CTNF 18/898,866 CTNF 99349 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. Notice to Applications This communication is in response to the Application filed on September 27, 2024 . Claims 1-15 are pending. Information Disclosure Statement The information disclosure statement(s) (IDS(s)) submitted on September 27, 2024 and May 14, 2026 are in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statements are being considered and attached by the examiner. Priority Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copies have been filed as Application No. JP2023-178067 , filed on October 16, 2023 . 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a learning unit ”, and “a defect inspection unit ”, in claims 1 and 12 , described in paragraphs, [0024] and [0025] respectively and thus, claims 2-11 and 13-15 are similarly interpreted. “an image feature extraction unit ” and “image generation units ” in claim 3 described in paragraph [0028] . Regarding the claim limitation above, 112(f) is invoked because “unit” is a non-functional generic placeholder expressed merely by the function it performs. Although claims 1-11 and 12-15 are drafted as system and method claims, the term “unit” is Applicant’s claim term preceding a functional limitation of “learning/inspection/extraction/generation” . Because Applicant fails to recite sufficiently definite structure for the term “unit” , the claimed limitation is akin to a generic term. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tachi et al. , US 20240005477 A1, (hereinafter “ Tachi ”) . Regarding claim 1 , Tachi teaches a defect inspection system having an imaging device for acquiring an observation image of a sample ([0068] “ The image acquirer 310 functions as an input data acquirer and acquires an input image (input data) in cooperation with the communication unit 120.” ) , comprising: a learning unit that trains a multiple non-defective product image estimation model that captures an image of the sample to acquire a learning image and estimates a plurality of non-defective product images of the sample for one input image using the learning image ([0046] “The image reconstructor 220 functions as a data reconstructor, and generates a reconstructed image (reconstructed data) as reference data based on the input image acquired by the image acquirer 210 by using a generative model trained using an input image of a plurality of non-defective products regarding the same inspection target.” ) ([0041] “The information processing device 100 acquires a plurality of input images (hereinafter, also referred to as “normal input images”) of a non-defective product, learns the images as correct images, and generates a deep learning model (generative model) for generating a reconstructed image from the input images.” ) ; and a defect inspection unit that captures an image of an inspection target sample using the imaging device to acquire an inspection target image, inputs the inspection target image into the multiple non-defective product image estimation model trained, outputs a plurality of estimated non-defective product images corresponding to the inspection target image, and extracts a defective part using the plurality of estimated non-defective product images ([0041] “The information processing device 100 acquires a plurality of input images (hereinafter, also referred to as “normal input images”) of a non-defective product, learns the images as correct images, and generates a deep learning model (generative model) for generating a reconstructed image from the input images.” ) ([0064] “The image of the inspection target captured in advance by the imaging device may be stored in a storage device outside the information processing device 100, and the information processing device 100 may sequentially acquire a predetermined number of images of the inspection target stored in the storage device as the input images.” ) ([0055] “FIG. 5 illustrates a distribution of the values of the abnormality scores for the non-defective product and the defective product in an orthogonal coordinate system in which the horizontal axis and the vertical axis indicate the normalized abnormality score and the number of samples (input images) used for the selection of an index parameter, respectively.” ) ([0091] “Further, in the present embodiment, defect detection can be performed on a difference between a luminance value of the input image and a luminance value of the reconstructed image. As illustrated in FIG. 10, for example, it is assumed that an input image IM1 includes a linear defect F added in the manufacturing process in addition to a linear feature C originally included in the non-defective product. In a reconstructed image IM2 corresponding to the input image IM1, the feature C of the input image IM1 is reconstructed, but the defect F is not reconstructed. Therefore, a difference image IM3 is generated by subtracting a luminance value of the reconstructed image IM2 from a luminance value of the input image IM1, and the defect F remains in the difference image IM3.” ) ([0092] “ Since the difference image IM3 does not include the feature C of the non-defective product, the linear defect F can be detected by, for example, a linear defect detection algorithm or the like.” ) . Regarding claim 2 , Tachi teaches the defect inspection system according to claim 1 , wherein the multiple non-defective product image estimation model has a plurality of non-defective product image estimation models ([0046] “The image reconstructor 220 functions as a data reconstructor, and generates a reconstructed image (reconstructed data) as reference data based on the input image acquired by the image acquirer 210 by using a generative model trained using an input image of a plurality of non-defective products regarding the same inspection target.” ) ([0041] “The information processing device 100 acquires a plurality of input images (hereinafter, also referred to as “normal input images”) of a non-defective product, learns the images as correct images, and generates a deep learning model (generative model) for generating a reconstructed image from the input images.” ) ([0057] “More specifically, the index selector 240 has, for example, a multilayer convolutional neural network (second neural network), and performs deep learning (or machine learning) on the index parameters for a plurality of input images of the non-defective product and the defective product regarding the same inspection target.” wherein multiple non-defective product image estimation model is multiple neural networks) . Regarding claim 3 , Tachi teaches the defect inspection system according to claim 1 , wherein the multiple non-defective product image estimation model includes an image feature extraction unit in common and a plurality of image generation units that generate a single non-defective product image based on an output of the image feature extraction unit ([0057] “More specifically, the index selector 240 has, for example, a multilayer convolutional neural network (second neural network), and performs deep learning (or machine learning) on the index parameters for a plurality of input images of the non-defective product and the defective product regarding the same inspection target.” wherein multiple non-defective product image estimation model is multiple neural networks) ([0046] “The image reconstructor 220 functions as a data reconstructor, and generates a reconstructed image (reconstructed data) as reference data based on the input image acquired by the image acquirer 210 by using a generative model trained using an input image of a plurality of non-defective products regarding the same inspection target. More specifically, the image reconstructor 220 extracts a feature amount from the input image using a trained first neural network, and reconstructs (restores) the input image based on the extracted feature amount to generate the reconstructed image.” wherein the image feature extraction unit is the image reconstructor that extracts a feature amount) . Regarding claim 4 , Tachi teaches the defect inspection system according to claim 2 , wherein the defect inspection unit creates a single integrated estimated non-defective product image from the plurality of estimated non-defective product images using statistical processing, and compares the inspection target image with the integrated estimated non-defective product image to extract the defective part ([0046] “The image reconstructor 220 functions as a data reconstructor, and generates a reconstructed image (reconstructed data) as reference data based on the input image acquired by the image acquirer 210 by using a generative model trained using an input image of a plurality of non-defective products regarding the same inspection target. More specifically, the image reconstructor 220 extracts a feature amount from the input image using a trained first neural network, and reconstructs (restores) the input image based on the extracted feature amount to generate the reconstructed image.” ) ([0058] “In the present embodiment, the index selector 240 trains the second neural network so as to maximize a difference (distance) between a distribution of an abnormality score of the non-defective product and a distribution of an abnormality score of the defective product (that is, a distance between a statistic of the abnormality score of the non-defective product and a statistic of the abnormality score of the defective product) as an objective variable with feature amounts of a plurality of input images of the non-defective product and the defective product as explanatory variables, and generates a trained model. The statistics may be, for example, histograms.” ) ([0091] “ Further, in the present embodiment, defect detection can be performed on a difference between a luminance value of the input image and a luminance value of the reconstructed image. As illustrated in FIG. 10, for example, it is assumed that an input image IM1 includes a linear defect F added in the manufacturing process in addition to a linear feature C originally included in the non-defective product. In a reconstructed image IM2 corresponding to the input image IM1, the feature C of the input image IM1 is reconstructed, but the defect F is not reconstructed. Therefore, a difference image IM3 is generated by subtracting a luminance value of the reconstructed image IM2 from a luminance value of the input image IM1, and the defect F remains in the difference image IM3 .”) ([0092] “Since the difference image IM3 does not include the feature C of the non-defective product, the linear defect F can be detected by, for example, a linear defect detection algorithm or the like.” ) . Regarding claim 5 , Tachi teaches the defect inspection system according to claim 2 , wherein the defect inspection unit compares the inspection target image with each of the plurality of estimated non-defective product images, creates a defect map that is an image showing the defective part, performs statistical processing on a plurality of the defect maps, and outputs an integrated defect map ([0078] “For example, in a case where only the abnormality score of the index parameter 1 is set to be used in the score calculator 330, the determiner 340 can determine that the inspection target is a non-defective product in a case where the maximum value of the abnormality score (abnormality score map) of luminescence is equal to or less than a predetermined first threshold value set in advance, and can determine that the inspection target is a defective product in a case where the maximum value exceeds the first threshold value. Therefore, in the abnormality score map, a region in which the first threshold value is exceeded is estimated to be a defective region such as a flaw in the inspection target. ”) ([0058] “In the present embodiment, the index selector 240 trains the second neural network so as to maximize a difference (distance) between a distribution of an abnormality score of the non-defective product and a distribution of an abnormality score of the defective product (that is, a distance between a statistic of the abnormality score of the non-defective product and a statistic of the abnormality score of the defective product) as an objective variable with feature amounts of a plurality of input images of the non-defective product and the defective product as explanatory variables, and generates a trained model. The statistics may be, for example, histograms.” ) . Regarding claim 6 , Tachi teaches the defect inspection system according to claim 4 , wherein the statistical processing creates an image showing at least one of an average, a median value, a most frequent value, and a maximum value of pixel values at the same position on a plurality of images to be processed ([0058] “In the present embodiment, the index selector 240 trains the second neural network so as to maximize a difference (distance) between a distribution of an abnormality score of the non-defective product and a distribution of an abnormality score of the defective product (that is, a distance between a statistic of the abnormality score of the non-defective product and a statistic of the abnormality score of the defective product) as an objective variable with feature amounts of a plurality of input images of the non-defective product and the defective product as explanatory variables, and generates a trained model. The statistics may be, for example, histograms.” ) . Regarding claim 7 , Tachi teaches the defect inspection system according to claim 1 , wherein the learning unit repeatedly updates internal parameters of the multiple non-defective product image estimation model so as to minimize a reconstruction error, which is a difference between the learning image and an estimated non-defective product image estimated from a noise image obtained by adding a defective product feature to the learning image, and the learning image is a non-defective product image ([0040] “By learning (deep learning or machine learning) the index parameters, an index parameter effective for separating a non-defective product from a defective product is selected. The index parameters can be set, for example, as a hue, saturation, and a density, which are color attributes of the input images and a reconstructed image, and a shape, a size, and the like of an object, or a combination thereof.” ) ([0053] “The score calculator 230 calculates abnormality scores by using a plurality of index parameters based on a plurality of input images of a non-defective product and a defective product and a plurality of reconstructed images corresponding to the input images.” ) ([0057] “By inputting a plurality of input images of the same inspection target, abnormality scores are accumulated for each pair of an input image and a reference image, and the learning of the index parameters is deepened.” ) . Regarding claim 8 , Tachi teaches the defect inspection system according to claim 7 , wherein the learning unit repeatedly updates the internal parameters of the multiple non-defective product image estimation model so as to minimize the reconstruction error and maximize a non-defective product conversion amount of the noise image to the estimated non-defective product image ([0040] “By learning (deep learning or machine learning) the index parameters, an index parameter effective for separating a non-defective product from a defective product is selected. The index parameters can be set, for example, as a hue, saturation, and a density, which are color attributes of the input images and a reconstructed image, and a shape, a size, and the like of an object, or a combination thereof. ”) ([0057] “By inputting a plurality of input images of the same inspection target, abnormality scores are accumulated for each pair of an input image and a reference image, and the learning of the index parameters is deepened.” ) . Regarding claim 9 , Tachi teaches the defect inspection system according to claim 1 , wherein the learning unit performs at least one of brightness conversion, contrast conversion, and distortion addition on the learning image before inputting the learning image into the multiple non-defective product image estimation model ([0070] “To cope with this, it is possible to adopt a configuration in which filter processing is performed on the input image and the reconstructed image in advance. A filter may be a noise removal filter (e.g., a low-pass filter, a high-pass filter, or a band-pass filter). With such a configuration, an effect of noise on an abnormality score is reduced, and it is possible to prevent or suppress the degradation of the determination performance (separation performance) for non-defective and defective products due to noise.” ) . Regarding claim 10 , Tachi teaches the defect inspection system according to claim 1 , wherein the defect inspection unit extracts the defective part using some of the estimated non-defective product images among the plurality of estimated non-defective product images ([0091] “Further, in the present embodiment, defect detection can be performed on a difference between a luminance value of the input image and a luminance value of the reconstructed image. As illustrated in FIG. 10, for example, it is assumed that an input image IM1 includes a linear defect F added in the manufacturing process in addition to a linear feature C originally included in the non-defective product. In a reconstructed image IM2 corresponding to the input image IM1, the feature C of the input image IM1 is reconstructed, but the defect F is not reconstructed. Therefore, a difference image IM3 is generated by subtracting a luminance value of the reconstructed image IM2 from a luminance value of the input image IM1, and the defect F remains in the difference image IM3.” ) ([0092] “Since the difference image IM3 does not include the feature C of the non-defective product, the linear defect F can be detected by, for example, a linear defect detection algorithm or the like.” ) . Regarding claim 11 , Tachi teaches the defect inspection system according to claim 6 , comprising: a display device, wherein the display device has an input field for inputting the number of estimated non-defective product images to be output from the multiple non-defective product image estimation model on a screen ([0063] “The operation display unit 130 includes an input unit and an output unit. The input unit includes, for example, a keyboard, a mouse, and the like, and is used for a user to perform various instructions (inputs) such as character input and various settings using the keyboard, the mouse, and the like.” ) ([0064] “The image of the inspection target captured in advance by the imaging device may be stored in a storage device outside the information processing device 100, and the information processing device 100 may sequentially acquire a predetermined number of images of the inspection target stored in the storage device as the input images.” ) ([0055] “FIG. 5 illustrates a distribution of the values of the abnormality scores for the non-defective product and the defective product in an orthogonal coordinate system in which the horizontal axis and the vertical axis indicate the normalized abnormality score and the number of samples (input images) used for the selection of an index parameter, respectively.” ) . Regarding claim 12 , the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 12 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above). Regarding claim 13 , the claim recites similar limitations to claim 2 but in the form of a method. Therefore, claim 13 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above). Regarding claim 14 , the claim recites similar limitations to claim 4 but in the form of a method. Therefore, claim 14 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above). Regarding claim 15 , the claim recites similar limitations to claim 5 but in the form of a method. Therefore, claim 15 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 9:00 - 5:00. 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, Emily Terrell can be reached on (571)- 270-3717. 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. /AMANDA H PEARSON/Examiner, Art Unit 2666 /MING Y HON/Primary Examiner, Art Unit 2666 Application/Control Number: 18/898,866 Page 2 Art Unit: 2666 Application/Control Number: 18/898,866 Page 4 Art Unit: 2666 Application/Control Number: 18/898,866 Page 5 Art Unit: 2666 Application/Control Number: 18/898,866 Page 6 Art Unit: 2666 Application/Control Number: 18/898,866 Page 7 Art Unit: 2666 Application/Control Number: 18/898,866 Page 8 Art Unit: 2666 Application/Control Number: 18/898,866 Page 9 Art Unit: 2666 Application/Control Number: 18/898,866 Page 10 Art Unit: 2666 Application/Control Number: 18/898,866 Page 11 Art Unit: 2666 Application/Control Number: 18/898,866 Page 12 Art Unit: 2666 Application/Control Number: 18/898,866 Page 13 Art Unit: 2666 Application/Control Number: 18/898,866 Page 14 Art Unit: 2666 Application/Control Number: 18/898,866 Page 15 Art Unit: 2666
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