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 .
Priority
Acknowledgement is made of Applicant’s claim of priority and benefit of the provisional Application No. 63/599,813, filed on November 16, 2023.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on 01/01/2025 and 05/13/2025 were reviewed and the listed references were noted.
Drawings
The 6 page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-20 are pending.
Claim Objection
System Claim 15 recites its dependency from Claim 14. Based on the structural dependencies of method claims, it appears that Claim 15 should depend from independent Claim 11. Appropriate correction is requested.
Examiner’s Note
Claim 20 recites “A computer program product, comprising: one or more computer-readable storage mediums, and program instructions collectively stored on the one or more computer-readable storage mediums, …”. Paragraph [0084] of Applicant’s specification states “As defined herein, a "computer-readable storage medium" is not a transitory, propagating signal per se.” Accordingly, because of the stated disavowal, Claim 20 is statutory.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art.
b. Ascertaining the differences between the prior art and the claims at issue.
c. Resolving the level of ordinary skill in the pertinent art.
d. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 9-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (CN 112488962 A – IDS, Machine translated to English is provided) in view of Kudo et al. (US 11,948,349 - IDS).
Consider Claim 1, Su discloses “A method, comprising: receiving, by a conditional network, conditional information including one or more color histograms for an input image, wherein the input image has a first coloration” (Su, Paragraphs [n0009]-[n0011], the image is input into the color adjustment model, interpreted as “a conditional network”, and obtain the color histogram of the image); “generating, by the conditional network and based on the one or more color histograms, a plurality of scalar parameters” (Su, Paragraph [n0012] discloses “The color histogram is input into the color adjustment model, and the color adjustment model outputs the target value of the color adjustment parameter”. Also, see Paragraph [n0033]); “and generating, from the input image, an output image having a second coloration different from the first coloration (Su, Paragraph [n0052] discloses “the image of the single frame can be directly input into the color adjustment model, or the image of the single frame can be processed first to obtain its color histogram, and then the color histogram can be input into the color adjustment model, and then the color adjustment model outputs the target value of the color adjustment parameter.” Paragraph [0093 discloses “During training, the parameters of each layer of the network are constantly changed to fit the input and label output as closely as possible, and to make the parameters of each convolutional kernel of the network converge to a stable state.” And finally, Paragraph [n0094] discloses “Once trained, the network can be used as a color adjustment model to predict color adjustment parameters. For example, when the projector plays a movie, a color histogram is calculated for each frame of the image, and the color histogram is input into the color adjustment model, which can then output the predicted RGB current value”. Although, Su discloses a color adjustment model that uses machine learning methods, such as convolutional neural networks (CNN) or support vector machines (SVM) for its color adjustment invention (see for example, Su, Paragraphs[n0053] or [n0099]), Su does not explicitly disclose use of a generative network for image enhancement invention. However, in an analogous field of endeavor, Kudo discloses use of a generative adversarial network (GAN), which enhances an image by receiving a low resolution input image and generates a high resolution output image (Kudo, Column 7, lines 23-36).
Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine Su with the teachings of Kudo to use a generative network for performing color enhancement/adjustment to an input image. One of ordinary skill in the art would be motivated to replace the CNN network of Su with the GAN network of Kudo and the results of the substitution would have been predictable. Accordingly, the combination of Su and Kudo discloses the invention of Claim 1.
Consider Claims 2-4, the combination of Su and Kudo discloses “The method of claim 1, wherein the one or more color histograms are for one or more different channels” (Su, Paragraphs [n0055]-[n0056]); “The method of claim 2, wherein the one or more color histograms correspond to one or more color spaces.” (Su, Paragraph [n0054], discloses “The images used in this embodiment are RGB images. In other embodiments, other formats such as YUV or HSV may also be used”; and “The method of claim 3, wherein the one or more color spaces includes at least one of a Red-Green-Blue (RGB) color space, a YUV color space, or a CIELAB color space” (emphasis added) (Su, Paragraph [n0054] discloses “The images used in this embodiment are RGB images. In other embodiments, other formats such as YUV or HSV may also be used”).
Consider Claim 9, the combination of Su and Kudo discloses “The method of claim 1, wherein the conditional network includes a multilayer perceptron network for each histogram of a different channel” (Su, Paragraphs [n0034], wherein a multilayer convolutional network is being disclosed).
Consider Claim 10, the combination of Su and Kudo discloses “The method of claim 1, wherein the base generative network includes a plurality of convolutional blocks, wherein each convolutional block receives one or more scalar parameters of the plurality of scalar parameters” (Su, Paragraphs [n0095] discloses “Adjust the image color according to the target value of the color adjustment parameters”).
Claims 11-14 recite systems with elements corresponding to the steps of the methods recited in Claims 1-4, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims, respectively. Additionally, the rationale and motivation to combine the Su and Kudo references, presented in rejection of Claim 1, apply to these claims. Finally, the combination of Su and Kudo discloses one or more processors and computer-readable media (for example, see Kudo, Fig. 1:18, 20, and 22).
Claim 19 recites a system with elements corresponding to the steps of the methods recited in Claims 9 and 10 combined. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in Claims 9 and 10. Additionally, the rationale and motivation to combine the Su and Kudo references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Su and Kudo discloses one or more processors and computer-readable media (for example, see Kudo, Fig. 1:18, 20, and 22).
Claim 20 recites a computer program product comprising one or more computer-readable storage mediums storing programming instructions corresponding to the steps of the method recited in Claim 1. Therefore, the recited instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in Claim 1. Additionally, the rationale and motivation to combine the Su and Kudo references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of Su and Kudo discloses one or more computer-readable mediums (for example, see Kudo, Fig. 1:20 and Column 5, lines 24-27).
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (CN 112488962 A – IDS, Machine translated to English is provided) in view of Kudo et al. (US 11,948,349 - IDS), and in further view of Dongpei Su (US 2021/0374907, hereinafter referred to as “Su ‘907”).
Consider Claim 5, the combination of Su and Kudo is not relied on to disclose “The method of claim 1, wherein the base generative network and the conditional network are trained jointly by minimizing a loss function.” In an analogous field of endeavor, Su ‘907, in its modular training method, discloses minimization of a loss function (Su ‘907, Paragraph [0044]).
Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Su and Kudo with the teachings of Su ‘907 to train the machine learning models by minimizing its loss function. One of ordinary skill in the art would be motivated to combine Su, Kudo, and Su ‘907 to minimize the difference between the predicted output image and the actual target image. Accordingly, the combination of Su, Kudo, and Su ‘907 discloses the invention of Claim 5.
Consider Claim 6, the combination of Su, Kudo, and Su ‘907 discloses “The method of claim 5, wherein the loss function includes a pixel error between a ground truth image and a version of a training image output from the base generative network” (Su ‘907, Paragraph [0044]). The proposed combination as well as the motivation for combining the Su, Kudo, and Su ‘907 references presented in the rejection of Claim 5, apply to Claim 6 and are incorporated herein by reference. Thus, the method recited in Claim 6 is met by Su, Kudo, and Su ‘907.
Claims 15 and 16 recite systems with elements corresponding to the steps of the methods recited in Claims 5 and 6, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims, respectively. Additionally, the rationale and motivation to combine the Su, Kudo, and Su ‘907 references, presented in rejection of Claim 5, apply to these claims. Finally, the combination of Su, Kudo, and Su ‘907 discloses one or more processors and computer-readable media (for example, see Kudo, Fig. 1:18, 20, and 22).
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Su et al. (CN 112488962 A – IDS, Machine translated to English is provided) in view of Kudo et al. (US 11,948,349 - IDS), in further view of Dongpei Su (US 2021/0374907, hereinafter referred to as “Su ‘907”), and still in further view of Treder et al. (US 2023/0112647).
Consider Claims 7 and 8, although, as seen above in the analysis of Claim 5, the combination of Su, Kudo, and Su ‘907 discloses minimization of a loss function for the machine learning model, this combination is not relied on to disclose “The method of claim 5, wherein the loss function includes a structural similarity error between a ground truth image and a version of a training image output from the base generative network” and “The method of claim 5, wherein the loss function includes at least one of a cosine error or a feature reconstruction error, wherein each error is calculated between a version of a training image output from the base generative network and a ground truth version of the training image.” However, in an analogous field of endeavor, Treder discloses minimization of a loss function that represents either structural similarity index (Treder, Paragraph [0120]) and/or the reconstruction error (Treder, Paragraph [0073]).
Accordingly, before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to combine the combination of Su, Kudo, and Su ‘907 with the teachings of Treder to establish minimization of a loss function based on the error in similarity structure or reconstruction error, as Claims 7 and 8 recite. One of ordinary skill in the art would be motivated to combine Su, Kudo, and Su ‘907 with Treder to facilitate minimization of the loss function generated with different computational methodologies based on the architectural need of a network for system efficiency (Treder, Paragraph [0120]). Accordingly, the combination of Su, Kudo, Su ‘907, and Treder discloses the inventions of Claims 7 and 8.
Claims 17 and 18 recite systems with elements corresponding to the steps of the methods recited in Claims 7 and 8, respectively. Therefore, the recited elements of these claims are mapped to the proposed combination in the same manner as the corresponding steps in their corresponding method claims, respectively. Additionally, the rationale and motivation to combine the Su, Kudo, Su ‘907, and Treder references, presented in rejection of Claim 7, apply to these claims. Finally, the combination of Su, Kudo, Su ‘907, and Treder discloses one or more processors and computer-readable media (for example, see Kudo, Fig. 1:18, 20, and 22).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Khodadadeh et al. (US 2022/0237830), Paragraphs [0030] and [0036]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Siamak HARANDI whose telephone number is (571)270-1832. The examiner can normally be reached Monday - Friday 9:30 - 6:00 ET.
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/Siamak Harandi/Primary Examiner, Art Unit 2662
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