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 .
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 5, 8 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (“Estimating the Resize Parameter in End-to-end Learned Image Compression,” arXiv:2204.12022v1 [eess.IV] 26 Apr 2022, already of record, referred to herein as “Chen”).
Regarding claim 1, Chen discloses: A method for processing visual media data, comprising:
determining, during a conversion between visual media data and a bitstream of the visual media data (Chen: Fig. 1, page 1, disclosing a compression process that converts source video data into an encoded bitstream), a resampling ratio of a dimension of a neural-network post-filter (NNPF) generated picture relative to a cropped dimension for a current picture to which a filtering process using NNPFs is applied (Chen: section I, pp. 1-2, disclosing a resize factor M using an auxiliary neural network that is used to resize video in a compression workflow; Fig. 3, section III(A), pp. 3-4 disclosing use of the neural network in post filtering to resample images relative to a cropped dimension), based on at least one syntax element (Chen: section I, pp. 2, disclosing that the estimated resize parameter is signaled in the bitstream),
wherein a value of the resampling ratio is constrained to a range with an endpoint (Chen: section III(B), pp. 4, disclosing that a resampling grid may exceed dimensions of an input source so that a warped image is then cropped and the resize factor determined), and wherein the endpoint of the range is based on a value of 16 (Chen: Table 1, page 5, disclosing a ResizeParamNet operation based on C=16); and
performing the conversion based on the resampling ratio (Chen: Fig. 3, section III(C), pp. 5-6, disclosing use of the resize factor to compress image data used for generating an output image).
Regarding claim 4, Chen discloses: The method of claim 1, wherein the resampling ratio of the dimension of the NNPF generated picture relative to the cropped dimension for the current picture comprises a resampling ratio of a width of the NNPF generated picture relative to a cropped width for the current picture (Chen: section III(B), pp. 4, disclosing resampling of the neural network post filtering picture relative to a cropped width).
Regarding claim 5, Chen discloses: The method of claim 1, wherein the resampling ratio of the dimension of the NNPF generated picture relative to the cropped dimension for the current picture comprises a resampling ratio of a height of the NNPF generated picture relative to a cropped height for the current picture (Chen: Chen: section III(B), pp. 4, disclosing resampling of the neural network post filtering picture relative to a cropped height).
Regarding claim 8, Chen discloses: The method of claim 1, wherein the conversion includes encoding the visual media data into the bitstream (Chen: Fig. 1, section I, pp. 1, disclosing input source images that are encoded).
Regarding claim 9, Chen discloses: The method of claim 1, wherein the conversion includes decoding the visual media data from the bitstream (Chen: Fig. 1, section I, pp. 1, disclosing decoding and reconstruction of the source image data).
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 2, 3, 10, 11, 12, 15, 16, 17, 18, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Choi et al. (US 2022/0217403 A1, already of record, referred to herein as “Choi”).
Regarding claim 2, Chen discloses: The method of claim 1, as discussed above.
Chen does not explicitly disclose: wherein the at least one syntax element is coded using a descriptor of ue(v).
However, Choi discloses: wherein the at least one syntax element is coded using a descriptor of ue(v) (Choi: paragraphs [0085] – [0089], disclosing signaling of neural network coding information; paragraph [0097], Table 1, disclosing use of a descriptor of ue(v) to code syntax elements).
At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the syntax of Choi in the method of Chen.
One would have been motivated to modify Chen in this manner in order to better signal a plurality of parameters useful for neural network based coding and better achieve increases in compressibility and accuracy associated with such neural network based coding (Choi: paragraphs [0003] – [0004] and [0097]).
Regarding claim 3, Chen and Choi disclose: The method of claim 1, wherein when the at least one syntax element is not present in the bitstream, a value of the at least one syntax element is inferred to be equal to 0 (Choi: paragraph [0102], disclosing that the value of a syntax element may be inferred to be zero when another syntax element is not present).
The motivation for combining Chen and Choi has been discussed in connection with claim 2, above.
Regarding claim 10, Chen discloses: An apparatus for processing visual media data comprising:
[…]
determine, during a conversion between visual media data and a bitstream of the visual media data (Chen: Fig. 1, page 1, disclosing a compression process that converts source video data into an encoded bitstream), a resampling ratio of a dimension of a neural-network post-filter (NNPF) generated picture relative to a cropped dimension for a current picture to which a filtering process using NNPFs is applied (Chen: section I, pp. 1-2, disclosing a resize factor M using an auxiliary neural network that is used to resize video in a compression workflow; Fig. 3, section III(A), pp. 3-4 disclosing use of the neural network in post filtering to resample images relative to a cropped dimension), based on at least one syntax element (Chen: section I, pp. 2, disclosing that the estimated resize parameter is signaled in the bitstream),
wherein a value of the resampling ratio is constrained to a range with an endpoint (Chen: section III(B), pp. 4, disclosing that a resampling grid may exceed dimensions of an input source so that a warped image is then cropped and the resize factor determined), and wherein the endpoint of the range is based on a value of 16 (Chen: Table 1, page 5, disclosing a ResizeParamNet operation based on C=16); and
perform the conversion based on the resampling ratio (Chen: Fig. 3, section III(C), pp. 5-6, disclosing use of the resize factor to compress image data used for generating an output image).
Chen does not explicitly disclose: a processor; and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to…
However, Choi discloses: a processor; and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to… (Choi: paragraph [0029], disclosing one or more processors and associated non-transitory computer-readable medium storing instructions to cause the processor(s) to implement a coding method).
At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the hardware implementation of Choi in the apparatus of Chen.
One would have been motivated to modify Chen in this manner in order to better implement neural network coding on a number of computing devices (Choi: paragraphs [0032] – [0033]).
Regarding claim 11, Chen and Choi disclose: The apparatus of claim 10, wherein the at least one syntax element is coded using a descriptor of ue(v) (Choi: paragraphs [0085] – [0089], disclosing signaling of neural network coding information; paragraph [0097], Table 1, disclosing use of a descriptor of ue(v) to code syntax elements).
The motivation for combining Chen and Choi has been discussed in connection with claim 10, above.
Regarding claim 12, Chen and Choi disclose: The apparatus of claim 10, wherein when the at least one syntax element is not present in the bitstream, a value of the at least one syntax element is inferred to be equal to 0 (Choi: paragraph [0102], disclosing that the value of a syntax element may be inferred to be zero when another syntax element is not present).
The motivation for combining Chen and Choi has been discussed in connection with claim 10, above.
Regarding claim 15, the claim recites analogous limitations to claim 10, above, and is therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 11, above, and is therefore rejected on the same premise.
Regarding claim 17, the claim recites analogous limitations to claim 12, above, and is therefore rejected on the same premise.
Regarding claim 18, the claim recites analogous limitations to claim 10, above, and is therefore rejected on the same premise.
Regarding claim 19, the claim recites analogous limitations to claim 11, above, and is therefore rejected on the same premise.
Regarding claim 20, the claim recites analogous limitations to claim 12, above, and is therefore rejected on the same premise.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of McCarthy et al. (“Additional SEI messages for VSEI (Draft 2),” Joint Video Experts Team (JVET) of ITU-T SG 16 WG 3 and Iso/IEC JTC 1/2C 29 27th Meeting, 13-22 Jul. 2022, Document: JVET-AA2006-v2, already of record, referred to herein as “McCarthy”).
Regarding claim 7, Chen discloses: The method of claim 1, as discussed above.
Chen does not explicitly disclose: wherein the at least one syntax element is included in a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message.
However, McCarthy discloses: wherein the at least one syntax element is included in a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message (McCarthy: section 8.28.1, pp. 4, disclosing neural-network post-filter characteristics SEI message syntax).
At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the neural-network post-filter characteristics SEI message of McCarthy in the method of Chen.
One would have been motivated to modify Chen in this manner in order to better specify characteristics of a neural network used in the post-filtering process (McCarthy: pp. 5-6).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Choi as applied to claim 10 above, and further in view of McCarthy.
Regarding claim 14, Chen and Choi disclose: The apparatus of claim 10, as discussed above.
Chen and Choi do not explicitly disclose: wherein the at least one syntax element is included in a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message.
However, McCarthy discloses: wherein the at least one syntax element is included in a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message (McCarthy: section 8.28.1, pp. 4, disclosing neural-network post-filter characteristics SEI message syntax).
At the time the application was effectively filed, it would have been obvious for a person having ordinary skill in the art to use the neural-network post-filter characteristics SEI message of McCarthy in the method of Chen and Choi.
One would have been motivated to modify Chen and Choi in this manner in order to better specify characteristics of a neural network used in the post-filtering process (McCarthy: pp. 5-6).
Allowable Subject Matter
Claims 6 and 13 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.
The following is a statement of reasons for the indication of allowable subject matter:
Chen, either alone or in combination with other prior art of record, does not teach, suggest, or disclose where the resampling ratio is in the range of 1/16 to 16, inclusive.
Conclusion
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CHRISTOPHER T. BRANIFF
Primary Examiner
Art Unit 2484
/CHRISTOPHER BRANIFF/Primary Examiner, Art Unit 2484