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 Interpretation
Claim 21 (New) recites:
A method for storing a bitstream of a video, comprising:
applying a neural network (NN)-based model comprising a first module for prediction fusion and a second module for hyper scale decoder to the video data, wherein at least one of the followings of the first module for prediction fusion and/or the second module for hyper scale decoder is satisfied: the number of channels in a convolutional layer being smaller than or equal to a first threshold number, the number of convolutional layers being smaller than or equal to a second threshold number, or a kernel size being smaller than or equal to a threshold size; and
generating the bitstream based on the first module for prediction fusion and the second module for hyper scale decoder;
storing the bitstream in a non-transitory computer-readable recording medium.
The bitstream, defined by how the bitstream was generated, only describes the content of the information in the bitstream and as result is descriptive language. See MPEP §2111.05.
The bitstream has no functional relationship with the “method for storing of a video….” The claim scope (in light of the specification) describes the generation of bitstream in terms of how the video gets encoded within the bitstream, there is provided no functional relationship between the bitstream’s contents once generated and the process for storing the bitstream. As result, claim language directed to the contents of the bitstream is considered non-functional descriptive language and will be given not patentable weight. See Id. Thus, the claim scope is just a method for storing a bitstream of a video and is anticipated by Alshina et al., US 20250245488 A1 which recites a storage medium storing data (Alshina: Memory Store(s) 44, ¶ [0241]; Fig. 16).
Response to Amendment
In response to the Office Action mailed June 9, 2026, claims 1-19 and 21 remain pending and subject to examination. No claims have been amended, claim 20 has been canceled, and claim 21 has been added. Support for the new claim was found throughout the specification, claims, and drawings as originally filed in the present application. No new matter has been added.
Response to Arguments
Applicant's arguments filed September 9, 2026, have been fully considered but they are not persuasive.
Independent claims 1, 18, 19 and-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Alshina et al., US 20250245488 A1, (hereinafter referred to as “Alshina”).
Independent claims 1, 18, 19 and new claim 21 require that at least one of the following three threshold limitations be satisfied:
(1) the number of channels in a convolutional layer is smaller than or equal to a first threshold number;
(2) the number of convolutional layers is smaller than or equal to a second threshold number; or
(3) a kernel size is smaller than or equal to a threshold size.
Applicant argues, Alshina does not disclose all features recited in independent claims because Alshina does not disclose at least the comparison between a parameter (i.e., number of channels, convolution layers or kernel size) and an identified threshold. Remarks, page 11. More specifically, applicant argues “Alshina does not disclose a corresponding parameter as being at or below a meaningful upper limit. The fact that a parameter may take an arbitrary positive integer value does not, by itself, disclose the claimed upper-bound relationship.” Id., page 11.
The Examiner respectfully disagrees. In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., the comparison) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
None of the independent claims disclose performing a comparison as part of the method, or instructions for executed by a processor. Based on the Examiner's broadest reasonable interpretation of the claim, the comparison is not performed by the computing device and may be disclosed by virtue of teaching a parameter number equal to or less than a threshold number.
The Examiner agrees that a threshold may be reasonably interpreted as an upper bound limit. Alshina discloses restricting the number of channels to a positive integer. Alshina, ¶ ¶ [0008], [0253]. Integers are whole numbers not including fractions or decimals. Meaning, the actual number of channels used must be equal to or bound to any number in the positive integer number space. A restriction on the number of channels disclosed by Alshina is a thresholding. According to Alshina, any decimal or fractional number above a positive integer value is not allowed. For example, Alshina discloses 2 channels (Id, ¶ [0008]), in this case the number of channels is equal to a disclosed threshold. Therefore, Alshina discloses “the number of channels in a convolutional layer is smaller than or equal to a first threshold number,” as recited in the claim.
Independent claims 1, 18, 19 and 21 require that at least one of the three threshold limitations be satisfied. Alshina discloses at least one of the threshold limitations (i.e., “the number of channels in a convolutional layer is smaller than or equal to a first threshold number).
For at least these reasons, independent claims 1, 18, 19 and-20 stand rejected under 35 U.S.C. 102(a)(2) as being anticipated by Alshina.
Claims 2-17 depend on independent claim 1 respectively and are therefore unpatentable at least by virtue of their dependency.
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 1-4 and 9-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Alshina et al., US 20250245488 A1, (hereinafter referred to as “Alshina”).
Regarding claim 1 (Original), Alshina discloses a method for video processing, comprising:
applying, for a conversion between visual data and a bitstream of the visual data, a neural network (NN)-based model (Alshina: Embodiments of the present disclosure generally relate to the field of encoding and decoding data based on a neural network architecture. ¶ [0002]) comprising a first module for prediction fusion and a second module for hyper scale decoder to the video data (Alshina: Prediction Fusion Net and Hyperscale Decoder Fig. 7A. ¶¶ [0038]-[0039]), wherein at least one of the followings of the first module for prediction fusion and/or the second module for hyper scale decoder is satisfied: the number of channels in a convolutional layer being smaller than or equal to a first threshold number (Alshina: the C number of channels is a positive integer ¶ [0008],[0033]), the number of convolutional layers being smaller than or equal to a second threshold number (Alshina: As seen in figures 6-12 the number of convolutional layers may be restricted to any positive integer value. ¶ [0008], [0253]), or a kernel size being smaller than or equal to a threshold size (Alshina: As seen in figures the Kernel size be any value KxK for example, where K is an integer); and performing the conversion based on the first module for prediction fusion and the second module for hyper scale decoder (Alshina: Embodiments of the present disclosure generally relate to the field of encoding and decoding data based on a neural network architecture. ¶ [0002]).
Regarding claim 2 (Original), Alshina discloses the method of claim 1, wherein the number of convolutional layers in the first module for prediction fusion is one of: 6, 5, 4, or 3 (Alshina: Figs. 9A, 12).
Regarding claim 3 (Original), Alshina discloses the method of claim 1 (Note: The following claim recites alternative language), wherein the number of channels for each layer in the first module for prediction fusion is adjustable (Alshina: The number of channels changes from one neural network layer to another. ¶ [0041]), and/or
wherein the bitstream comprises a syntax element indicating the number of channels for each layer in the first module for prediction fusion, and/or
wherein a plurality of first modules for prediction fusion are stored, and wherein the bitstream comprises a syntax element indicating which first module for prediction fusion is used in the conversion, and/or
wherein the first module for prediction fusion is applied to one or more of: luma components or chroma components, and/or
wherein the first module for prediction fusion comprises a chroma prediction fusion net with convolution layers where each convolutional layer has a number of channels that is a multiple of
C
4
,
C
8
,
C
16
, or
C
32
and a luma prediction fusion net with convolution layers where each convolutional layer has a number of channels that are twice the number of channels in the chroma prediction fusion net, wherein C is an integer number.
Regarding claim 4 (Original), Alshina discloses, the method of claim 3 (Note: The following claim recites alternative language), wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
17
4
C
,
11
4
C
,
9
4
C
,
7
4
C
,
5
4
C
, or
C
(Alshina: The number of channels allocated for primary component “Y” coding is C.sub.p=128. ¶ [0171]); and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
17
8
C
,
11
8
C
,
9
8
C
,
7
8
C
,
5
8
C
, or
1
2
C
(Alshina: The number of channels allocated for secondary components “UV” coding C.sub.s=64. ¶ [0172]), or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
33
8
C
,
21
8
C
,
17
8
C
,
13
8
C
,
9
8
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
33
16
C
,
21
16
C
,
17
16
C
,
13
16
C
,
9
16
C
, or
1
2
C
, or
wherein the number of channels in a first convolutional layer in the luma prediction fusion net is
7
2
C
, and the number of channels in a first convolutional layer in the chroma prediction fusion net is
7
4
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
13
4
C
,
11
4
C
,
9
4
C
,
7
4
C
,
5
4
C
, or
C
; and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
13
8
C
,
11
8
C
,
9
8
C
,
7
8
C
,
5
8
C
, or
1
2
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
25
8
C
,
21
8
C
,
17
8
C
,
13
8
C
,
9
8
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
25
16
C
,
21
16
C
,
17
16
C
,
13
16
C
,
9
16
C
, or
1
2
C
.
Regarding claim 9 (Original), Alshina discloses the method of claim 1 (Note: The claim recites alternative language), wherein the second module for hyper scale decoder comprises 4x3 convolutional kernels, or 3x4 convolutional kernels, or combinations thereof, and/or
wherein the second module for hyper scale decoder comprises 4x4 convolutional kernels implemented with a two-dimensional (2D) convolution layer plus a pixel shuffling layer, and/or
wherein the second module for hyper scale decoder comprises a 5x5 upsampling layer, followed by a convolution layer, followed by a 5x5 upsampling layer, and/or
wherein the number of convolutional layers in the second module for hyper scale decoder is one of: 6, 5, 4, or 3 (Alshina: Hyper Scale Decoder Fig. 9a, 910), and/or
wherein the number of channels in the second module for hyper scale decoder is adjustable, and/or
wherein the bitstream comprises a flag indicating whether the second module for hyper scale decoder is used in the conversion, and/or
wherein a plurality of second modules for hyper scale decoder are stored, and wherein the bitstream comprises a syntax element indicating which second module for hyper scale decoder is used in the conversion, and/or
wherein the second module for hyper scale decoder is applied to at least one of: luma components, or chroma components (Alshina: Y and UV Fig. 7A).
Regarding claim 10 (Original), Alshina discloses the method of claim 1, wherein the second module for hyper scale decoder comprises a chroma hyper scale decoder with convolution layers where each convolutional layer has a number of channels that is a multiple of
C
4
,
C
8
,
C
16
, or
C
32
and a luma hyper scale decoder with convolution layers where each convolutional layer has a number of channels that are twice the number of channels in the chroma hyper scale decoder (Alshina: ¶¶ [0171]-[0172], Fig. 7A).
Regarding claim 11 (Original), Alshina discloses the method of claim 10 (Note: The claim recites alternative language), wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
5
4
C
,
9
4
C
,
7
4
C
,
5
4
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
5
8
C
,
9
8
C
,
7
8
C
,
5
8
C
, and
1
2
C
, respectively (Alshina: ¶¶ [0171]-[0172], Fig. 7A), or
wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
11
8
C
,
19
8
C
,
15
8
C
,
11
8
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
11
16
C
,
19
16
C
,
15
16
C
,
11
16
C
, and
1
2
C
, respectively, or
wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
3
2
C
,
2
C
,
3
2
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
3
4
C
,
C
,
3
4
C
, and
1
2
C
, respectively, or
wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
5
4
C
,
7
4
C
,
5
4
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
5
8
C
,
7
8
C
,
5
8
C
, and
1
2
C
, respectively, or
wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
2
C
,
2
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
C
,
C
, and
1
2
C
, respectively, or
wherein the number of channels for each convolutional layer in the luma hyper scale decoder is equal to:
C
,
C
,
C
, and
C
, respectively; and the number of channels for each convolutional layer in the chroma hyper scale decoder is equal to:
1
2
C
,
1
2
C
,
1
2
C
, and
1
2
C
, respectively.
Regarding claim 12 (Original), Alshina discloses the method of claim 1 (Note: The claim recites alternative language), wherein the second module for hyper scale decoder comprises 4 convolutional layers of which a kernel size is 2x2 and with pixel shuffle, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers where first two convolutional layers of the 4 convolutional layers are convolutional layers with a kernel size being 2x2 and implemented with pixel shuffler and/or last two convolutional layers of the 4 convolutional layers are group convolutional layers with group of 2 and kernel size being 3x3 (Alshina: Hyper Scale Decoder Net. Figs. 9A and 11).
Regarding claim 13, Alshina discloses the method of claim 1 (Note: The claim recites alternative language), wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 3x3 and with pixel shuffler, and wherein the number of channels after pixel shuffle layer is:
C
,
C
,
C
,
a
n
d
C
, respectively (Alshina: Hyper Scale Decoder Net. Figs. 9A and 11).
Regarding claim 14 (Original), Alshina discloses the method of claim 1 (Note: The following claim recites alternative language), wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 4x3 and with pixel shuffle, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
3
2
C
,
5
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers comprising upsampling layer with kernel size being 5x5, followed by a convolution layer, followed by an upsampling layer with kernel size being 5x5, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 3x3 and with pixel shuffle, and wherein the number of channels in each layer after pixel shuffle layer is:
2
C
,
3
2
C
,
3
2
C
, and
2
C
, respectively, or
wherein the second module for hyper scale decoder comprises 5 convolutional layers with kernel size being 4x3, and wherein the number of channels in each layer is:
C
,
3
2
C
,
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 5 convolutional layers with kernel size being 4x3, and wherein the number of channels in each layer is:
C
,
C
,
C
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 4x3, and wherein the number of channels in each layer is:
C
,
3
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 4x3, and wherein the number of channels in each layer is:
3
2
C
,
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 4x3, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
C
,
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 5 convolutional layers with kernel size being 3x3 and with pixel shuffler, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
3
2
C
,
5
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 5 convolutional layers with kernel size being 3x3 and with pixel shuffler, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
C
,
C
,
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 4 convolutional layers with kernel size being 3x3 and with pixel shuffler, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
3
2
C
,
3
2
C
, and
C
, respectively, or
wherein the second module for hyper scale decoder comprises 3 convolutional layers with kernel size being 3x3 (Alshina: Hyper Scale Decoder Net. Figs. 9A and 11) and/or 1x1 and a pixel shuffle layer, and wherein the number of channels in each layer after pixel shuffle layer is:
C
,
C
, and
16
C
, respectively.
Regarding claim 15 (Original), Alshina discloses the method of claim 1, wherein the first module for prediction fusion comprises one of: a LeakyReLU activation layer or an ReLU activation layer, and/or wherein the second module for hyper scale decoder comprises one of: a quantized LeakyReLU activation layer or a quantized ReLU activation layer (Alshina: Fig 9A).
Regarding claim 16 (Original), Alshina discloses the method of claim 1, wherein the conversion includes encoding the visual data into the bitstream (Alshina: Fig 15).
Regarding claim 17 (Original), Alshina discloses the method of claim 1, wherein the conversion includes decoding the visual data from the bitstream (Alshina: Fig 15).
Regarding claim 18 (Original), claim 1 is sustainably similar to claim 18, therefore, claim 18 is rejected for the same reasons as claim 1 (Alshina: Figs. 15-16).
Regarding claim 19 (Original), claim 1 is sustainably similar to claim 18, therefore, claim 19 is rejected for the same reasons as claim 1 (Alshina: Figs. 15-16).
Regarding claim 21 (New), Alshina discloses a method for storing a bitstream of a video, comprising: applying a neural network (NN)-based model comprising a first module for prediction fusion and a second module for hyper scale decoder to the video data, wherein at least one of the followings of the first module for prediction fusion and/or the second module for hyper scale decoder is satisfied: the number of channels in a convolutional layer being smaller than or equal to a first threshold number, the number of convolutional layers being smaller than or equal to a second threshold number, or a kernel size being smaller than or equal to a threshold size; and generating the bitstream based on the first module for prediction fusion and the second module for hyper scale decoder; storing the bitstream in a non-transitory computer-readable recording medium. (Alshina: Memory Store(s) 44, ¶ [0241], Figs. 15-16)
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Alshina in further view of Ding et al., US 20220353521 A1 (hereinafter referred to as “Ding”).
Regarding claim 5, Alshina does not explicitly disclose the method of claim 1, wherein the number of convolutional layers in the luma prediction fusion net is 5, and the number of convolutional layers in the chroma prediction fusion net is 5 (Alshina: The Prediction Fusion Net including multiple convolutional layers. Fig. 9A).
However, Ding discloses wherein the number of convolutional layers in the luma prediction fusion net is 5, and the number of convolutional layers in the chroma prediction fusion net is 5 (Ding: FIG. 10, includes four convolution layers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Alshina with wherein the number of convolutional layers in the luma prediction fusion net is 5, and the number of convolutional layers in the chroma prediction fusion net is 5, as taught by Ding, since the number of convolution layers in video coding is known to vary in order to improve coding efficiency.
Regarding claim 6, Alshina discloses, the method of claim 5 (Note: The following claim recites alternative language), wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
3
C
,
5
2
C
,
2
C
,
3
2
C
, or
C
(Alshina: The number of channels allocated for primary component “Y” coding is C.sub.p=128. ¶ [0171]), and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
3
2
C
,
5
4
C
,
C
,
3
4
C
, or
1
2
C
(Alshina: The number of channels allocated for secondary components “UV” coding C.sub.s=64. ¶ [0172]), or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
13
4
C
,
9
4
C
,
7
4
C
,
5
4
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
13
8
C
,
9
8
C
,
7
8
C
,
5
8
C
, or
1
2
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
21
8
C
,
17
8
C
,
13
8
C
,
9
8
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
21
16
C
,
17
16
C
,
13
16
C
,
9
16
C
, or
1
2
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
7
2
C
,
11
4
C
,
2
C
,
3
2
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
7
4
C
,
11
8
C
,
C
,
3
4
C
, or
1
2
C
.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Alshina in further view of Salehifar et al., US 20210099710 A1 (hereinafter referred to as “Salehifar”).
Regarding claim 7, Alshina does not disclose the method of claim 1, wherein the number of convolutional layers in the luma prediction fusion net is 4, and the number of convolutional layers in the chroma prediction fusion net is 4.
However, wherein the number of convolutional layers in the luma prediction fusion net is 4, and the number of convolutional layers in the chroma prediction fusion net is 4 (Salehifar: FIG. 9 may include five convolution layers)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Alshina with wherein the number of convolutional layers in the luma prediction fusion net is 4, and the number of convolutional layers in the chroma prediction fusion net is 4, as taught by Salehifar, since the number of convolution layers in video coding is known to vary in order to improve coding efficiency.
Regarding claim 8, Alshina discloses the method of claim 7 (Note: The following claim recites alternative language), wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
4
C
,
3
C
,
2
C
or,
C
(Alshina: The number of channels allocated for primary component “Y” coding is C.sub.p=128. ¶ [0171]), and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
2
C
,
3
2
C
,
C
, or
1
2
C
Alshina: The number of channels allocated for secondary components “UV” coding C.sub.s=64. ¶ [0172]), or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
7
2
C
,
5
2
C
,
3
2
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
7
4
C
,
5
4
C
,
3
4
C
, or
1
2
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
15
4
C
,
11
4
C
,
7
4
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
15
8
C
,
11
8
C
,
7
8
C
, or
1
2
C
, or
wherein for each convolutional layer in the luma prediction fusion net, the number of channels is equal to one of:
11
8
C
,
23
8
C
,
15
8
C
, or
C
, and for each convolutional layer in the chroma prediction fusion net, the number of channels is equal to one of:
31
16
C
,
23
16
C
,
15
16
C
, or
1
2
C
, and/or
wherein C is equal to 128 (Alshina: The number of channels allocated for primary component “Y” coding is C.sub.p=128. ¶ [0171]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/DLB/Patent Examiner, Art Unit 2482
/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482
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