F
, ([0127], [0072- 0073] teaches the loss function as the sum of the weight and the distortion).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Racape as disclosed above to reduce the coding complexity of video data (Racape [0127]).
Claims 11- 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hu Chen (US 20230069953 A1) (hereinafter Chen) in view of Wen Gao (US 20230269378 A1) (hereinafter Gao) further in view of Vladimir Tankovich (US 20210264632 A1) (hereinafter Tankovich):
Regarding Claim 11, Chen in view of Gao teach the method according to claim 10. Chen further teaches wherein the post-processing network further comprises:
a feature down-sampling branch configured to down-sample a feature map of the original visual signal to obtain a down-sampled feature map ([0072- 0074] teaches down- sampling branch that produces downsampled feature maps); and
a feature up-sampling branch configured to up-sample the enhanced down-sampled feature map to an up-sampled feature map ([0066], [0072], [0086] teaches feature up sampling branch that produces upsampled feature maps).
Chen does not explicitly teach the following limitations; however, in an analogous art, Tankovich teaches a base block configured to transform a down-sampled feature map to an enhanced down-sampled feature map ([0035], [0038], [0042] and [0060] teaches the residual block that transforms the down-sampled feature map to enhanced down sampled feature map).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Tankovich as disclosed above to increase the speed, and accuracy of feature maps. (Tankovich [0002]).
Regarding Claim 12, Chen in view of Gao and Tankovich teach the method according to claim 11. Tankovich further teaches one or more base blocks configured to enhance a feature map ([0035], [0038], [0042] and [0060] teaches the residual blocks that enhance the feature maps); and
one or more down-sampling block corresponding to the one or more base blocks and configured to down-sample the enhanced feature map ([0038]- [0039] teaches the down sampling blocks that down sample the feature maps).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Tankovich as disclosed above to increase the speed, and accuracy of feature maps. (Tankovich [0002]).
Regarding Claim 13, Chen in view of Gao and Tankovich teach the method according to claim 12. Chen further teaches wherein the feature up-sampling branch ([0065] teaches the feature up sampling branch) further comprises:
one or more up-sampling block configured to up-sample a feature map ([0072] teaches multiple upsampling blocks that up-sample the feature map); and
Chen does not explicitly teach the following limitations; however, in an analogous art, Tankovich teaches one or more base blocks configured to enhance the up-sampled feature map to an enhanced up-sampled feature map ([0035], [0038], [0042] and [0060] teaches the residual block that transforms the up-sampled feature map to enhanced up-sampled feature map).
It would have been obvious to the person having ordinary skill in the art before the effective filling date of the claimed invention to modify the video processing system as disclosed by Chen in view of Gao to further add the teachings of Tankovich as disclosed above to increase the speed, and accuracy of feature maps. (Tankovich [0002]).
Regarding Claim 14, Chen in view of Gao and Tankovich teach the method according to claim 12. Chen further teaches one or more down-channel block configured to reduce a channel size of a combination of the enhanced feature map and the up-sampled feature map ([0074] teaches combining maps from the enhanced feature map with the up-sampled map to reduce the chancel size by two).
Conclusion
THIS ACTION IS MADE FINAL. 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 MAHMOUD KAMAL ABOUZAHRA whose telephone number is (703)756-1694. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jamie Atala can be reached at (571) 272-7384. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAHMOUD KAMAL ABOUZAHRA/Examiner, Art Unit 2486
/JAMIE J ATALA/Supervisory Patent Examiner, Art Unit 2486
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