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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/16/2025 has been entered.
Claim status
Claims 1-20 are pending; claims 1, 18 and 20 are independent.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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
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.
Claim(s) 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (WO2021232323), using English translation, hereinafter Zhang, and further in view of Jiang (US 2024/0347013).
Regarding claim 18, Zhang teaches a non-transitory, computer-readable memory storing backlight extraction model data representing a neural network for backlight extraction of a local dimming display (Pages 24-25, wherein a method steps in of the application can be implemented by the processor executing software instructions; a computer program or instruction can be stored in the computer readable storage medium) that is trained using a loss function that includes a first regularization term and a second regularization term, wherein the first regularization term includes a first regularization value that is different than a second regularization value included in the second regularization term (fig. 2, an artificial intelligence chip 207 and pages 11-13, wherein to input a sample image and a target backlight image into a first neural network to obtain a predicted backlight image, predicting the backlight image and the target backlight image to obtain the loss value according to the loss function the loss value is fed back according to a certain mechanism. Including artificial intelligence chip periodically according to the received sample data for training the first neural network and a second neural network generating a parameter of the first neural network and the parameter of the second neural network to calculate a sample image),
Zhang does not expressly disclose wherein the loss function applies the first regularization value to a first set of pixels of an image, and wherein the loss function applies the second regularization value to a second set of pixels of the image having pixels of the image not included in the first set of pixels.
However, Jiang discloses” applies a first power regularization for a first local dimming region set of the two local dimming region sets and applies a second power regularization for a second local dimming region set of the two local dimming region sets, the first power regularization being different than the second power regularization, wherein the first local dimming region set comprises a first set of pixels of an image, and wherein the second local dimming region set comprises a second set of pixels of the image having pixels of the image not included in the first set of pixels”, fig. 3 and Paras 0037-0040.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a method of Zhang by applying the teaching of Jiang using a local dimming (LD) algorithm may perform the level mapping using a machine learning (ML) technique. For instance, a trained ML model may output brightness and/or dimming values corresponding to specific statistics/ backlight unit (BLU) stats, as a known technique to yield a predictable result.
Regarding claim 19, Zhang in view of Jiang teaches a local dimming display control system, comprising: at least one processor (fig. 2, a processor 201, a processor 202) and the non-transitory, computer readable memory of claim 18, wherein the at least one processor is configured to execute the neural network using the backlight extraction model data in order to determine backlight extraction values for the image (fig. 4 and Pages 12-13, the processor 202 according to the sample image and the target backlight image for training the first neural network to generate the parameter of the first neural network).
Claim(s) 1-14, 16-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2019/0355313), hereinafter Sun, in view of Zhang (WO2021232323), using English translation, hereinafter Zhang, and further in view of Jiang (US 2024/0347013).
Regarding claim 1, Sun teaches a method of controlling backlights for a local dimming display (fig. 2), comprising the steps of:
determining backlight extraction values for at least two local dimming region sets of a local dimming display (fig. 2, S201, Paras 0069 and 0076, wherein determining, according to an input luminance value of each pixel in an input image to be displayed, a backlight signal value of each backlight area in a backlight module is to extract the backlight signal value of each backlight area); and
controlling the local dimming display in accordance with the backlight extraction values in order to display image data representing an image (fig. 2, S204, Paras 0072 and 0088, wherein determining, according to the backlight signal value of each pixel and the relationship between the corresponding grayscale value when the backlight module is always on and the peak driving threshold, an output luminance value of each pixel. See fig. 3B and Para 0094).
Sun apparently does not explicitly teach the italicized portions of:
determining backlight extraction values for at least two local dimming region sets of a local dimming display through use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function that applies a first power regularization for a first local dimming region set of the two local dimming region sets and applies a second power regularization for a second local dimming region set of the two local dimming region sets, the first power regularization being different than the second power regularization, wherein the first local dimming region set comprises a first set of pixels of an image, and wherein the second local dimming region set comprises a second set of pixels of the image having pixels of the image not included in the first set of pixels; and
However, Zhang discloses “use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function”, see fig. 2, 207 an artificial intelligence chip 207 and pages 11-13.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a method of Sun by applying the teaching of Zhang using a neural network input a sample image and a target backlight image into a first neural network to obtain a predicted backlight image, predicting the backlight image and the target backlight image to obtain the loss value according to the loss function the loss value is fed back according to a certain mechanism. Including artificial intelligence chip periodically according to the received sample data for training the first neural network and a second neural network generating a parameter of the first neural network and the parameter of the second neural network to calculate a sample image, as a known technique to yield a predictable result.
Sun in view of Zhang apparently does not explicitly teach the italicized portions of:
determining backlight extraction values for at least two local dimming region sets of a local dimming display through use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function that applies a first power regularization for a first local dimming region set of the two local dimming region sets and applies a second power regularization for a second local dimming region set of the two local dimming region sets, the first power regularization being different than the second power regularization, wherein the first local dimming region set comprises a first set of pixels of an image, and wherein the second local dimming region set comprises a second set of pixels of the image having pixels of the image not included in the first set of pixels.
However, Jiang discloses” applies a first power regularization for a first local dimming region set of the two local dimming region sets and applies a second power regularization for a second local dimming region set of the two local dimming region sets, the first power regularization being different than the second power regularization, wherein the first local dimming region set comprises a first set of pixels of an image, and wherein the second local dimming region set comprises a second set of pixels of the image having pixels of the image not included in the first set of pixels”, see fig. 3 and Paras 0037-0040.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a method of Sun in view of Zhang by applying the teaching of Jiang using a local dimming (LD) algorithm may perform the level mapping using a machine learning (ML) technique. For instance, a trained ML model may output brightness and/or dimming values corresponding to specific statistics/ backlight unit (BLU) stats, as a known technique to yield a predictable result.
Regarding claim 2, Sun in view of Zhang and in view of Jiang teaches the method of claim 1, wherein, for each local dimming region set of the at least two local dimming regions sets, one or more local dimming regions are selected to belong to the local dimming region set based on projected backlight extraction values derived from image data (fig. 3B and Paras 0094-0095, Sun).
Regarding claim 3, Sun in view of Zhang and in view of Jiang teaches the method of claim 2, wherein a local dimming region threshold is determined based on the projected backlight extraction values, and wherein the local dimming region threshold is determined by averaging the projected backlight extraction values (figs 6, 7 and Para 0095, wherein adjusting the output luminance value of the pixel is performed, during local dimming, the backlight signal value of the pixel is smaller than 255. Therefore, when the output luminance value is adjusted according to the input luminance value, it is required to increase the output luminance value to be greater than Vbl, as shown in Segment A in the FIG. 7, Sun).
Regarding claim 4, Sun in view of Zhang and in view of Jiang teaches the method of claim 1, wherein the at least two local dimming region sets includes the first local dimming region set corresponding to one or more first local dimming regions on the local dimming display and the second local dimming region set corresponding to one or more second local dimming regions on the local dimming display (fig. 9 and Para 0115, wherein a local dimming circuit 901, configured to determine, according to an input luminance value of each pixel in an input image to be displayed, a backlight signal value of each backlight area in a backlight module, Sun),
suppression of luminance is applied by the loss function differently for the one or more first local dimming regions and the one or more second local dimming regions (fig. 5, S501 and Para 0082, wherein determining, according to the backlight signal value of each backlight area and the corresponding grayscale value when the backlight module is always on, a maximum power consumption allowance, Sun).
Regarding claim 5, Sun in view of Zhang and in view of Jiang teaches the method of claim 4 wherein the first power regularization and the second power regularization are configured so that, when applied, the dark region(s) of the given image are suppressed more than the bright region(s) of the image (fig. 3B and Para 0094, wherein to obtain relationship between the pixel grayscale (pixel luminance value) and the display luminance, it can be seen that the corresponding grayscale value when the backlight signal value of the pixel is lower than the grayscale value when the backlight module is always on, that is, backlight luminance L1, is subjected to transmittance compensation in a manner A, and the corresponding grayscale value when the backlight signal value of the pixel is higher than the grayscale value when the backlight module is always on, that is, backlight luminance L pec, is subjected to transmittance compensation in a manner B or C, and the obtained display luminance L1*A and L pec*B (or C) have a uniform transition and continuous grayscale, Sun).
Regarding claim 6, Sun in view of Zhang and in view of Jiang teaches the method of claim 5, wherein power control parameter data is used to control power consumption of the local dimming display when displaying the given image on the local dimming display (fig. 5, S501 and Para 0082, Sun).
Regarding claim 7, Sun in view of Zhang and in view of Jiang teaches the method of claim 6, wherein the power control parameter data includes a single power control parameter (fig. 5, S501 and Para 0082, Sun).
Regarding clam 8, Sun in view of Zhang and in view of Jiang teaches the method of claim 1, but Sun in view of Zhang does not expressly disclose wherein the first power regularization of the loss function includes a first regularization term and the first power regularization includes a second regularization term.
However, Jiang discloses “wherein the first power regularization of the loss function includes a first regularization term and the first power regularization includes a second regularization term”, see fig. 3 and Paras 0037-0040.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a method of Sun in view of Zhang by applying the teaching of Jiang using a local dimming (LD) algorithm may perform the level mapping using a machine learning (ML) technique. For instance, a trained ML model may output brightness and/or dimming values corresponding to specific statistics/ backlight unit (BLU) stats, as a known technique to yield a predictable result.
Regarding claim 9, Sun in view of Zhang and in view of Jiang teaches the method of claim 8, wherein the first regularization term includes a first regularization value that is different than a second regularization value included in the second regularization term (fig. 3 and Paras 0037-0040, Jiang).
Regarding claim 10, Sun in view of Zhang and in view of Jiang teaches the method of claim 8, wherein the machine learning model is trained using a training process that includes, for a given input image: determining local dimming perceptual data that represents a perceived appearance of the given input image as though the given input image is being displayed on the local dimming display (fig. 8 and page 15, wherein based on the area neural network dimming principle schematic diagram. After receiving the backlight image and the liquid crystal pixel compensation image from the processor 202, the processor 20 controls the backlight module 203 to provide light to the liquid crystal panel 204 according to the backlight image. Under the common action of the backlight image and the liquid crystal pixel compensation image, the liquid crystal display 200 displays the second image. the dynamic range of the second neural network displayed by the liquid crystal display 200 is greater than the dynamic range of the first neural network, Zhang).
Regarding claim 11, Sun in view of Zhang and in view of Jiang teaches the method of claim 10, wherein the training process further includes, for the appearance of the given input image as though the given input image is being displayed on a target display characterized by predetermined display characteristic data (fig. 4, S404 and Page 14, wherein inputting the first image into the second neural network for operation, extracting the characteristic of the first image by the second neural network, after calculating, outputting the liquid crystal pixel compensation image of the first image by the second neural network, Zhang).
Regarding claim 12, Sun in view of Zhang and in view of Jiang teaches the method of claim 11, wherein the predetermined display characteristic data includes ideal diffuser values representing an ideal diffuser for a display (fig. 10, S9011 and page 16, wherein the processor 202 determines the diffusion backlight image of the sample image according to the target backlight image, Zhang).
Regarding claim 13, Sun in view of Zhang and in view of Jiang teaches the method of claim 12, wherein the local dimming perceptual data and the target perceptual data are each determined using a perceptual uniform (PU) encoder (fig. 7 and page 14, wherein the structure of the second neural network is similar to the encoder and decoding network structure of the U-net, Zhang).
Regarding claim 14, Sun in view of Zhang and in view of Jiang teaches the method of claim 13, wherein ambient luminance data is used by the PU encoder for determining the local dimming perceptual data and the target perceptual data (fig. 7 and page 14, Zhang).
Regarding claim 16, Sun in view of Zhang and in view of Jiang teaches the method of claim 1, wherein determining the backlight extraction values includes post-processing, and wherein projected backlight extraction values and initial backlight extraction values are used for determining the backlight extraction values (Paras 0079-0080, Zhang).
Regarding claim 17, Sun in view of Zhang and in view of Jiang teaches the method of claim 16, wherein the initial backlight extraction values are determined using a deep neural network (DNN) trained for backlight extraction (Page 7 deep neural network, DNN Zhang).
Regarding claim 20, Sun teaches a local dimming display control system (Abstract), comprising: at least one processor and non-transitory, computer readable memory storing computer instructions that, when executed by the at least one processor (Paras 0139 and 0140), cause the local dimming display control system to determine backlight extraction values for two or more local dimming region sets (fig. 2, S201, Paras 0069 and 0076, wherein determining, according to an input luminance value of each pixel in an input image to be displayed, a backlight signal value of each backlight area in a backlight module is to extract the backlight signal value of each backlight area. In fig. 2, S204, Paras 0072 and 0088, wherein determining, according to the backlight signal value of each pixel and the relationship between the corresponding grayscale value when the backlight module is always on and the peak driving threshold, an output luminance value of each pixel. See fig. 3B and Para 0094).
Sun apparently does not explicitly teach the italicized portions of:
determine backlight extraction values for two or more local dimming region sets through use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function that applies a first regularization value to a first set of pixels of an image, and wherein the loss function applies a second regularization value to a second set of pixels of the image having pixels of the image not included in the first set of pixels, the first regularization value being different than the second regularization value
However, Zhang discloses “use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function”, see fig. 2, 207 an artificial intelligence chip 207 and pages 11-13.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a local dimming display control system of Sun by applying the teaching of Zhang using a neural network to input a sample image and a target backlight image into a first neural network to obtain a predicted backlight image, predicting the backlight image and the target backlight image to obtain the loss value according to the loss function the loss value is fed back according to a certain mechanism. Including artificial intelligence chip periodically according to the received sample data for training the first neural network and a second neural network generating a parameter of the first neural network and the parameter of the second neural network to calculate a sample image, as a known technique to yield a predictable result.
Sun in view of Zhang apparently does not explicitly teach the italicized portions of:
determine backlight extraction values for two or more local dimming region sets through use of a trained machine learning model, wherein the trained machine learning model is trained using a loss function that applies a first regularization value to a first set of pixels of an image, and wherein the loss function applies a second regularization value to a second set of pixels of the image having pixels of the image not included in the first set of pixels, the first regularization value being different than the second regularization value
However, Jiang discloses” applies a first regularization value to a first set of pixels of an image, and wherein the loss function applies a second regularization value to a second set of pixels of the image having pixels of the image not included in the first set of pixels, the first regularization value being different than the second regularization value”, see fig. 3 and Paras 0037-0040.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified a method of Sun in view of Zhang by applying the teaching of Jiang using a local dimming (LD) algorithm may perform the level mapping using a machine learning (ML) technique. For instance, a trained ML model may output brightness and/or dimming values corresponding to specific statistics/ backlight unit (BLU) stats, as a known technique to yield a predictable result.
8. Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun, in view of Zhang, in view of Jiang and further in view of Hiramatsu (US 2022/0319443), hereinafter Hiramatsu.
Regarding claim 15, Sun in view of Zhang and in view of Jiang teaches the method of claim 13, but Sun in view of Zhang and in view of Jiang does not expressly disclose wherein a point spread function (PSF) is used to determine local dimming diffuser data, and wherein the local dimming diffuser data is input into the PU encoder as a part of determining the perceived appearance of the local dimming perceptual data.
However, Hiramatsu discloses “wherein a point spread function (PSF) is used to determine local dimming diffuser data, and wherein the local dimming diffuser data is input into the PU encoder as a part of determining the perceived appearance of the local dimming perceptual data”, see fig. 18 and Para 0156.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified the method of Sun in view of Zhang and in view of Jiang by applying the teaching of Hiramatsu to include a neural network receives a target display image, predicts and outputs predictive values of an optimal local dimming pattern to calculate an intensity distribution formed on the screen surface of the liquid crystal display panel by weighting a point spread function (PSF), as a known technique to yield a predictable result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
JI (US 2019/0353961), relates to the field of display technology, and in particular to a method for acquiring backlight diffusion transmission parameter, a display control method and a display control device.
Shin (US 2021/0096423), related a display device that includes a backlight unit. The backlight unit may operate according to a dimming operation method. The display device may have satisfactory display quality.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAIFELDIN E ELNAFIA whose telephone number is (571)270-5852. The examiner can normally be reached 9-5.
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/S.E.E/Examiner, Art Unit 2625 11/16/2025
/WILLIAM BODDIE/Supervisory Patent Examiner, Art Unit 2625