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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/25/2026 has been entered.
Response to Arguments
Applicant’s arguments have been considered but are moot in view of new grounds of rejections.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 6-9, 13-19, 21 are rejected under 35 U.S.C. 103 as being unpatentable over US20220292724-Ren et al (Hereinafter referred to as “Ren”), in view of US 20220101112 A1-Brown et al (Hereinafter referred to as “Brown”), in further view of US 20190124348 A1-Yang.
Regarding claim 1, Ren discloses a method for video processing (Fig. 8A), comprising:
obtaining a first machine learning (ML) model for processing a video ([0019], GAN, student network), wherein the first ML model is trained based on one or more second ML models ([0019], Gan, teacher network), wherein a loss for training the first ML model comprises a non-linear weighting function depending on labels of training samples, an output of the first ML model, and outputs of the one or more second ML model ([0117-0120], wherein an adversarial loss includes logarithms. Thus, will be non-linear), or wherein a loss for training the first ML model comprises a linear weighting function depending on labels of training samples, an output of the first ML model, and outputs of the one or more second ML models (optional and not a required limitation since the former limitation was elected)
While Ren does disclose encoding, Ren fails to explicitly disclose that the encoding introduced actually performs a conversion between a current video block of the video and a bitstream of the video.
However, in the same field of endeavor, Brown discloses obtaining a first machine learning (ML) model for processing a video ([0085], student model), wherein the first ML model is trained based on one or more second ML models ([0086], teacher model), wherein a loss for training the first ML model comprises a non-linear weighting function depending on labels of training samples, an output of the first ML model, and outputs of the one or more second ML model ([0070-0071], discloses a nonlinear loss function) performing, according to the first ML model, a conversion between a current video block of the video and a bitstream of the video ([0258], discloses a GPU with video encoder and decoders; [0407], graphics processor includes a video codec engine to encode, decode, or transcode media to, from, or between one or more media encoding formats, including, but not limited to Moving Picture Experts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, as well as the Society of Motion Picture & Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats. If a teacher network includes or uses a graphic processor with video encoding engine capable of AVC encoding/decoding, then one of ordinary skilled in the art would understand that the teacher system does inherently perform conversion between video blocks and bitstreams. In addition, Brown discloses lossless compression logic in [0336], which is well known to perform conversion between a video block and a bitstream).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Ren to disclose performing, according to the first ML model, a conversion between a current video block of the video and a bitstream of the video as taught by Brown, to improve efficiency, accuracy, and efficacy of image processing ([0512], Brown).
Ren and Brown fail to disclose wherein usage of the first ML model to perform the conversion depends on coding information.
However, in the same field of endeavor, Yang discloses wherein usage of the first ML model to perform the conversion depends on coding information ([0103], a decoder uses a ML operation to apply a prediction enhancement operation, which is a conversion, that depends on coding information in the form of a coding flag. In addition, Yang discloses in [0105], selecting a ML algorithm using coding information to generate prediction).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Ren and Brown to disclose wherein usage of the first ML model to perform the conversion depends on coding information as taught by Yang, to improve efficiency by reducing residual data without increasing header data of a video signal in size (Yang, [0111]).
Regarding claim 2, Ren discloses the method of claim 1, wherein the first ML model is of a first type and the one or more second ML models are of a second type different from the first type ([0019], wherein student and teacher network).
Regarding claim 3, Ren discloses the method of claim 2, wherein the one or more second ML models are trained before training of the first ML model and the first ML model is trained to approach features and/or outputs of the one or more second ML models (Fig. 8a, [0019],
wherein the one or more second ML models have the same structure as the first ML model; or wherein at least one of the one or more second ML models has a learning capacity larger than the first ML model ([0019], wherein student network contains less parameters than the teacher and the trained student has lower computational cost than the teacher).
Regarding claim 6, Ren discloses the method of claim 1, wherein the first ML model is used in video coding and/or compression ([0152]).
Regarding claim 8, Ren discloses the method of claim 1, wherein the first ML model is used in end-to-end video coding/compression ([0152]).
Regarding claim 9, Ren discloses the method of claim 1, wherein the one or more second ML models are used to supervise training of the first ML model ([0019]).
Regarding claim 13, Ren discloses the method of claim 1, wherein usage of the first ML model to perform the conversion depends on coding information ([0164]).
Regarding claim 14, Ren discloses the method of claim 13, wherein the coding information comprises at least one of: a block size of the current video block, a temporal layer of the current video block, a type of a slice comprising the current video block, a type of a frame comprising the current video block, or a colour component; or wherein the first ML model is used to process a luma component during the conversion ([0064], video frames contain video blocks).
Regarding claim 15, Ren discloses the method of claim 1, wherein the first ML model comprises a neural network ([0002]).
Regarding claim 16, Brown discloses the method of claim 1, wherein the conversion includes encoding the current video block into the bitstream ([0258]).
Regarding claim 17, Brown discloses the method of claim 1, wherein the conversion includes decoding the current video block from the bitstream ([0258]).
Regarding claim 18, analyses are analogous to those presented for claim 1 and are applicable for claim 18.
Regarding claim 19, analyses are analogous to those presented for claim 1 and are applicable for claim 19.
Regarding claim 21, analyses are analogous to those presented for claim 1 and are applicable for claim 21.
Claim(s) 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over US20220292724-Ren et al (Hereinafter referred to as “Ren”), in view of US 20220101112 A1-Brown et al (Hereinafter referred to as “Brown”), in further view of US 20190124348 A1-Yang, in view of 12210976 B2-Liu et al (Hereinafter referred to a “Liu”).
Regarding claim 4, Ren discloses the method of claim 1 (See claim 1),
Ren fails to disclose wherein the first ML model and the one or more second ML models are of the same type.
However, in the same field of endeavor, Liu discloses wherein the first ML model and the one or more second ML models are of the same type (column 3, lines 60-67, wherein a first and second teacher model share same parameters).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Ren to disclose wherein the first ML model and the one or more second ML models are of the same type as taught by Liu, to improve contrastive learning framework from unlabeled videos (column 1, lines 30-36, Liu).
Regarding claim 5, Liu discloses the method of claim 4, wherein a model of the first ML model and the one or more second ML models is trained to approach ground truths of training samples and features and/or outputs of others of the first ML model and the one or more second ML models (column 7, lines 40-65, wherein the labels of the predictive logits are interpreted as ground truths); wherein the first ML model and the one or more second ML models have the same structure(column 3, lines 60-67, wherein a first and second teacher model share same parameters); or wherein at least two of the first ML model and the one or more second ML models have different structures.
Claim(s) 7 rejected under 35 U.S.C. 103 as being unpatentable over US20220292724-Ren et al (Hereinafter referred to as “Ren”), in view of US 20220101112 A1-Brown et al (Hereinafter referred to as “Brown”), in further view of US 20190124348 A1-Yang, in view of US 20210099710 A1-Salehefar et al (Hereinafter referred to as “Sal”).
Regarding claim 7, Ren discloses the method of claim 6 (See claim 6),
Ren fails to disclose wherein the first ML model is used for loop-filtering in the video coding and/or compression; wherein the first ML model is used for post-filtering in the video coding and/or compression; wherein the first ML model is used for at least one of the following in the video coding and/or compression: down-sampling, or up-sampling; wherein the first ML model is used to generate a prediction signal in the video coding and/o compression; wherein the first ML model is used to filter a prediction signal in the video coding and/or compression; or wherein the first ML model is used for entropy coding in the video coding and/or compression.
However, in the same field of endeavor, Sal discloses wherein the first ML model is used for loop-filtering in the video coding and/or compression ([0112], CNN as in-loop filtering); wherein the first ML model is used for post-filtering in the video coding and/or compression ([0151, CNN as post filtering); wherein the first ML model is used for at least one of the following in the video coding and/or compression: down-sampling, or up-sampling ([0095], down sampling); wherein the first ML model is used to generate a prediction signal in the video coding and/or compression ([0131], prediction sample); wherein the first ML model is used to filter a prediction signal in the video coding and/or compression; or wherein the first ML model is used for entropy coding in the video coding and/or compression ([0130], entropy encoding).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Ren to disclose wherein the first ML model is used for loop-filtering in the video coding and/or compression; wherein the first ML model is used for post-filtering in the video coding and/or compression; wherein the first ML model is used for at least one of the following in the video coding and/or compression: down-sampling, or up-sampling; wherein the first ML model is used to generate a prediction signal in the video coding and/o compression; wherein the first ML model is used to filter a prediction signal in the video coding and/or compression; or wherein the first ML model is used for entropy coding in the video coding and/or compression as taught by Sal, to improve the subjective/objective image qualities ([0114], Sal).
Allowable Subject Matter
Claims 10-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LERON BECK whose telephone number is (571)270-1175. The examiner can normally be reached M-F 8 am-5pm.
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LERON . BECK
Examiner
Art Unit 2487
/LERON BECK/Primary Examiner, Art Unit 2487
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