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
Acknowledgment is made of applicant’s claim for foreign priority based on an application filed in Korea on Dec. 5, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/13/24, 5/29/25 and 2/11/26 are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claims 1, 13 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhao et al. (CN 114494698 A).
As to Claim 1, Zhao teaches A display apparatus comprising: at least one memory storing one or more instructions; and at least one processor configured to execute the one or more instructions, wherein the one or more instructions, when executed by the at least one processor, cause the display apparatus to:
obtain an input image, obtain a contour prediction result comprising information related to a prediction of whether a contour would be generated in an image output by the display apparatus based on the input image by inputting the input image into the contour prediction model (Zhao discloses “A semantic segmentation method for traditional culture images based on edge prediction” in [0008]; “S3. During the model upsampling prediction process, the edge prediction method is used to determine the category of blurred edge pixels, and multiple blurred edge pixels with uncertain labels are selected” in [0011], see also [0068]), and
based on the contour prediction result, perform at least one of a first blurring process or a second blurring process on the input image, wherein the first blurring process is different from the second blurring process (Zhao discloses “S5. The predicted category of the blurred edge pixels and the label prediction map after bilinear interpolation are mixed to obtain the final fine edge prediction map” in [0013]; “At the same time, uncertain edge pixels are selected in the upsampling prediction stage of the model and the selected predicted edge points are classified by a shared point classifier to determine the edge pixel labels on the label map after the original bilinear interpolation” in [0037]; “Specifically, since upsampling is an iterative process, the prediction map at the beginning of each iteration is considered a coarse prediction map, and the final iteration will generate a label prediction map of the same size as the input image” in [0067]. Here, the interpolation and upsampling process refer to blurring process.)
Claim 13 recites similar limitations as claim 1 but in a method form. Therefore, the same rationale used for claim 1 is applied.
Claim 19 recites similar limitations as claim 1 but in a computer readable medium form. Therefore, the same rationale used for claim 1 is applied.
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 of this title, 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 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (CN 114494698 A) in view of TSURUYAMA et al. (CN115115529A).
As to Claim 2, Zhao teaches The display apparatus of claim 1. The combination of TSURUYAMA further teaches wherein the contour prediction result comprises a contour generation probability, the contour generation probability comprising a probability that a contour would be generated in an image output by the display apparatus based on the input image, and wherein the one or more instructions, when executed by the at least one processor, cause the display apparatus to: based on the contour generation probability being less than a threshold, perform the first blurring process on the input image, and based on the contour generation probability being equal to or greater than the threshold, perform the second blurring process on the input image (TSURUYAMA discloses “Therefore, the image generation unit 33 is configured to generate a first blurred image using blurred values whose uncertainty is less than a threshold among the blurred values of each pixel of the captured image obtained in step S3 (that is, blurred values with uncertainty greater than or equal to the threshold are discarded)” in [0149].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of TSURUYAMA so as to perform a different blurring process based on a threshold value.
Claim 14 is rejected based upon similar rationale as Claim 2.
Claims 3, 10-11, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Farre Guiu et al. (US 2023/0007365 A1).
As to Claim 3, Zhao teaches The display apparatus of claim 1, wherein the one or more instructions, when executed by the at least one processor, cause the display apparatus to: obtain the contour prediction result by converting the input image into a vector representation to generate image embedding and inputting the image embedding into the contour prediction model (Zhao discloses deep learning semantic segmentation method in [0003]. Farre Guiu further discloses “Content segmentation software code 110/210 is further configured to predict, using trained content subsection boundary prediction ML model 160/260 and the embedding vectors…” in [0028], see also Fig 2.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Farre Guiu so as to explain a machine learning model to convert the input content to embedding vector within a boundary prediction model.
As to Claim 10, Zhao teaches The display apparatus of claim 1, wherein the one or more instructions, when executed by the at least one processor, cause the display apparatus to:
generate a blurred image by performing a blurring process on a training image, obtain feedback on a contour generation result of the blurred image, generate image embedding by converting the training image into a vector representation, and provide the image embedding and a label corresponding to the feedback to the contour prediction model as training data (Zhao discloses deep learning semantic segmentation method in [0003]. Farre Guiu further discloses “Content segmentation software code 110/210 is further configured to predict, using trained content subsection boundary prediction ML model 160/260 and the embedding vectors…” in [0028], see also Fig 2; “In those implementations, content segmentation software code 110/210 may use the feedback provided by content editor 144 to tune hyperparameters of content subsection boundary prediction ML model 160/260/460 so as to improve its predictive performance” in [0054].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Farre Guiu so as to provide feedback to system correcting or ratifying segmentation predictions made by content subsection boundary prediction ML model (Farre Guiu, [0054]).
As to Claim 11, Zhao teaches The display apparatus of claim 10, wherein the one or more instructions, when executed by the at least one processor, cause the display apparatus to:
obtain at least one of an user feedback through a user interface including an inquiry about whether a contour is generated in the blurred image, or the feedback corresponding to a contour generation result of the blurred image, obtained through a contour detection algorithm (Farre Guiu further discloses “Content segmentation software code 110/210 is further configured to predict, using trained content subsection boundary prediction ML model 160/260 and the embedding vectors…” in [0028], see also Fig 2; “In those implementations, content segmentation software code 110/210 may use the feedback provided by content editor 144 to tune hyperparameters of content subsection boundary prediction ML model 160/260/460 so as to improve its predictive performance” in [0054].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Farre Guiu so as to provide feedback to system correcting or ratifying segmentation predictions made by content subsection boundary prediction ML model (Farre Guiu, [0054]).
Claim 15 is rejected based upon similar rationale as Claim 3.
Claim 20 is rejected based upon similar rationale as Claims 1 & 3.
Claims 4, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Sun (CN112887597A).
As to Claim 4, Zhao teaches The display apparatus of claim 1, wherein the first blurring process comprises downscaling, blurring, and upscaling the input image, and the second blurring process comprises downscaling, blurring, and upscaling the input image to generate a blurred image, and lowering a color gray scale of the blurred image through dimming (Sun discloses “Specifically, the underlying grayscale image G0 is subjected to Gaussian convolution and downsampling to obtain the upper layer image G1. Then, the G1 image is subjected to Gaussian blur and downsampling again to obtain the top layer image G2… As shown in Figure 4, the topmost image G2 is upsampled…” in [0042]; “and the highlight increment curve is used to determine the increment that each gray value in the low-frequency information needs to be decreased (i.e., darkening)” in [0047]; see also Fig 4 below:
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Sun so as to adjust gray scale after the blurring process to provide a smooth transitions and reduce sharp edges.
As to Claim 9, Zhao teaches The display apparatus of claim 1, wherein a blurred image generated by performing the second blurring process on the input image includes fewer contours than a blurred image generated by performing the first blurring process on the input image (Sun discloses “Specifically, the underlying grayscale image G0 is subjected to Gaussian convolution and downsampling to obtain the upper layer image G1. Then, the G1 image is subjected to Gaussian blur and downsampling again to obtain the top layer image G2… As shown in Figure 4, the topmost image G2 is upsampled…” in [0042], see also Fig 4. Here, different scaling factor between two downsampling may cause different contours. In other words, the high resolution image may have more contours than the lower resolution image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Sun so as to compress data, remove noise, extract abstract features and reconstruct outputs.
Claim 16 is rejected based upon similar rationale as Claim 4.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Luo et al. (CN 101706948 B).
As to Claim 5, Zhao teaches The display apparatus of claim 1, wherein the second blurring process comprises contour interpolation of a blurred image generated by blurring the input image, wherein the contour interpolation comprises detecting a boundary region where a gray scale difference between a first pixel and a second pixel included in the input image is equal to or greater than a preset value, and interpolating an intermediate gray scale between a gray scale of the first pixel and a gray scale of the second pixel in the boundary region
(Luo discloses “2) Detect the image by forming a grid with a 2*2 window as a unit. Determine the variance of the grid. If the variance is less than a set threshold, it is a smooth region and bilinear interpolation is used. Otherwise, it is an edge region and step 3) is performed” in [0013]; “3) For the midpoint J of the window, calculate the value of the midpoint based on the principle of strong aggression using the four reference points in the window, and interpolate the midpoint according to the calculated value” in [0014]; “For each grid, determine whether its variance is less than a certain set threshold. If it is less than the threshold, it is a smooth region and bilinear interpolation can be used. If it is greater than the threshold, it is a non-smooth region and the subsequent steps are performed.” in [0035]; “As shown in Figure 2, for the edge, the gray value of the interpolation point moves towards the stronger side, that is, t increases the result point t’ of the bilinear interpolation. Therefore, the edge width of the enlarged image will not increase with the enlargement” in [0054].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Luo so that the adaptive interpolation strategy preserves the edge features of the image (Luo, [0021]).
Claim 17 is rejected based upon similar rationale as Claim 5.
Claims 6-8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Sun and Enomoto (US 2002/0006230 A1).
As to Claim 6, Zhao teaches The display apparatus of claim 1, wherein the second blurring process comprises downscaling, blurring, and upscaling the input image, and wherein a second blurring intensity used in the second blurring process is lower than a first blurring intensity used in the first blurring process (Sun discloses “Specifically, the underlying grayscale image G0 is subjected to Gaussian convolution and downsampling to obtain the upper layer image G1. Then, the G1 image is subjected to Gaussian blur and downsampling again to obtain the top layer image G2… As shown in Figure 4, the topmost image G2 is upsampled…” in [0042]. It is obvious that the edge region has different blurring intensity from the non-edge region. For example, Enomoto discloses “…a synthesized unsharp image signal S3 having a lower degree of unsharpness with respect to a region mainly containing a high-contrast edge portion of a subject, and a higher degree of unsharpness with respect to a flat region” in [0180].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Sun so as to compress data, remove noise, extract abstract features and reconstruct outputs. The combination of Enomoto is to explain the different blurring intensity between edge region and non-edge region.
As to Claim 7, Zhao in view of Sun and Enomoto teaches The display apparatus of claim 6, wherein a second scaling factor used in the second blurring process is smaller than a first scaling factor used in the first blurring process (Sun discloses two downsampling steps in Fig 4. Here, the second downsampling has lower scaling factor than the first downsampling.)
As to Claim 8, Zhao in view of Sun and Enomoto teaches The display apparatus of claim 6, wherein a size of a second blur filter used in the second blurring process is smaller than a size of a first blur filter used in the first blurring process (Sun discloses two downsampling steps in Fig 4. Here, the second downsampling process is applied on G1 after the first downsampling process to obtain G2. Thus, the filter size of the second blurring process is smaller than the filter size of the first blurring process.)
Claim 18 is rejected based upon similar rationale as Claim 6.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Enomoto.
As to Claim 12, Zhao teaches The display apparatus of claim 1, wherein the input image comprises a video image on which an image process has been performed in a YUV color space or a graphic image on which an image process has been performed in an RGB or an RGBA color space (Zhao discloses video segmentation in [0003]. Enomoto also discloses image process in RGB color space in [0059].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Zhao with the teaching of Enomoto to indicate a color image in R,G,B color space.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEIMING HE whose telephone number is (571)270-1221. The examiner can normally be reached on Monday-Friday, 8:30am-5:00pm.
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/WEIMING HE/
Primary Examiner, Art Unit 2611
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