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 information disclosure statement (IDS) submitted on 02/24/2025 was considered by the examiner.
Drawings
The drawings were received on 01/21/2025. These drawings are acceptable.
Claim Rejections – 35 USC § 102
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 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.
Claims 1, 2, and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Besenbruch et al. US 2022/0272345 A1, hereafter Besenbruch.
Regarding claim 1, Besenbruch discloses a method for visual data processing (image encoding) [title], comprising:
obtaining, for a conversion between a current visual data unit of visual data and a bitstream of the current visual data unit (receiving an input image; transmitting the first bitstream and the second bitstream) [0010; 0018], first syntax information corresponding to adaptive masking and scaling and/or second syntax information corresponding to an adaptive offset process (obtain…an entropy scale parameter oy) [0015]; and
performing the conversion based on the first syntax information and/or the second syntax information processing by applying neural network-based module processing (entropy encoding the quantizied latent residuals into a second bitstream, using the entropy scale parameter oy) [0017].
Regarding claim 2, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
Besenbruch further discloses for the current visual data unit, quantized latent samples y are obtained based on quantized hyper latent samples z and quantized residual latent samples w (processing the quantized z hyperlatent representation using a hyperdecoder trained neural network to obtain a location entropy parameter u, an entropy scale parameter oy, and a context matrix Ay of the y latent representation ) [0011], and
wherein the quantized residual latent samples w are determined based on the first syntax information and parameter information derived based on the quantized hyper latent samples z (processing the y latent representation, the location entropy parameter uy, and the context matrix Ay, using an implicit encoding solver, to obtain quantized latent residuals) [0016].
Regarding claim 17, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
Besenbruch further discloses the conversion includes encoding the current visual data unit into the bitstream (encoding computer system including an encoding computer) [0030].
Regarding claim 18, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
Besenbruch further discloses the conversion includes decoding the current visual data unit from the bitstream (image or video decoding) [0041].
Claim 19 is drawn to an apparatus adapted to implement the method of claim 1, and are therefore rejected in the same manner as above. However, the claims also recite a processor, which Besenbruch also teaches (processor) [0102].
Regarding claim 20, computer readable medium claim 20 is drawn to the instructions corresponding to method claim 1. Therefore, computer readable medium claim 20 corresponds to method claim 1 and is rejected for the same reasons of unpatentability as used above.
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.
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 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Besenbruch in view of Laroche et al WO 2021/123357 A1, hereafter Laroche.
Regarding claim 3, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
However while Besenbrush discloses neural network image compression, Besenbrush fails to explicitly disclose high-level syntax for neural network image compression including wherein the first syntax information is used in performing an adaptive quantization process, wherein the first syntax information includes a first syntax element included in the bitstream for specifying a number of filters used in the adaptive quantization process, and wherein the first syntax element is coded with 8 bits.
Laroche further discloses the first syntax information is used in performing an adaptive quantization process, wherein the first syntax information includes a first syntax element included in the bitstream for specifying a number of filters used in the adaptive quantization process, and wherein the first syntax element is coded with 8 bits (number of filters signaled is decoded using the alf_luma_num_filters_signalled_minus 1 syntax element) [page 19],
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the number of filters syntax, as disclosed by Laroche, with the invention disclosed by Besenbruch, the motivation being simplify parsing process [page 1].
Regarding claim 4, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
However while Besenbrush discloses neural network image compression, Besenbrush fails to explicitly disclose high-level syntax for neural network image compression including the first syntax information is used in performing a block- based skipping process, wherein the first syntax information includes a second syntax element included in the bitstream for specifying a number of filters used in the block-based skipping process, and wherein the second syntax element is coded with 8 bits.
Laroche, in an analogous environment, discloses the first syntax information is used in performing a block- based skipping process, wherein the first syntax information includes a second syntax element included in the bitstream for specifying a number of filters used in the block-based skipping process, and wherein the second syntax element is coded with 8 bits (coding modes based on temporal prediction …SKIP) [page 13].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the skip syntax, as disclosed by Laroche, with the invention disclosed by Besenbruch, the motivation being simplify parsing process [page 1].
Regarding claim 5, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
However while Besenbrush discloses neural network image compression, Besenbrush fails to explicitly disclose high-level syntax for neural network image compression including the first syntax information is used in performing latent domain masking and scaling, wherein the first syntax information includes a third syntax element included in the bitstream for specifying a number of filters used in the latent domain masking and scaling, and wherein the third syntax element is coded with 8 bits.
Laroche, in an analogous environment, discloses the first syntax information is used in performing latent domain masking and scaling, wherein the first syntax information includes a third syntax element included in the bitstream for specifying a number of filters used in the latent domain masking and scaling, andwherein the third syntax element is coded with 8 bits (number of filters signaled is decoded using the alf_luma_num_filters_signalled_minus 1 syntax element) [page 19],
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the number of filters syntax, as disclosed by Laroche, with the invention disclosed by Besenbruch, the motivation being simplify parsing process [page 1].
Regarding claim 6, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
However while Besenbrush discloses neural network image compression, Besenbrush fails to explicitly disclose high-level syntax for neural network image compression including the first syntax information includes a list of flags included in the bitstream, and wherein the list of flags is used to indicate a three dimensional array specifying parameters for the adaptive masking and scaling.
Laroche, in an analogous environment, discloses the first syntax information includes a list of flags included in the bitstream, and wherein the list of flags is used to indicate a three dimensional array specifying parameters for the adaptive masking and scaling (three types of APS parameters) [page 19].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the number of filters syntax, as disclosed by Laroche, with the invention disclosed by Besenbruch, the motivation being simplify parsing process [page 1].
Regarding claim 7, Besenbruch addresses all of the features with respect to claim 1 as outlined above.
However while Besenbrush discloses neural network image compression, Besenbrush fails to explicitly disclose high-level syntax for neural network image compression including the second syntax information includes a fourth syntax element included in the bitstream for specifying whether the adaptive offset process is used for the current visual data unit, and wherein the fourth syntax element is coded with one bit.
Laroche, in an analogous environment, discloses the second syntax information includes a fourth syntax element included in the bitstream for specifying whether the adaptive offset process is used for the current visual data unit, and wherein the fourth syntax element is coded with one bit (SAO_parameters()…pic_alf_enabled_present_flag) [page 27].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the number of filters syntax, as disclosed by Laroche, with the invention disclosed by Besenbruch, the motivation being simplify parsing process [page 1].
Allowable Subject Matter
Claims 8-16 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.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rapaka et al. US 2015/0016540 A1 discloses offset delay parameter for video decoding
Conclusion
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STEFAN GADOMSKI
Primary Examiner
Art Unit 2485
/STEFAN GADOMSKI/Primary Examiner, Art Unit 2485