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)(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) 1-11, 18-23, and 25-27 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al., US 2022/0078418.
Regarding claim 1, Huang discloses a method for decoding a signal from a bitstream (fig. 5; para 0012 and 0040; a method and apparatus of video decoding incorporating Deep Neural Network), the method applied to an electronic decoding apparatus (para 0012 and 0040; a video decoder) comprising:
decoding, from the bitstream, a set of features and a residual signal (figs. 1B, 2B, and 5; para 0010, 0029, and 0040; the method receives a video bitstream corresponding to one or more pictures in a video sequence in step 510. Each picture is decoded using a decoding process comprising one or a combination of a residual decoding process to generate reconstructed residual from the video bitstream);
obtaining a prediction signal (figs. 1B, 2B, and 5; para 0010, 0029, and 0040; a prediction process to generate a prediction signal related to each picture);
outputting the signal (fig. 5, element 540; para 0040; provide output data from an DNN) including:
determining whether to output a first reconstructed signal or a second reconstructed signal (figs. 1B, 2B, and 5; para 0011-0012, 0029, and 0040; the output data from DNN output is provided for the decoding process in step 540), or
combining the first reconstructed signal and the second reconstructed signal (this limitation is not addressed based on the “OR” condition); and
wherein the first reconstructed signal is obtained based on the residual signal and the prediction signal (figs. 1B, 2B, and 5; para 0010, 0029, and 0040; a reconstruction process to generate reconstructed picture from the reconstructed residual and the prediction signal); and the second reconstructed signal is obtained by processing the set of features and the prediction signal through applying one or more layers of a first neural network (figs. 1B, 2B, and 5; para 0032-0035 and 0040; target signal is processed using DNN (Deep Neural Network) in step 530, where the target signal provided to DNN input corresponds to the reconstructed residual, output from the prediction process, the reconstruction process or said at least one filtering process, or a combination thereof).
Regarding claim 2, the method according to claim 1, Huang discloses wherein the combining the first reconstructed signal and the second reconstructed signal comprises:
processing the first reconstructed signal and the second reconstructed signal by applying a second neural network (para 0032-0035 and 0040).
Regarding claim 3, the method according to claim 2, Huang discloses wherein the second neural network is applied:
on a frame level (para 0035 and 0038), or
on a block level (para 0037-0038), or
on predetermined shapes obtained by applying a mask indicating at least one area within a subframe, or
on predetermined shapes obtained by applying a pixel-wise soft mask.
Regarding claim 4, the method according to claim 1, Huang discloses wherein the determination is performed:
on a frame level (para 0035 and 0038), or
on a block level (para 0037-0038), or
on predetermined shapes obtained by applying a mask indicating at least one area within a subframe, or
on predetermined shapes obtained by applying a pixel-wise soft mask.
Regarding claim 5, the method according to claim 1, Huang discloses wherein in obtaining the second reconstructed signal, the prediction signal is added to an output of the first neural network (para 0032-0035 and 0040).
Regarding claim 6, the method according to claim 2, Huang discloses wherein at least one of the first neural network or the second neural network is a convolutional neural network (para 0032-0035 and 0040).
Regarding claim 7, the method according to claim 1, Huang discloses wherein the decoding the set of features and the residual signal is performed by a decoder of an autoencoder (para 0010, 0029, and 0040), wherein a training of the first neural network and the autoencoder is performed in an end-to-end manner (para 0030-0035).
Regarding claim 8, the method according to claim 1, Huang discloses wherein the decoding is performed by a hybrid block-based decoder (para 0006-0011).
Regarding claim 9, the method according to claim 1, Huang discloses wherein the decoding the set of features and the residual signal includes applying one or more of
a hyperprior,
an autoregressive model, and
a factorized entropy model (para 0009-0010).
Regarding claim 10, the method according to claim 1, Huang discloses wherein the signal to be decoded is a current frame, wherein the prediction signal is obtained from at least one previous frame and at least one motion field (para 010, 0035, and 0038).
Regarding claim 11, the method according to claim 1, Huang discloses wherein the signal to be decoded is a current motion field, wherein the prediction signal is obtained from at least one previous motion field (para 010, 0035, and 0038).
Regarding claim 18, Huang discloses a method for encoding a signal into a bitstream (fig. 6; para 0012 and 0040; a method and apparatus of video encoding incorporating Deep Neural Network), comprising:
obtaining a prediction signal (figs. 1A, 2A, 3-4, and 6; para 0009, 0031, and 0041; a prediction process to generate a prediction signal related to each picture);
obtaining a residual signal from the signal and the prediction signal (figs. 1A, 2A, 3-4, and 6; 0009, 0031, and 0041; the intra/Inter prediction data (i.e., the intra/Inter prediction signal) is supplied to the subtractor 116 to form prediction errors, also called residues or residual, by subtracting the Intra/Inter prediction signal from the signal associated with the input picture);
processing the signal and the prediction signal by applying one or more layers of a neural network, so as to obtain a set of features (figs. 1A, 2A, 3-4, and 6; 0009, 0031-0032, and 0041; a reconstruction process to generate reconstructed picture from reconstructed residual and the prediction signal, and at least one filtering process applied to the reconstructed picture in step 620. Target signal using DNN (Deep Neural Network) is processed in step 630, where the target signal provided to DNN input corresponds to the reconstructed residual, output from the prediction process, the reconstruction process or said at least one filtering process, or a combination thereof);
encoding the set of features and the residual signal into the bitstream (figs. 1A, 2A, 3-4, and 6; 0009, 0031-0032, and 0041; the output data from DNN output is provided for the encoding process in step 640).
Regarding claim 19, the method according to claim 18, Huang discloses wherein the neural network is a convolutional neural network (para 0032-0035 and 0040).
Regarding claim 20, the method according to claim 18, Huang discloses wherein the encoding the set of features and the residual signal is performed by an encoder of an autoencoder (para 0009, 0029, and 0041).
Regarding claim 21, the method according to claim 20, Huang discloses wherein a training of the neural network and the autoencoder is performed in an end-to-end manner (para 0030-0035).
Regarding claim 22, the method according to claim 18, Huang discloses wherein the encoding the set of features and the residual signal is performed by a hybrid block-based encoder (para 0006-0011).
Regarding claim 23, the method according to claim 18, Huang discloses wherein the encoding the set of features and the residual signal includes applying one or more of
a hyperprior,
an autoregressive model, and
a factorized entropy model (para 0009-0010).
Regarding claim 25, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 26, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 27, this claim recites substantially the same limitations that are performed by claim 18 above, and it is rejected for the same reasons.
Allowable Subject Matter
Claims 12-17 and 24 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:
The prior art made of record and considered pertinent to the applicant's disclosure, taken individually or in combination, does not teach the claimed invention having the following limitations, in combination with the remaining claimed limitations.
Regarding dependent claim 12, the prior art does not teach or suggest the claimed invention having “wherein the residual signal represents an area, and wherein the decoding from the bitstream the residual signal further comprises: decoding a first flag from the bitstream, setting samples of the residual signal within a first area included in the area equal to a default sample value based on the first flag being equal to a predefined value”, and a combination of other limitations thereof as recited in the claims.
Regarding dependent claim 13, the prior art does not teach or suggest the claimed invention having “wherein the set of features represents an area, and wherein the decoding from the bitstream the set of features further comprises: decoding a second flag from the bitstream, setting values of the features within a second area included in the area equal to a default feature value based on the second flag being equal to a predefined value”, and a combination of other limitations thereof as recited in the claims.
Regarding claims 14-15, the claim has been found allowable due to its dependencies to claim 12 above.
Regarding dependent claim 16, the prior art does not teach or suggest the claimed invention having “wherein the residual signal represents an area, the set of features represents the area, and wherein the decoding from the bitstream the set of features and the residual signal further comprises: decoding a third flag from the bitstream, setting samples of the residual signal within a third area included in the area equal to a default sample value and values of the features within a fourth area included in the area equal to a default feature value based on the third flag being equal to a predefined value”, and a combination of other limitations thereof as recited in the claims.
Regarding claim 17, the claim has been found allowable due to its dependencies to claim 16 above.
Regarding dependent claim 24, the prior art does not teach or suggest the claimed invention having “wherein the residual signal represents an area, and prior to the encoding the residual signal into the bitstream, the following operations are performed: determining whether or not to set samples of the residual signal within a first area included in the area equal to a default sample value, encoding a first flag into the bitstream, the first flag indicating whether or not the samples are set equal to the default sample value. encoding a second flag into the bitstream, the second flag indicating whether or not the samples are equal to the default feature value”, and a combination of other limitations thereof as recited in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al., US 2020/0389658 discloses provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image.
Li et al., US 2022/0210402 discloses a method of video coding using neural network based inter-frame prediction.
Jang, US 2025/0301145 discloses providing a feature encoding/decoding method and apparatus with improved encoding/decoding efficiency.
Hinz et al., US 2016/0014425 discloses a scalable video decoder is described which is configured to reconstruct a base layer signal from a coded data stream to obtain a reconstructed base layer signal.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665
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