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 .
Status of Claims
The following is a Non-Final Office Action in response to the correspondence filed on 09/05/2025.
Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Information Disclosure Statement
The IDSs received on date 06/25/2026 and 12/17/2025 are in compliance with the provisions of 37 CFR 1.97, being reviewed and considered by the Examiner.
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- 12 and 15- 20 are rejected under 35 U. S. C. 103 as being unpatentable over Shoujiang Ma (US 20220295116 A 1) (hereinafter Ma) in view of Zhao Wang "Multi-density attention network for loop filtering in video compression." arXiv preprint arXiv:2104.12865 (2021) (hereinafter Zhao):
Regarding claim 1, Ma teaches a computer-implemented method comprising:
performing a decode of a frame of a video ([0037] teaches decoding the bitstream to reconstruct a frame of the video) that converts the frame from a transform domain to a pixel domain ([0036]- [0037] teach inverse transforming the coefficients into reconstructed pixel samples) to generate an input for a video filter operation ([0037], and [0041] teach the reconstructed samples being the input to the CNNLF filter operation);
generating a first set of features ([0049], and [0030] teach generating feature maps) by a machine learning model ([0030], and [0045] teach a deep learning CNN model performing the filtering) based on the input for the video filter operation ([0046], and [0049] teach generating the feature maps from the reconstructed sample input);
generating a modified version of the frame ([0041], and [0046] teach outputting A filtered reconstructed video frame); and
transmitting the modified version of the frame to storage or a display device ([0036]- [0037], and [0125] teach storing the filtered frame in a frame buffer and outputting it to a display).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches generating a first set of features at a first resolution ([Pg. 3, section 3.1], and [Pg. 4, section 3.2] teach a full density branch generating features at the full resolution);
generating a second set of features at a second lower resolution ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations), by the machine learning model ([Pg. 4, section 3.2], and [Pg. 3, section 3.2] teach the branches being part of one neural network model) in parallel with the generating the first set of features ([Pg. 1, abstract], and [ Pg. 3, section 3.1] teach parallel full resolution and half resolution convolution streams), based on the input for the video filter operation ([Pg. 5, section 4], and [Pg. 3, section 3.2] teach the branches operating on the reconstructed frame input to the loop filter):
upsampling the second set of features to the first resolution to generate an upsampled second set of features ([Pg. 4, section 3.2] teaches upsampling the half resolution feature map back to the original resolution); and
generating a modified version of the frame based on the first set of features and the upsampled second set of features ([Pg. 3, section 3.1], and [Pg. 5, section 3.2] teach generating the output from the fused full density and upsampled half density features).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 2, Ma in view of Zhao teaches the computer-implemented method of claim 1. Zhao further teaches generating a third set of features at a third resolution that is lower than the second lower resolution ([Pg. 4, section 3.2] teaches adding a quarter density branch at a resolution lower than the half density branch), by the machine learning model in parallel with the generating the first set of features and the second set of features ([Pg. 1, abstract], and [Pg. 3, section 3.2] teach the multi density branches being parallel streams of one network), based on the input for the video filter operation ([Pg. 5, section 4], and [Pg.3 , section 3.2] teach the branches operating on the reconstructed frame input to the loop filter); and
upsampling the third set of features to the first resolution to generate an upsampled third set of features ([Pg. 4, section 3.2], and [Pg.3, section 3.1] teach up sampling each reduced density feature map back to the original resolution before element wise some fusion),
wherein the generating the modified version of the frame is based on the first set of features, the upsampled second set of features, and the upsampled third set of features ([Pg. 3, section 3.1], and [Pg. 2, section 2.1] teach generating the output from the fused responses of all density branches).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 3, Ma in view of Zhao teaches the computer-implemented method of claim 1. Ma further teaches wherein the generating the first set of features, the generating the second set of features at the second lower resolution, and the generating the modified version of the frame ([0046], [0049], and [0041] teach the feature generation and filtered frame output being performed within the CNNLF) occur within a loop filter of a decoder ([0037], [0036], and [0041] teach the CNN LF being an in loop filter stage of the video decoder).
Regarding claim 4, Ma teaches a computer-implemented method comprising ([0026] teaches the disclosed material being implemented as instructions executed by one or more processors):
performing a video coding for a frame of a video ([0037] teaches the video decoder decoding the bitstream to generate output video) that generates a residual of the frame ([0037] teaches inverse quantization and inverse transform generating reconstructed pixel residuals);
generating a first set of features ([0030], and [0049] teach the hidden convolutional layer generating M channel feature maps) at a first resolution, ([0048], and [0050] teach the feature maps being at the NxN resolution of the input layer) by a machine learning model, ([0030] teaches the CNNLF being a deep learning neural network based model) based on the residual of the frame ([0041] teaches the inputs of the CNNLF including residual samples after inverse quantization and inverse transform);
generating a modified version of the frame ([0041], and [0046] teach the CNNLF outputting restored, filtered reconstructed video frame samples); and
transmitting the modified version of the frame to storage or a display device ([0036], and [0037] teach the decoder with a frame buffer outputting the output video for presentation via a display).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches generating a second set of features ([Pg. 4, section 3.2] teaches the half density branch generating a second set of feature representations) at a second lower resolution, ([Pg. 3, section 3.1], and [Pg. 4, section 3.2] teach the second branch operating at half resolution, lower than the full resolution branch) by the machine learning model in parallel with the generating the first set of features, ([Pg.1 ,abstract], and [Pg. 4, section 3.2] teach parallel multi resolution streams within one network model) based on the residual of the frame ( [Pg.5 , section 4], and [Pg. 6, section 5.1] teach the network branches operating on the reconstructed frame formed from the residual);
upsampling the second set of features to the first resolution ([Pg. 4, section 3.2] teaches upsampling the half resolution feature map back to the original resolution) to generate an upsampled second set of features ([Pg. 4, section 3.2] teaches the pixel shuffle up sampling layer generating the upsampled feature map); and
generating a modified version of the frame based on the first set of features and the upsampled second set of features ([Pg. 3, section 3.1], and [Pg. 5, section 3.2] teach generating the output from the fused full density and upsampled half density features).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 5, Ma in view of Zhao teaches the computer-implemented method of claim 4. Zhao further teaches generating a third set of features ([Pg. 4, section 3.2] teaches adding a quarter density branch generating a third set of features) at a third resolution that is lower than the second lower resolution ([Pg. 4, section 3.2] teaches the quarter density branch being at a resolution lower than the half density branch), by the machine learning model in parallel with the generating the first set of features and the second set of features ([Pg. 1, Abstract], and [Pg. 3, section 3.2] teach the multi density branches being parallel streams of one network), based on the residual of the frame ([Pg. 5, section 4] teaches the network operating on the reconstructed frame formed from the residual); and
upsampling the third set of features to the first resolution to generate an upsampled third set of features ([Pg. 4, section 3.2], and [Pg. 3, section 3.1] teach up sampling each reduced density feature map back to the original resolution before element wise some fusion),
wherein the generating the modified version of the frame is based on the first set of features, the upsampled second set of features, and the upsampled third set of features ([Pg. 3, section 3.1], and [Pg. 2, section 1] teach generating the output from the fused responses of all density branches).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 6, Ma in view of Zhao teaches the computer-implemented method of claim 4. Ma further teaches wherein the generating the first set of features, ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the generating the modified version of the frame ([0041] teaches the CNNLF outputting the restored reconstructed samples) occur within a loop filter ([0030], and [0037] teach the CNNLF being an in loop filter stage whose filtered samples are used in inter prediction) of a decoder ([0036] teaches the video decoder having the in loop convolutional neural network loop filter).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the generating the second set of features at the second lower resolution ([Pg. 4, section 3.2] teaches a half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 7, Ma in view of Zhao teaches the computer-implemented method of claim 4. Ma further teaches wherein a viewer device comprises a decoder and the machine learning model, ([0036], and [0037] teach the decoder including the CNN LF and outputting video for presentation to a user via a display) and the generating the first set of features, ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the generating the modified version of the frame ([0041] teaches the CNNLF outputting the restored reconstructed samples) occur within a loop filter of the decoder ([0036], and [0037] teach the CNNLF applied in loop within the video decoder).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the generating the second set of features at the second lower resolution ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 8, Ma in view of Zhao teaches the computer-implemented method of claim 4. Ma further teaches wherein a viewer device comprises the machine learning model, ([0036], and [0037] teach the decoder that presents video to a user including the CNNLF) and the computer-implemented method further comprises receiving, in a bitstream for the video, an indication of a set of model parameters for the machine learning model ([0037], and [0042] teach the decoder receiving CNN parameters signaled in the bitstream) for the generating the first set of features ([0046], and [0049] teach transmitting the CNNLF parameters of the convolutional layer that generates the feature maps).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the generating the second set of features at the second lower resolution ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 9, Ma in view of Zhao teaches the computer-implemented method of claim 4. Ma further teaches wherein a viewer device comprises a decoder and the machine learning model, ([0040] teaches the decoding device including the decoder components and the CNNLF and presenting video to a viewer via a display) and the generating the first set of features, ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the generating the modified version of the frame ([0040], and [0041] teach the CNNLF generating the output video of restored reconstructed samples) occur in a post-processor of the viewer device that is separate from the decoder ([0039], [0040], and [0041] teach the CNNLF applied as an out of loop stage on the decoded output, not used for prediction, prior to display).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the generating the second set of features at the second lower resolution ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 10, Ma in view of Zhao teaches the computer-implemented method of claim 4. Zhao further teaches before the generating the second set of features at the second lower resolution, ([Pg. 4, section 3.2] teaches the down sampling layer being applied first, before the half density residual blocks) downsampling the frame from the first resolution to the second lower resolution ([Pg.3, section 3.1], and [Pg. 4, section 3.2] teach down sampling from the full resolution scale to the half resolution scale).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 11, Ma in view of Zhao teaches the computer-implemented method of claim 10. Zhao further teaches wherein the downsampling comprises performing a strided convolution ([Pg. 4, section 3.2] teaches the down sampling layer being a convolution layer with stride of 2) on the frame at the first resolution ([Pg. 4, section 3.2], and [Pg. 3, section 3.1] teach the strided down sampling being applied to the full resolution representation).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Recording claim 12, Ma in view of Zhao teaches the computer-implemented method of claim 4. Zhao further teaches wherein the upsampling comprises ([Pg. 4, section 3.2] teaches an up sampling layer resizing the half resolution feature map back to the original resolution) interleaving a plurality of channels into one channel ([Pg. 4, section 3.2] teaches the up sampling layer being realized by the pixel shuffle procedure).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 15, Ma teaches an apparatus comprising ([0029], and [0150] teach an apparatus and computing platform for convolutional neural work loop filtering for video decode);
a coupling to a display ([0150], and [0155] teach the platform coupled to a display through an HDMI or display port interface); and
a media player ([0025], [0129], and [0149] teaches a smartphone, or smart television video processor implementing the decoders) to:
perform a video decoding for a frame of a video ([0037] teaches the video decoder decoding the bitstream to generate output video) to generate a residual of the frame ([0037] teaches inverse quantization and inverse transform generating reconstructed pixel residuals),
generate a first set of features ([0030], and [0049] teach the hidden convolutional layer generating M channel feature maps) at a first resolution ([0048], and [0050] teach the feature maps being at the NxN resolution of the input layer), by a machine learning model of the media player ([0030], and [0129] teach the CNN LF being a deep learning model included in the decoders of the video processor), based on the residual of the frame ([0041] teaches the inputs of the CNNLF including residual samples after inverse quantization and inverse transform);
wherein the second set of features is generated by the machine learning model of the media player ([0030], and [0040] teach the decoder including the CNNLF deep learning model);
generate a modified version of the frame ([0041], and [0046] teach the CNNLF outputting restored, filtered reconstructed video frame samples); and
transmit the modified version of the frame to the coupling to the display ([0040], and [0124] teach the CNNLF filtered reconstructed frame being output for presentation to a user via a display).
Ma does not explicitly teach the following limitations; However, in an analogous art, Zhao teaches generate a second set of features ([Pg.4 , section 3.2] teaches the half density branch generating a second set of feature representations) at a second lower resolution, ([Pg.3 , section 3.1] teaches the second branch operating at half resolution, lower than the full resolution branch) in parallel with the generating the first set of features, ([Pg. 1, Abstract], and [Pg. 3, section 3.1] teach parallel full resolution and half resolution convolution streams) based on the residual of the frame, ([Pg. 5, section 4], and [Pg. 6, section 5.1] teach the network branches operating on the reconstructed frame formed from the residual)
upsample the second set of features to the first resolution ([Pg. 4, section 3.2] teaches up sampling the half resolution feature map back to the original resolution) to generate an upsampled second set of features, ([Pg.4, section 3.2] teaches the pixel shuffle up sampling layer generating the upsampled feature map), and
generate the modified version of the frame based on the first set of features and the upsampled second set of features ([Pg.3, section 3.1], and [Pg. 5, section 3.2] teach generating the output from the fused full density and upsampled half density features).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 16, Ma in view of Zhao teaches the apparatus of claim 15. Ma further teaches by the machine learning model of the media player ([0036] teaches the CNNLF being a component of the decoder),
wherein the media player is to generate the modified version of the frame ([0041] teaches the outputs of the CNNLF being the restored reconstructed samples).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches that the media player is further to generate a third set of features ([Pg. 4, section 3.2] teaches adding a quarter density branch generating a third set of features) at a third resolution that is lower than the second lower resolution ([Pg. 4, section 3.2] teaches the quarter density branch being at a resolution lower than the half density branch), in parallel with the generating the first set of features and the second set of features ([Pg. 1, abstract], and [Pg. 3, section 3.2] teach the multi density branches being parallel streams of one network), based on the residual of the frame ([Pg.5 , section 5.4] teaches the network operating on the reconstructed frame formed from the residual); and
upsample the third set of features to the first resolution to generate an upsampled third set of features ([Pg. 4, section 3.2], and [Pg. 3, section 3.1] T drop sampling each reduced density feature map back to the original resolution before element wise some fusion), based on the first set of features, the upsampled second set of features, and the upsampled third set of features ([Pg. 3, section 3.1], and [Pg. 2, section 1] teach generating the output from the fused responses of all density branches).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 17, Ma in view of Zhao teaches the apparatus of claim 15. Ma further teaches wherein the media player comprises A decoder ([0036], and [0129] teach the video processor implementing the video decoder having the in loop CNNLF), and the media player is to generate the first set of features ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the modified version of the frame ([0041] teaches the CNNLF outputting the restored reconstructed samples) within a loop filter of the decoder of the media player ([0030], [0036], and [0037] teach the CNNLF being an in loop filter stage of the video decoder whose filtered samples are used in inter prediction).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the second set of features ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 18, Ma in view of Zhao teaches the apparatus of claim 15. Ma further teaches further comprising a post processor ([0039], and [0040] teaching out of loop CNNLF stage applied to the decoded output that is not used for interpretation), wherein the media player is to generate the first set of features ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the modified version of the frame ([0040], and [0041] teach the CNNLF generating the output video of restored reconstructed samples) within the post processor ([0040 close bracket, and [0041] teach the CNNLF operations being performed in the out of loop stage prior to display).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the second set of features ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 19, Ma in view of Zhao teaches the apparatus of claim 15. Ma further teaches wherein the media player is to receive, in a bitstream for the video, an indication of a set of model parameters for the machine learning model ([0037], [0042], and [0119] teach the decoder receiving CNN parameters signaled in the bitstream) to use to generate the first set of features ([0046], and [0049] teach transmitting the CNNLF parameters of the convolutional layer that generates the maps).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the second set of features ([Pg. 3, section 3.1], and [Pg. 4, section 3.2] teach the half density branch being convolution layers whose parameters are learned).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Regarding claim 20, Ma in view of Zhao teaches the apparatus of claim 15. Ma further teaches wherein the media player is to generate the first set of features ([0045], and [0049] teach the feature maps being generated by a hidden convolutional layer of the CNNLF) and the modified version of the frame ([0041] teaches the CNNLF outputting the restored reconstructed samples) in response to a flag in a bitstream for the video being set ([0043], [0112], and [0122] teach the decoder performing CNNLF processing in response to the bitstream CNNLF flag being set to on).
Ma does not explicitly teach the following limitations; however, in an analogous art, Zhao teaches the second set of features ([Pg. 4, section 3.2] teaches half density branch generating half resolution feature representations).
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 coding and decoding as disclosed by Ma to add the teachings of Zhao as disclosed above to improve video compression proficiency (Zhao [Pg. 6, section 5.]).
Claims 13- 14 are rejected under 35 U. S. C. 103 as being unpatentable over Shoujiang Ma (US 20220295116 A 1) (hereinafter Ma) in view of Zhao Wang "Multi-density attention network for loop filtering in video compression." arXiv preprint arXiv:2104.12865 (2021) (hereinafter Zhao) further in view of Yixin Du (US 20230143147 A1) (hereinafter Du):
Regarding claim 13, Ma in view of Zhao teaches the computer-implemented method of claim 4; however, do not explicitly teach the generating the modified version of the frame comprises performing a cross-component sample offset operation
However, in an analogous art, Du teaches wherein the generating the modified version of the frame comprises ([0026], and [0033] teach the in loop filter applying multiple filtering operations to produce the recovered frames) performing a cross-component sample offset operation ([0033], [0050], and [0051] teach the in loop multi-filter system including a CCSO filter generating corrected samples using an orthogonal color plane).
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 coding and decoding as disclosed by Ma in view of Zhao to add the teachings of Du as disclosed above to improve the quality of video data (Du [0005]).
Regarding claim 14, Ma in view of Zhao teaches the computer-implemented method of claim 4; however, do not explicitly teach selecting one of the modified version of the frame and another version of the frame as input to a cross-component sample offset operation.
However, in an analogous art, Du teaches further selecting one of the modified version of the frame and another version of the frame ([0026], and [0047] teach selecting filtering operations and alternative filtered versions of the reconstructed samples as the CCSO input) as input to a cross-component sample offset operation ([0050] teaches the CCSO receiving reconstructed samples as its input).
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 coding and decoding as disclosed by Ma in view of Zhao to add the teachings of Du as disclosed above to improve the quality of video data (Du [0005]).
Conclusion
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.
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, 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.
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.
/MAHMOUD KAMAL ABOUZAHRA/Examiner, Art Unit 2486
/JAMIE J ATALA/Supervisory Patent Examiner, Art Unit 2486
Prosecution Insights