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 § 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.
Claims 26 and 27 are 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.
Claim 26 recites the limitation “the third value in the repeater block” in the last line. There is insufficient antecedent basis for this limitation in the claim.
Claim 27 recites the limitation “the second value in the repeater block” in the last line. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. Three categories of subject matter are found to be judicially recognized exceptions to 35 U.S.C. § 101 (i.e. patent ineligible) (1) laws of nature, (2) physical phenomena, and (3) abstract ideas. To be patent-eligible, a claim directed to a judicial exception must as whole be directed to significantly more than the exception itself. Hence, the claim must describe a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.
Claim(s) 25-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. Claim(s) are directed toward processing data which encompasses all forms of data in contrast to limited forms of data, such as image data or video data. Since the claims encompasses all forms of data, they are directed toward all practical uses of the processing steps (i.e. mathematical formula), which is essentially a claim to the mathematical formula itself (i.e. abstract idea), which corresponds to concepts identified as abstract ideas by the courts, such as the Arrhenius equation in Diehr and the mathematical formula for hedging in Bilski. Furthermore, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
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)(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.
(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 25, 27-34, 39 and 40 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Julio Zamora-Esquivel et al. [Adaptive Convolutional Kernels].
Regarding claim 25, Julio teaches:
25. (New) A method (i.e. This novel adaptive kernel is used to perform a second convolution operation over the input image in order to generate the output features- Abstract), comprising:
identifying a first group within a set of inputs (i.e. using a sliding window of 3 × 3- page 1999, col 2, last ¶, fig. 1), wherein the first group comprises a first plurality of values (i.e. input image- fig. 1) and wherein the set of inputs is divided into n x n or n x m repeater blocks (i.e. using a sliding window of 3 × 3- page 1999, col 2, last ¶, fig. 1 and fig. 7);
selecting a first weight tensor, wherein the first weight tensor is selected based on a first value having one of the following positions in a repeater block of the set of inputs: top left position, top right position, bottom left position and bottom right position (i.e. An adaptive kernel (K) is defined by a dynamic filter that changes its weights by itself depending on the input image. This adaptive kernel can be generated using an array of traditional kernels Q. For instance, each element (u, v) in a 3 × 3 Adaptive Kernel could be generated by a 3x3 linear filter Qu,v, as shown in Figure 1- page 1999, ¶8, fig. 1);
generating a first weighted sum by applying the first weight tensor to the first plurality of values (i.e. This new dynamically generated kernel K is convolved again with the input image X to generate
∑
u
,
v
x
u
,
v
K
u
,
v
- page 2000, ¶2);
identifying a second group within the set of inputs, wherein the second group comprises a second plurality of values; selecting a second weight tensor, wherein the second weight tensor is selected based at least in part on a position of a second value, wherein the second weight tensor is different than the first weight tensor; generating a second weighted sum by applying the second weight tensor to the second plurality of values; identifying a third group within a set of inputs, wherein the third group comprises a third plurality of values;
selecting a third weight tensor, wherein the third weight tensor is selected based at least in part on a position of a third value; and generating a third weighted sum by applying the third weight tensor to the third plurality of values (i.e. By sliding the window through all the input image X, we generate the final filtered image. Given that the convolutional kernel Ku,v- page 2000, ¶3, Figure 5. Every input window is convolved by a different filter generated on the fly using the input image- page 2002, fig. 5);
wherein the first and third weight tensors are the same, further comprising performing one or more of: generating a first output value by processing the first weighted sum with a non-linear activation function; generating a second output value by processing the second weighted sum with a non-linear activation function; generating a third output value by processing the third weighted sum with a non-linear activation function (i.e. In the final step, the output pixel is computed using hyperbolic tangent as the activation function like f(s) = tanh(S). Other activation functions could be used in the same fashion, e.g. sigmoid or Relu- page 2000, ¶3).
Regarding claim 27, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the position of the first value in the repeater block and the position of the second value in the repeater block are different (i.e. By sliding the window through all the input image X, we generate the final filtered image. Given that the convolutional kernel Ku,v- page 2000, ¶3, Figure 5. Every input window is convolved by a different filter generated on the fly using the input image- page 2002, fig. 5).
Regarding claim 28, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the first weight tensor is selected based on a distance of the first value to a repeater block boundary (i.e. By sliding the window through all the input image X, we generate the final filtered image- page 2000, ¶3).
Regarding claim 29, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the first value is a centrally located value within the first group (i.e. see center value of the 3 × 3 Adaptive Kernel overlaps the center pixel in the image window- figs. 1 and 2).
Regarding claim 30, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the second value is a centrally located value within the second group, and the third value is a centrally located value within the third group (i.e. see center value of the 3 × 3 Adaptive Kernel overlaps the center pixel in the image window- figs. 1 and 2).
Regarding claim 31, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the method is applied in a convolutional layer of a neural network filter (i.e. this work introduces the concept of adaptive kernels applied to convolutional layers… Adaptive kernels enable accurate recognition with significant lower memory requirements; this is accomplished by reducing the number of kernels and the number of layers needed as compared to typical CNN configurations- Abstract).
Regarding claim 32, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the method is applied in the head (i.e. To further down on the analysis of adaptive kernels, we performed three additional experiments: we implemented a ResNet18 where we replace the initial layer with adaptive kernels- page 2000, ¶10), trunk/body, or tail of a neural network feature.
Regarding claim 33, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the method is applied in only a subset of convolutional layer layers of a given neural network filter or neural network part or applied only to a subset of channels (i.e. To further down on the analysis of adaptive kernels, we performed three additional experiments: we implemented a ResNet18 where we replace the initial layer with adaptive kernels- page 2000, ¶10, Table 6-7 and 9, [6] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016).
Regarding claim 34, Julio teaches all the limitations of claim 25 and Julio further teaches:
wherein the set of inputs are values corresponding to a video picture (i.e. the input image is used to define the weights of a dynamically changing kernel, named adaptive kernel- Abstract).
Regarding claim 39, computer-readable medium storing instructions claim 39 corresponds to apparatus claim 25, and therefore is also rejected for the same rationale as listed above.
Regarding claim 40, apparatus claim 40 is drawn to the apparatus using/performing the same method as claimed in claim 25. Therefore, apparatus claim 40 corresponds to method claim 25, and is rejected for the same rationale 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Julio Zamora-Esquivel et al. [Adaptive Convolutional Kernels] in view of Ze Wang et al. [Adaptive Convolutions with Per-pixel Dynamic Filter Atom].
Regarding claim 26, Julio teaches all the limitations of claim 25.
However, Julio does not teach explicitly:
wherein the position of the first value in the repeater block and the position of the third value in the repeater block are the same.
In the same field of endeavor, Ze teaches:
wherein the position of the first value in the repeater block and the position of the third value in the repeater block are the same (i.e. First, in atom convolution, the input features with c0 channels are convolved spatially only with each of the m generated dynamic filter atoms, and output the intermediate features ˜Z with c0m channels- page 12284, ¶10).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Ze to significantly improve accuracy (Ze- Section 4.1).
Claims 35, 38 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Julio Zamora-Esquivel et al. [Adaptive Convolutional Kernels] in view of Philippe Bordes et al. [US 20230188713 A1].
Regarding claim 35, Julio teaches all the limitations of claim 25.
However, Julio does not teach explicitly:
wherein the method is performed as a filtering step or as part of a filtering step in an encoding or decoding process.
In the same field of endeavor, Ze teaches:
wherein the method is performed as a filtering step or as part of a filtering step in an encoding or decoding process (i.e. In one implementation, to perform in-loop filtering of a version of reconstructed samples of a block, only a single offset parameter is signaled in the bitstream. Based on the version of reconstructed samples, a pixel-wise weight mask is generated using a neural network. Because the neural network parameters are known at both the encoder and decoder- Abstract… FIG. 9 illustrates an example of using a Convolutional Neural Network (CNN) to restore images after reconstruction- ¶0016).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Philippe to make the number of parameters in a CNN independent of its input size, meaning that a trained CNN can restore images of various sizes (Phillipe- ¶0081).
Regarding claim 38, Julio teaches all the limitations of claim 25.
However, Julio does not teach explicitly:
38. (New) An apparatus configured to perform the method of claim 25, wherein the apparatus is an encoder or decoder.
In the same field of endeavor, Ze teaches:
38. (New) An apparatus configured to perform the method of claim 25, wherein the apparatus is an encoder or decoder (i.e. In one implementation, to perform in-loop filtering of a version of reconstructed samples of a block, only a single offset parameter is signaled in the bitstream. Based on the version of reconstructed samples, a pixel-wise weight mask is generated using a neural network. Because the neural network parameters are known at both the encoder and decoder- Abstract… FIG. 9 illustrates an example of using a Convolutional Neural Network (CNN) to restore images after reconstruction- ¶0016).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Philippe to make the number of parameters in a CNN independent of its input size, meaning that a trained CNN can restore images of various sizes (Phillipe- ¶0081).
Regarding claim 41, Julio teaches all the limitations of claim 40.
However, Julio does not teach explicitly:
wherein the apparatus is an encoder or decoder.
In the same field of endeavor, Ze teaches:
wherein the apparatus is an encoder or decoder (i.e. In one implementation, to perform in-loop filtering of a version of reconstructed samples of a block, only a single offset parameter is signaled in the bitstream. Based on the version of reconstructed samples, a pixel-wise weight mask is generated using a neural network. Because the neural network parameters are known at both the encoder and decoder- Abstract… FIG. 9 illustrates an example of using a Convolutional Neural Network (CNN) to restore images after reconstruction- ¶0016).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Philippe to make the number of parameters in a CNN independent of its input size, meaning that a trained CNN can restore images of various sizes (Phillipe- ¶0081).
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Julio Zamora-Esquivel et al. [Adaptive Convolutional Kernels] in view of Tetsu Wada [US 20060274953 A1].
Regarding claim 36, Julio teaches all the limitations of claim 25.
However, Julio does not teach explicitly:
wherein positions within the set of inputs are defined in a checkerboard arrangement.
In the same field of endeavor, Tetsu teaches:
wherein positions within the set of inputs are defined in a checkerboard arrangement (i.e. FIG. 6 is a diagram sowing a filter coefficient used to interpolate an image in the checkerboard like pattern shown in FIG. 5- ¶0055).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Tetsu to reduce the volume of data to be compressed without the image quality being deteriorated (Tetsu- ¶0028).
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Julio Zamora-Esquivel et al. [Adaptive Convolutional Kernels] in view of Hongtao Wang et al. [US 20240015284 A1].
Regarding claim 37, Julio teaches all the limitations of claim 25.
However, Julio does not teach explicitly:
further comprising: performing a shuffle or unshuffled operation on one or more values.
In the same field of endeavor, Hongtao teaches:
further comprising: performing a shuffle or unshuffled operation on one or more values (i.e. Filter unit 312 may then apply a 3×3 CNN filter (410), a PReLU filter (412), another 3×3 CNN filter (414), and a pixel shuffle filter (416)- ¶0169).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Julio with the teachings of Hongtao to improve the output video data (Hongtao- ¶0029).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLIFFORD HILAIRE whose telephone number is (571)272-8397. The examiner can normally be reached 5:30-1400.
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
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