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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. In addition, the Device claimed by independent claim 19 is drafted in a way that does not specify the structural limitations of the Device. For these reasons, it is neither possible to give a definite construction to claim 19, nor is it possible to ascertain the scope of protection sought by claim 19. The non-compliance with the substantive provisions is such an extent that a meaningful search of the whole claimed subject matter of independent claim 19 could not be carried out.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 7, 19,21,24, and 28 are rejected under 35 U.S.C. 102A2 as being anticipated by us 20240129546 a1-Dinh et al (Hereinafter referred to as “DinH”).
Regarding claim 1, DinH discloses a method for decoding a current block (Fig.11 and 15), comprising:
receiving a compressed bitstream ([0084], transmitted to a decoding side as a bitstream; [0085], wherein obtained from bitstream);
decoding a transform block of transform coefficients from the compressed bitstream ([0085]),
wherein the transform coefficients are in a transform domain ([0085], wherein transform feature map is inverse quantized. These are still coefficients that are in the transform domain);
inputting the transform block to a machine-learning model to obtain a residual block ([0085], wherein an inverse-transformed residual block 635 is obtained as the inverse-quantized transform feature map 620 and the coding context feature map 631 are input to the inverse-transform neural network 625)
wherein the residual block is in a pixel domain ([0085], fig 6, wherein an inverse transform converts transform domain coefficients into pixel domain samples); and
using the residual block to reconstruct the current block ( [0085], Fig. 6)A reconstructed block 645 of the current block is obtained by performing addition 640 on the residual block 635 and the prediction block 603).
Regarding claim 7, Dinh discloses the method of claim 1, wherein the machine-learning model is trained to perform one of an inverse linear transform or an inverse non-linear transform ([0079], linear inverse transform).
Regarding claim 19, analyses are analogous to those presented for claim 1 and are applicable for claim 19, processor ([0007])
Regarding claim 21, analyses are analogous to those presented for claim 1 and are applicable for claim 21.
Regarding claim 24, analyses are analogous to those presented for claim 1 and are applicable for claim 24, processor ([0007])
Regarding claim 28, analyses are analogous to those presented for claim 7 and are applicable for claim 28.
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) 2-4, 10, 22-23, 25-27, 30 are rejected under 35 U.S.C. 103 as being unpatentable over us 20240129546 a1-Dinh et al (Hereinafter referred to as “DinH”), in view of US 20220103839 A1-Van Rozendaal et al (Hereinafter referred to as:Van”)
Regarding claim 2, Dinh discloses the method of claim 1 (see claim 1)
Dinh fails to disclose decoding a latent space representation of the transform block from the compressed bitstream; and obtaining, based on the latent space representation, a probability distribution for decoding the transform block.
However, in the same field of endeavor, Van discloses decoding a latent space representation from the compressed bitstream ([0129-0130], decoding latent code; [0094], The codes (e.g., codes z) can also be referred to as latents, latent variables or latent representations; [0141], wherein hyperdecoder of the hypercodec can use the hyperlatent space representation (z.sub.1) to generate a hyperprior model ); and obtaining, based on the latent space representation, a probability distribution for decoding ([0106], wherein the code model receives the code z representing an encoded image or portion thereof and generates a probability distribution P(z) over a set of compressed codewords that can be used to represent the code z. In some examples, the code model can include a probabilistic auto-regressive generative model; [0109]) determines the probability of a code in latent space; [0141], wherein the hyperprior model can include a probability distribution over the parameters of the latent space representation (z.sub.2) and the hyperlatent space representation (z.sub.1). In some examples, the hyperprior model can include a probability distribution over the parameters of the latent space representation (z.sub.2), the hyperlatent space representation (z.sub.1), and the hyperdecoder).
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 Dinh to disclose decoding a latent space representation of the transform block from the compressed bitstream; and obtaining, based on the latent space representation, a probability distribution for decoding the transform block as taught by Van, to improve compression performance for the image to compress ([0212], Van).
Regarding claim 3, Van discloses the method of claim 2, wherein obtaining the probability distribution comprises: inputting the latent space representation into a context parameter extractor machine-learning model to obtain a parameter ([0094], deep neural network); and obtaining the probability distribution based on the parameter ([0094])The deep neural network can include a probabilistic model (also referred to as a prior or code model) that can losslessly compress the codes z from the latent code space. The probabilistic model can generate a probability distribution over the set of codes z that can represent encoded data based on the input data. In some cases, the probability distribution can be denoted as (P(.
Regarding claim 4, Van discloses the method of claim 3, wherein the parameter is one of: at least one of a mean or a standard deviation of a Gaussian distribution of the probability distribution ([0178], standard deviation); an index of the probability distribution into a look-up-table; or the probability distribution ([0094], probability distribution).
Regarding claim 10, Dinh method of claim 1 (see claim 1),
Dinh fails to disclose wherein decoding the transform block of coefficients from the compressed bitstream comprises decoding at least two of the transform coefficients in parallel
However, in the same field of endeavor, Van discloses wherein decoding the transform block of coefficients from the compressed bitstream comprises: decoding at least two of the transform coefficients in parallel ([0228]).
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 Dinh to disclose decoding the transform block of coefficients from the compressed bitstream comprises decoding at least two of the transform coefficients in parallel as taught by Van, to improve compression performance for the image to compress ([0212], Van).
Regarding claim 22, analyses are analogous to those presented for claim 2 and are applicable for claim 22.
Regarding claim 23, analyses are analogous to those presented for claim 3 and are applicable for claim 23.
Regarding claim 25, analyses are analogous to those presented for claim 2 and are applicable for claim 25.
Regarding claim 26, analyses are analogous to those presented for claim 3 and are applicable for claim 26.
Regarding claim 27, analyses are analogous to those presented for claim 4 and are applicable for claim 27.
Regarding claim 30, analyses are analogous to those presented for claim 10 and are applicable for claim 30.
Claim(s) 9 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over us 20240129546 a1-Dinh et al (Hereinafter referred to as “DinH”), in view of US 20230412808 A1-Holland et al (hereinafter referred to as “Holland”).
Regarding claim 9, Dinh discloses the method of claim 1 (See claim 1),
Dinh fails to disclose wherein an indication of a bitrate is further input to the machine-learning model.
However, in the same field of endeavor, Holland discloses wherein an indication of a bitrate is further input to the machine-learning model (target bitrate is provided as an input to machine learning)
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 Dinh to disclose wherein an indication of a bitrate is further input to the machine-learning model as taught by Holland, to improved compression efficiency and/or video quality([0003], Holland)
Regarding claim 29, analyses are analogous to those presented for claim 9 and are applicable for claim 29.
Conclusion
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LERON . BECK
Examiner
Art Unit 2487
/LERON BECK/Primary Examiner, Art Unit 2487
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