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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/4/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection 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.
Claims 21, 23-26 and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US Pub. 2021/0166360) in view of PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing, by Furuta et al.
With respect to claim 21, Kim discloses A method comprising:
obtaining multiple framing images wherein the training images include a training standard dynamic range (SDR) image and a training high dynamic range (HDR) image, (see paragraph 0042, wherein …LDR-HDR data pairs containing diverse scenes. The specifications are given in Table 1. The HDR video is professionally filmed and mastered, and both the LDR and HDR data are normalized to be in the range [0, 1]. For the synthesis of training data, we randomly cropped 20 subimages of size 40×40 per frame with the frame stride of 30…);
training a neural network, using the training images and a set of color grading actions, for converting SDR images into HDR images, wherein training the neural network comprises Table 1 for the color transformation “a set of color grading actions”; for further explanation see paragraph 0046, wherein … …where θ is the set of model parameters, n is the number of training samples, ILDR is the input LDR image “training images”, F is the non-linear mapping function of the ITM-CNN giving the prediction of the network as F(ILDR; θ), and IHDR is the ground truth HDR image “color grading action”);
receiving an input SDR image: and converting the input SDR image into the HDR image using the training images and one or more color grading actions from the set of color grading actions, (see figure 4 for the LDR “SDR” image conversion to HDR image using the CNN), as claimed.
However, Kim fails to explicitly disclose training the neural network comprises reinforcement learning in which a reward based on similarity to the training HDR image reinforces the set of color grading actions, (emphasis added) as claimed.
Furuta teaches training the neural network comprises reinforcement learning in which a reward based on similarity to the training HDR image reinforces the set of color grading actions, (emphasis added, see Algorithm 1 and section V), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of image restoration using image analysis. The teaching of Furuta using a reinforcement learning with reward can be incorporated in to Kim’s system as suggested (see paragraph 0038 training a neural network), for suggestion, and modifying the system yields high dynamic images with color enhancement and image restoration from standard dynamic images (see Furuta Abstract), for motivation.
With respect to claim 23, combination of Kim and Furuta further discloses wherein training the neural network comprises: applying a first color grading action from the set of color grading actions to the training SDR image; wherein the first color grading action is selected based on the training HDR image, (see Kim paragraph 0045-0046, the parameters are subject to the HDR images), as claimed.
With respect to claim 24, combination of Kim and Furuta further discloses wherein the neural network is configured to extract contextual features or color features from the training SDR image, (see Kim paragraph 0042, videos are converted to YUV color space “color features”), as claimed.
With respect to claim 25, combination of Kim and Furuta further discloses wherein the set of color grading actions includes at least are of adjusting brightness, adjusting contrast, adjusting color Saturation or adjusting exposure, (see Kim paragraph 0035, match desired brightness), as claimed.
Claims 26 and 28-30 are rejected for the same reasons as set forth in the rejections of claims 21 and 23-25, because claims 26 and 28-30 are claiming subject matter of similar scope as claimed in claims 21 and 23-25.
Claims 22, 27, 37 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (US Pub. 2021/0166360) in view of PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing, by Furuta et al. as applied to claim 21 above, and further in view of Zink et al (2022/0078386).
With respect to claim 22, combination of Kim and Furuta discloses all the elements as claimed and as rejected in claim 21 above. However, combination of Kim and Furuta fail to disclose receiving a user input to modify the one or more color grading actions; and modifying the HDR image based on the user input, as claimed.
Zink teaches a user input to modify the one or more color grading actions; and modifying the HDR image based on the user input, (see figure 4A, numerical 460 and paragraph 0043, creative profile manually created), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of converting SDR to HDR images using image analysis. The teaching of Zink to manually creating creative profile for the HDR conversion can be incorporated in to Kim and Furuta’s system as suggested (see Kim paragraph 0006, images to be viewed on TV, and TV has a manual input for color changes), for suggestion, and modifying the system yields high dynamic images from standard dynamic images (see Zink paragraph 0002), for motivation.
Claim 27 is rejected for the same reasons as set forth in the rejections of claims 22, because claim 27 is claiming subject matter of similar scope as claimed in claim 22.
With respect claim 37, combination of Kim, Furuta and Zink further discloses wherein obtaining the multiple training images includes obtaining a plurality of pairs of training SDR images and training HDR images, (see Zink paragraph 0046, wherein …In this aspect, the processor generates a generic model for the ML algorithm using only, for example, SDR-HDR pair data…), as claimed.
Claim 38 is rejected for the same reasons as set forth in the rejections of claim 37, because claim 38 is claiming subject matter of similar scope as claimed in claim 37.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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