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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 9, 11-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al (CN 109410143 B).
Regarding claim 1, Bhagwan et al. teaches a code recognition method (see para [0016]; “the barcode can be more quickly and accurately recognized”, see also para [0031]; “image analysis used to identify a barcode (115) from an image) comprising: receiving a color image including a portion in which a code is inscribed (see Figs. 1, 2 and 9, para [0029]; “The barcode system includes a barcode (115) which imaged by a color image sensor (120) in an imaging device (105)” see also para [0066]; “a barcode image is acquired from a color imaging array (step 905)”); generating a first image in which a first color correction is applied to the color image so as to change at least one of a ratio of RGB and a combination of HSV (see para [0067]-[0069]; “The barcode image is separated into color channels (step 910)…. the color channels can be separated into a variety of color formats, including red/green/blue (RGB) format and cyan/yellow/magenta (CYM) formats…..Weighting factors are associated with each of the color channels (step 915)…. the weighting factors may be assigned values which reflect the relative magnitudes of the contributions of the individual color channels to the barcode image…At least one of the weighting factors is reduced to produce a transformed image (step (920). The pixel magnitudes of the color channel associated with the reduced weighting factor are multiplied by the reduced weighting factor… the proportional influence of the color channel is simply reduced in the transformed image”, see also para [0071]; “adapting the weighting factors to produce altered weighting factors. These altered weighting factors are applied to the color channels to produce a second transformed image which is analyzed to produce a second set of decoded data”, Note; the system multiplies the color channels by specific weights (e.g., multiplying the Blue channel by 0.8 to reduce its influence, Multiplying a pixel's magnitude by a reduced weighting factor (like a number less than 1) scales down the brightness or dominance of that specific color channel and repeatedly tune these weights to test or decode the image in different ways); generating a third image in which a second color correction different from the first color correction is applied to the color image so as to change at least one of a ratio of RGB or a combination of HSV (see para [0053]; “the initial decoding attempt weighted the red channel at 0% and the green channel at 100%....Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel…..the algorithm shifts the weighting toward the red channel by approximately 5% for each new attempt” see also para [0071]; “the predictive algorithm may include performing an initial weighting and analysis of the barcode image and then adapting the weighting factors to produce altered weighting factors. These altered weighting factors are applied to the color channels to produce a second transformed image”, Note; the altered weighting constitute a second color correction different from the first color correction because the altered factor produce a different relative RGB ratio or contribution and second transformed image corresponds to the claimed third image); and performing recognition of the code using the second image and the fourth image (see para [0016]; “the barcode can be more quickly and accurately recognized”, see also claim 7; “analyzing the second transformed image to produce a second set of decoded data”). However, Bhagwan et al. does not teach generating a second image in which a first preprocessing digital filter that performs preprocessing digital filter correction is applied to the first image; generating a fourth image in which a second preprocessing digital filter that performs preprocessing digital filter correction is applied to the third image.
In the same field of endeavor, Xingjie et al. teaches generating a second image in which a first preprocessing digital filter that performs preprocessing digital filter correction is applied to the first image; generating a fourth image in which a second preprocessing digital filter that performs preprocessing digital filter correction is applied to the third image (see page 2, 2nd para; “performing color correction on the standard image to generate a corrected image; and deblurring the corrected image by wiener filtering to obtain an enhanced image”, see also page 12, 2nd pare “In S702, an original image is input. In S704, the image is preprocessed. In S706, color correction based on a white balance algorithm. In S708, an enhanced image is obtained based on the deblurring of the wiener filtered image. In S710, a second enhanced image is obtained based on image enhancement by the deep convolutional neural network. In S712, a second enhanced image is output”, and see page 12 last para; “through a multi-step-based image enhancement method, the method enhances the effectiveness of the algorithm by enhancing blurred images in different directions”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. in order to improve the authentication efficiency in face recognition and save a large amount of labor cost (see page 2 2nd para ).
Regarding claim 2, the rejection of claim 1 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein in a case where the recognition of the code using the second image fails, the method executes recognition of the code using the fourth image (see para [0053; “the initial decoding attempt weighted the red channel at 0% and the green channel at 100%. This decoding attempt is the left most data point and did not result in a level of confidence sufficient to exceed the confidence threshold…. Consequently, the algorithm adjusted the weightings… Performing another decoding attempt resulted in an increased confidence level”).
Regarding claim 3, the rejection of claim 1 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein in a case where the recognition of the code using the second image fails (see Fig. 3, para [0053]; “the initial decoding attempt weighted the red channel at 0% and the green channel at 100%. This decoding attempt is the left most data point and did not result in a level of confidence sufficient to exceed the confidence threshold”), the method executes generating the third image (see para [0053]; “the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel”, see also para [0071]; “These altered weighting factors are applied to the color channels to produce a second transformed image”), and performing recognition of the code using the fourth image (see para [0016]; “the barcode can be more quickly and accurately recognized”).
Xingjie et al. in the combination further teach generating the fourth image (see page 3, 4th para [0046]; “spatially converting the standard image from three primary colors to YC C A space; and pair YC C And carrying out color b r correction on the spatial standard image to generate the corrected image”).
Regarding claim 9, Bhagwan et al. teaches an inspection device comprising: an imaging unit configured to capture a color image including a portion in which a code is inscribed (see para [0003]; “A system for noise removal in barcode images includes a color imaging camera having a color imaging array and image processing software. The image processing software acquires a barcode image from the color imaging array”); and a code recognition module configured to recognize the code (see para [0031]; “image analysis used to identify a barcode (115) from an image” see also para [0016; “the barcode can be more quickly and accurately recognized”), wherein a code recognition module including a color-tone correction unit configured to generate a first image and a third image by applying a first color correction and a second color correction that change at least one of a ratio of RGB and a combination of HSV of the color image (see para [0067]-[0069]; “The barcode image is separated into color channels (step 910)…. the color channels can be separated into a variety of color formats, including red/green/blue (RGB) format and cyan/yellow/magenta (CYM) formats…..Weighting factors are associated with each of the color channels (step 915)…. the weighting factors may be assigned values which reflect the relative magnitudes of the contributions of the individual color channels to the barcode image…At least one of the weighting factors is reduced to produce a transformed image (step (920). The pixel magnitudes of the color channel associated with the reduced weighting factor are multiplied by the reduced weighting factor… the proportional influence of the color channel is simply reduced in the transformed image”, see also para [0071]; “adapting the weighting factors to produce altered weighting factors. These altered weighting factors are applied to the color channels to produce a second transformed image which is analyzed to produce a second set of decoded data”, see para [0053]; “the initial decoding attempt weighted the red channel at 0% and the green channel at 100%....Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel…..the algorithm shifts the weighting toward the red channel by approximately 5% for each new attempt” see also para [0071]; “the predictive algorithm may include performing an initial weighting and analysis of the barcode image and then adapting the weighting factors to produce altered weighting factors. These altered weighting factors are applied to the color channels to produce a second transformed image”, Note; the system multiplies the color channels by specific weights (e.g., multiplying the Blue channel by 0.8 to reduce its influence, Multiplying a pixel's magnitude by a reduced weighting factor (like a number less than 1) scales down the brightness or dominance of that specific color channel and repeatedly tune these weights to test or decode the image in different ways, and the altered weighting constitute a second color correction different from the first color correction because the altered factor produce a different relative RGB ratio or contribution and second transformed image corresponds to the claimed third image), and a recognition unit configured to recognize the code using the second image and the fourth image (see claim 7; “analyzing the second transformed image to produce a second set of decoded data”). However, Bhagwan et al. does not teach a preprocessing digital filter correction unit configured to generate a second image and a fourth image by applying first preprocessing digital filter correction and second preprocessing digital filter correction to the first image and the third image, respectively.
In the same field of endeavor, Xingjie et al. teaches a preprocessing digital filter correction unit configured to generate a second image and a fourth image by applying first preprocessing digital filter correction and second preprocessing digital filter correction to the first image and the third image, respectively (see page 2, 2nd para; “performing color correction on the standard image to generate a corrected image; and deblurring the corrected image by wiener filtering to obtain an enhanced image”, see also page 12, 2nd pare “In S702, an original image is input. In S704, the image is preprocessed. In S706, color correction based on a white balance algorithm. In S708, an enhanced image is obtained based on the deblurring of the wiener filtered image. In S710, a second enhanced image is obtained based on image enhancement by the deep convolutional neural network. In S712, a second enhanced image is output”, and see page 12 last para; “through a multi-step-based image enhancement method, the method enhances the effectiveness of the algorithm by enhancing blurred images in different directions”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. in order to improve the authentication efficiency in face recognition and save a large amount of labor cost (see page 2 2nd para ).
Regarding claim 11, the rejection of claim 9 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein the recognition unit recognizes the code using the fourth image in a case where the recognition of the code using the second image fails (see para [0053]; “did not result in a level of confidence sufficient to exceed the confidence threshold……Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel”, see also para [0071]; “These altered weighting factors are applied to the color channels to produce a second transformed image which is analyzed to produce a second set of decoded data”).
Regarding claim 12, the rejection of claim 9 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein in a case where the recognition unit fails to recognize the code using the second image (see para [0053]; “This decoding attempt is the left most data point and did not result in a level of confidence sufficient to exceed the confidence threshold”), the color-tone correction unit generates the third image by applying the second color correction to the color image and the recognition unit recognizes the code using the fourth image (see para [0053]; “Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel”, see also para [0071]; “These altered weighting factors are applied to the color channels to produce a second transformed image which is analyzed to produce a second set of decoded data”).
Xingjie et al. in the combination further teaches the preprocessing digital filter correction unit generates the fourth image by applying the second preprocessing digital filter correction to the third image, (see 2nd page 9th para; “performing color correction on the standard image to generate a corrected image; and the image enhancement module is used for carrying out deblurring processing on the corrected image through wiener filtering so as to obtain an enhanced image”).
Regarding claim 13, the rejection of claim 9 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein the preprocessing digital filter correction unit determines the first preprocessing digital filter according to the first color correction (see para [0047]; “combination B may be most useful in recognizing barcodes printed with green ink”, see also para [0049]; “Combination G uses only the green channel and may be useful for decoding blue barcodes”, see also para [0050]; “Combination D may be particularly suited for decoding blue barcodes and may also be useful for reading black barcodes”).
Xingjie et al. in the combination further teach and determines the second preprocessing digital filter according to the second color correction (see 2nd page 9th para; “performing color correction on the standard image to generate a corrected image; and the image enhancement module is used for carrying out deblurring processing on the corrected image through wiener filtering so as to obtain an enhanced image”).
Regarding claim 19, the scope of claim 19 is fully incorporated in claim 1, and the
rejection of claim 1 analysis is equally applicable here (see also para [0004]; “a computer readable storage medium having a computer readable program code embodied therewith” of Bhagwan et al.).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al. as applied in claim 1 above and further in view of Vakrat et al (US 20090185058 A1).
Regarding claim 4, the rejection of claim 1 is fully incorporated herein. The combination of Bhagwan et al and Xingjie et al does not teach further comprising: determining the first preprocessing digital filter according to the first color correction; and determining the second preprocessing digital filter according to the second color correction.
In the same field of endeavor, Vakrat et al. teaches further comprising: determining the first preprocessing digital filter according to the first color correction; and determining the second preprocessing digital filter according to the second color correction (see claims 7-8; “employing a color correction three-dimensional look-up table to generate a color-corrected image from the input image; and generating the noise-reduction lookup input image by low-pass filtering the color-corrected image…… wherein determining at least one noise reduction component for the pixel includes determining at least one noise reduction component for each color component of the pixel”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and a device for noise reduction three-dimensional look-up table (LUT) and a noise reduction unit of Vakrat et al. in order to change the strength of the NR operation with respect to each color in order to produce a more pleasing result (see claims 7-8).
Claims 5-6, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al. as applied in claim 1 above and further in view of Toedtli et al (US 20180107915 A1).
Regarding claim 5, the rejection of claim 1 is fully incorporated herein. The combination of Bhagwan et al and Xingjie et al does not teach wherein the code has a square or rectangular shape having a side of 0.8 mm or more and 15 mm or less.
In the same field of endeavor, Toedtli et al. teaches wherein the code has a square or rectangular shape (see para [0033]; “the barcode may be one of a “linear barcode”, also denominated as a “one-dimensional barcode”, which represents data by varying a width and a spacing of parallel lines, a “two-dimensional barcode” which represents data within an arrangement of bars, rectangles, squares, hexagons, or any other”) having a side of 0.8 mm or more and 15 mm or less (see para [0110]; “a fashion in which the at least two different kinds of modules are actually distributed over the area of the barcode. By way of example, for printing a 10×10 mm barcode of 16×16 modules at 300 dpi, an average number of pixels for one module can be calculated as approximately 7.38”, see also para [0163]; “the exemplary barcode 110 exhibits a size of ⅖ of an inch which equals 10.16 mm… to be as big as the printing device 118 allows, which is usually ½ inch which equals 12.7 mm”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and a method for serializing products using a barcode and checking authenticity of barcodes of Toedtli et al. in order to enhance the security of a barcode, mechanisms that allow differentiating between an original barcode and a copied barcode still have to be developed (see para [0033]).
Regarding claim 6, the rejection of claim 5 is fully incorporated herein.
Toedtli et al. in the combination further teach wherein the code is a symbol of Data Matrix (see para [0164]; “This method is, generally, used for generating standard barcodes, such as the Data Matrix barcode”).
Regarding claim 14, the rejection of claim 9 is fully incorporated herein.
Toedtli et al. in the combination further teach wherein the code has a square or rectangular shape having a side of 0.8 mm or more and 15 mm or less(see para [0033]; “the barcode may be one of a “linear barcode”, also denominated as a “one-dimensional barcode”, which represents data by varying a width and a spacing of parallel lines, a “two-dimensional barcode” which represents data within an arrangement of bars, rectangles, squares, hexagons, or any other”, see also para [0110]; “a fashion in which the at least two different kinds of modules are actually distributed over the area of the barcode. By way of example, for printing a 10×10 mm barcode of 16×16 modules at 300 dpi, an average number of pixels for one module can be calculated as approximately 7.38”, see also para [0163]; “the exemplary barcode 110 exhibits a size of ⅖ of an inch which equals 10.16 mm… to be as big as the printing device 118 allows, which is usually ½ inch which equals 12.7 mm”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and a method for serializing products using a barcode and checking authenticity of barcodes of Toedtli et al. in order to enhance the security of a barcode, mechanisms that allow differentiating between an original barcode and a copied barcode still have to be developed (see para [0033]).
Regarding claim 15, the rejection of claim 14 is fully incorporated herein.
Toedtli et al. in the combination further teach wherein the code is a symbol of Data Matrix (see para [0164]; “This method is, generally, used for generating standard barcodes, such as the Data Matrix barcode”).
Claims 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al. as applied in claim 1 above and further in view of Vakrat et al (US 20090185058 A1).
Regarding claim 7, the rejection of claim 1 is fully incorporated herein. The combination of Bhagwan et al and Xingjie et al does not teach further comprising: for any N (N is an integer greater than or equal to 3, generating a (2k-1)-th image in which a k-th color correction (k is an integer satisfying 3 ≤ k ≤ N) different from the first to (k-1)-th color corrections is applied to the color image so as to change at least one of a ratio of RGB and a combination of HSV; generating a (2k)-th image in which a k-th preprocessing digital filter that performs preprocessing digital filter correction is applied to the (2k-1)-th image; and performing code recognition using the second image, the fourth image, ..., the (2N-2)-th image, and the (2N)-th image.
In the same field of endeavor, Blasinski et al. teaches further comprising: for any N (N is an integer greater than or equal to 3), generating a (2k-1)-th image in which a k-th color correction (k is an integer satisfying 3 ≤ k ≤ N) different from the first to (k-1)-th color corrections is applied to the color image so as to change at least one of a ratio of RGB and a combination of HSV; generating a (2k)-th image in which a k-th preprocessing digital filter that performs preprocessing digital filter correction is applied to the (2k-1)-th image; and performing code recognition using the second image, the fourth image, ..., the (2N-2)-th image, and the (2N)-th image (see para [0031]; “Data Extraction From Color Channels includes obtaining three grayscale images corresponding to C, M, and Y printing layers. These grayscale images are threshold and binary images are obtained, from which data in each colorant channel is extracted using the data extraction algorithm of the monochrome counterpart”, see also para [0032; “Binarization is performed. Binarization includes utilizing a local thresholding algorithm…… t.sub.C, t.sub.M, t.sub.Y are the threshold values used to binarize the estimated C, M, and Y channels… we fixed the thresholds t.sub.C, t.sub.M, t.sub.Y for C, M, and Y channels to binarize the grayscale images” Note; the three C,M,Y grayscale images implies the (2K-1), and the (2K)-th images; the resulting binary images). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and new framework for extending monochrome barcodes to color is provided of Blasinski et al. in order to effectively increases the capacity of monochrome barcodes by a factor of three (see para [0031]).
Regarding claim 8, the rejection of claim 7 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein the method executes recognition of the code using the (2k)-th image in a case where the recognition of the code using the (2k-2)-th image fails (see para [0053]; “did not result in a level of confidence sufficient to exceed the confidence threshold. Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel”).
Regarding claim 16, the rejection of claim 9 is fully incorporated herein.
Blasinski et al. in the combination further teach wherein for any N (N is an integer greater than or equal to 3), the color-tone correction unit further generates a (2k-1)-th image in which a k-th color correction (k is an integer satisfying 3 ≤ k ≤ N) different from the first to (k-1)-th color corrections is applied to the color image so as to change at least one of a ratio of RGB and a combination of HSV, the preprocessing digital filter correction unit further generates a (2k)-th image in which a k-th preprocessing digital filter that performs preprocessing digital filter correction is applied to the (2k-1)-th image, and the recognition unit recognizes a code using the second image, the fourth image, ..., the (2N-2)-th image, and the (2N)-th image (see para [0031]; “Data Extraction From Color Channels includes obtaining three grayscale images corresponding to C, M, and Y printing layers. These grayscale images are threshold and binary images are obtained, from which data in each colorant channel is extracted using the data extraction algorithm of the monochrome counterpart”, see also para [0032; “Binarization is performed. Binarization includes utilizing a local thresholding algorithm…… t.sub.C, t.sub.M, t.sub.Y are the threshold values used to binarize the estimated C, M, and Y channels… we fixed the thresholds t.sub.C, t.sub.M, t.sub.Y for C, M, and Y channels to binarize the grayscale images” Note; the three C,M,Y grayscale images implies the (2K-1), and the (2K)-th images; the resulting binary images). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and new framework for extending monochrome barcodes to color is provided of Blasinski et al. in order to effectively increases the capacity of monochrome barcodes by a factor of three (see para [0031]).
Regarding claim 17, the rejection of claim 16 is fully incorporated herein.
Bhagwan et al. in the combination further teach wherein the recognition unit executes recognition of the code using the (2k)-th image in a case where the recognition of the code using the (2k-2)-th image fails (see para [0053]; “did not result in a level of confidence sufficient to exceed the confidence threshold. Consequently, the algorithm adjusted the weightings to include approximately 5% of the red channel and only 95% of the green channel”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al. as applied in claim 1 above and further in view of Cheong et al (US 20050001033 A1).
Regarding claim 10, the rejection of claim 9 is fully incorporated herein. The combination of Bhagwan et al and Xingjie et al does not teach wherein the code recognition module further includes a trimming unit that extracts a portion in which the code is inscribed from the color image.
In the same field of endeavor, Cheong et al. teach wherein the code recognition module further includes a trimming unit that extracts a portion in which the code is inscribed from the color image (see para [0020]; “An image-processing portion 13 extracts a code image from the raw image” see also claim 17; “the image-processing portion extracts a code image region, in which a background image portion is excluded, from the raw image, discriminates the shape and type of the code image, and discriminates cells included in the code image region on this basis”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and a method for recognizing a code from a code image that is expressed physically or electronically and extracting data represented in the code image is provided of Cheong et al. in order to the effect of illumination can be reduced, and colors close to the original color can be obtained (see para [0020]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bhagwan et al. in view of Xingjie et al. as applied in claim 9 above and further in view of Doe et al. (US 20120189189 A1).
Regarding claim 18, the rejection of claim 9 is fully incorporated herein. The combination of Bhagwan et al and Xingjie et al does not teach further comprising, an appearance inspection module configured to receive the color image and inspect a presence or absence of abnormality of a product using an image generated by changing at least one of a ratio of RGB and a combination of HSV with respect to the color image.
In the same field of endeavor, Doe et al. teach comprising, an appearance inspection module configured to receive the color image (see [0017]; “the capture of images, image processing, the computation of figures of merit or scoring, and the assessment figures of merit or scores to select an optimal representation or channel are carried out electronically by one or more software programs that operation on a controller or computer”, see also para [0018]; “capture a full-color image so as to maximize the amount of information contained in the image”, and para [0021]; “the original image being an RGB digital image output by a color camera”), and inspect a presence or absence of abnormality of a product using an image generated by changing at least one of a ratio of RGB and a combination of HSV with respect to the color image (see para [0032]; “As seen in FIGS. 4a-4c, to identify scrub marks 24, if any, on bond pads 22a, the RGB image of the semiconductor device was duplicated and the duplicate was converted to an HSV color space representation. As seen in FIG. 4a, the hue channel of the HSV color space representation is quite dark and the contrast is such that scrub marks, if any exist on bond pads 22, are not visible. In FIG. 4b, the value channel of the color space representation provides good contrast overall, but does not reveal the presence, if any, of scrub marks 24 on bond pads 22. In FIG. 4c, the saturation channel or component of the HSV color space representation clearly shows the presence of scrub marks 24 on bond pads 22”). Accordingly, it would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to modify a method for noise removal from color barcode images of Bhagwan et al. in view of the use of method for an image enhancement method, an image enhancement device, an electronic device and a computer readable medium of Xingjie et al. and a method of optimizing an optical inspection and fabrication process of Doe et al. in order to optimize images and optical systems used for automated optical inspection to take advantages of the capabilities of machine vision (see para [0021]).
Conclusion
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