DETAILED ACTION
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.
Claim(s) 1, 2, 6, 11, 12, 16, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by MA( CN 111901592).
Regarding claim 1, MA teaches a system ( [0031], computer) for deep learning-based video processing (DLVP), the system comprising:
a first neural network associated with generating kernel weights ( [0056], S102 uses reconstructed pixels as labels to synchronously update the neural network weights of the predictive coding module ); and
a second neural network associated with filtering image pixels ([0054], S101, with the help of a neural network, generates predicted pixels based on the original pixels or further enhances the predicted pixels after obtaining them) for the DLVP, the second neural network using a second hardware device,
wherein the second neural network is configured to receive a first image and the kernel weights, and generate filtered image data based on the first image and the kernel weights(([0054], S101, with the help of a neural network, generates predicted pixels based on the original pixels) , and
wherein the first neural network is configured to receive a second image and generate the kernel weights based on the second image, the first image preceding the second image in a series of images([0056], S102 uses reconstructed pixels as labels to synchronously update the neural network weights of the predictive coding module ).
Regarding claim 2, MA teaches the system of claim 1, wherein the first neural network comprises a plurality of encoders, a plurality of decoders, and a weight predictor associated with generating the kernel weights based on image data decoded by the plurality of decoders( [0056], S102 … synchronously update the neural network weights of the predictive coding module at the encoder-decoder end).
Regarding claim 6, MA teaches the system of any of claim 2, wherein the second neural network comprises a first plurality of filtering layers and a second plurality of filtering layers ( [0025], updating the weights of all layers and updating only the weights of a few layers).
Claims 11, 12, 16 recite the methods in claims 1, 2, 6 , thus are also rejected.
Claim 20 recites the device for claim 1. Since MA also teaches a device (( [0031], computer).
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.
Claim(s) 3-5, 7-10, 13-15, 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over MA.
Regarding claim 3, MA teaches the system of claim 2.
MA does not expressly teach wherein the plurality of encoders comprises a convolution layer, a parametric rectified linear unit (PReLU) layer, and a pooling layer.
However, official notice is taken that it is routine and conventional to implement neural network encoders comprising a convolution layer, a parametric rectified linear unit (PReLU) layer, and a pooling layer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the encoders in MA following industry standard practice, with motivation for fast implementation and cost savings.
Regarding claim 4, MA teaches the system of claim 2.
MA does not expressly teach wherein the plurality of decoders comprises a upsampling layer, a convolution layer, and a PReLU layer.
However, official notice is taken that it is routine and conventional to implement neural network decoders comprising a upsampling layer, a convolution layer, and a PReLU layer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the encoders in MA following industry standard practice, with motivation for fast implementation and cost savings.
Regarding claim 5, MA teaches the system of claim 2.
MA does not expressly teach wherein the weight predictor comprises a 3x3 convolution layer associated with generating the kernel weights.
However, official notice is taken that it is routine and conventional to implement neural network layers with 3x3 patches.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the encoders in MA following industry standard practice, with motivation for fast implementation and cost savings.
Regarding claim 7, MA teaches the system of claim 6, wherein the first plurality of filtering layers comprises a convolution layer ([0024], updating the weights of convolutional layers, pooling layers, activation layers, etc.).
MA does not teach an average pooling layer.
However, official notice is taken that it is routine and conventional to implement a pooling layer with an average pooling layer.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the encoders in MA following industry standard practice, with motivation for fast implementation and cost savings.
Regarding claim 8, MA teaches the system of claim 7, wherein the convolution layer receives the kernel weights ( [0025], updating the weights of all layers and updating only the weights of a few layers) .
Regarding claim 9, MA teaches the system of claim 6, wherein the second plurality of filtering layers comprises a convolution layer ([0024], updating the weights of convolutional layers, pooling layers, activation layers, etc.).
MA does not teach an upsampling layer.
However, official notice is taken that it is routine and conventional to implement a convolutional layer with an upsampling area.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the encoders in MA following industry standard practice, with motivation for higher resolution images, fast implementation and cost savings.
Regarding claim 10, MA teaches the system of claim 9, wherein the convolution layer receives the kernel weights([0024], updating the weights of convolutional layers, pooling layers, activation layers, etc.).
Claims 13-15, 17-19 recite the methods in claims 3-5, 7-10, thus are also rejected.
Conclusion
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JIANGENG SUN
Examiner
Art Unit 2671
/Jiangeng Sun/Examiner, Art Unit 2671