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 § 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.
Claims 19 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim 19 claimed a computer-readable storage medium. It can be interpreted as a carrier wave signal.
The claim 20 claimed a computer program product. It can be interpreted as a software.
They fail to fall within a statutory category of invention. It is not a process occurring as a result of executing the software, a machine programmed to operate in accordance with the software nor a manufacture structurally and functionally interconnected with the software in a manner which enables the software to act as a computer component and realize its functionality. It is also clearly not directed to a composition of matter. Therefore, it is non-statutory under 35 U.S.C. 101.
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.
Claim(s) 1 – 5, 9 – 12 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Park et al. (“Densely Connected Hierarchical Network for Image Denoising”, IDS), hereinafter referred as Park.
Regarding claim 1, Park discloses an image noise reduction processing method (abstract), comprising:
inputting target image data into an image noise reduction model to obtain noise-reduced image data outputted by the image noise reduction model, the target image data comprising pixel values of each channel of a target image (Fig. 2 – 3, section 3. Proposed network architecture);
wherein the image noise reduction model comprises a down-sampling model, an up-sampling model, and an output layer that are cascaded (Fig. 2 and 4, section 3.1. Proposed block architectures), the down-sampling model comprises n cascaded down-sampling modules (Fig. 2 and 4, section 3 and 3.1), and the up-sampling model comprises n cascaded up-sampling modules that are in one-to-one correspondence with the n down-sampling modules (Fig. 2 and 4, section 3 and 3.1); each down-sampling module comprise a first down-sampling module, a second down-sampling module (Fig. 2 and 4, section 3 and 3.1), and a fusion module cascaded with the first down-sampling module and the second down-sampling module (Fig. 2, section 3: “densely connected residual block (DCR block)”); the first down-sampling module comprises a first down-sampling layer and a first convolution layer that are cascaded (Fig. 4(a), section 3.1, down-sampling layer connected with convolution layer), and the second down-sampling module comprises a second down-sampling layer (Fig. 2 and 4, section 3 and 3.1, the second down-sampling module comprises a second down-sampling layer).
Regarding claim 2 (depends on claim 1), Park disclosed the method wherein inputting the target image data into the image noise reduction model to obtain the noise-reduced image data outputted by the image noise reduction model comprises: inputting the target image data into the down-sampling model, and down-sampling, by the down-sampling modules in the down-sampling model, the target image data to obtain down-sampled feature data (Fig. 2 and 4, section 3.1. Proposed block architectures); inputting the down-sampled feature data into the up-sampling model, and up-sampling, by the up-sampling modules in the up-sampling model, the down-sampled feature data to obtain up-sampled feature data (Fig. 2 and 4, section 3.1. Proposed block architectures); and obtaining, by the output layer, the noise-reduced image data based on the up-sampled feature data and the target image data (Fig. 2 - 4, section 3 and 3.1).
Regarding claim 3 (depends on claim 2), Park disclosed the method wherein image data resolution of the channels of the target image is the same, and down-sampling, by the down-sampling modules in the down-sampling model, the target image data to obtain the down-sampled feature data comprises:
for an ith down-sampling module, down-sampling input data of the ith down-sampling module to obtain intermediate down-sampled feature data outputted by the ith down-sampling module; wherein when i=1, the input data of the ith down-sampling module is the target image data, and when i is greater than 1, the input data of the ith down-sampling module is intermediate down-sampled feature data outputted by an i-1th down-sampling module; and taking intermediate down-sampled feature data outputted by the last down-sampling module as the down-sampled feature data (Fig. 2 - 4, section 3 and 3.1; intermediate down-sampling in Fig. 2, 16x16).
Regarding claim 4 (depends on claim 3), Park disclosed the method wherein up-sampling, by the up-sampling modules in the up-sampling model, the down-sampled feature data to obtain the up-sampled feature data comprises: for an ith up-sampling module, up-sampling input data of the ith up-sampling module to obtain intermediate up-sampled feature data outputted by the ith up-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512); wherein when i=1, the input data of the ith up-sampling module is the down-sampled feature data, and when i is greater than 1, the input data of the ith up-sampling module is aggregated feature data obtained by fusing intermediate up-sampled feature data outputted by the i-1th up-sampling module and intermediate down-sampled feature data outputted by a down-sampling module corresponding to the ith up-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512); and taking intermediate up-sampled feature data outputted by the last up-sampling module as the up-sampled feature data (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512).
Regarding claim 5 (depends on claim 4), Park disclosed the method wherein obtaining, by the output layer, the noise-reduced image data based on the up-sampled feature data and the target image data comprises: inputting the up-sampled feature data and the target image data into the output layer for fusion to obtain the noise-reduced image data outputted by the output layer (Fig. 2, final output layer, section 3 and 3.1).
Regarding claim 9 (depends on claim 3), Park disclosed the method wherein down-sampling the input data of the ith down-sampling module to obtain the intermediate down-sampled feature data outputted by the ith down-sampling module comprises: down-sampling, by the first down-sampling layer, the input data of the ith down-sampling module to obtain first down-sampled feature data outputted by the first down-sampling layer (Fig. 2 - 4, section 3 and 3.1); convolving, by the first convolution layer, the first down-sampled feature data to obtain first convolution feature data outputted by the first convolution layer (Fig. 2 - 4, section 3 and 3.1); down-sampling, by the second down-sampling layer, the input data of the ith down-sampling module to obtain second down-sampled feature data outputted by the second down-sampling layer (Fig. 2 - 4, section 3 and 3.1); and fusing, by the fusion module, the first convolution feature data and the second down-sampled feature data to obtain the intermediate down-sampled feature data outputted by the fusion module (Fig. 2, section 3: “densely connected residual block (DCR block)”).
Regarding claim 10 (depends on claim 4), Park disclosed the method wherein the up-sampling modules each comprise a second convolution layer and an up-sampling layer that are cascaded; and up-sampling the input data of the ith up-sampling module to obtain the intermediate up-sampled feature data outputted by the ith up-sampling module comprises: convolving, by the second convolution layer, the input data of the ith up-sampling module to obtain second convolution feature data outputted by the second convolution layer (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512); and up-sampling, by the up-sampling layer, the second convolution feature data to obtain the intermediate up-sampled feature data outputted by the up-sampling layer (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512).
Regarding claim 11 (depends on claim 1), Park disclosed the method wherein the image noise reduction model is applied in a RAW image noise reduction module, an RGB image noise reduction module, or a YUV image noise reduction module in an ISP chip; and correspondingly, a format of the target image is a RAW format, an RGB format, or a YUV format (abstract, section 4.3).
Regarding claim 12 (depends on claim 10), Park disclosed the method, wherein the up-sampling layer up-samples input data of the up-sampling layer by convolution, unpooling, or interpolation (Fig. 2 - 4, section 3 and 3.1).
Regarding claim 20, it is corresponding to claim 1, thus, it is interpreted and rejected for the same reason set forth for claim 1.
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) 6 – 8 and 21 – 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Zhang et al. (US Patent Application Publication 2023/0081171), hereinafter referred as Zhang.
Regarding claim 6 (depends on claim 2), Park disclosed the method wherein image data resolution of the channels of the target image is same, the down-sampling model further comprises a down-sampling module, and inputting the target image data into the down-sampling model, and down-sampling, by the down-sampling modules in the down-sampling model, the target image data to obtain down-sampled feature data comprises:
inputting a first channel pixel value of the target image comprised in the target image data into the down-sampling module to obtain channel feature data outputted by the additional down-sampling module (Fig. 2 – 3, section 3. Proposed network architecture);
fusing the channel feature data with a second channel pixel value of the target image comprised in the target image data to obtain candidate target image data (Fig. 2, section 3: “densely connected residual block (DCR block)”);
for an ith down-sampling module, down-sampling input data of the ith down-sampling module to obtain intermediate down-sampled feature data outputted by the ith down-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate down-sampling in Fig. 2, 16x16);
wherein when i=1, the input data of the ith down-sampling module is the candidate target image data, and when i is greater than 1, the input data of the ith down-sampling module is intermediate down-sampled feature data outputted by an i-1th down-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate down-sampling in Fig. 2); and
taking intermediate down-sampled feature data outputted by the last down-sampling module as the down-sampled feature data (Fig. 2 - 4, section 3 and 3.1; intermediate down-sampling in Fig. 2, 16x16).
However, Park fails to explicitly disclose the method wherein image data resolution of the channels of the target image is different, the down-sampling model further comprises an additional down-sampling module.
However, in a similar field of endeavor Zhang discloses a method for image processing (abstract). In addition, Zhang discloses the method wherein image data resolution of the channels of the target image is different ([0071]), the down-sampling model further comprises an additional down-sampling module ([0083]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Park, and image data resolution of the channels of the target image is different, the down-sampling model further comprises an additional down-sampling module. The motivation for doing this is that the application of Park can meet specific needs like image restoration or super-resolution.
Regarding claim 7 (depends on claim 6), Park disclosed the method wherein the up-sampling model further comprises an up-sampling module, and inputting the down-sampled feature data into the up-sampling model, and up-sampling, by the up-sampling modules in the up-sampling model, the down-sampled feature data to obtain up-sampled feature data comprises:
for an ith up-sampling module, up-sampling input data of the ith up-sampling module to obtain intermediate up-sampled feature data outputted by the ith up-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512);
wherein when i=1, the input data of the ith up-sampling module is the down-sampled feature data, and when i is greater than 1, the input data of the ith up-sampling module is aggregated feature data obtained by fusing intermediate up-sampled feature data outputted by the i-1th up-sampling module and intermediate down-sampled feature data outputted by a down-sampling module corresponding to the ith up-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2, 512); and
inputting first intermediate channel feature data corresponding to the first channel pixel value and comprised in intermediate up-sampled feature data outputted by the last up-sampling module into the up-sampling module to obtain the up-sampled feature data outputted by the up-sampling module (Fig. 2 - 4, section 3 and 3.1; intermediate up-sampling in Fig. 2).
However, Park fails to explicitly disclose the method wherein the up-sampling model further comprises an additional up-sampling module.
However, in a similar field of endeavor Zhang discloses a method for image processing (abstract). In addition, Zhang discloses the method the up-sampling model further comprises an additional up-sampling module ([0083]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Park, and the up-sampling model further comprises an additional up-sampling module. The motivation for doing this is that the application of Park can meet specific needs like image restoration or super-resolution.
Regarding claim 8 (depends on claim 7), Park disclosed the method wherein obtaining, by the output layer, the noise-reduced image data based on the up-sampled feature data and the target image data comprises: inputting the up-sampled feature data and the first channel pixel value in the target image data into the output layer for fusion to obtain candidate noise-reduced image data outputted by the output layer (Fig. 2, section 3: “densely connected residual block (DCR block)”); and obtaining the noise-reduced image data based on the candidate noise-reduced image data and second intermediate channel feature data that corresponds to the second channel pixel value and is comprised in the intermediate up-sampled feature data outputted by the last up-sampling module (Fig. 2 – 4, section 3 and 3.1).
Regarding claim 21 (depends on claim 6), Park disclosed the method wherein down-sampling the input data of the ith down-sampling module to obtain the intermediate down-sampled feature data outputted by the ith down-sampling module comprises: down-sampling, by the first down-sampling layer, the input data of the ith down-sampling module to obtain first down-sampled feature data outputted by the first down-sampling layer (Fig. 2 - 4, section 3 and 3.1); convolving, by the first convolution layer, the first down-sampled feature data to obtain first convolution feature data outputted by the first convolution layer (Fig. 2 - 4, section 3 and 3.1); down-sampling, by the second down-sampling layer, the input data of the ith down-sampling module to obtain second down-sampled feature data outputted by the second down-sampling layer (Fig. 2 - 4, section 3 and 3.1); and fusing, by the fusion module, the first convolution feature data and the second down-sampled feature data to obtain the intermediate down-sampled feature data outputted by the fusion module (Fig. 2 - 4, section 3 and 3.1).
Regarding claim 22 (depends on claim 7), Park disclosed the method wherein the up-sampling modules each comprise a second convolution layer and an up-sampling layer that are cascaded (Fig. 2 - 4, section 3 and 3.1); and up-sampling the input data of the ith up-sampling module to obtain the intermediate up-sampled feature data outputted by the ith up-sampling module (Fig. 2 - 4, section 3 and 3.1) comprises: convolving, by the second convolution layer, the input data of the ith up-sampling module to obtain second convolution feature data outputted by the second convolution layer (Fig. 2 - 4, section 3 and 3.1); and up-sampling, by the up-sampling layer, the second convolution feature data to obtain the intermediate up-sampled feature data outputted by the up-sampling layer (Fig. 2 - 4, section 3 and 3.1).
Claim(s) 18 – 19 and 23 – 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Yang et al. (US Patent Application Publication 2021/0287342), hereinafter referred as Yang.
Regarding claim 18, Park discloses a computer program (section 3), implements steps of the method according to claim 1 (see claim 1 rejection for details).
However, Park fails to explicitly disclose the computer program is implemented by an electronic device, comprising a memory and a processor, the memory storing a computer program.
However, in a similar field of endeavor Yang discloses a method and computer program for denoising an image (abstract). In addition, Yang discloses the computer program is implemented by an electronic device, comprising a memory and a processor, the memory storing a computer program (Fig. 1, [0039 – 0042]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Park, and the computer program is implemented by an electronic device, comprising a memory and a processor, the memory storing a computer program. The motivation for doing this is that the application of Park can be extended by using a substantial machine/computer to reduce human error.
Regarding claims 19 and 23, they are corresponding to claim 18, thus, they are interpreted and rejected for the same reason set forth for claim 18.
Regarding claim 24 (depends on claim 23), Yang disclosed the chip wherein the chip is an image signal processor (ISP) chip ([0039], ISP).
Regarding claim 25 (depends on claim 24), Park disclosed the chip wherein the image noise reduction model is applied in a RAW image noise reduction module, an RGB image noise reduction module, or a YUV image noise reduction module in the ISP chip. (abstract, section 4.3)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIAN YANG whose telephone number is (571)270-7239. The examiner can normally be reached on Monday-Thursday 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/QIAN YANG/Primary Examiner, Art Unit 2677
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