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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-9 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (TW200913730A, hereinafter referred to as Huang) in view of Onishi et al. (CN117651967A, hereinafter referred to as Onishi).
Regarding claim 1, Huang teaches a grayscale map generator configured to selectively identify a luminance map data based on a saturation map and a luminance map that correspond to an input image (met by image saturation brightness can be determined by calculating the maximum gray level value (Mij) of the luminance map), and configured to generate a grayscale map based on the identified luminance map data (met by requiring a wide range of adjustments to the grayscale value of the image element of image (500); further met by depending on the saturation of the image, different gain values can be selected to adjust the image (500)). This is read in (Page 3, Paragraph 3-4).
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Huang fails to teach a noise corrector configured to generate processed image data for which a noise value for the input image is corrected based on the grayscale map. However, Onishi amends this deficiency.
Onishi teaches a trained model (36) for performing noise removal in the optical image of the object F corresponding to the light detector (21). This is read in (Page 12, Paragraph 6).
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Therefore, it would have been prima facia obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Huang to incorporate the teachings of Onishi in order to provide an optical image processing method, a machine learning method, a trained model, a machine learning pre-processing method, an optical image processing module, an optical image processing program and an optical image processing system capable of effectively removing noise in the optical image (Abstract).
Regarding claim 5, Onishi as read in the rejection of claim 1, incorporated herein, meets the processed image data is image data for which the noise value is subtracted from pixel data of pixels corresponding to the input image (met by performing noise removal in the optical image).
Regarding claim 6, Onishi further teaches wherein the noise value is DC offset noise value associated with pixel data of pixels corresponding to the input image (met by condition information includes shadow correction coefficient, offset, noise factor). The noise factor and offset condition information meet the claimed limitation by enabling the same process and desired outcome as that of claim 6. This is read in (Page 6, Paragraph 3).
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Regarding claim 7, Huang teaches a dark area detector configured to detect a dark area from among areas of the grayscale map (met by if the image of image 500 is sufficiently saturated, the brightness of image 500 is also low). This is read in (Page 3, Paragraph 2).
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Further, Onishi teaches and a noise calculator configured to calculate the noise value for the input image based on pixel data for at least one pixel included in the dark area (met by constant D is information representing dark current noise). This is read in (Page 8, Paragraph 3).
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Regarding claim 8, Onishi teaches to identify data including the lowest pixel data from among data of the grayscale map (met by the horizontal axis of the histogram is the luminance value of the camera (2), and the vertical axis of the histogram is the frequency). Therefore, Onishi is able to identify data including the lowest pixel data (luminance and/or frequency of a luminance value) from data of the grayscale map. This is read in (Page 9, Paragraph 1 (abbreviated)).
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Further, Onishi teaches detecting an area corresponding to data including the identified lowest pixel data as the dark area (met by analyze the luminance value and the noise of each of the plurality of measurement areas). This implicitly allows the prior art to determine one of the measurement areas as having a lower luminance value than the others. This is read in (Page 13, Last Paragraph)
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Regarding claim 9, Onishi as read in the rejection of claim 7, incorporated herein, meets determine the pixel data for the at least one pixel included in the dark area as the noise value (met by constant D is information representing dark current noise).
Claims 2, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Onishi and in further view of Hong (WO2013025219A1).
Regarding claim 2, Huang in view of Onishi fails to teach a saturation map generator configured to generate the saturation map based on pixels corresponding to a first color filter, pixels corresponding to a second color filter, and pixels corresponding to a third color filter. However, Hong amends this deficiency.
Hong teaches an input overlay image frame is converted into a color saturation image frame C(x, y), for example by converting (block 635) the RGB version of the overlay image frame to the S component of an HSV color space. The three color filters of the claimed invention are met by the RBG color space format of the image, storing data in three color channels which may be separated (i.e. filtered) and converted into saturation values as is commonly known in the art. This is read in (Paragraph [0047]).
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Therefore, it would have been prima facia obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Huang in view of Onishi to incorporate the teachings of Hong in order to provide methods and systems are disclosed for creating a blended image (Abstract).
Regarding claim 12, the claim is substantially identical to the subject matter of claims 1 and 2, the analyses of which are incorporated herein.
Claims 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Onishi and in further view of Macazaga Zuazo et al. (US20250166203A1, hereinafter referred to as Macazaga Zuazo).
Regarding claim 13, Huang in view of Onishi fails to teach an image processing method comprising: identifying data based on a first threshold value from among data of a luminance map for an input image; identifying data that is less than or equal to a second threshold value from among data of a saturation map for the input image; detecting a dark area based on the data that is less than or equal to the first threshold value and the data that is less than or equal to the second threshold value. However, Macazaga Zuazo amends this deficiency.
Macazaga Zuazo teaches a luminance threshold as well as a saturation threshold. This enables the features of claim 13 in combination with the previously taught features of Huang in view of Onishi. This is read in (Paragraph [0070]).
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Huang as read in the rejection of claim 7, incorporated herein, enables the detection of a dark area. Onishi as read in the rejection of claim 6, incorporated herein, meets calculating a direct current DC offset noise value for the input image based on the detected dark area.
Regarding claim 14, Macazaga Zuazo teaches classifying and separating pixels corresponding to the input image into pixels corresponding to each of a plurality of channels (met by receive a video frame in RGB). The RGB color space would allow one of ordinary skill to separate the image into these separate color channels. This is read in (Paragraph [0119]).
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Regarding claim 15, Macazaga Zuazo as read in the rejection of claim 14, incorporated herein, meets wherein the plurality of channels includes a red channel, a green channel, and a blue channel (met by the RGB color space).
Regarding claim 16, Onishi as read in the rejection of claim 1, incorporated herein, meets performing pre-processing to remove pixel noise from the input image (met by performing noise removal in the optical image).
Allowable Subject Matter
Claims 3-4, 10-11, 17-20 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.
Claim 3 recites generating the saturation map based on a standard deviation between pixel data of pixels corresponding to the first, second, and third color filters. The prior art of reference fails to teach this feature.
Claim 4 recites converting data having a threshold value or higher from among data of the saturation map into data corresponding to white; and converting data less than the threshold value from among the data of the saturation map into data of the luminance map. The prior art of reference fails to teach these features.
Claim 10 recites wherein the noise calculator is configured to calculate: a first color average obtained by calculating an average between pixel data of pixels corresponding to a first color filter from among pixels included in the dark area; a second color average obtained by calculating an average between pixel data of pixels corresponding to a second color filter from among pixels included in the dark area; and a third color average obtained by calculating an average between pixel data of pixels corresponding to a third color filter from among pixels included in the dark area. The prior art of reference fails to teach these features.
Claim 11 recites wherein the noise calculator is configured to correct the input image by: subtracting the first color average from pixel data for pixels corresponding to the first color filter from among pixels corresponding to the input image; subtracting the second color average from pixel data for pixels corresponding to the second color filter from among pixels corresponding to the input image; and subtracting the third color average from pixel data for pixels corresponding to the third color filter from among pixels corresponding to the input image. The prior art of reference fails to teach these features.
Claim 17 recites generating the saturation map based on a standard deviation between pixel data of the pixels corresponding to each of the plurality of channels from among pixels included in a unit pixel group. The prior art of reference fails to teach this feature.
Claim 18 recites detecting a common region between an area corresponding to data identified based on the first threshold value and an area corresponding to data less than or equal to the second threshold value from among areas of the input image. The prior art of reference fails to teach this feature.
Claim 19 recites calculating an average value between pixel data of pixels corresponding to a channel of a color from among pixels included in the detected dark area. The prior art of reference fails to teach this feature.
Claim 20 recites correcting the input image by subtracting the calculated average value from pixel data of pixels corresponding to the channel of color from among pixels corresponding to the input image. The prior art of reference fails to teach this feature.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JAMES BODNARK whose telephone number is (703)756-5378. The examiner can normally be reached 8a-5p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at (571) 272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW JAMES BODNARK/Examiner, Art Unit 2668
/UTPAL D SHAH/Primary Examiner, Art Unit 2668
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