CTNF 18/286,507 CTNF 100278 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Continued Examination Under 37 CFR 1.114 07-42-04 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's RCE submission filed on 04/22/2026 has been entered. Priority Receipt is acknowledged that application is a National Stage application of PCT/JP2022/007813. Receipt is acknowledged that application claims priority to foreign application with application number JP2021-074653 dated 04/27/2021. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78. Response to Amendment The amendment filed 04/22/2026 has been entered. Applicant’s amendments to the claims have overcome each and every 35 U.S.C. 112 rejection previously set forth in the Final Office Action mailed 01/16/2026. Claims 1-3 and 5-15 remain pending in the application. Response to Arguments Applicant’s arguments, in the Remarks filed 04/22/2026, have been considered but they are not persuasive. Applicant argues that Lu in view of Tandia fails to teach or suggest “wherein the one or more computer systems divide the input image by a size that selectively includes one pattern area and one non-pattern area of an object represented by one closed figure.” Examiner respectfully disagrees. Given the broadest reasonable interpretation of the claim limitation, a “closed figure” representing an object may be an image (a closed figure defined by the four borders making up the image) of a semiconductor wafer (an object). The divided images, or patches, taught by Lu are a size that selectively includes at least one pattern area (darker area) and at least one non-pattern area (lighter area), as demonstrated by FIG. 5 in the claim 1 rejection below. The examiner also notes that the metes and bounds of the claim limitation above is not defined by the claim or the specification, as further outlined in the 35 U.S.C. 112(b) rejection below. Applicant argues that the relied upon prior art “does not achieve the object of using an appropriate number of pattern variations as in the present invention.” It is noted that the features upon which applicant relies (i.e., an appropriate number of pattern variations) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns , 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). A recitation of the intended objective of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. Since Lu produces images divided by a size that satisfies all recited limitations, it meets the claim. In view of the foregoing, Lu in view of Tandia is relied upon to teach independent claims 1, 9, and 10. Claim Objections 07-29-01 AIA Claim s 3, 5, 8-10, and 14 are objected to because of the following informalities: In claims 3 and 5, “the plurality of inspection sub-images” should read “the plurality of inspection sub-images divided input images ”. In claim 8, “divide the input image into the plurality of inspection sub-images while providing an overlapped region, evaluate the degree of discrepancy of the divided input image and the output image, and allow a display device to display identification information corresponding to the number of inspection sub-images with a degree of discrepancy being a predetermined value or more among the inspection sub-images constituting the overlapped region” should read “divide the input image into the plurality of inspection sub-images divided input images while providing an overlapped region, evaluate the degree of discrepancy of the divided input image and the output image, and allow a display device to display identification information corresponding to the number of inspection sub-images divided input images with a degree of discrepancy being a predetermined value or more among the inspection sub-images divided input images constituting the overlapped region”. In claims 9 and 10, “divide the input image” should read “divide the an input image”. In claim 14, “in a training image” should read “in a the training image” . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-3 and 5-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claims 1, 9, and 10 recite the limitation “closed figure”, which renders the claims indefinite. The metes and bounds of what is considered a “closed figure” is not defined by the claim or the specification, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Dependent claims 2-3, 5-8, and 11-15 are similarly rejected due to their dependence on a rejected base claim. For examination purposes, the “closed figure” will be interpreted to be a shape with no open endings formed by line segments that are all connected. For example, wherein the image is of a semiconductor wafer, which is an object represented by one closed figure that is defined by the four borders that make up the image, that also includes pattern and non-pattern areas of the wafer in the image. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-3, 5-7, and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (U.S. Patent No. 2019/0287230 A1), hereinafter Lu, in view of Tandia et al. (U.S. Patent No. 2020/0410660 A1), hereinafter Tandia . Regarding claim 1, Lu teaches a system configured to detect defects on a semiconductor wafer (Lu, abstract: “Defects on semiconductor wafers can be discovered using these approaches”; para 22: “The model is configured to receive an image of a wafer and determine presence of one or more anomalies in the image”) , wherein the system is provided with one or more computer systems specifying the defects included in a received input image (Lu, para 83: “Computer subsystem 202 may be configured to perform any of the functions described herein using the output of the detector 207 and/or the electron beam images”; para 103: “The methods also may include any other step(s) that can be performed by the processor and/or computer subsystem(s) or system(s) described herein”) , wherein the one or more computer systems are provided with a training device including an autoencoder trained in advance (Lu, para 50: “the model includes a variational autoencoder”) by inputting a plurality of images at different locations (Lu, see Figure 2 attached below wherein images of different locations are utilized to train the autoencoder ; para 51: “The autoencoder can be trained on clean sites”) , wherein the one or more computer systems divide images by a size that selectively includes one pattern area and one non-pattern area of an object represented by one closed figure (Lu, see input patches in the leftmost column of Figure 5, attached below ; para 71: “FIG. 5 illustrates input and reconstructed SEM patches with an autoencoder”; Each patch image is a closed figure representing an object, which is the semiconductor wafer. Each patch includes a pattern area, a dark line, and a non-pattern area, a lighter area of the wafer. ) , wherein the one or more computer systems input the divided input images to the autoencoder (Lu, para 46: “reconstructed images can be generated from input SEM images by applying the model at 103”; see para 71 citation above ) , and wherein the one or more computer systems compare an output image output from the autoencoder with the input image (Lu, para 48: “a difference between reconstructed and original SEM images may be calculated at 104 to locate the anomaly patterns (e.g., defects)”; para 73: “The reconstruction error can be defined as the difference between the original input vector x and the reconstruction”; see also para 68 ) . PNG media_image1.png 401 609 media_image1.png Greyscale PNG media_image2.png 506 428 media_image2.png Greyscale However, Lu fails to explicitly teach wherein the autoencoder is trained by inputting a plurality of images at different locations included in a training image and wherein the input image is divided into the divided input images ( Emphasis added ) . While Lu teaches that the autoencoder can be trained using patch images, Lu fails to teach explicitly wherein a plurality of the patches are from the same training image (Lu, para 64: “The semi-supervised model can be trained with same patch size images from the clean sites of the same SEM layer, then the detection output patches can be passed to this model”) . However, Tandia teaches a similar system for detecting defects on semiconductor wafers (Tandia, abstract: “anomaly detection system for super-high-resolution images”; para 29: “For example, the tile size and overlap may be different for the content of the image (e.g., a mesh, semiconductor wafer, flat glass sheets) and the anomaly or defect that is being identified”) , further disclosing wherein the machine learned model is trained by inputting a plurality of images at different locations included in a training image (Tandia, para 34: “splitting the images based on the currently selected tile size and step size from operation 410 and operation 415. The resulting sub-images from the split are then labeled according to the presence of an anomaly. At operation 425, the new data set is used to train and build a CNN model”) . It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the different images from a training image, taught by Tandia, with the system of Lu in order to maximize the number of possible images for training from a given number of training images (Tandia, para 40: “Because the sub-images are overlap-split, all the features of original image may be captured in different patterns, but this results in an increase in the number of images in the dataset”) . Doing so can improve the performance of the autoencoder by training with a larger dataset. Additionally, Lu teaches that divided images are input to the autoencoder, but fails to teach explicitly wherein the divided images are from the same input image. Tandia further discloses dividing an input image into a plurality of divided input images (Tandia, steps 702-706 in Figure 7; p ara 49: “divide the image into tiles with a size and overlap between each tile”; para 50: “use a classifier model for each tile to identify anomaly presence in the tile”) . It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the divided input image of Tandia with the system of Lu in order to detect defects on the edges of divided images (Tandia, para 22: “if a defect were to present on the edge between two tiles then it may be undetected by the classifier as the portion appearing in each respective tile would not fall into the known defects of the CNN. Thus, a tile overlap is utilized to ensure a defect is not missed by being split along tile edges”) . Utilizing overlapping sub-images from the same input image ensures that all image features are represented in the tested dataset (Tandia, para 21: “The methods and techniques use an ‘overlapped-step-by-step’ process to crop the original images and generate many sub-images (tiles) to ensure all the features are included in the new image model database and each tile is large enough to possibly contain all features representing the different possible characteristics (defects) to be classified”; para 28: “Using tiling, with overlap, to divide the overall image into smaller sub-images for analysis may result in tile 310, tile 315, and tile 320. By using an overlap, the defect is fully captured in tile 315. There is a sufficient amount of the defect present in tile 315 for the CNN to recognize the defect and classify tile 315. This may result in appropriate classification of failing for the image with section 205”) . Regarding claim 2 (dependent on claim 1), Lu in view of Tandia teaches wherein the one or more computer systems are configured to divide the training image into a plurality of training sub-images ( Taught by the combination with Tandia in claim 1. The images captured at different locations are sub-images of the training image ) and train the autoencoder based on the plurality of divided training sub-images (Lu, sub-images are utilized to train the autoencoder, see Figure 2 and para 51 ) . Regarding claim 3 (dependent on claim 1), Lu in view of Tandia teaches wherein the one or more computer systems are configured to detect the defects included in the input image (Lu, para 22: "The model is configured to receive an image of a wafer and determine presence of one or more anomalies in the image") by training the autoencoder based on input of a training image (Lu, Figure 2 clean site images ; para 51: “The autoencoder can be trained on clean sites”) and inputting the divided input images to the autoencoder that is trained (Lu, Figure 5 and para 71; para 51: “The autoencoder can be trained on clean sites and inference can be run with test sites that may contain defects”; para 75: “Then the validation data, which contains some images with defects, was passed in”) . Regarding claim 5 (dependent on claim 1), Lu in view of Tandia teaches wherein the one or more computer systems are configured to divide the input image into the divided input images while providing an overlapped region (Tandia, see Fig. 1, para 26: “The whole image 105 is then divided into tile 110 size images for analysis. To prevent an anomaly or defect from being missed by falling on the edge between two tiles, an overlap is also determined”; see also para 21 ) . Regarding claim 6 (dependent on claim 1), Lu in view of Tandia teaches wherein the one or more computer systems are configured to evaluate a degree of discrepancy of the input image and the output image (Lu, Figure 6, para 72-73: “FIG. 6 a graph of reconstruction errors. A threshold is used to distinguish anomaly from nominal…The reconstruction error can be defined as the difference between the original input vector x and the reconstruction”; the reconstruction error represents a discrepancy between the image inputted to the autoencoder and the reconstructed output ) . Regarding claim 7 (dependent on claim 6), Lu in view of Tandia teaches wherein the one or more computer systems are configured to allow a display device (Tandia, para 53: “a display may be connected to the computer system performing the classification. When classification of an image is completed, the display may provide information about the classification of the image, such as with text or a graphical indicator”; see combination with Lu described below ) to display a frequency distribution of the degree of discrepancy ( Taught by Lu, see claim 6 and the histogram of Figure 6 ) or a distribution on the semiconductor wafer. Lu teaches a frequency distribution output from the computer system, but fails to explicitly teach a display device; while Tandia teaches displaying an output of the computer system on a display. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the display of Tandia with the system of Lu in order to display the results of the image processing to a user for decision making regarding the imaged product (Tandia, para 53: “Based on the classification result, the sorting mechanism may move the product associated with the classified image to a particular bin. For example, a product that is classified as passing may be moved to bin for distribution or sales, while a product that is classified as failing may be moved to a bin of rejects for trash or recycling”). Regarding claim 9, Lu teaches a non-transitory computer-readable medium storing program instructions executable on a computer system (Lu, para 92: “Program code or instructions for the processor 208 to implement various methods and functions may be stored in readable storage media, such as a memory in the electronic data storage unit 209 or other memory”) to perform a computer-implemented method of detecting defects on a semiconductor wafer (Lu, abstract: "Defects on semiconductor wafers can be discovered using these approaches"; para 22: "The model is configured to receive an image of a wafer and determine presence of one or more anomalies in the image") . The remaining claim limitations of claim 9 are met and rendered obvious by Lu in view of Tandia because the method steps of claim 9 are the same as those performed by the system in claim 1. Regarding claim 10, Lu teaches a system for processing image signals obtained based on irradiation of a semiconductor wafer with a beam (Lu, para 11: “The wafer inspection tool is configured to generate images of a wafer, and includes an electron beam source and a detector. The processor operates a model configured to find one or more anomalies in the image. The model is trained using semi-supervised machine learning based on only defect-free training images of semiconductor devices”) , wherein the system includes one or more computer systems (Lu, para 14: “An image of a wafer is received at a processor. The processor operates a model configured to find one or more anomalies in the image”) computing difference information between first image data and second image data (Lu, reconstructed image data and original image data, para 48: “a difference between reconstructed and original SEM images may be calculated at 104 to locate the anomaly patterns (e.g., defects)”) , and the one or more computer systems are configured to calculate a frequency for each degree of discrepancy between the first image data and the second image data (Lu, Figure 6, para 72-73: “FIG. 6 a graph of reconstruction errors. A threshold is used to distinguish anomaly from nominal…The reconstruction error can be defined as the difference between the original input vector x and the reconstruction”; the reconstruction error represents a discrepancy between the image inputted to the autoencoder and the reconstructed output ) . The remaining claim limitations of claim 10 are met and rendered obvious by Lu in view of Tandia because the one or more computer systems of claim 10 perform the same steps as those performed in claim 1. Regarding claim 11 (dependent on claim 10), Lu in view of Tandia teaches wherein the one or more computer systems are configured to generate a histogram indicating the frequency for each degree of discrepancy for each pixel of the first image data and the second image data (Lu, Figure 6, para 72-72: “FIG. 6 a graph of reconstruction errors. A threshold is used to distinguish anomaly from nominal…The reconstruction error can be defined as the difference between the original input vector x and the reconstruction” ; reconstruction error is calculated for each pixel, para 45: “The anomaly region can be identified by thresholding the pixel-level reconstruction error and/or probabilities”; first and second image data include the input and output images ) . Regarding claim 12 (dependent on claim 11), Lu in view of Tandia teaches wherein the one or more computer systems are configured to evaluate a shape of the histogram (Lu, para 74: “FIG. 6 shows that reconstruction error can exhibit two modal distributions, which makes it possible for auto-thresholding to separate anomalies from nominal”; para 75: “from the error histogram separation, the 200 steps model may be sufficient to separate anomaly from nominal validation images, although longer training can be performed to get better reconstructed images”) . Regarding claim 13 (dependent on claim 11), Lu in view of Tandia teaches wherein the one or more computer systems are configured to allow a display device to display (Tandia, displaying results taught by Tandia, see note below regarding combination; para 53: “a display may be connected to the computer system performing the classification. When classification of an image is completed, the display may provide information about the classification of the image, such as with text or a graphical indicator”) different histograms ( Histograms taught by Lu, see claim 6 and the histograms of Figure 6-7C ) obtained from different semiconductor wafers manufactured at different manufacturing timings (Tandia, classified image for a product, para 53: “operation 710 to output the classification for the image to a graphical user interface…When classification of an image is completed, the display may provide information about the classification of the image, such as with text or a graphical indicator. The computer system execution the classification may be connected to a system which captures images of the products and includes a sorting mechanism”; images for different semiconductor wafers manufactured at different manufacturing timings are separately processed; see also next citation ) . Lu teaches a histogram output from the computer system, but fails to explicitly teach a display device and histogram outputted for different semiconductor wafers; while Tandia teaches displaying the output of the computer system on a display and outputting classification results for each product manufactured. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the display and processing operation ( para 53 of Tandia ) of Tandia with the system of Lu in order to display the results of the image processing to a user for decision making regarding the imaged product (Tandia, para 53: “Based on the classification result, the sorting mechanism may move the product associated with the classified image to a particular bin. For example, a product that is classified as passing may be moved to bin for distribution or sales, while a product that is classified as failing may be moved to a bin of rejects for trash or recycling”) . Tandia teaches classification of each sub-image, and thus a person of ordinary skill in the art could combine the results of the classification with the histogram of Lu using known methods to represent the frequency of anomalies in an image. Regarding claim 14 (dependent on claim 10), in the combination of Lu in view of Tandia ( see combination rationale below ) , Lu teaches wherein the one or more of computer systems are provided with a training device including an autoencoder trained in advance (Lu, para 50: "the model includes a variational autoencoder") by inputting a plurality of images at different locations (Lu, see Figure 2 attached above wherein images of different locations are utilized to train the autoencoder ; para 51: “The autoencoder can be trained on clean sites”) , and wherein the one or more computer systems divide the second image data into a plurality of inspection sub-images (Lu, division of images that result in the divided input images in claim 10, see Figure 5; para 71: “FIG. 5 illustrates input and reconstructed SEM patches with an autoencoder”) , input the plurality of inspection sub-images to the autoencoder (Lu, Figure 5 and para 71; para 51: “The autoencoder can be trained on clean sites and inference can be run with test sites that may contain defects”; para 75: “Then the validation data, which contains some images with defects, was passed in”) , and compare the first image data which is output from the autoencoder with the second image data (Lu, para 48: "a difference between reconstructed and original SEM images may be calculated at 104 to locate the anomaly patterns (e.g., defects)"; para 73: "The reconstruction error can be defined as the difference between the original input vector x and the reconstruction"; see also para 68 ) . However, Lu fails to explicitly teach wherein the autoencoder is trained by inputting a plurality of images at different locations included in the training image ( Emphasis added ). While Lu teaches that the autoencoder can be trained using patch images, Lu fails to teach explicitly wherein a plurality of the patches are from the same training image (Lu, para 64: “The semi-supervised model can be trained with same patch size images from the clean sites of the same SEM layer, then the detection output patches can be passed to this model”) . However, Tandia teaches a similar system for detecting defects on semiconductor wafers (Tandia, abstract: “anomaly detection system for super-high-resolution images”; para 29: “For example, the tile size and overlap may be different for the content of the image (e.g., a mesh, semiconductor wafer, flat glass sheets) and the anomaly or defect that is being identified”) , further disclosing wherein the machine learned model is trained by inputting a plurality of images at different locations included in a training image (Tandia, para 34: “splitting the images based on the currently selected tile size and step size from operation 410 and operation 415. The resulting sub-images from the split are then labeled according to the presence of an anomaly. At operation 425, the new data set is used to train and build a CNN model”) . It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the different images from a training image, taught by Tandia, with the system of Lu in order to maximize the number of possible images for training from a given number of training images (Tandia, para 40: “Because the sub-images are overlap-split, all the features of original image may be captured in different patterns, but this results in an increase in the number of images in the dataset”) . Doing so can improve the performance of the autoencoder by training with a larger dataset. Regarding claim 15 (dependent on claim 10), Lu in view of Tandia teaches wherein the one or more computer systems are configured to evaluate the degree of discrepancy of the first image data and the second image data for each pixel (Lu, Figure 6, para 72-72: “FIG. 6 a graph of reconstruction errors. A threshold is used to distinguish anomaly from nominal…The reconstruction error can be defined as the difference between the original input vector x and the reconstruction”; reconstruction error is calculated for each pixel, para 45: “The anomaly region can be identified by thresholding the pixel-level reconstruction error and/or probabilities”) . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Tandia , in further view of Enno (WO Patent No. 2018/235266 A1) . Regarding claim 8 (dependent on claim 6), Lu in view of Tandia teaches wherein the one or more computer systems are configured to divide the input image into the divided input images while providing an overlapped region (Tandia, see Fig. 1, para 26: “The whole image 105 is then divided into tile 110 size images for analysis. To prevent an anomaly or defect from being missed by falling on the edge between two tiles, an overlap is also determined”; see also para 21 and combination in claim 1 ) , evaluate the degree of discrepancy of the input image and the output image (Lu, Figure 6, para 72-73: “FIG. 6 a graph of reconstruction errors. A threshold is used to distinguish anomaly from nominal…The reconstruction error can be defined as the difference between the original input vector x and the reconstruction”; the reconstruction error represents a discrepancy between the image inputted to the autoencoder and the reconstructed output ) , and allow a display device to display identification information (Tandia, para 53: “a display may be connected to the computer system performing the classification. When classification of an image is completed, the display may provide information about the classification of the image, such as with text or a graphical indicator”; see further combination with Lu described below ), but fails to explicitly teach wherein the identification information corresponds to the number of divided input images with a degree of discrepancy being a predetermined value or more among the divided input images constituting the overlapped region. Lu teaches a histogram output from the computer system that displays degrees of discrepancy, but fails to explicitly teach a display device; while Tandia teaches displaying the output of the computer system on a display. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the display of Tandia with the system of Lu in order to display the results of the image processing to a user for decision making regarding the imaged product (Tandia, para 53: “Based on the classification result, the sorting mechanism may move the product associated with the classified image to a particular bin. For example, a product that is classified as passing may be moved to bin for distribution or sales, while a product that is classified as failing may be moved to a bin of rejects for trash or recycling”). Further, Enno teaches a device/method for detecting abnormalities in images (Enno, para 33: “The inspection device 10 acquires an image of the inspection object 100 taken by the camera 30 via the communication network N, and determines whether or not there is an abnormality in the inspection object 100 using a trained model.”) . Enno discloses outputting identification information (Enno, results regarding classification, see also display unit in para 56 ) corresponding to the number of divided input images with a degree of discrepancy being a predetermined value or more (Enno, para 51: “the classification unit 13 may calculate an evaluation value based on the partial image and compare the evaluation value with a threshold value to classify the partial image into one that contains an abnormality and one that does not contain an abnormality”) among the divided input images constituting the overlapped region (para 8: “an image is divided into a plurality of first partial images and a plurality of second partial images different from the plurality of first partial images, and when it is determined that each of the plurality of first partial images and the plurality of second partial images contains an abnormality, the presence or absence of an abnormality in the object to be inspected is determined based on the overlap of those partial images”; the identification information corresponds to the number of sub-images, partial images, classified as containing an abnormality – the number being two partial images with the overlap, para 53: “The judgment unit 14 may judge whether or not there is an abnormality in the object to be inspected 100 based on the overlap of at least two types of partial images”) . It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the identification information, taught by Enno, with the system of Lu in view of Tandia in order to inform the decision regarding whether an abnormality exists based on multiple processed images, increasing the probability of accurate results from the system (Enno, para 8: “the presence or absence of an abnormality in the object to be inspected is determined based on the overlap of those partial images, thereby reducing erroneous determinations and enabling the presence or absence of an abnormality in the object to be inspected to be detected accurately even when there are a variety of possible abnormalities”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : U.S. Patent No. 2019/0346769 A1 ( see analyzed pattern regions in FIG. 6A and para 90 ) PNG media_image3.png 407 460 media_image3.png Greyscale JP Patent No. 2005277395 A ( see magnified image regions analyzed in FIG. 135 ) PNG media_image4.png 446 496 media_image4.png Greyscale Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMMA E DRYDEN whose telephone number is (571)272-1179. The examiner can normally be reached M-F 9-5 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW BEE can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /EMMA E DRYDEN/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/286,507 Page 2 Art Unit: 2677 Application/Control Number: 18/286,507 Page 3 Art Unit: 2677 Application/Control Number: 18/286,507 Page 4 Art Unit: 2677 Application/Control Number: 18/286,507 Page 5 Art Unit: 2677 Application/Control Number: 18/286,507 Page 6 Art Unit: 2677 Application/Control Number: 18/286,507 Page 7 Art Unit: 2677 Application/Control Number: 18/286,507 Page 8 Art Unit: 2677 Application/Control Number: 18/286,507 Page 9 Art Unit: 2677 Application/Control Number: 18/286,507 Page 10 Art Unit: 2677 Application/Control Number: 18/286,507 Page 11 Art Unit: 2677 Application/Control Number: 18/286,507 Page 12 Art Unit: 2677 Application/Control Number: 18/286,507 Page 13 Art Unit: 2677 Application/Control Number: 18/286,507 Page 14 Art Unit: 2677 Application/Control Number: 18/286,507 Page 15 Art Unit: 2677 Application/Control Number: 18/286,507 Page 16 Art Unit: 2677 Application/Control Number: 18/286,507 Page 17 Art Unit: 2677 Application/Control Number: 18/286,507 Page 19 Art Unit: 2677 Application/Control Number: 18/286,507 Page 20 Art Unit: 2677 Application/Control Number: 18/286,507 Page 21 Art Unit: 2677 Application/Control Number: 18/286,507 Page 22 Art Unit: 2677
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