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
Receipt is acknowledged that application claims priority to foreign application with application number KR10-2022-0029655 dated 03/08/2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 RCE submission filed on 03/12/2026 has been entered.
Response to Amendment
The amendment filed 02/10/2026 has been entered. Claims 1-20 remain pending in the application. Claims 1-13 are allowed.
Regarding claim 7, although it is indicated as “Currently Amended” in the claim set filed 02/10/2026, the claim is the same as the claim 7 presented in the most recently examined claim set filed 11/12/2025.
Response to Arguments
Applicant's arguments, filed 02/10/2026 have been fully considered but they are not persuasive. As indicated on pg. 7 of the remarks, amended independent claims 14 and 20 incorporate some of the features of allowed claim 1.
In claim 1, the first and second image capture data is generated by capturing each respective image displayed in the display device, as described in FIG. 1 and para 61 of the specification. The subsequently detected blur information represents a degree to which the dot patten is blurred when it is displayed. This information is utilized to deblur the second image capture data, which is used to generate compensation data for the display device. Thus, consistent with the specification and claims, the captured images are images of the display surface of the display device displaying an image/dot pattern. Specifically, the prior art fails to teach generating compensation data for luminance correction of the display device, based on the deblurring data (generated by deblurring the second image capture data), wherein the method also comprises generating first image capture data by capturing the first image displayed in the display device through an image capture device; displaying a second image in the display device; generating second image capture data by capturing the second image displayed in the display device through the image capture device.
The scope of claims 14 and 20 do not require that the first and second image capture data are images capturing the display device. Claim 20 recites “an image capture device that generates image capture data by capturing an image displayed in a display device”; however, it is not required that the image capture data includes the first and second image capture data referenced in the subsequent blur correction steps. Accordingly, the statement of reasons for the indication of allowable subject matter has been updated below to further clarify claim 1’s distinction from the prior art.
Furthermore, the rejection below relies on a new combination of prior art references.
Claim Objections
Claims 5 and 15 are objected to because of the following informalities: “a blurred dot pattern” should read “the blurred dot pattern” (Claim 5/15 references the blurred dot pattern in claim 4/14). Appropriate correction is required.
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.
Claims 14-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shyshkin et al. (U.S. Patent No. 2023/0007236 A1), hereinafter Shyshkin, in view of Yamaguchi et al. (U.S. Patent No. 2016/0295183 A1), hereinafter Yamaguchi.
Regarding claim 14, Shyshkin teaches an image processing method of preprocessing a capture image for optical compensation of a display device (Shyshkin, abstract: “systems and methods of optical correction for pixel evaluation and correction”), the image processing method comprising:
detecting blur information representing a degree to which a dot pattern is blurred in a first capture image including the dot pattern (Shyshkin, the scale of the correction data indicates a degree of blur, para 45: “the present embodiments display a number of display test patterns, each of which includes a sparse set of activated pixels which are spaced apart far enough so that at least some portion of the blurred images of each of the activated pixels in the captured images do not overlap or interfere with each other”; para 50: “The series of display test patterns are such that each pixel 110 of the display panel 220 may be corrected with correction data based on measurements in the form of the captured images by the camera 230 of the display panel 220 displaying the display test patterns”; see also claim 39);
deblurring a second capture image to qenerate deblurrinq data, based on the blur information (Shyshkin, correcting a display image using correction data, see step 314 in FIG. 3; para 64: “correction data stored in memory 106 is used by the controller 202 or in combination with a separate compensation block (not shown) to correct image data input to the display 250 for display on the display panel 22”) While Shyshkin teaches correcting a second image (image data input), thus teaching deblurring data (corrected input image), Shyshkin fails to explicitly teach qeneratinq compensation data for luminance correction of the display device, based on the deblurrinq data. However, Yamaguchi similarly teaches an image processing method of preprocessing a capture image for optical compensation of a display device (Yamaguchi, abstract). Yamaguchi teaches further: qeneratinq compensation data for luminance correction of the display device, based on deblurrinq data of a deblurred image (Yamaguchi, contrast data from the deblurring operation is transmitted to the luminance correction unit, para 43: “The image processing unit 220 comprises a deblurring unit 221 and a contrast conversion unit 222. The deblurring unit 221 corrects the blurring in the input image data that is input to the circuit 110 and transmits the correction image data to the contrast conversion unit 222. The input image data and the correction image data include pixel values. The contrast conversion unit 222 modulates a contrast of the pixel values in the correction image data so that the pixel values are in the acceptable range and transmits the modulated correction image data as a display image data to the display 122 of the projection unit 120. The contrast conversion unit 222 also transmits information used as contrast conversion information to the luminance correction unit 230”; see also para 44-45).
While Shyshkin utilizes a dot pattern to generate optical correction values of a display utilizing calibration and text patterns, Yamaguchi generates optical correction values of a display based on pixel values of the image to be displayed. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have utilized the deblurring data of the deblurred image to generate luminance correction values, as taught by Yamaguchi, with the method of Shyshkin in order to improve the display of the second image by correcting luminance values based on the pixel values of the input image (Yamaguchi, para 73: “visibility of the observation image will be improved because of improvement of contrast of the observation image”; para 98: “Convenience and visibility of the image display apparatus may be improved”), while still also benefitting from the initial calibration performed in the method of Shyshkin.
Regarding claim 15 (dependent on claim 14), Shyshkin in view of Yamaguchi teaches wherein the detecting of the blur information includes deriving a weight matrix for converting the blurred dot pattern included in the first capture image into an ideal dot pattern (Shyshkin, correction values to convert output luminance (observed blurred dot pattern) into expected luminance (ideal), para 25: “measure and correct pixels (and sub-pixels) whose output luminance varies from the expected luminance”; see also para 61).
Regarding claim 20, Shyshkin teaches an optical compensation system (Shyshkin, abstract: “systems and methods of optical correction for pixel evaluation and correction”) comprising: an image capture device that generates image capture data by capturing an image displayed in a display device (Shyshkin, para 50: “captured images by the camera 230 of the display panel 220 displaying the display test patterns”); and
a luminance correction device that detects blur information representing a degree to which a dot pattern is blurred in first image capture data including the dot pattern (Shyshkin, the scale of the correction data indicates a degree of blur, para 45: “the present embodiments display a number of display test patterns, each of which includes a sparse set of activated pixels which are spaced apart far enough so that at least some portion of the blurred images of each of the activated pixels in the captured images do not overlap or interfere with each other”; see also para 50 citation above and claim 39), and deblurs second image capture data to generate deblurrinq data, based on the blur information (Shyshkin, correcting a display image using correction data, see step 314 in FIG. 3; para 64: “correction data stored in memory 106 is used by the controller 202 or in combination with a separate compensation block (not shown) to correct image data input to the display 250 for display on the display panel 22”).
While Shyshkin teaches correcting a second image (image data input), thus teaching deblurring data (corrected input image), Shyshkin fails to explicitly teach qenerates compensation data for luminance correction of the display device, based on the deblurrinq data. However, Yamaguchi similarly teaches an image processing system of processing a capture image for optical compensation of a display device (Yamaguchi, abstract). Yamaguchi teaches further: qenerates compensation data for luminance correction of the display device, based on deblurrinq data of a deblurred image (Yamaguchi, contrast data from the deblurring operation is transmitted to the luminance correction unit, para 43: “The image processing unit 220 comprises a deblurring unit 221 and a contrast conversion unit 222. The deblurring unit 221 corrects the blurring in the input image data that is input to the circuit 110 and transmits the correction image data to the contrast conversion unit 222. The input image data and the correction image data include pixel values. The contrast conversion unit 222 modulates a contrast of the pixel values in the correction image data so that the pixel values are in the acceptable range and transmits the modulated correction image data as a display image data to the display 122 of the projection unit 120. The contrast conversion unit 222 also transmits information used as contrast conversion information to the luminance correction unit 230”; see also para 44-45).
While Shyshkin utilizes a dot pattern to generate optical correction values of a display utilizing calibration and text patterns, Yamaguchi generates optical correction values of a display based on pixel values of the image to be displayed. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have utilized the deblurring data of the deblurred image to generate luminance correction values, as taught by Yamaguchi, with the system of Shyshkin in order to improve the display of the second image by correcting luminance values based on the pixel values of the input image (Yamaguchi, para 73: “visibility of the observation image will be improved because of improvement of contrast of the observation image”; para 98: “Convenience and visibility of the image display apparatus may be improved”), while still also benefitting from the initial calibration performed in the method of Shyshkin.
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Shyshkin in view of Yamaguchi, Rykowski (U.S. Patent No. 9,135,851 B2), and Lee et al. (U.S. Patent No. 2019/0371268 A1), hereinafter Lee.
Regarding claim 16 (dependent on claim 15), Shyshkin in view of Yamaguchi teaches wherein the deriving of the weight matrix includes calculating a first weighted value for a target pixel corresponding to the dot pattern (Shyshkin, scale factor to correct luminance at a pixel, see para 61), and the first weighted value and a second weighted value are included in the weight matrix (Shyshkin, plurality of scale factors corresponding to each pixel in the image, para 56-57 and 61), but fails to explicitly teach a second weighted value for adjacent pixels adjacent to the target pixel through machine learning on the blurred dot pattern.
However, Rykowski similarly teaches a method for correcting target pixels in a dot pattern using a weight matrix (Rykowski, abstract: “generating a series of patterns for illuminating proper subsets of the light emitting elements of the display…A computing device analyzes the captured information, comparing the output of the activated light emitting elements to target output values, and determines correction factors to calibrate the display to better achieve the target output values”). Rykowski teaches wherein the light emitting pixels are nonadjacent, thus the elements between lit pixels are not illuminated (Rykowski, abstract and claim 2; see also FIG. 4A and col 7, ln 17-40). Accordingly, the pixels adjacent to the target pixels corresponding to the dot pattern will require a second weighted value, as opposed to the first weighted value for the target pixels, since the luminance value is different.
Shyshkin teaches generating weighted values to correct luminance corresponding to a displayed dot pattern, but fails to explicitly teach a second weighted value for pixels adjacent to a target pixel; however, Rykowski teaches the first and second weighted values as claimed, described above. One of ordinary skill in the art, before the effective filing date of the claimed invention, could have combined the weighted values for adjacent pixels, taught by Rykowski, with the method taught by Shyshkin in view of Yamaguchi using known methods. In doing so, each element merely would have performed the same functions as it did separately and would achieve the predictable results of generating corresponding luminance correction values for different pixels in a dot pattern on a display.
Furthermore, Lee teaches a method for correcting pixel luminance values (Lee, abstract) and discloses deriving of the weight matrix through machine learning (Lee, para 188: “The model learning unit 910-4 can use the learning data so that the artificial intelligence model has a criterion for correcting the luminance value of the image”; correction is based on correction function value, see para 14). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the machine learning algorithm of Lee with the method of Shyshkin in view of Yamaguchi and Rykowski in order to accurately and efficiently determine the correction values for pixels using a trained and validated model (Lee, para 4: “The more the artificial intelligence systems are used, the more the recognition rate is improved, and user's taste can be understood more accurately and thus, existing rule-based smart systems are gradually being replaced by deep learning-based artificial intelligence systems”).
Regarding claim 17 (dependent on claim 16), Shyshkin in view of Yamaguchi, Rykowski, and Lee teaches wherein a gradient descent algorithm is used for the machine learning (Lee, para 188: “The model learning unit 910-4 can also make an artificial intelligence model learn using, for example, a learning algorithm including an error back-propagation method or a gradient descent.”).
Regarding claim 18 (dependent on claim 16), Shyshkin in view of Yamaguchi, Rykowski, and Lee teaches wherein the deriving of the weight matrix includes: generating a deblurring dot image including a deblurring dot pattern by using the weight matrix (Shyshkin, corrected test pattern, para 50: “The series of display test patterns are such that each pixel 110 of the display panel 220 may be corrected with correction data”).
Shyshkin in view of Yamaguchi, Rykowski, and Lee teaches an ideal dot image including the ideal dot pattern (Shyshkin, expected luminance values for the test pattern image, para 25: “measure and correct pixels (and sub-pixels) whose output luminance varies from the expected luminance”; see also para 61), the deblurring dot image (Shyshkin, para 50 citation above), and the first and second weighted values (Taught in combination with Rykowski, see claim 16 rejection), but fails to explicitly teach calculating an error between an ideal dot image including the ideal dot pattern and the deblurring dot image; and adjusting the first weighted value and the second weighted value, based on the error.
However, Lee teaches calculating an error to train the machine learning model (Lee, error back-propagation, para 188: “The model learning unit 910-4 can also make an artificial intelligence model learn using, for example, a learning algorithm including an error back-propagation method”). Thus, in combination with the machine learning of Lee (See claim 16 rejection), Shyshkin in view of Yamaguchi, Rykowski, and Lee teaches calculating an error between an ideal dot image including the ideal dot pattern and the deblurring dot image; and adjusting the first weighted value and the second weighted value, based on the error (Lee teaches utilizing an error back-propagation method to train the model to correct luminance values, which involves minimizing the difference between the model’s predicted output, ideal dot pattern, and the actual output, deblurring dot pattern – dot patterns taught by Shyshkin).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Shyshkin in view of Yamaguchi and Stenman (U.S. Patent No. 2013/0208129 A1).
Regarding claim 19 (dependent on claim 14), Shyshkin in view of Yamaguchi teaches wherein the deblurring of the second capture image further includes: generating a first deblurring image by deblurring the second capture image (Yamaguchi, corrected image data, see citation in claim 14 rejection); but fails to teach detecting a noise through a spatial frequency analysis on the first deblurring image, the noise being a deblurred value out of a reference range in the deblurring of the second capture image; and replacing the noise with a value corresponding to the second capture image.
However, Stenman teaches an image processing method wherein spatial frequency analysis is utilized to remove noise using two images, specifically: detecting a noise through a spatial frequency analysis on the first image (Stenman, para 54: “applying a wavelet spatial frequency decomposition method, amplitude filtering and frequency filtering. Such other de-noising operation(s) may be executed before or after reducing the noise according to the method described above”), the noise being a value out of a reference range in the image (Stenman, para 44: “The values of amplitudes and frequencies of an image content may be measured and compared with amplitude and frequency of a reference value. Any deviations from a reference value can then be identified. The identified noise may be classified as allowable noise or not. Threshold levels for values may be defined for this purpose”); and replacing the noise with a value corresponding to a second image (Stenman, para 48-49: “After identification of the noises, and possibly allocation of threshold values to the different noises of the image content per pixel per image, the one or more pixels with values that deviate from the reference values (threshold value 2) are being removed. The removed image content of the pixel is than replaced with a replacement image content derived by overlapping the images of the series of subsequent images. This overlapping may be implemented by integrating an image content of a pixel from another image, whereby the replacement image content does not have noise (e.g. a colour or intensity value deviating from the reference value). The other image may be the previous image or the next or second next image in the series of images or any other image”).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have utilized the method of Stenman with the first deblurring image and second capture image of Shyshkin in view of Yamaguchi in order to further remove noise in the deblurred image (Stenman, para 4: “The quality of images decreases with increasing noise level. Therefore, many techniques have been developed to reduce noise in digital images”).
Allowable Subject Matter
Claims 1-13 are allowed. The following is a statement of reasons for the indication of allowable subject matter: In related prior art, Rykowski (U.S. Patent No. 9,135,851 B2) teaches displaying a first image in a display device, the first image including a dot pattern obtained by allowing target pixels spaced apart from each other emit light, at least two other pixels being disposed between the target pixels and capturing multiple images displayed on a display device (Rykowski, See abstract and Fig. 1 and 4A). Additionally, Shyshkin, relied upon to teach the similar method described in claim 14, teaches the deblurring of the dot pattern image and a second image. The combination of Shyshkin in view of Yamaguchi teaches utilizing deblurring image data to generate luminance compensation values.
However, the relied upon prior art (above and also referenced in the Non-final Office Action of 08/20/25) fails to teach generating compensation data for luminance correction of the display device, based on the deblurring data (of a second captured displayed image), wherein the first and second images are images capturing the display, recited in claim 1 as follows: displaying a first image in a display device, the first image including a dot pattern obtained by allowing target pixels spaced apart from each other to emit light, at least two other pixels being disposed between the target pixels; generating first image capture data by capturing the first image displayed in the display device through an image capture device; displaying a second image in the display device; generating second image capture data by capturing the second image displayed in the display device through the image capture device (see also Response to Arguments above).
In similar prior art, Bae (KR Patent No. 102153567 B1) teaches a luminance compensation device wherein a first and second image, the first image displaying a dot pattern, are captured images of the front of a display device (para 10 on pg. 7). Compensation values are determined for the first and second images, and luminance values are generated using the compensation data. However, Bae fails to disclose wherein the second image is deblurred based on first image data. The relevant teachings of Shyshkin, Yamaguchi, and Bae fail to teach the invention of claim 1 in reasonable combination.
In view of the foregoing, the prior art references alone or in reasonable combination are insufficient to teach the invention as a whole, as claimed in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
KR Patent No. 102153567 B1
U.S. Patent No. 2017/0116904 A1
U.S. Patent No. 2015/0146017 A1
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/EMMA E DRYDEN/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677