DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 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 – (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. Claim(s) 18-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being FILLIN "Insert either—clearly anticipated—or—anticipated—with an explanation at the end of the paragraph." \d "[ 3 ]" anticipated by Fuchigami ( WO 2020262683 A1 ) . Regarding claim 18. Fuchigami teaches a method comprising: convolving, with a plurality of kernel coefficients, first features of a first feature map associated with a first plurality of sample indices in a first feature map format to generate a plurality of first output values mapped to sample indices in the first feature map format ( see page 11 , lines 10-16 , the convolution process using various kernels is performed so as to detect the infarct region, and a feature map consisting of the feature data obtained by the convolution process is output. The kernel has an n × n pixel size (for example, n = 3), and each element is weighted. Specifically, weights such as a differential filter that emphasizes the edges of the input image are set. The convolution layer applies the kernel to the entire input image or feature map output from the previous processing layer, shifting the pixels of interest of the kernel ) ( see page 14 , lines 22-28 , t he first discriminator 61 is composed of a convolutional neural network having a plurality of processing layers including at least one of a convolutional layer and a pooling layer, performs at least one of the convolutional processing and the pooling processing in each processing layer, and outputs a feature map F1. .. FIG. 17 is a diagram showing an example of the feature map F1 output from the first discriminator 61. In FIG. 17, the resolution of the feature map F1 is set to 5 × 5 pixels for the sake of simplicity, but the present invention is not limited to this ) ; interpolating the first output values to provide first interpolated values of a second feature map mapped to first sample indices in one or more second feature map formats; and subsequent to commencement of interpolation of the first output values to provide the first interpolated values ( see Fig. 26, page , lines , the area has been detected. Therefore, the decoder 80B interpolates the region F25A having the same size as the feature map F25 and having the label "0" in the entire region in the region on the right side of the feature map F25. Further, the decoder 80B flips the infarct region of the label “2” included in the feature map F25 horizontally with respect to the right side of the feature map F25 and imparts it to the interpolated region F25A. As a result, the decoder 80B generates a feature map F26 having the same size as the normalized tomographic images Ss1 and Ss2 ) , convolving, with the plurality of kernel coefficients, second features of the first feature map associated with a second plurality of sample indices in the first feature map format to generate a plurality of second output values mapped to sample indices in the first feature map format ( see page 15, lines 7-11, The third discriminator 63 is composed of a convolutional neural network having a plurality of processing layers including at least one of a convolutional layer and a pooling layer, and the feature map F1 and the second discriminator output by the first discriminator 61 in the first processing layer. A superposed map is generated by superimposing the inverted feature map F2 output by the vessel 62 ). Regarding claim 19. The method of claim 18, wherein the first features of the first feature map comprise image signal intensity values associated with first pixel locations of an image frame and the second features of the first feature map comprise image signal intensity values associated with second pixel locations of the image frame ( see page 18, lines 31-36, the encoder 80A performs a convolution process using various kernels so that the infarct region can be detected based on the difference in the pixel values of the corresponding pixel positions between the divided normalized tomographic image Sh1 and the divided inverted tomographic image Sch1. A feature map is generated from the feature data obtained in the convolution process ) . Regarding claim 20. The method of claim 18, and further comprising: interpolating the second output values to second sample indices in the one or more second feature map formats ( see page 19 and 20 , lines 34-6 and 1-5 , the feature maps F21, F23, F25, and F27 upsampled to the same resolution as the divided normalized tomographic images Sh1 and Sh2 are shown. First, as shown in FIG. 24, when the label of the detected infarct region in the feature map F21 is "1", the infarct region is detected on the right brain side. Therefore, as shown in FIG. 24, the decoder 80B interpolates the region F21A having the same size as the feature map F21 in the region on the right side of the feature map F21 and having the label "0" in all regions. Therefore, a feature map F22 having the same size as the normalized tomographic images Ss1 and Ss2 is generated ) . Allowable Subject Matter Claims 1-17 are allowed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT XIN JIA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-5536 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 9:00 am-7:30pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Gregory Morse can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571)272-3838 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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