Image enhancement

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 The information disclosure statement (IDS) submitted on 05/21/2026, and 08/06/2026 are compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/23/2026 and 5/21/2026 have been entered. Response to Amendment Applicant’s Amendments filed on 04/23/2026 has been entered and made of record. Currently pending Claim(s): Independent Claim(s): Cancelled Claim(s): 1, 2, 4–8 and 11–20 1 and 20 3, 9 and 10 Response to Applicant’s Arguments This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 04/23/2026. Applicant’s Reply (April 23, 2026) includes substantive amendments to the claims. This Office action has been updated with new grounds of rejection addressing those amendments. Further Applicant’s Arguments/Remarks to Chui with respect to independent claims 1 and 20 have been considered but are moot because the arguments to Chui do not apply to the new primary being used in the current rejection and the arguments are now rejected under the combination Chen et al. (CN 113763296 A) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement") as explained in the body of the rejection. Applicant argues, on page 8 stating: PNG media_image1.png 60 529 media_image1.png Greyscale The Examiner respectfully disagrees with the applicant’s line of reasoning, the examiner understands the classification output maybe fed from one model to another as an inference-time input but Claim 1 does not recite or specify this in the limitations. The claim recites “a machine learning model was trained to form the trained machine learning model by a process comprising utilising as a learning objective a reduction or minimisation of a combination of both: i) a first loss, wherein the first loss is a loss between an output image of the machine learning model that applies the at least one visual parameter and a target training image and ii) a second loss, wherein the second loss is a loss between a second classification output, different from the first classification output, and a known classification of the target training image”. Sharma, in the Final Rejection was applied to have taught the limitation “wherein a machine learning model was trained to form the trained machine learning model by a process comprising utilizing as a learning objective a reduction or minimization of a combination of both: i) …and ii) …”. Sharma met this limitation as it was cited on page 4036 col. 1 lines 3-4, page 4037 col. 1 lines 12-19, and page 4040, col. 2, lines 27–45, and [Table 5]). Applicant argues on page 8 stating: PNG media_image2.png 170 601 media_image2.png Greyscale The Examiner respectfully disagrees with the applicant’s line of reasoning, as explained above, arguments do not apply to the combination Chen et al. (CN 113763296 A) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement"). The Examiner understands that the enhancement model after training uses a combined input based on the same input image. Sharma is not being incorporated to teach the combined input for the same input image. Chen discloses determining a colour characteristic and semantic feature for the same to processed image (See Chen, ¶ [0128], step S202, obtaining the video frame to be processed in the target video frame set; obtaining the global colour characteristic corresponding to the video frame to be processed; obtaining the image semantic feature corresponding to the video frame to be processed) using a fully trained target generation model (See Chen, ¶ [0151], according to the loss function, performing iterative training for the initial generation model; when the initial generation model satisfies the convergence condition; and determining an initial generation model satisfying the convergence condition as a target generation model). The colour characteristic and the semantic feature are combined to make a combined feature for input (See Chen, ¶ [0131], the global colour feature and the image semantic feature for splicing processing to obtain the target image feature). Applicant’s arguments with respect to the cited art of Chui have been considered but are moot because the new ground of rejection (Chen et al. (CN 113763296 A) in view of Sharma) does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 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 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 non-obviousness. Claims 1–2, 5–8, 11, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma"). Regarding claim 1, Chen teaches an image processing method (See Chen, [Abstract], the embodiment of the invention claims an image processing method), the method including: by a computer processing system implementing a trained machine learning model, the trained machine learning model having been formed by training a machine learning model, after completion of said training (See Chen, ¶ [0133], Specifically, the computer device obtains the target image characteristic corresponding to the video frame to be processed, the target image characteristic input target generation model. ¶ [0151], according to the loss function, performing iterative training for the initial generation model; when the initial generation model satisfies the convergence condition; and determining an initial generation model satisfying the convergence condition as a target generation model. Note: Examiner interprets the trained model as the target generation model and the target generation model is the product after training the initial generation model): receiving as an input to the trained machine learning model (See Chen, ¶ [0133], Specifically, the computer device obtains the target image characteristic corresponding to the video frame to be processed, the target image characteristic input target generation model. Note: Examiner interprets the target generation model as the trained model) a combination of: image characteristics of an input image (See Chen, ¶ [0128], step S202, obtaining the video frame to be processed in the target video frame set; obtaining the global colour characteristic corresponding to the video frame to be processed. Note: the Examiner interprets the image characteristics as the global colour characteristic and the to be processed frame as the input image), wherein the image characteristics include variables that change between an image before processing by the trained machine learning model and after the image has been processed by the trained machine learning model (See Chen, ¶ [0134], Optionally, the enhanced parameter of M image information dimension comprises a brightness enhancement parameter, contrast enhancement parameter and saturation enhancement parameter. Note: the color features change when the saturation, contrast or brightness is enhanced or changed. Therefore, the examiner interprets the color features as the variable image characteristic); and a first classification output for the input image (See Chen, ¶ [0128], step S202, …, obtaining the image semantic feature corresponding to the video frame to be processed. ¶ [0117], Specifically, the computer device obtains the image semantic feature corresponding to the video frame to be processed, the candidate video frame can be input to the feature extraction model ,… , when using MobileNetV2, obtaining the image semantic feature corresponding to the video frame to be processed, taking the output image characteristic of the upper layer of the full connection classification layer in MobileNetV2, as the image semantic feature corresponding to the video frame to be processed. Note: Examiner interprets the first classification as the semantic feature and the input image as the to be processed image), the first classification output relating the input image (See Chen, ¶ [0128], step S202, …, obtaining the image semantic feature corresponding to the video frame to be processed) to [a set of image classes]; and generating, through application of the trained machine learning model to the received input (See Chen, ¶ [0131], the global colour feature and the image semantic feature for splicing processing to obtain the target image feature. ¶ [0132], step S204, the target image characteristic input target generating model. Note: Examiner interprets the target generation model as the trained model and received input is the target image characteristic), at least one visual parameter usable to generate a processed image relative to the input image (See Chen, ¶ [0132], step S204, the target image characteristic input target generating model, through the target generating model output target image characteristic respectively the enhanced parameter of M image information dimension. Note: Examiner interprets the visual parameters as the enhanced parameter); wherein: [a machine learning model was trained to form the trained machine learning model by a process comprising utilising as a learning objective a reduction or minimisation of a combination of both: i) a first loss, wherein the first loss is a loss between an output image of the machine learning model that applies the at least one visual parameter and a target training image and ii) a second loss, wherein the second loss is a loss between a second classification output, different from the first classification output, and a known classification of the target training image]. However, Chen fails to teach a set of classes; a machine learning model was trained to form the trained machine learning model by a process comprising utilising as a learning objective a reduction or minimisation of a combination of both: i) a first loss, wherein the first loss is a loss between an output image of the machine learning model that applies the at least one visual parameter and a target training image and ii) a second loss, wherein the second loss is a loss between a second classification output, different from the first classification output, and a known classification of the target training image. Sharma, working in the same field of endeavor, teaches: a set of classes (See Sharma, [Pg. 4038, Col. 1, ln. 25–27], To fine-tune the network, we replace the 1000-way classification layer with a C-way softmax layer, where C is the number of classes in the target dataset. Note: Examiner interprets C as the set of classes for the input image. Sharma classifies images based on multiple classes); a machine learning model was trained to form the trained machine learning model by a process comprising utilising as a learning objective a reduction or minimization (See Sharma, [Pg. 4037, Col. 1, ln. 12–19], We believe training our network in this manner, offers a natural way to encourage the filters to apply a transformation that enhances the image structures for an accurate classification, as the classification network is regularized via enhancement networks. Moreover, joint optimization helps minimize the overall cost function of the whole architecture, hence leading to better results) of a combination of both: i) a first loss, wherein the first loss is a loss between an output image of the machine learning model that applies the at least one visual parameter and a target training image (See Sharma, [Pg. 4036, Col. 1, ln. 3–4]. The Stage 1-2 cascade with two loss functions - MSE (enhancement). [Pg. 4035, Col. 2, ln. 24–28], To compare the reconstruction image Y′ with the ideal T, we use MSE loss as a measure of image quality, although we note that more complex loss functions could be used [10]) and ii) a second loss, wherein the second loss is a loss between a second classification output, different from the first classification output, and a known classification of the target training image (See Sharma, [Pg. 4036, Col. 1, ln. 3–7]. The Stage 1-2 cascade with two loss functions - MSE (enhancement) and softmax-loss L (classification) - enables joint optimization by end-to-end propagation of gradients in both ClassNet and EnhanceNet using SGD optimizer. [Pg. 4036, Col. 1, ln. 17–19], ClassNet that is fed to a C-way softmax function, y is the vector of true labels for image I, and C is the number of classes). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference to a set of classes; a machine learning model was trained to form the trained machine learning model by a process comprising utilising as a learning objective a reduction or minimisation of a combination of both: i) a first loss, wherein the first loss is a loss between an output image of the machine learning model that applies the at least one visual parameter and a target training image and ii) a second loss, wherein the second loss is a loss between a second classification output, different from the first classification output, and a known classification of the target training image based on the method of Sharma’s reference. The suggestion/motivation would have been to provide loss function to encapsulate the visual and multi-classification errors differences in order to enhance the visual features of a target image (See Sharma, [Pg. 4040, Col. 2, ln. 27–45] and [Table 5]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Sharma with Chen to obtain the invention as specified in claim 1. Regarding claim 2, Chen in view of Sharma the method of claim 1, wherein the image characteristics define colour and brightness semantics of the input image, or comprise data representing a colour histogram of the input image, for example an RGB histogram (See Chen, ¶ [0116], extracting the global colour characteristic corresponding to the candidate video frame according to the colour histogram corresponding to the candidate video frame). Regarding claim 5, Chen in view of Sharma teaches the method of claim 1, wherein the trained machine learning model is a first trained machine learning model (See Chen, ¶ [0135], Specifically, each regression network in the target generating model can comprise one or more full connection layer, after the full connection layer can be connected with an activation function layer. Note: Examiner interprets the target generating model as the trained machine learning model) and the first classification output comprises an output of a second trained machine learning model (See Chen, ¶ [0115], inputting the candidate video frame into the feature extraction model; according to the convolution layer in the feature extraction model), different to the first trained machine learning model, wherein the second trained machine learning model is trained to classify images into one of a plurality of scene classes (See Chen, ¶ [0115], inputting the candidate video frame into the feature extraction model; according to the convolution layer in the feature extraction model. ¶ [0117], Specifically, the computer device obtains the image semantic feature corresponding to the video frame to be processed, the candidate video frame can be input to the feature extraction model ,… , when using MobileNetV2, obtaining the image semantic feature corresponding to the video frame to be processed, taking the output image characteristic of the upper layer of the full connection classification layer in MobileNetV2, as the image semantic feature corresponding to the video frame to be processed. Note: the feature extraction model is a different model designed to extract the semantic features). Regarding claim 6, Chen in view of Sharma teaches the method of claim 1, wherein the combination of image characteristics of the input image and the first classification output for the input image is a concatenation of data defining the image characteristics of the input image and the first classification output for the input image (See Chen, ¶ [0120], Specifically, the computer device obtains the to-be-processed video frame corresponding to the global colour feature and image semantic feature, can perform feature fusion extraction to the global colour feature and image semantic feature). Regarding claim 7, Chen in view of Sharma teaches the method of claim 1, wherein the at least one visual parameter comprises one or more of: (i) brightness, (ii) contrast, (iii) saturation, (iv) vibrance, (v) whites, (vi) blacks, (vii) shadows and (viii) highlights (See Chen, ¶ [0122], The M enhanced parameters may include a brightness enhancement parameter, a contrast enhancement parameter and a saturation enhancement parameter. Note: Chen only needs to teach one). Regarding claim 8, Chen in view of Sharma teaches the method of claim 1, [wherein the first loss is a mean square error loss between the output image and the target training image, and/or the second loss is a multi-class cross entropy loss between the second classification output and the known classification of the target training image]. However, Chen fails to teach wherein the first loss is a mean square error loss between the output image and the target training image, and/or the second loss is a multi-class cross entropy loss between the second classification output and the known classification of the target training image. Sharma, working in the same field of endeavor, teaches: wherein the first loss is a mean square error loss between the output image and the target training image (See Sharma, [Pg. 4036, Col. 1, ln. 3–4]. The Stage 1-2 cascade with two loss functions - MSE (enhancement). [Pg. 4035, Col. 2, ln. 24–28], To compare the reconstruction image Y′ with the ideal T, we use MSE loss as a measure of image quality, although we note that more complex loss functions could be used [10]), and/or the second loss is a multi-class cross entropy loss between the second classification output and the known classification of the target training image (See Sharma, [Pg. 4036, Col. 1, ln. 3–7]. The Stage 1-2 cascade with two loss functions - MSE (enhancement) and softmax-loss L (classification) - enables joint optimization by end-to-end propagation of gradients in both ClassNet and EnhanceNet using SGD optimizer. [Pg. 4036, Col. 1, ln. 17–19], ClassNet that is fed to a C-way softmax function, y is the vector of true labels for image I, and C is the number of classes. Note: softmax loss is known in the art as a combination of cross entropy and softmax activation function. The examiner interprets the softmax loss as the cross entropy loss). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s reference wherein the first loss is a mean square error loss between the output image and the target training image, and/or the second loss is a multi-class cross entropy loss between the second classification output and the known classification of the target training image based on the method of Sharma’s reference. The suggestion/motivation would have been to provide loss function to encapsulate the visual and multi-classification errors differences in order to enhance the visual features of a target image (See Sharma, [Pg. 4040, Col. 2, ln. 27–45] and [Table 5]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Sharma with Chen to obtain the invention as specified in claim 8. Regarding claim 11, Chen in view of Sharma teaches the method of claim 1, wherein the machine learning model comprises a first multilayer perceptron configured to provide the at least one visual parameter (See Chen, ¶ [0135], Specifically, each regression network in the target generating model can comprise one or more full connection layer, after the full connection layer can be connected with an activation function layer. See also ¶ [0035], the sample video frame corresponding to the sample global colour characteristic and sample image semantic feature input initial generation model. See also ¶ [0136], As shown in FIG. 5, FIG. 5 is a structure schematic diagram of a regression network in a target generating model. Note: Examiner interprets the first multilayer perceptron as the generation model and neural network is a multilayer perceptron) and a second multilayer perceptron, configured to provide the second classification output (See Chen, ¶ [0115], inputting the candidate video frame into the feature extraction model; according to the convolution layer in the feature extraction model. See also, ¶ [0117], the feature extraction model can be convolutional neural network module. Note: the feature extraction model is a different model designed to extract the semantic features and a convolutional network is still a multilayer perceptron). Regarding claim 15, Chen in view of Sharma teaches the method of claim 1, further comprising, by the computer processing system, applying the at least one visual parameter to generate a processed image relative to the input image (See Chen, ¶ [0074], An aspect of the present application provides a computer device, comprising: a processor and a memory; See also ¶ [0122], so it can according to the brightness enhancement parameter; contrast enhancement parameter and saturation enhancement parameter, each video frame in the target video frame set for image enhancement to obtain the target video frame set corresponding to each video frame in the target image data). Regarding claim 20, claim 20 is rejected the same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to the claim 20, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference. Furthermore, Chen teaches non-transitory computer-readable storage storing instructions for a computer processing system, wherein the instructions, when executed by the computer processing system cause the computer processing system to perform a method comprising (See Chen, ¶ [0074], An aspect of the present application provides a computer device, comprising: a processor and a memory). Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma") further in view of Wang et al. (US 2019/0139262 A1, hereafter, "Wang"). Regarding claim 4, Chen in view of Sharma teaches the method of claim 1, [wherein the first classification output comprises a feature vector determined by another trained machine learning model]. However, Chen and Sharma fail to teach wherein the first classification output comprises a feature vector determined by another trained machine learning model. Wang, working in the same field of endeavor, teaches: wherein the first classification output comprises a feature vector determined by another trained machine learning model (See Wang, ¶ [0052], Step 2, extracting a vehicle type feature x.sub.1 and a color feature x.sub.2, particularly, in this embodiment, 128 dimensional vectors output by a vehicle model classification depth convolution neural network output layer are taken as the extracted vehicle type feature). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference to wherein the first classification output comprises a feature vector determined by another trained machine learning model based on the method of Wang’s reference. The suggestion/motivation would have been to extract more features to accurately process and analyze (See Wang, ¶ [0003–0011]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Wang with Chen and Sharma to obtain the invention as specified in claim 4. Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma") further in view of Gao et al. ("NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction", hereafter, "Gao"). Regarding claim 12, Chen in view of Sharma teaches the method of claim 11, [wherein the machine learning model comprises a third multilayer perceptron, the third multilayer perception configured to reduce the dimensionality of the input to the trained machine learning model, wherein the first multilayer perceptron and the second multilayer perception are both attached to the third multilayer perceptron]. However, Chen and Sharma fail to teach wherein the machine learning model comprises a third multilayer perceptron, the third multilayer perception configured to reduce the dimensionality of the input to the trained machine learning model, wherein the first multilayer perceptron and the second multilayer perception are both attached to the third multilayer perceptron. Gao, working in the same field of endeavor, teaches: wherein the machine learning model comprises a third multilayer perceptron, the third multilayer perception configured to reduce the dimensionality of the input to the trained machine learning model, wherein the first multilayer perceptron and the second multilayer perception are both attached to the third multilayer perceptron (See Gao, [Pg. 3208, Col. 1, ln. 22–30], Figure 1 shows the NDDR-CNN network structure for two tasks. It can easily be extended to K-task problems. Let the number of channels for the single-task features be D. Then NDDR-CNN for K tasks can be constructed by: 1) concatenating the features from K tasks according to the channel dimension, and 2) using 1 × 1 convolution with (filters ×1 × 1× channels) = (C × 1 × 1 × KC) to conduct dimensionality reduction, where C is the channel dimension size of the output features from each task. See also [Figure 1], Single-Task Network, NDDR layers. PNG media_image3.png 281 713 media_image3.png Greyscale Note: Examiner is interpreting the NDDR reduction layer as the third CNN). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference wherein the machine learning model comprises a third multilayer perceptron, the third multilayer perception configured to reduce the dimensionality of the input to the trained machine learning model, wherein the first multilayer perceptron and the second multilayer perception are both attached to the third multilayer perceptron based on the method of Gao’s reference. The suggestion/motivation would have been improving the processing and accuracy of multitasks while maintaining high accuracy (See Gao, [Pg. 3212, Col. 2, ln. 20–43] and [Table 7]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Gao with Chen and Sharma to obtain the invention as specified in claim 12. Regarding claim 13, Chen teaches the method of claim 12, wherein the first multilayer perception (See Chen, ¶ [0135], Specifically, each regression network in the target generating model can comprise one or more full connection layer, after the full connection layer can be connected with an activation function layer. See also ¶ [0035], the sample video frame corresponding to the sample global colour characteristic and sample image semantic feature input initial generation model. See also ¶ [0136], As shown in FIG. 5, FIG. 5 is a structure schematic diagram of a regression network in a target generating model. Note: Examiner is interpreting the first multilayer perceptron as the generation model and neural network is a multilayer perceptron) and the second multilayer perception (See Chen, ¶ [0115], inputting the candidate video frame into the feature extraction model; according to the convolution layer in the feature extraction model. See also, ¶ [0117], the feature extraction model can be convolutional neural network module. Note: the feature extraction model is a different model designed to extract the semantic features and a convolutional network is still a multilayer perceptron) and [the third multilayer perception comprise a convolutional neural network]. However, Chen and Sharma fail to teach the third multilayer perception comprise a convolutional neural network. Gao, working in the same field of endeavor, teaches: the third multilayer perception comprise a convolutional neural network (See Gao, [Pg. 3208, Col. 1, ln. 22–30], Figure 1 shows the NDDR-CNN network structure for two tasks. It can easily be extended to K-task problems. Let the number of channels for the single-task features be D. Then NDDR-CNN for K tasks can be constructed by: 1) concatenating the features from K tasks according to the channel dimension, and 2) using 1 × 1 convolution with (filters ×1 × 1× channels) = (C × 1 × 1 × KC) to conduct dimensionality reduction, where C is the channel dimension size of the output features from each task. See also [Figure 1], Single-Task Network, NDDR layers. PNG media_image3.png 281 713 media_image3.png Greyscale Note: Examiner is interpreting the NDDR reduction layer as the third CNN). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference the third multilayer perception comprise a convolutional neural network based on the method of Gao’s reference. The suggestion/motivation would have been improving the processing and accuracy of multitasks while maintaining high accuracy (See Gao, [Pg. 3212, Col. 2, ln. 20–43] and [Table 7]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Gao with Chen and Sharma to obtain the invention as specified in claim 13. Claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma") further in view of Pettigrew et al. (US 20160284070 A1, hereafter, "Pettigrew"). Regarding claim 14, Chen in view of Sharma teaches the method claim 1, [wherein the at least one visual parameter corresponds to visual parameter that is adjustable by a slider in a photo editing application]. However, Chen and Sharma fail to teach wherein the at least one visual parameter corresponds to visual parameter that is adjustable by a slider in a photo editing application. Pettigrew, working in the same field of endeavor, teaches: wherein the at least one visual parameter corresponds to visual parameter that is adjustable by a slider in a photo editing application (See Pettigrew, ¶ [0040], The example system or method receives user input via one or more sliders (a user either moves the slider to the left or to the right), analyzes the image, and enhances the image based on the user input and the analysis. The enhancement is determined based on the image analysis and the details of the image (e.g. light, shadows, colors, contrast, etc.)). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference to wherein the at least one visual parameter corresponds to visual parameter that is adjustable by a slider in a photo editing application based on the method of Pettigrew’s reference. The suggestion/motivation would have been to more control and desirable visualization (See Pettigrew, ¶ [0014]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Pettigrew with Chen and Sharma to obtain the invention as specified in claim 14. Regarding claim 16, Chen in view of Sharma teaches the method of claim 15, further comprising, by the computer processing system, [causing display on a display device a graphical user interface, wherein the graphical user interface is configured to allow the user to further adjust at least one said visual parameter of the processed image]. However, Chen and Sharma fail to teach causing display on a display device a graphical user interface, wherein the graphical user interface is configured to allow the user to further adjust at least one said visual parameter of the processed image. Pettigrew, working in the same field of endeavor, teaches: causing display on a display device a graphical user interface, wherein the graphical user interface is configured to allow the user to further adjust at least one said visual parameter of the processed image (See Pettigrew, ¶ [0040], The example system or method receives user input via one or more sliders (a user either moves the slider to the left or to the right), analyzes the image, and enhances the image based on the user input and the analysis. The enhancement is determined based on the image analysis and the details of the image (e.g. light, shadows, colors, contrast, etc.)). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference to causing display on a display device a graphical user interface, wherein the graphical user interface is configured to allow the user to further adjust at least one said visual parameter of the processed image based on the method of Pettigrew’s reference. The suggestion/motivation would have been to more control and desirable visualization (See Pettigrew, ¶ [0014]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Pettigrew with Chen and Sharma to obtain the invention as specified in claim 16. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma") further in view of Saeed Rad et al. ("Benefiting from multitask learning to improve single image super-resolution", hereafter, "Saeed Rad"). Regarding claim 17, Chen in view Sharma teaches the method of claim 1, [wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image, the degraded image used to generate the output image of the machine learning model during training]. However, Chen and Sharma fail to teach wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image, the degraded image used to generate the output image of the machine learning model during training. Saeed Rad, working in the same field of endeavor, teaches: wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image, the degraded image used to generate the output image of the machine learning model during training (See Saeed Rad, [Pg. 307, Col. 1, ln. 38–40], Training the proposed network in a supervised manner requires a considerable number of training examples with ground-truth for both semantic segmentation and super resolution tasks. Note: The examiner interprets the ground truth as the target training image and training examples are the degraded (lower quality) image). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference wherein the first loss is a mean square error loss between the output image and the target training image, and/or the second loss is a multi-class cross entropy loss between the second classification output and the known classification of the target training image based on the method of Saeed Rad’s reference. The suggestion/motivation would have been to more accurately produce high quality image of known categories and unknown categories (See Saeed Rad, [Pg. 310, Col. 2, 4.5 Results on standard benchmarks]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Saeed Rad with Chen and Sharma to obtain the invention as specified in claim 17. Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 113763296 A, hereafter, “Chen”) in view of Sharma et al. ("Classification-Driven Dynamic Image Enhancement", hereafter, "Sharma"), Saeed Rad et al. ("Benefiting from multitask learning to improve single image super-resolution", hereafter, "Saeed Rad"), and further in view of Barbosa et al. (US 11,341,367 B1, hereafter, "Barbosa"). Regarding claim 18, Chen in view of Sharma further in view of Saeed Rad teaches the method of claim 17, wherein: [a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model]. However, Chen and Sharma fail to teach a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model. Saeed Rad, working in the same field of endeavor, teaches: a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair (See Saeed Rad, [Pg. 307, Col. 1, ln. 38–40], Training the proposed network in a supervised manner requires a considerable number of training examples with ground-truth for both semantic segmentation and super resolution tasks. Note: The examiner is interpreting the ground truth as the target training image and training examples are the degraded (lower quality) image); a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair (See Saeed Rad, [Pg. 307, Col. 1, ln. 38–40], Training the proposed network in a supervised manner requires a considerable number of training examples with ground-truth for both semantic segmentation and super resolution tasks. Note: The examiner is interpreting the ground truth as the target training image and training examples are the degraded (lower quality) image and there are more than one examples). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s and Sharma’s reference a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair based on the method of Saeed Rad’s reference. The suggestion/motivation would have been to more accurately produce high quality image of known categories and unknown categories (See Saeed Rad, [Pg. 310, Col. 2, 4.5 Results on standard benchmarks]). However, Chen, Sharma and Saeed Rad fail to teach was generated by applying a first degradation model to the target training image of the first image pair; was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model. Barbosa, working in the same field of endeavor, teaches: was generated by applying a first degradation model to the target training image of the first image pair (See Barbosa, [Pg. 15, Col. 8, ln. 15–23], The synthetic training data generator then generates many synthetic images 132 (and corresponding labels 134 and/or algorithm metadata 133) by overlaying one or more of the object images 204A-204M ( or variants thereof, for example images resulting from being transformed or filtered in some manner as described herein) over ones of the background images 254A-204N (or variants thereof, again resulting from being transformed or filtered in some manner as described herein). Note: the synthetic images are generated using class labels and the combination of the images the examiner is interpreting as a degradation); was generated by applying a second degradation model to the target training image of the second image pair (See Barbosa, [Pg. 15, Col. 8, ln. 15–23], The synthetic training data generator then generates many synthetic images 132 (and corresponding labels 134 and/or algorithm metadata 133) by overlaying one or more of the object images 204A-204M (or variants thereof, for example images resulting from being transformed or filtered in some manner as described herein) over ones of the background images 254A-204N (or variants thereof, again resulting from being transformed or filtered in some manner as described herein). Note: the synthetic images are generated using class labels and the combination of the images the examiner is interpreting as a degradation); the first image pair is different to the second image pair and the first degradation model is different to the second degradation model (See Barbosa, [Pg. 15, Col. 8, ln. 15–23], The synthetic training data generator then generates many synthetic images 132 (and corresponding labels 134 and/or algorithm metadata 133) by overlaying one or more of the object images 204A-204M ( or variants thereof, for example images resulting from being transformed or filtered in some manner as described herein) over ones of the background images 254A-204N (or variants thereof, again resulting from being transformed or filtered in some manner as described herein). Note: different combination are used to generate different images and degradations). Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Chen’s, Sharma’s and Saeed Rad’s reference was generated by applying a first degradation model to the target training image of the first image pair; was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model based on the method of Barbosa’s reference. The suggestion/motivation would have been to provide time efficient data and increase the training performance (See Barbosa, [Pg. 12, Col. 1, ln. 6–34]). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Barbosa with Chen, Sharma and Saeed Rad to obtain the invention as specified in claim 18. Allowable Subject Matter Claim 19 is 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. Claim 19 contains subject matter that is not disclosed or made obvious in the cited art. In regard to claim 19, when considering claim 19 as a whole, prior art of record fails to disclose or render obvious, alone or in combination: “wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class”. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chalom et al. (US 20160284095 A1) teaches methods, apparatuses and systems may provide for operating a machine learning device by obtaining training image data, conducting an offline prediction analysis of the training image data with respect to one or more real-time parameters of an image capture device, and generating one or more parameter detection models based on the offline prediction analysis. Additionally, methods, apparatuses and systems may provide for operating the image capture device by obtaining a candidate image associated with the image capture device, determining that the candidate image corresponds to a particular type of scene represented in a parameter prediction model, and adjusting one or more real-time parameters of the image capture device based at least in part on one or more parameter values associated with the particular type of scene. Wilensky (US 20200410730 A1) teaches image modification styles learned from a limited set of modified images are described. A learned style system receives a selection of one or more modified images serving as a basis for a modification style. For each modified image, this system creates a modification memory, which includes a representation of the image content and modification parameters describing modification of this content to produce the modified image. These modification memories are packaged into style data, used to apply the modification style to input images. When applying a style, the system generates an image representation of an input image and determines measures of similarity between the input image's representation and representations of each modification memory in the style data. The system determines parameters for applying the modification style based, in part, on these similarity measures. The system modifies the input image according to the determined parameters to produce a styled image. Zhang et al. (US 20220292654 A1) teaches the present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DION J SATCHER/Patent Examiner, Art Unit 2676 /SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676