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 .
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-12, 18-19 and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dangi (US2021/0360179).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding claim 1, Dangi discloses an apparatus for processing image data, the apparatus comprising:
a memory ([0095]); and
one or more processors coupled to the memory, the one or more processors ([0095-0096]) configured to:
obtain image data associated with an image frame (abstract; [0006-008]);
obtain, based on an output of one or more trained machine learning models that use the image data as input ([0163; 0231; 0241]),
a tuning map indicating a plurality of settings for adjusting a parameter of the one or more processors, wherein each value in the tuning map corresponds to a respective setting for one or more pixels of an image frame associated with the image data ([0104-0105; 0116; 0126]); and
generate an output image frame at least in part by processing a plurality of pixels of the image data using the tuning map ([0073-0076; 0157]), wherein each pixel of the plurality of pixels is processed using a respective setting of the plurality of settings indicated in the tuning map for adjusting the parameter ([0237]).
Regarding claim 2, Dangi discloses the apparatus of claim 1, wherein the plurality of settings indicated in the tuning map spatially vary across the image frame ([0039-0043; 0119]).
Regarding claim 3, Dangi discloses the apparatus of claim 1, wherein the one or more processors are configured to: obtain a plurality of tuning maps including the tuning map, each tuning map of the plurality of tuning maps including respective settings for adjusting a respective parameter of a plurality of parameters of the one or more processors ([0237; 0245]).
Regarding claim 5, Dangi discloses the apparatus of claim 1, wherein each value in the tuning map corresponds to a respective pixel in the image frame ([0104; 0109]).
Regarding claim 6, Dangi discloses the apparatus of claim 3, wherein the one or more processors are configured to use each value in the tuning map to adjust each respective pixel in the image frame based on the parameter ([0105; 0116; 0152]).
Regarding claim 7, Dangi discloses the apparatus of claim 1, wherein the one or more processors include an image signal processor (ISP: [0004; 0051]).
Regarding claim 8, Dangi discloses the apparatus of claim 1, wherein the plurality of settings include one or more tuned settings ([0085; 0090-0092]).
Regarding claim 9, Dangi discloses the apparatus of claim 1, wherein the image data is raw image data having a plurality of color components corresponding to a color filter array of an image sensor ([0015; 0108]).
Regarding claim 10, Dangi discloses the apparatus of claim 9, wherein input of the image data to the one or more trained machine learning models includes input of the raw image data to the one or more trained machine learning models ([0083; 0188; 0190]).
Regarding claim 11, Dangi discloses the apparatus of claim 1, wherein, to generate the output image frame, the one or more processors are configured to: demosaic the image data before processing the plurality of pixels of the image data using the tuning map ([0185]).
Regarding claim 12, Dangi discloses the apparatus of claim 1, wherein, to obtain the image data, the one or more processors are configured to receive the image data from an image sensor that captures the image data ([0003; 0082-0083]).
Regarding claim 18, Dangi discloses the apparatus of claim 1, wherein parameters of the one or more processors include one or more demosaicing parameters and the plurality of settings include one or more demosaicing settings corresponding to the one or more demosaicing parameters ([0325]), wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to demosaic at least one pixel of the image data based on the one or more demosaicing settings (claim 15; [0371]).
Regarding claim 19, Dangi discloses the apparatus of claim 1, wherein parameters of the one or more processors are associated with at least one of noise reduction, sharpening, tone mapping, or color saturation (abstract; [0004-0005; 0011; 0109]).
Regarding claim 22 the claim is a method of the apparatus claim 1. Therefore, claim 22 is analyzed and rejected as claim 1.
Allowable Subject Matter
Claims 4, 13-17 and 20-21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 4, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: ”wherein the one or more processors are configured to: obtain, based on an output of the one or more trained machine learning models, an additional tuning map indicating a plurality of additional settings for adjusting an additional parameter of the one or more processors, wherein each value in the additional tuning map corresponds to a respective additional setting for the one or more pixels of the image frame associated with the image data; and generate the output image frame at least in part by processing the plurality of pixels of the image data using the additional tuning map”.
Regarding claim 13, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein the one or more processors are configured to: obtain metadata corresponding to the image data, wherein an output of the one or more trained machine learning models is based on input of the metadata and the image data to the one or more trained machine learning models.
Regarding claim 14, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein parameters of the one or more processors include a plurality of gain parameters and the plurality of settings include a plurality of gain settings corresponding to the plurality of gain parameters, each gain parameter of the plurality of gain parameters corresponding to one of a plurality of color channels, wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to perform one or more multiplier operations for at least one pixel based on the plurality of gain settings.
Regarding claim 15, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein parameters of the one or more processors include a plurality of offset parameters and the plurality of settings include a plurality of offset settings corresponding to the plurality of offset parameters, each offset parameter of the plurality of offset parameters corresponding to one of a plurality of color channels, wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to perform one or more addition operations for at least one pixel based on the plurality of offset settings.
Regarding claim 16, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein parameters of the one or more processors include one or more gamma parameters and the plurality of settings include one or more gamma settings corresponding to the one or more gamma parameters, wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to adjust tone of at least one pixel based on the one or more gamma settings.
Regarding claim 17, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein parameters of the one or more processors include one or more Gaussian filter parameters and the plurality of settings include one or more Gaussian filter settings corresponding to the one or more Gaussian filter parameters, wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to apply a Gaussian filter to at least one pixel based on a Gaussian curve, wherein a shape of the Gaussian curve is based on the one or more Gaussian filter settings. The prior art of Yan (US12150400) discloses method for inputting the density maps and one or more GT density maps corresponding to the density maps to a second loss function to output second loss data corresponding to the density maps, wherein the GT density maps are obtained by performing a gaussian distribution.
Regarding claim 20, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to: process, based on the tuning map, a first pixel of the plurality of pixels of the image data based on a first setting indicated by a first value of the tuning map for the parameter; and process, based on the tuning map, a second pixel of the plurality of pixels of the image data based on a second setting indicated by a second value of the tuning map for the parameter, wherein the plurality of settings include at least the first setting and the second setting.
Regarding claim 21, the prior art of Chuang (US2019/0311464) discloses a tuning input comprising a set of color balance parameters for the point; generating a second version of the mesh by adding the point and the set of color balance parameters for the point to the first version of the mesh. The prior art of Hwang (US10643306) discloses an application of the at least one neural network that causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. The prior art of Hwang (US2025/0267355) discloses systems and techniques for processing image data using a composite image signal processor (ISP) that uses one or more trained machine learning models to identify custom settings for different pixels of the image data. The prior art of Lin (US2014/0049662) discloses responsive to a determination that the spectral content has changed by more than the threshold value, the spectral capture parameter for the first region is adjusted, and the adjusted spectral capture parameter is applied to an imaging system for capture of a successive frame. The prior art of McNamer (US2014/0099022) discloses color of pixel of image that is modified by performing inverse look-up in sparse table, the color difference is identified on corresponding entry, and color difference is applied on target pixel. The prior art of Jeon (US2022/0165232) discloses tuning of the gamma curve that includes, as the result of the comparison, until the measured gamma curve for each color falls within a range of the target gamma curve for each color. Thus, while many references teach machine learning models, tunning input and other imaging processing, none of the references alone or in combination, provide a motivation to teach the apparatus of claim 1 in combination with: wherein, to process the plurality of pixels of the image data using the tuning map, the one or more processors are configured to: process, based on the tuning map, a first pixel of the plurality of pixels of the image data based on a first setting indicated by a first value of the tuning map for the parameter; and process, based on an additional tuning map, the first pixel of the plurality of pixels based on a second setting indicated by a first value of the additional tuning map for a second parameter of the one or more processors, wherein the plurality of settings include at least the first setting and the second setting.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNG H LAM whose telephone number is (571)272-7367. The examiner can normally be reached 9AM-5PM.
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/HUNG H LAM/Primary Examiner, Art Unit 2639 06/27/26
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