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 .
Information Disclosure Statement
IDS filed 12/31/24 is acknowledged, the references therein relating to the general background of applicant’s invention with the exception of the references detailed below, which have particular relevance.
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)(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.
1) Claims 16 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. patent application publication 2024/0078189 by Clark et al.
2) Regarding claim 16, Clark teaches a device (figure 7; a computer system) comprising: at least one processor (paragraph 136; a processor); a main memory (figure 7, item 704; a memory); a cache memory (paragraph 136; various cache memories); a convolutional neural network (paragraph 165; a CNN) configured to convert a plurality of pixels from a first color space to a second color space (figure 13; paragraph 166 and 187; color space conversion can be performed on image data, the image data including pixels [paragraph 170]), the convolutional neural network organized into execution-separable layers; and logic to load a first layer of the convolutional neural network from the main memory to the cache memory, process by the at least one processor, in the cache memory, the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the convolutional neural network from the main memory to the cache memory, and, process by the at least one processor, in the cache memory, the pixels through the second layer (paragraphs 165 and 188-190; layers of a CNN can be loaded into a circular buffer [i.e. a cache], processed, then output to the ReLU circuit [i.e. a main memory] where further layers are then sent to the circular buffer for processing; NOTE: a circular buffer inherently evicts data from itself as the cache fills, thereby disclosing eventual eviction of each CNN layer).
3) Regarding claim 20, Clark teaches the device of claim 16, wherein the device is a color printer (paragraph 139; device 700 can include a printer).
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.
4) Claim(s) 1, 7-10 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2024/0078189 by Clark et al., and further in view of U.S. patent application publication 2021/0133522 by Chang et al.
5) Regarding claim 1, Clark teaches a device (figure 7; a computer system) comprising: a main memory (figure 7, item 704; a memory); a cache memory (paragraph 136; various cache memories); one or more convolutional neural networks (paragraph 165; a CNN) configured to convert a plurality of pixels from a source color space to a destination color space (figure 13; paragraph 166 and 187; color space conversion can be performed on image data, the image data including pixels [paragraph 170]), and wherein the one or more convolutional neural networks are organized into execution-separable layers; and logic to load a first layer of the one or more convolutional neural networks from the main memory to the cache memory, process the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the one or more convolutional neural networks from the main memory to the cache memory, and process the pixels through the second layer (paragraphs 165 and 188-190; layers of a CNN can be loaded into a circular buffer [i.e. a cache], processed, then output to the ReLU circuit [i.e. a main memory] where further layers are then sent to the circular buffer for processing; NOTE: a circular buffer inherently evicts data from itself as the cache fills, thereby disclosing eventual eviction of each CNN layer).
Clark does not specifically teach wherein the one or more convolutional neural networks utilize a source color space profile and a destination color space profile in sequence, wherein the source color space profile and the destination color space profile are each an International Color Consortium (ICC) profile, wherein the source color space is a display device color space and the destination color space is a printer device color space.
Chang teaches the one or more convolutional neural networks utilize a source color space profile and a destination color space profile in sequence, wherein the source color space profile and the destination color space profile are each an International Color Consortium (ICC) profile, wherein the source color space is a display device color space and the destination color space is a printer device color space (paragraphs 37 and 38; ICC profiles can be used for a source monitor and converted to an output profile of a printer).
Clark and Chang are combinable because they are both from the color space conversion field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Clark with Chang to add obtaining color space profiles for a monitor and a printer. The motivation for doing so would have been to “optimize the use of memory and processing resources during color conversion” (paragraph 28). Therefore it would have been obvious to combine Clark with Chang to obtain the invention of claim 1.
6) Regarding claim 7, Clark teaches the device of claim 1, wherein the first color space and the second color space are both three-dimensional color spaces or both four-dimensional color spaces (paragraph 11 and 186; input color space could be chroma444 [i.e. three dimensions] and the output color space could be RGB as disclosed in paragraph 187).
7) Regarding claim 8, Clark teaches the device of claim 1, wherein one of the color spaces is a three-dimensional color space and the other color space is a four-dimensional color space (paragraph 11 and 186; input could be any of the 3D color spaces listed and output could be CMYK or vice versa).
8) Regarding claim 9, Clark teaches the device of claim 1, wherein the device is a color printer (paragraph 139; device 700 can include a printer).
9) Regarding claim 10, Clark teaches a device (figure 7; a computer system) comprising: a main memory (figure 7, item 704; a memory); a cache memory (paragraph 136; various cache memories); one or more convolutional neural networks (paragraph 165; a CNN) configured to convert a plurality of pixels from a source color space to a destination color space (figure 13; paragraph 166 and 187; color space conversion can be performed on image data, the image data including pixels [paragraph 170]), and wherein the one or more convolutional neural networks are organized into execution-separable layers; and logic to load a first layer of the convolutional neural network from the main memory to the cache memory, process the pixels through the first layer, evict the first layer from the cache memory, load a second layer of the convolutional neural network from the main memory to the cache memory, and process the pixels through the second layer (paragraphs 165 and 188-190; layers of a CNN can be loaded into a circular buffer [i.e. a cache], processed, then output to the ReLU circuit [i.e. a main memory] where further layers are then sent to the circular buffer for processing; NOTE: a circular buffer inherently evicts data from itself as the cache fills, thereby disclosing eventual eviction of each CNN layer).
Clark does not specifically teach wherein the convolutional neural network utilizes a single International Color Consortium (ICC) profile without an intermediate conversion.
Chang (as combined with Clark in the rejection of claim 1 above) teaches the convolutional neural network utilizes a single International Color Consortium (ICC) profile without an intermediate conversion (paragraph 24; single profile can be utilized for direct conversion).
10) Claims 13-15 are taught in the same manner as described in the rejection of claims 7-9 above, respectively.
11) Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2024/0078189 by Clark et al., and further in view of U.S. patent application publication 2021/0133522 by Chang et al. as applied to claim 1 above, and further in view of U.S. patent application publication 2023/0329646 by Zhou et al.
12) Regarding claim 2, Clark does not specifically teach the device of claim 1, wherein the convolutional neural network comprises exactly six convolutional layers.
Zhou teaches the device of claim 1, wherein the convolutional neural network comprises exactly six convolutional layers (paragraph 340; CNN can be exactly six layers).
Clark and Zhou are combinable because they are both from the convolutional neural network field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Clark with Zhou to add six CNN layers. The motivation for doing so would have been to “implement classification and remediation algorithms to at least partially automate various aspects of patient care in healthcare applications” (paragraph 5). Therefore it would have been obvious to combine Clark with Zhou to obtain the invention of claim 2.
13) Claims 11 is taught in the same manner as described in the rejection of claim 2 above.
14) Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2024/0078189 by Clark et al. as applied to claim 16 above, and further in view of U.S. patent application publication 2023/0214340 by Luo.
15) Regarding claim 17, Clark does not specifically teach the device of claim 16, wherein the at least one processor may process, in the cache memory, multiple image pixels through at least the first layer in parallel using multiple execution threads.
Luo teaches the device of claim 16, wherein the at least one processor may process, in the cache memory, multiple image pixels through at least the first layer in parallel using multiple execution threads (paragraphs 34 and 37; CNN layer can be processed in parallel).
Clark and Zhou are combinable because they are both from the convolutional neural network field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Clark with Luo to add parallel processing. The motivation for doing so would have been for efficiency. Therefore it would have been obvious to combine Clark with Luo to obtain the invention of claim 17.
16) Regarding claim 18, Luo (as combined with Clark in the rejection of claim 17 above) teaches the device of claim 17, wherein multiple consecutive layers of the convolutional neural network are cached and processed simultaneously (paragraph 34; parallel processing could be applied to each layer).
17) Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2024/0078189 by Clark et al., and further in view of U.S. patent application publication 2023/0214340 by Luo as applied to claim 16 above, and further in view of U.S. patent application publication 2019/0045168 by Chaudhuri et al.
Clark does not specifically teach the device of claim 18, wherein the multiple consecutive layers are 3->32 and 32->64.
Chaudhuri teaches the device of claim 18, wherein the multiple consecutive layers are 3->32 and 32->64 (paragraph 28; figure 4; input can be an RGB image [i.e. 3 layers] and can advance through the convolution layers as shown from input [3] to 32 and then to 64).
Clark and Chaudhuri are combinable because they are both from the convolutional neural network field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Clark with Luo to add particular color space convolutions. The motivation for doing so would have been to create a sense of depth in an image (paragraph 2). Therefore it would have been obvious to combine Clark with Chaudhuri to obtain the invention of claim 19.
Allowable Subject Matter
Claims 3-6 and 12 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:
Regarding claims 3 and 12, the prior art does not appear to contain a valid combination of references that disclose a device using a convolutional neural network to convert pixels from a first color space to a second color space wherein layers are loaded into a cache memory from a main memory, processed and evicted wherein the convolutional neural network has exactly six convolutional layers and each layer has a filter size of 1. Similar prior art such as U.S. patent application publication 2024/0078189 by Clark et al. discloses a CNN for conversion of image data between color spaces using cache memory for processing layers but lacks disclosure of the CNN comprising exactly six layers with each layer having a filter size of one.
Claim 4 is objected to because it depends upon claim 3.
Regarding claims 5 and 6, the prior art does not appear to contain a valid combination of references that disclose a device using a convolutional neural network to convert pixels from a first color space to a second color space wherein layers are loaded into a cache memory from a main memory, processed and evicted wherein the convolutional neural network has exactly six convolutional layers and the layers are configured as 3->32->64->128->128->64->4 or 3->64->128->256->128->64->4 in describing the feature map depth of each layer. Similar prior art such as U.S. patent application publication 2024/0078189 by Clark et al. discloses a CNN for conversion of image data between color spaces using cache memory for processing layers but lacks disclosure of the CNN comprising exactly six layers with each layer having the feature map depth claimed.
Conclusion
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/ Primary Examiner, Art Unit 2683
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