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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 13, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 – 3, 8 – 10, 12, 14 – 17 and 21 are rejected under 35 U.S.C 103 as being unpatentable over Chen et al. Patent Application Publication No. CN-113379726-A (hereinafter Chen) in view of Zhou Patent Application Publication No. CN-111062910-A (hereinafter Zhou), Liu Patent Application Publication No. CN-109886952-A (hereinafter Liu) and further in view of Liu US Patent Application Publication No. US-20220309639-A1 (hereinafter Liu639).
Regarding claim 1, Chen discloses a method for processing an image, comprising: acquiring a in an original wiring image, and determining a first to-be- detected region of the original wiring image (Chen in [0054 – 0056] discloses, “Step 10: acquiring an original image of a workpiece to be detected; in another embodiment, the workpiece to be tested may be an integrated circuit or a circuit board. The detection equipment comprises an image acquisition device, and the original image is an image obtained after the image acquisition device photographs the workpiece to be detected”); performing boundary sharpening on the first to-be-detected region to determine a second to-be-detected region (Chen in [0064] discloses, “the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation ... the boundary of the large object is smoothed”. Chen in [0063] discloses about second detected region, “And performing morphological processing on the binary image to determine the detection image, wherein the detection image comprises line information of the original image”).
Chen doesn’t disclose the following limitation as further recited in the claim.
Zhou discloses mark area, based on the mark area (Zhou in [0005 - 0006] discloses, “1) Selecting a certain pixel point (L, L) at the initial position of the image, and taking the pixel point as an initial center marking point; l=1 to 4 pixels. 2) Setting a local selected area A and a selected area B by taking the central marking point as the center, wherein the selected area B is a sub-area of the selected area A”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Zhou into the system of Chen because it would allow the system to consistently locate the correct wire region instead of searching the whole image.
Chen and Zhou in the combination do not disclose the following limitation as further recited in the claim.
Liu discloses generating a target detection image based on the first to-be- detected region and the second to-be-detected region (Liu in [0072 – 0074] discloses, “an expansion module 607, configured to perform a first expansion operation on the binarized image to obtain a first expanded image. The expansion module 607 is further configured to perform a second expansion operation on the first expanded image to obtain a second expanded image. A difference-making module 608 is configured to make a difference between the second expanded image and the first expanded image to obtain a defect point image”. Difference between the second expanded image and the first expanded image equates to based-on first and second regions).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Liu into the system of Chen in view of Zhou because it would reduce noise and irregularities around the wire boundary.
Chen, Zhou and Liu in the combination do not disclose the following limitation as further recited in the claim.
Liu639 discloses performing image recognition on the target detection image to detect a crack defect included in the original wiring image (Liu639 in [0008] discloses, “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying defect types existing in the sample image”. Liu639 in [0070] further discloses about detecting defect in wiring image, “When the product is a wire mesh product, the defect types of the product include: double mesh defects, breakage defects, impurity defects, and fine mesh deviation defects. Among them, the breakage defect is the first type of defect”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Liu639 into the system of Chen in view of Zhou and Liu because it would actually determine if there is crack present in the original wiring image.
Summary of Citations (Liu639)
Paragraph [0008]; “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying defect types existing in the sample image”.
Paragraph [0070]; “When the product is a wire mesh product, the defect types of the product include: double mesh defects, breakage defects, impurity defects, and fine mesh deviation defects. Among them, the breakage defect is the first type of defect”.
Summary of Citations (Liu)
Paragraph [0072 – 0074]; “an expansion module 607, configured to perform a first expansion operation on the binarized image to obtain a first expanded image. the expansion module 607 is further configured to perform a second expansion operation on the first expanded image to obtain a second expanded image. a difference-making module 608 is configured to make a difference between the second expanded image and the first expanded image to obtain a defect point image”.
Summary of Citations (Zhou)
Paragraph [0005 - 0006]; “1) Selecting a certain pixel point (L, L) at the initial position of the image, and taking the pixel point as an initial center marking point; l=1 to 4 pixels. 2) Setting a local selected area A and a selected area B by taking the central marking point as the center, wherein the selected area B is a sub-area of the selected area A”.
Summary of Citations (Chen)
Paragraph [0006 – 0007]; “acquiring an original image of a workpiece to be detected; carrying out image processing on the original image to obtain a detection image”.
Paragraph [0054 – 0056]; “Step 10: acquiring an original image of a workpiece to be detected; in another embodiment, the workpiece to be tested may be an integrated circuit or a circuit board. The detection equipment comprises an image acquisition device, and the original image is an image obtained after the image acquisition device photographs the workpiece to be detected”.
Paragraph [0063]; “And performing morphological processing on the binary image to determine the detection image, wherein the detection image comprises line information of the original image”.
Paragraph [0064]; “the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation ... the boundary of the large object is smoothed”.
Regarding claim 2, Chen in the combination discloses the method for processing the image according to claim 1, wherein acquiring the mark area in the original wiring image comprises: acquiring the original wiring image (Chen in [0054 – 0056] discloses, “Step 10: acquiring an original image of a workpiece to be detected; in another embodiment, the workpiece to be tested may be an integrated circuit or a circuit board. The detection equipment comprises an image acquisition device, and the original image is an image obtained after the image acquisition device photographs the workpiece to be detected”).
Zhou further discloses performing grayscale processing on the original wiring image to obtain a grayscale wiring image (Zhou in [0027] discloses, “FIG. 1 (a) is an original gray scale of an imprint defect (dashed circle mark area) of a stamping in an embodiment”), and performing binarization processing on the grayscale wiring image to obtain a binarized wiring image (Zhou in [0011] discloses, “judging whether the gray value of the center mark point is larger than a threshold value T, if so, setting the gray value of the center mark point to 255, otherwise, setting the gray value of the center mark point to 0, and carrying out binarization processing on the center mark point”).
Liu639 discloses filtering the binarized wiring image based on an attribute feature possessed by the mark area to obtain the mark area (Liu639 in [0020 – 0021] discloses comparing each region against a preset condition (filtering), “to obtain a binarized image after threshold segmentation; counting the areas and position coordinates of different communication areas in the binary image by using a contour tracking algorithm, wherein when the areas of the communication areas in the binary image are larger than a preset threshold value, the corresponding communication areas are defect areas, and when the areas of the communication areas in the binary image are smaller than the preset threshold value, the corresponding communication areas are normal areas”.
Summary of Citations (Chen)
Paragraph [0054 – 0056]; “Step 10: acquiring an original image of a workpiece to be detected; in another embodiment, the workpiece to be tested may be an integrated circuit or a circuit board. The detection equipment comprises an image acquisition device, and the original image is an image obtained after the image acquisition device photographs the workpiece to be detected”.
Summary of Citations (Zhou)
Paragraph [0011]; “judging whether the gray value of the center mark point is larger than a threshold value T, if so, setting the gray value of the center mark point to 255, otherwise, setting the gray value of the center mark point to 0, and carrying out binarization processing on the center mark point”.
Paragraph [0027]; “FIG. 1 (a) is an original gray scale of an imprint defect (dashed circle mark area) of a stamping in an embodiment”.
Summary of Citations (Liu639)
Paragraph [0020 – 0021]; “to obtain a binarized image after threshold segmentation; counting the areas and position coordinates of different communication areas in the binary image by using a contour tracking algorithm, wherein when the areas of the communication areas in the binary image are larger than a preset threshold value, the corresponding communication areas are defect areas, and when the areas of the communication areas in the binary image are smaller than the preset threshold value, the corresponding communication areas are normal areas”.
Regarding claim 3, Zhou in the combination discloses the method for processing the image according to claim 2, wherein performing binarization processing on the grayscale wiring image to obtain the binarized wiring image comprises: acquiring a current brightness value of each pixel point included in the grayscale wiring image and determining whether the current brightness value is greater than a first preset threshold; in response to the current brightness value being greater than the first preset threshold, replacing the current brightness value of the pixel point with a first preset brightness value (Zhou [0011] discloses, “judging whether the gray value of the center mark point is larger than a threshold value T, if so, setting the gray value of the center mark point to 255, otherwise, setting the gray value of the center mark point to 0, and carrying out binarization processing on the center mark point”); in response to the current brightness value being less than the first preset threshold, replacing the current brightness value of the pixel point with a second preset brightness value (Zhou [0011] discloses, “otherwise, setting the gray value of the center mark point to 0, and carrying out binarization processing on the center mark point”); and generating the binarized wiring image based on each pixel point after the replacement of the current brightness value (Zhou in [0012] discloses, “performing binarization processing on the point (N-L+1, M-L+1) by using the threshold value; and finishing threshold segmentation of the whole image”).
Summary of Citations (Zhou)
Paragraph [0011]; “judging whether the gray value of the center mark point is larger than a threshold value T, if so, setting the gray value of the center mark point to 255, otherwise, setting the gray value of the center mark point to 0, and carrying out binarization processing on the center mark point”.
Paragraph [0012]; “performing binarization processing on the point (N-L+1, M-L+1) by using the threshold value; and finishing threshold segmentation of the whole image”.
Regarding claim 8, Liu639 in the combination discloses the method for processing the image according to claim 1, wherein performing boundary sharpening on the first to-be-detected region to determine the second to-be-detected region comprises (Liu639 discloses about detection on the product image (first region to determine), “Performing morphological detection on the product image using a second localization and detection network associated with the second type of classification result; when a locating box for locating the second type of defect is not detected, generating a second type of detection result; when a locating box for locating the second type of defect is detected”): performing dilation and erosion processing on the first to-be-detected region to obtain an intermediate detection region (Liu639 in [0090] discloses about morphological closing (dilation and erosion), “performing a morphological closing operation on the product image, performing binarization processing on the image after processed by the morphological closing operation, and performing region detection on a binary image obtained”);
Chen further discloses performing erosion and dilation processing on the intermediate detection region to obtain the second to-be-detected region (Chen in [0064] discloses, “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed”, wherein the resulting image after morphological processing equates to second to be detected region).
Summary of Citations (Chen)
Paragraph [0064]; “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed”.
Summary of Citations (Liu639)
Paragraph [0087]; “Performing morphological detection on the product image using a second localization and detection network associated with the second type of classification result; when a locating box for locating the second type of defect is not detected, generating a second type of detection result; when a locating box for locating the second type of defect is detected”.
Paragraph [0090]; “performing a morphological closing operation on the product image, performing binarization processing on the image after processed by the morphological closing operation, and performing region detection on a binary image obtained”.
Regarding claim 9, Chen in the combination discloses the method for processing the image according to claim 8, wherein performing dilation and erosion processing on the first to-be-detected region to obtain the intermediate detection region comprises (Chen in [0064] discloses, “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed”): extracting a first pixel point to be processed from the first to-be-detected region and deleting the first pixel point to be processed, wherein a size of the first pixel point to be processed is less than or equal to a first preset pixel value (Chen in [0064] discloses, “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation, wherein when the corrosion operation is performed on the binary image, the morphological processing is mainly used for eliminating boundary points of an object in the image and enabling the boundary points to contract inwards, so that the object smaller than structural elements is removed), and a current brightness value of the first pixel point to be processed is greater than a first preset threshold (Chen in [0062]; “the brightness or gray value of each pixel point on the original image can be determined firstly, when the brightness of one pixel point is greater than or equal to the preset brightness or the gray value is greater than or equal to the preset gray value”); and performing smoothing processing on a boundary line of a metal wire included in the first to-be-detected region (Chen in [0064] discloses, “the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed, and the area of the large object is not obviously changed”) and disconnecting an adhesion between two adjacent metal wires to obtain the intermediate detection region (Chen in [0064] discloses, “the object is separated at a fine point, the boundary of the large object is smoothed” wherein fine points equates to adhesion. The nearby object or line the ‘object is separated’ from implies to having two adjacent metal wire).
Summary of Citations (Chen)
Paragraph [0062]; “the brightness or gray value of each pixel point on the original image can be determined firstly, when the brightness of one pixel point is greater than or equal to the preset brightness or the gray value is greater than or equal to the preset gray value”.
Paragraph [0064]; “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation, wherein when the corrosion operation is performed on the binary image, the morphological processing is mainly used for eliminating boundary points of an object in the image and enabling the boundary points to contract inwards, so that the object smaller than structural elements is removed ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed”.
Paragraph [0064]; “the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed, and the area of the large object is not obviously changed”.
Regarding claim 10, Chen in the combination discloses the method for processing the image according to claim 8,wherein performing erosion and dilation processing on the intermediate detection region to obtain the second to-be-detected region comprises (Chen in [0064] discloses, “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed”, wherein the resulting image after morphological processing equates to second to be detected region): extracting a second pixel point to be processed from the intermediate detection region (Chen in [0018 - 0019] discloses, “when the number of adjacent pixel areas of any pixel point is equal to a first preset number, determining the pixel point as a first characteristic point; and when the number of the adjacent pixel regions of any pixel point is greater than or equal to a second preset number, determining the pixel point as a second characteristic point”) and filling the second pixel point to be processed (Chen in [0064] discloses, “If the distance between the two objects is relatively short, the two objects are communicated together, so that the hollow space of the object after the image segmentation is filled”), wherein a size of the second pixel point to be processed is less than or equal to a first preset pixel value (Chen in [0014 – 0015]; “obtaining the line length of each line in the detection image; and when the line length of any line is smaller than the preset length, deleting the line”) and a current brightness value of the second pixel point to be processed is less than a first preset threshold (Chen in [0062]; “when the brightness of the pixel point is less than the preset brightness or the gray value is less than the preset gray value, the brightness value of the pixel point is adjusted to the minimum value”); and filling a disconnected portion of a metal wire included in the intermediate detection region (Chen in [0064] discloses, “If the distance between the two objects is relatively short, the two objects are communicated together, so that the hollow space of the object after the image segmentation is filled”) and performing secondary smoothing processing on a boundary line of the metal wire without changing an area of the metal wire to obtain the second to-be-detected region (Chen in [0064] discloses, “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed, and the area of the large object is not obviously changed”).
Summary of Citations (Chen)
Paragraph [0014 – 0015]; “obtaining the line length of each line in the detection image; and when the line length of any line is smaller than the preset length, deleting the line”.
Paragraph [0018 - 0019]; “when the number of adjacent pixel areas of any pixel point is equal to a first preset number, determining the pixel point as a first characteristic point; and when the number of the adjacent pixel regions of any pixel point is greater than or equal to a second preset number, determining the pixel point as a second characteristic point”.
Paragraph [0062]; “when the brightness of the pixel point is less than the preset brightness or the gray value is less than the preset gray value, the brightness value of the pixel point is adjusted to the minimum value”.
Paragraph [0064]; “the morphological processing mainly includes corrosion operation, expansion operation, opening operation and closing operation ... when the binary image is subjected to opening operation, the image is subjected to corrosion operation and then expansion operation, so that the small object is eliminated, the object is separated at a fine point, the boundary of the large object is smoothed, and the area of the large object is not obviously changed”.
Paragraph [0064]; “If the distance between the two objects is relatively short, the two objects are communicated together, so that the hollow space of the object after the image segmentation is filled”.
Regarding claim 12, Liu639 in the combination discloses the method for processing the image according to claim 1, wherein performing image recognition on the target detection image to detect the crack defect included in the original wiring image comprises (Liu639 in [0008] discloses, “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying defect types existing in the sample image”. Liu639 in [0070] further discloses about detecting defect in wiring image, “When the product is a wire mesh product, the defect types of the product include: double mesh defects, breakage defects, impurity defects, and fine mesh deviation defects. Among them, the breakage defect is the first type of defect”): performing image recognition on the target detection image by a preset image recognition model to detect the crack defect included in the original wiring image (Liu639 in [0057] discloses about training classification model, “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image”. Furthermore, Liu639 in [0059] discloses about image supplied to a model, “performing product defect detection, inputting a product image acquired into the defect detection framework, using the classification network to classify defect types in the product image, detecting defects of the product image according to a localization and detection network associated with a classification result”. The network being first trained using sample defect image equates to having a preset image recognition model) wherein the image recognition model comprises one or more of: an edge detection model, a convolutional neural network model, a recurrent neural network model, or a deep neural network model (Liu639 in [0058] discloses about deep neural network model, “the classification network is implemented using an image segmentation algorithm based on deep learning. For example, network models such as Alxnet, Vgg, Resnet, Inception net, Densenet, Googlenet, Nasnet and Xception can be used to build a classification network. Since the Densenet (Dense Convolutional Network) network model”)”).
Summary of Citations (Liu639)
Paragraph [0008]; “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying defect types existing in the sample image”.
Paragraph [0057]; “training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image”.
Paragraph [0058]; “the classification network is implemented using an image segmentation algorithm based on deep learning. For example, network models such as Alxnet, Vgg, Resnet, Inception net, Densenet, Googlenet, Nasnet and Xception can be used to build a classification network. Since the Densenet (Dense Convolutional Network) network model”.
Paragraph [0059]; “performing product defect detection, inputting a product image acquired into the defect detection framework, using the classification network to classify defect types in the product image, detecting defects of the product image according to a localization and detection network associated with a classification result”
Paragraph [0070]; “When the product is a wire mesh product, the defect types of the product include: double mesh defects, breakage defects, impurity defects, and fine mesh deviation defects. Among them, the breakage defect is the first type of defect”.
Regarding claim 14, Chen in the combination discloses non-transitory computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to perform the method claim 1 (Chen in [0040] discloses, “the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application”).
Summary of Citations (Chen)
Paragraph [0040]; “the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application”.
Regarding claim 15, Chen discloses an electronic device, comprising: a processor; and a memory configured to store instructions executable by the processor; wherein the processor is configured to perform the method of claim 1 by executing the instructions (Chen in [0038] discloses, “an embodiment of the present application provides a line detection apparatus, including a processor, a memory ... one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing steps”).
Summary of Citations (Chen)
Paragraph [0038]; “an embodiment of the present application provides a line detection apparatus, including a processor, a memory ... one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing steps”.
Regarding claim 16, apparatus claim 16 corresponds to method claim 2. Therefore, the
rejection analysis and motivation to combine of claim 2 is applicable to claim 16.
Regarding claim 17, apparatus claim 17 corresponds to method claim 3. Therefore, the
rejection analysis and motivation to combine of claim 3 is applicable to claim 17.
Regarding claim 21, apparatus claim 21 corresponds to method claim 8. Therefore, the
rejection analysis and motivation to combine claim 8 is applicable to claim 21.
Claims 4 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Chen in view of Zhou, Liu and Liu639 and further in view of Gao Patent Application Publication No. CN-108775919-A (hereinafter Gao).
Regarding claim 4, Liu639 in the combination discloses the method for processing the image according to claim 1, further comprising: acquiring, by a preset image acquisition device (Liu639 in [0036] discloses, “As shown in FIG. 1, the product defect detection system 100 comprises an image acquisition device 1000 and a product defect detection device 2000”), the original wiring image from a metal wire surface to be detected (Liu639 in [0046] discloses, “Take wire mesh products as an example, the defect categories of wire mesh products mainly include three types of defect: breakage, impurities and double mesh”).
Chen, Zhou, Liu and Liu639 in the combination don’t disclose the following limitation as further recited in the claim.
Gao discloses the image acquisition device comprises a large target surface industrial camera and an industrial telecentric lens (Gao in [Page – 2, Paragraph – 4] discloses, “an image capture device and position adjusting mechanism; said image obtaining device comprises an ultra-high-resolution CCD camera, large-field telecentric lens. the coaxial light source and high-brightness loop; under the ultra-high resolution CCD camera part connected with the large view field telecentric lens, the wide view field telecentric lens is fixed with the coaxial light source”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Gao into the system of Chen in view of Zhou, Liu and Liu639 because it would allow to have a higher image quality for small crack.
Summary of Citations (Liu639)
Paragraph [0036]; “As shown in FIG. 1, the product defect detection system 100 comprises an image acquisition device 1000 and a product defect detection device 2000”.
Paragraph [0046]; “Take wire mesh products as an example, the defect categories of wire mesh products mainly include three types of defect: breakage, impurities and double mesh”.
Summary of Citations (Gao)
[Page – 2, Paragraph – 4]; “an image capture device and position adjusting mechanism; said image obtaining device comprises an ultra-high-resolution CCD camera, large-field telecentric lens. the coaxial light source and high-brightness loop; under the ultra-high resolution CCD camera part connected with the large view field telecentric lens, the wide view field telecentric lens is fixed with the coaxial light source”.
Regarding claim 18, apparatus claim 18 corresponds to method claim 4. Therefore, the
rejection analysis and motivation to combine of claim 4 is applicable to claim 18.
Claims 5 and 19 are rejected under 35 U.S.C 103 as being unpatentable over Chen in view of Zhou, Liu, Liu639 and Gao and further in view of Li Patent Application Publication No. CN-110487511-A (hereinafter Li).
Regarding claim 5, Liu639 in the combination discloses the method for processing the image according to claim 4, wherein acquiring, by the preset image acquisition device (Liu639 in [0036] discloses, “As shown in FIG. 1, the product defect detection system 100 comprises an image acquisition device 1000 and a product defect detection device 2000”), the original wiring image from the metal wire surface to be detected comprises (Liu639 in [0046] discloses, “Take wire mesh products as an example, the defect categories of wire mesh products mainly include three types of defect: breakage, impurities and double mesh”).
Chen, Zhou, Liu, Liu639 and Gao in the combination don’t disclose the following limitation as further recited in the claim.
Li discloses configuring an incident angle of a light source between the industrial telecentric lens and the metal wire surface to be detected (Li in [Page – 2, Paragraph – 13] discloses, “The above-mentioned photosensitive module gold wire photographing component includes a camera group and a ring light source, the camera group is a telecentric lens with a high depth of field, the ring light source is a low angle ring light source, and the low angle ring light source is located in the high depth of field Telecentric lens below”); and controlling, based on the incident angle, the large target surface industrial camera to acquire the original wiring image from the metal wire surface to be detected through the industrial telecentric lens (Li in [Page – 4, Paragraph – 9] discloses about gold wire bonding (metal wire surface), “The photosensitive module gold wire camera component 22 includes a camera group 222 and a ring light source 221. The camera group is a telecentric lens with a high depth of field. The ring light source 221 is a low-angle ring light source. The low-angle ring light source is located under the telecentric lens with a high depth of field. To photograph the defect of the gold wire bonding of the photosensitive module”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Li into the system of Chen in view of Zhou, Liu, Liu639 and Gao because it would make the crack stand out more clearly for later detection by using the illumination angle.
Summary of Citations (Li)
[Page – 2, Paragraph – 13]; “The above-mentioned photosensitive module gold wire photographing component includes a camera group and a ring light source, the camera group is a telecentric lens with a high depth of field, the ring light source is a low angle ring light source, and the low angle ring light source is located in the high depth of field Telecentric lens below”.
[Page – 4, Paragraph – 9]; “The photosensitive module gold wire camera component 22 includes a camera group 222 and a ring light source 221. The camera group is a telecentric lens with a high depth of field. The ring light source 221 is a low-angle ring light source. The low-angle ring light source is located under the telecentric lens with a high depth of field. To photograph the defect of the gold wire bonding of the photosensitive module”.
Regarding claim 19, apparatus claim 19 corresponds to method claim 5. Therefore, the
rejection analysis and motivation to combine of claim 5 is applicable to claim 19.
Claim 6 is rejected under 35 U.S.C 103 as being unpatentable over Chen in view of Zhou, Liu, Liu639, Li and Gao and further in view of Liu Patent Application Publication No. WO-2016011967-A1 (hereinafter Liu967).
Regarding claim 6, Li in the combination discloses the method for processing the image according to claim 5, wherein the light source comprises one or more of: a ring-shaped light source (Li in [Page – 2, Paragraph – 13] discloses, “The above-mentioned photosensitive module gold wire photographing component includes a camera group and a ring light source”).
Chen, Zhou, Liu, Liu639, Gao and Li in the combination don’t disclose the following limitation as further recited in the claim.
Liu967 discloses one or more point light sources, and one or more strip light sources; and wherein the incident angle ranges from 60
°
to 85
°
(Liu967 in [0005] discloses, “The led light source has a strip-shaped light source, a face-shaped light source, a ring-shaped light source, an arched light source, a four-sided non-shaded light source, etc., wherein the ring-shaped light source has a variety of illumination angle types, such as 0 ° , 30 ° , 45 ° , 60 ° , 90 °, a red light source, a blue light source, a white light source, an infrared light source”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Liu967 into the system of Chen in view of Zhou, Liu, Liu639, Gao and Li because it would allow the system to adapt to different wire arrangements by using one or more strip light sources.
Summary of Citations (Li)
[Page – 2, Paragraph – 13]; “The above-mentioned photosensitive module gold wire photographing component includes a camera group and a ring light source”.
Summary of Citations (Liu967)
Paragraph [0005]; “The led light source has a strip-shaped light source, a face-shaped light source, a ring-shaped light source, an arched light source, a four-sided non-shaded light source, etc., wherein the ring-shaped light source has a variety of illumination angle types, such as 0 ° , 30 ° , 45 ° , 60 ° , 90 °, a red light source, a blue light source, a white light source, an infrared light source, an ultraviolet light source, by color division, and a voltage driving type and a current driving type, respectively”.
Claim 11 is rejected under 35 U.S.C 103 as being unpatentable over Chen in view of Zhou, Liu, Liu639 and further in view of Liu Patent Application Publication No. CN-113240673-A (hereinafter Liu673) and Chen Patent Application Publication No. CN-113989194-A (hereinafter Chen194).
Regarding claim 11, Chen in the combination discloses the method for processing the image according to claim 1.
Chen, Zhou, Liu and Liu639 in the combination don’t disclose the following limitation as further recited in the claim.
Liu673 discloses generating the target detection image based on the first to-be-detected region and the second to-be-detected region comprises: performing a difference operation on a first grayscale value of each pixel point included in the first to-be-detected region and a second grayscale value of each pixel point included in the second to-be-detected region to obtain a third grayscale value (Liu673 in [Page – 7, Paragraph – 7] discloses, “for each sub-original image, residual calculation is performed on the sub-original image and its corresponding sub-repaired image (that is, the sub-repaired image obtained by defect repair of the sub-original image) to obtain the sub-original image and its corresponding sub-original image. The sub-residual image of the repaired image, the sub-residual image represents the difference of each pixel between the sub-original image and the corresponding sub-repaired image”) and determining whether the third grayscale value satisfies a preset condition in response to the third grayscale value satisfying the preset condition, determining the third grayscale value as a target grayscale value of the pixel point (Liu673 in [Page – 7, Paragraph – 12] discloses, “the threshold is set to 200. That is: for a pixel in the sub-residual image, if the gray value of the pixel is greater than 200, the pixel value of the pixel is set to 1, otherwise the pixel value of the pixel is set to 0”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Liu673 into the system of Chen in view of Zhou, Liu and Liu639 because it would allow the system to preserve useful metal wire information from the original ROI.
Chen, Zhou, Liu, Liu639 and Liu673 in the combination don’t disclose the following limitation as further recited in the claim.
Chen194 discloses in response to the third grayscale value failing to satisfy the preset condition, replacing the third grayscale value and determining the replaced third grayscale value as the target grayscale value of the pixel point (Chen194 in [Page – 2, Paragraph – 11] discloses, “Set the grayscale value of the pixel point in the original image that is smaller than the binarized grayscale value to 0, and set the grayscale value of the pixel point in the original image that is greater than or equal to the binarized grayscale value The value is set to a third threshold value”); and generating the target detection image based on the target grayscale value of each pixel point (Chen194 in [Page – 7, Paragraph – 8] discloses about image being binarized according to those output values and assigning grayscale values to individual pixels, “Step 50: Binarize the original image according to the target gray value. Wherein, after the target gray value is determined, the original image is binarized according to the target gray value. Specifically, the gray values of all pixels in the original image are determined, and when the pixel When the grayscale value of the pixel point is greater than or equal to the target grayscale value, determine the grayscale value of the pixel point to the maximum grayscale value of the original image, and when the grayscale value of the pixel point is less than the target grayscale value, The gray value of the pixel is determined to be the minimum gray value of the original image. In a preferred embodiment, the maximum grayscale value of the original image is 255, and the minimum grayscale value is 0”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Chen194 into the system of Chen in view of Zhou, Liu, Liu639 and Liu673 because it would make the resulting image simpler for the recognition algorithm to identify cracks more accurately.
Summary of Citations (Chen194)
[Page – 2, Paragraph – 11]; “Set the grayscale value of the pixel point in the original image that is smaller than the binarized grayscale value to 0, and set the grayscale value of the pixel point in the original image that is greater than or equal to the binarized grayscale value The value is set to a third threshold value”.
[Page – 7, Paragraph – 8]; “Step 50: Binarize the original image according to the target gray value. Wherein, after the target gray value is determined, the original image is binarized according to the target gray value. Specifically, the gray values of all pixels in the original image are determined, and when the pixel When the grayscale value of the pixel point is greater than or equal to the target grayscale value, determine the grayscale value of the pixel point to the maximum grayscale value of the original image, and when the grayscale value of the pixel point is less than the target grayscale value, The gray value of the pixel is determined to be the minimum gray value of the original image. In a preferred embodiment, the maximum grayscale value of the original image is 255, and the minimum grayscale value is 0”.
Summary of Citations (Liu673)
[Page – 7, Paragraph – 7]; “for each sub-original image, residual calculation is performed on the sub-original image and its corresponding sub-repaired image (that is, the sub-repaired image obtained by defect repair of the sub-original image) to obtain the sub-original image and its corresponding sub-original image. The sub-residual image of the repaired image, the sub-residual image represents the difference of each pixel between the sub-original image and the corresponding sub-repaired image”.
[Page – 7, Paragraph – 12]; “the threshold is set to 200. That is: for a pixel in the sub-residual image, if the gray value of the pixel is greater than 200, the pixel value of the pixel is set to 1, otherwise the pixel value of the pixel is set to 0”.
Allowable Subject Matter
Claims 7 and 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.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 7, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Chen, Zhou, Liu, Gao, Li, Liu967, Liu639 and Braganca (US-20220399236-A1) doesn’t disclose the limitation, calculating a center point position of the mark area based on a starting coordinate position of the mark area in the original wiring image and a dimensional feature of attribute features possessed by the mark area; determining a dimension of the first to-be-detected region based on a proportion of a metal wire included in the original wiring image relative to the original wiring image. Braganca in [0042] discloses about calculating a center point position of the mark area based on dimensional feature of the mark area, “Using bitmap 192, the exact pixel locations of die center 153a and corners 153b-153e can be easily identified as shown in FIG. 3c. Corners 153b- 153e are identified as the furthest white pixel out in each direction. Center 153a is identified as the average position between the four corners”. However, none of the prior in combination discloses about determining the region of interest based on the proportion of the metal wire in the original image, determining a dimension of the first to-be-detected region based on a proportion of a metal wire included in the original wiring image relative to the original wiring image.
Regarding claim 20, apparatus claim 20 corresponds to method claim 7. Therefore, claim 20 is allowable for the same reason mentioned above for claim 7.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
09/05/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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