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 .
Response to Arguments
All claim rejections under 35 U.S.C. § 101 are withdrawn.
Applicant’s arguments with respect to claims 1-3, rejected under 35 U.S.C. § 102, and claims 4 & 5, rejected under 35 U.S.C. § 103, 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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18/189,282, filed on 03/24/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/24/2023 and 08/27/2025 was/were filed. The submission(s) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US5963665A), hereinafter referred to as Kim, in view of Kawada et al. (JP2012122965A), hereinafter referred to as Kawada.
Regarding claim 1, Kim teaches an image lightness adjustment method comprising:
generating a first histogram from image data in accordance with a lightness value (Kim, col. 3 lines 8-13, “To achieve still yet another one of the objects above, there is provided a circuit for enhancing an image by histogram-equalizing an image signal represented by a predetermined number of gray levels in a picture unit, the circuit comprising: first calculating means for calculating a gray level distribution of the image signal”; teaches obtaining a brightness/gray-level histogram of an image);
shifting the first histogram for average lightness in the first histogram to have a predetermined lightness value (Kim, col. 3 lines 13-18, “second calculating means for calculating a mean level of the image signal; compensation means for calculating a compensated mean level by adding a brightness compensation value according to a predetermined correction function to the mean level based on the mean brightness of the input image”; teaches finding the mean (gray) level of the image/histogram and then compensating/shifting the average to be at a mean brightness);
dividing the first histogram into a second histogram and a third histogram having a lightness value larger than a lightness value of the second histogram with the predetermined lightness value as a reference (Kim, col. 3 lines 18-22, “third calculating means for dividing the gray level distribution into a predetermined number of sub-images based on the mean level and calculating a cumulative density function for each sub-image”; teaches dividing the histogram into a predetermined number of sub-images (sub-histograms); col. 4 lines 3-7, “Based on the mean level Xm, the input image is divided into two sub-image groups denoted by {X}L and {X}U, wherein all the samples in {X}L are equal to or less than the mean level Xm and those in {X}U are greater than the mean level Xm.”; teaches dividing the histogram into two sub-images (sub-histograms), {X}L and {X}U, with {X}U having a brightness value larger than a brightness value of {X}L. both of which use the mean brightness level Xm- as a reference);
enlarging the second histogram at a first predetermined magnification ratio on a smaller side of the lightness value to obtain a fourth histogram including the lightness value equal to or larger than a minimum lightness value; enlarging the third histogram at a second predetermined magnification ratio on a larger side of the lightness value to obtain a fifth histogram including the lightness value equal to or smaller than a maximum lightness value (Kim, col. 4 lines 9-53:
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teaches enlarging {X}L by a factor of Xm to scale the range into (0, Xm) to include the brightness value equal to or larger than a minimum lightness value, as well as enlarging {X}U by a factor of (XL--1 – Xm+1), then shifting it by Xm+1 to scale the range into (Xm+1, XL=1) to include the brightness value equal to or smaller than a maximum brightness value);
synthesizing the fourth histogram and the fifth histogram to obtain a sixth histogram (Kim, col. 4:
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teaches an output Y- that combines the enlarged histograms together); and
adjusting the lightness value of the image data, based on the sixth histogram (Kim, col. 4 lines 48-62, “That is, the samples of {X}L are mapped into (0,Xm) according to the CDF of equation (3), and the samples of {X}U are mapped into (Xm+1, XL-1), respectively. Thus, each sub-image is equalized independently by the mean-separate histogram equalization (MSHE) according to the present invention.
Also, it can easily be seen that the average output of the mean-separate histogram equalization (MSHE) is given by (0.5+Xm)/2 when the number of gray levels L are large enough and the PDF of the input image X is symmetrical about the mean level of the input image. That is, the average output of the proposed mean-separate algorithm is the arithmetical average of the middle gray level (0.5) and ideal mean level (0.5), while the average output of the conventional histogram is 0.5.”; teaches adjusting the brightness of the image in such a way that the upper and lower histograms are more balanced, creating better contrast in the image).
Kim fails to teach an image lightness adjustment method specifically applicable to an inspection device that inspects manufactured products to distinguish between normal and abnormal products, the method being used to adjust the lightness of image data of the inspected product, comprising:
generating new image data for inspecting the inspected product.
Kawada teaches an image lightness adjustment method applicable to an inspection device that inspects manufactured products to distinguish between normal and abnormal products (Kawada, ¶ 18, “According to the present invention, it is possible to easily inspect an object for abnormalities in appearance.”; teaches inspecting objects for abnormalities), the method being used to adjust the lightness of image data of the inspected product (Kawada, ¶ 28-29, “Next, the image analysis unit 3 derives a brightness histogram of the reference image 11 (S2).
Specifically, as shown in FIG. 4, a grayscale image is created for the image 11, the brightness (luminance) of each pixel is determined, and the brightness distribution for 256 gradations is determined as a reference histogram 12.
At this time, the brightness of each pixel is calculated, for example, by the formula "brightness=0.299×R component+0.587×G component+0.144×B component".
In this reference histogram 12, the horizontal axis indicates gradation, which is expressed in 256 gradations from 0 to 255.
The vertical axis indicates brightness, which is expressed in pixels (px).
Next, in the reference histogram 12, a first gradation 12H when the brightness is at the first peak value P1, which is the highest, and a second gradation 12L when the brightness is at the second peak value P2, which is the second highest after the first peak value P1, are derived.
Then, the median value 12M of these first and second gradations 12H, 12L is set as a binarization level that serves as a threshold value for performing binarization processing (S3).
Next, a binarization process is performed on the reference image 11 based on the set binarization level, and a reference binarized image 13 is obtained, which is a binarized image in which multiple pixels are composed of only one value (first value) or zero value (second value) (S4).”; teaches determining a brightness histogram of an image, creating a grayscale image of it, and then binarizing the image based on brightness thresholds), comprising:
generating new image data for inspecting the inspected product (Kawada, ¶ 50, “As described above, in this embodiment, an inspection image 21 of the object 10 is acquired, and a binarization process is performed on this inspection image 21 to obtain a simplified inspection binary image 23 in which multiple pixels are binarized”; teaches obtaining a simplified inspection binary image).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement the abnormal product identification as taught by Kawada using the brightness histogram adjustment methods of Kim.
The suggestion/motivation for doing so would have been that “there is no need to set or adjust a light source or the like. Therefore, according to the present invention, it is possible to easily inspect an object for abnormalities in appearance” (Kawada, ¶ 7). Rather than having to adjust the physical environment of the inspection area, which may not always be possible, it is more efficient to perform image binarization to determine defects of an object.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Kim with Kawada to obtain the invention as specified in claim 1.
Regarding claim 2, Kim in view of Kawada teaches the image lightness adjustment method according to claim 1, wherein the predetermined lightness value is an average value of the minimum lightness value and the maximum lightness value (Kim, col. 3 line 64 – col. 4 line 2, “Here {X} denotes a given image, and Xm denotes the mean brightness level (hereinafter, abbreviated as "mean level") of {X}. The given image {X} is composed of L discrete gray levels denoted by {X0, X1, . . . , XL-1 }, where X0 =0 represents a block level and XL-1 =1 represents a while level. Also, it is assumed that Xm
ϵ
{X0, X1, . . . , XL-1 }.”; col. 5 lines 9-16:
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teaches a mean (brightness) level and a compensated mean level that uses the average value of the of the minimum brightness value and the maximum brightness value. In a case where the brightness compensation value is 0, the compensated mean level is the mean level.).
Regarding claim 3, Kim in view of Kawada teaches the image lightness adjustment method according to claim 1, wherein the predetermined lightness value is a preset value (Kim, col. 5 lines 9-16:
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teaches a compensated mean level that was determined by adding a preset brightness compensation predetermined by a compensation function. The compensated mean level may also be preset to be the mean level if the compensation is 0.).
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Kawada as applied to claims 1-3 above, and further in view of Dai et al. (CN109035182A), hereinafter referred to as Dai.
Regarding claim 4, Kim in view of Kawada teaches the image lightness adjustment method according to claim 1.
Kim in view of Kawada fails to teach wherein the minimum lightness value denotes a minimum value of possible lightness values, and the maximum lightness value denotes a maximum value of possible lightness values.
Dai teaches wherein the minimum lightness value denotes a minimum value of possible lightness values, and the maximum lightness value denotes a maximum value of possible lightness values (Dai, pg. 2, “1 is a flow chart of an adaptive dynamic double histogram equalization method according to the present invention, which assumes that an input image has an input bit of 8 and a 256-channel color digital image of 256, which is processed according to a flowchart. The steps should be as follows:
a) Since the input image is multi-channel, the luminance channel should be taken as the luminance map. For example, the image is converted to the HSV space, and the V channel is taken as the luminance map. At this time, the maximum gray level Lmax=255, the minimum gray level Lmin=0, or take the Y channel of the YCbCr space, at this time Lmax=235, Lmin=16, the initial histogram of the statistical brightness image, normalized to obtain the histogram The array h(j), where j represents the histogram gray level (Lmin ≤ j ≤ Lmax).”; teaches the minimum brightness level being the lowest possible brightness value and the maximum brightness level being the highest possible brightness value).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement the boundaries and preset values of brightness as taught by Dai into the brightness histogram adjustment methods of Kim in view of Kawada.
The suggestion/motivation for doing so would have been to ensure that all possible brightness values in an image are utilized, ensuring that there is good contrast in an image after equalization and the various objects/features of an image can be properly discerned from each other.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Kim in view of Kawada with Dai to obtain the invention as specified in claim 4.
Regarding claim 5, Kim in view of Kawada teaches the image lightness adjustment method according to claim 1.
Kim in view of Kawada fails to teach wherein either one of the minimum lightness value or the maximum lightness value is a preset value, or both the minimum lightness value and the maximum lightness value are preset values.
Dai teaches wherein either one of the minimum lightness value or the maximum lightness value is a preset value, or both the minimum lightness value and the maximum lightness value are preset values. (Dai, pg. 2, “1 is a flow chart of an adaptive dynamic double histogram equalization method according to the present invention, which assumes that an input image has an input bit of 8 and a 256-channel color digital image of 256, which is processed according to a flowchart. The steps should be as follows:
a) Since the input image is multi-channel, the luminance channel should be taken as the luminance map. For example, the image is converted to the HSV space, and the V channel is taken as the luminance map. At this time, the maximum gray level Lmax=255, the minimum gray level Lmin=0, or take the Y channel of the YCbCr space, at this time Lmax=235, Lmin=16, the initial histogram of the statistical brightness image, normalized to obtain the histogram The array h(j), where j represents the histogram gray level (Lmin ≤ j ≤ Lmax).”; teaches the minimum brightness level being 0 or 16 and the maximum brightness being 255 or 235, depending on if the 8-bit image is in HSV or YCbCr color space).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement the boundaries and preset values of brightness as taught by Dai into the brightness histogram adjustment methods of Kim in view of Kawada.
The suggestion/motivation for doing so would have been to ensure that all possible brightness values in an image are utilized, ensuring that there is good contrast in an image after equalization and the various objects/features of an image can be properly discerned from each other.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Kim in view of Kawada with Dai to obtain the invention as specified in claim 5.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Lei whose telephone number is (703)756-5602. The examiner can normally be reached Monday - Friday: 7:30AM - 5:00PM ET.
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/ANNA LEI/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672