CTNF 18/860,358 CTNF 87613 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. Prior arts cited in this office action: Pati et al. (US 20230169666 A1, hereinafter “Pati”) Gao et al. (US 20240223775 A1, hereinafter “Gao”) 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. Claim 8 recites the limitation wherein the loss function Fi is defined as a weighted sum of element losses calculated using a plurality of element loss functions fij (j = 1 to M, where M is the number of element losses), and has a different weight for each region Ri of the second learning image. There is insufficient antecedent basis for this limitation in the claim since during the amendment the claim or claims that claim 8 would depend on has been crossed out. Appropriate amendment is requested. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Pati et al. (US 20230169666 A1, hereinafter “Pati”) in view of Gao et al. (US 20240223775 A1, hereinafter “Gao”) . Regarding claim 1: Gao teaches a sample observation method (Pati Abstract, [0003], where Pati discloses Various methods and systems are provided for automatically registering and stitching images), comprising: acquiring a first learning image and a second learning image corresponding to the first learning image (Pati [0003], [0052], where Pati discloses an Embodiments for registering images are provided herein. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth. The method further includes automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory), learning, an estimation processing parameter of an estimation engine that estimates the second learning image from the first learning image is learned using the first learning image and the second learning image (Pati [0056], fig. 3, where Pati discloses In an example, predicted shift vectors output by the CNN may be used to move an input image, the ground truth shift vectors may be used to move the same image, and then the differences between the first moved image the second moved image may be determined. The mask may then be used so that, instead of comparing all of the differences in pixel values at all regions of the images, only the differences in non-masked regions of the images are considered. in other words, image 308 and mask 318 are used to generate an image that only shows the region of interest), wherein, in learning of the estimation processing parameter, an estimated image, which is estimated from the first learning image, and the second learning image corresponding to the first learning image are divided into regions Ri (i = 1 to N, where N is the number of regions (Pati [0056]-[0057, fig. 3 where Pati teaches Loss 328 may be used to train the TMPN 320 such that predicted shift vectors 324 may be equal to ground truth shift vectors 326. Trained TMPN 320 may then be implemented to generate a transformation matrix based on an input image pair), and the learning is performed using a loss function Fi of evaluating a loss between a pixel group Pi of the second learning image and a pixel group Qi of the estimated image included in each region Ri on the basis of a predetermined criterion (Pati [0040], [0056]-[0057], where Pati teaches Calculating loss 328 may include using the mask 318 to scale loss function values, such that loss 328 is calculated based on image data within the ROI, as defined by mask 318). Pati fails to teach using a plurality of loss function corresponding to each region in the first image and the estimated image. However, examiner believe that Pati teaches all the limitation of applicant’s invention as claimed (for example in the case where N=1 or even two, meaning one region the mask would only allow the loss for that particular region. However, for the sake of completeness, one can take a look at Gao. Where Gao teaches It should be noted that, in the foregoing block 403, the first loss function, the second loss function, and the third loss function each may include a plurality of loss functions. In this case, the computer device performs weighted summation on loss values determined according to the loss functions included in the first loss function, to obtain the first loss value, performs weighted summation on loss values determined according to the loss functions included in the second loss function, to obtain the second loss value, and performs weighted summation on loss values determined according to the loss functions included in the third loss function, to obtain the third loss value. Alternatively, the computer device does not perform the weighting operation in block 403, and perform all weighting operations in block 404 (Gao abstract, [0055]-[0061], [0176]-[0177]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to estimate an image by using a plurality of loss function corresponding to different area of an image, such that the system is more efficient and each loss function is tailored to specific area that would allow for better comparison between areas of the two images to determine the similarities and differences between them. In other words, determining how the system is performed in reconstructing a particular image or the quality of the image reconstructed in comparison to the original image (Gao Abstract). Regarding claim 2: Pati in view of Gao teaches wherein, in a case of capturing the first learning image and the second learning image, one or more of conditions of an image resolution, the number of frames added, and a focus position are changed, such that the second learning image has higher quality than the first learning image (Pati [0056]-[0057]; Gao [0162], where the ground truth image has better quality than the predicted or the generated image). Regarding claim 3: Pati in view of Gao teaches wherein the estimation engine uses a convolutional neural network, and wherein the estimation processing parameter is updated through error back propagation processing such that the loss calculated by the loss function is reduced (Gao [0096], [0106], [0139], [0186]). Regarding claim 4: Pati in view of Gao teaches wherein a luminance gradient image is acquired by applying a differential filter to the second learning image, the estimated image, which is estimated from the first learning image, and the second learning image are divided into an edge region R1 and a non-edge region R2 of a circuit pattern by using the luminance gradient image and an edge determination threshold, and the estimation processing parameter is learned using a loss function F1 for the edge region R1 and a loss function F2 for the non-edge region R2 (Gao abstract, [0004]-[0005], [0010]-[0013], [0103]-[0104], [0161], [0213]). Regarding claim 5: Pati in view of Gao teaches wherein the estimated image, which is estimated from the first learning image, and the second learning image are divided into a region R1' of a first layer pattern to a region RN' of an Nth layer pattern by using the second learning image and a layer determination threshold, and the estimation processing parameter is learned using a loss function Fi' (i = 1 to N, where N is the number of regions) for the region Ri' (Gao abstract, [0004]-[0005], [0010]-[0013], [0103]-[0104], [0161], [0213]). Regarding claim 9: Pati in view of Gao teaches wherein the loss function Fi is defined as a weighted sum of element losses calculated using a plurality of element loss functions fij (j = 1 to M, where M is the number of element losses), and has a different weight for each region Ri of the second learning image (Gao abstract, [0055]-[0061], [0176]-[0177]) . 12-151-08 AIA 07-43 12-51-08 Claim s 6-7, 10-11 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. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEDNEL CADEAU whose telephone number is (571)270-7843. The examiner can normally be reached Mon-Fri 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, Chieh Fan can be reached at 571-272-3042. 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. /WEDNEL CADEAU/Primary Examiner, Art Unit 2632 June 11, 2026 Application/Control Number: 18/860,358 Page 2 Art Unit: 2632 Application/Control Number: 18/860,358 Page 3 Art Unit: 2632 Application/Control Number: 18/860,358 Page 4 Art Unit: 2632 Application/Control Number: 18/860,358 Page 5 Art Unit: 2632 Application/Control Number: 18/860,358 Page 6 Art Unit: 2632 Application/Control Number: 18/860,358 Page 7 Art Unit: 2632 Application/Control Number: 18/860,358 Page 8 Art Unit: 2632
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