CTFR 18/339,444 CTFR 90214 DETAILED ACTION Applicant's amendment of February 3, 2026 overcomes the following: Claim objections Rejection of claims 11-12 and 15-17 under 35 U.S.C. 112(b), pre-AIA 35 U.S.C. 112, second paragraph Applicant has amended claims 1-20. Claims 1-20 are pending. Response to Arguments Applicant’s arguments filed on February 3, 2026 with respect to pending claims have been considered but are moot in view of the new ground(s) of rejection. The amended claims resulted in changes to the scope and contents; therefore, the grounds of rejection are modified accordingly. It is noted that previously applied prior arts remain in effect. 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 1-2, 4-8, 13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over UM et al. (US PG Pub. No. 2020/0226716 A1), hereafter referred to as UM, in view of Miyashita et al. (US PG Pub. No. 2022/0180475 A1), hereafter referred to as Miyashita . Regarding claim 1 , UM discloses an image stitching method having computational operations for image stitching (Par. [0004]: a method in which a media processing apparatus synthesizes a 360-degree image by stitching source images received ; Par. [0042]: media processing apparatus 100 receives sources images from the media source 10, defines a stitching-related workflow for the source images, and stitches the source images to synthesize a 360-degree image ) processed by a processor included in a computer, the computational operations (Par. [0035-36]: FIG. 1 is diagram illustrating a network-based media processing apparatus… Blocks illustrated in FIG. 1 are implemented as respective individual devices or as an integrated device… The term “device” refers to a computing device capable of performing operations or processing of data, and examples of the device include a controller, a memory ; Par. [0107]: constitutional elements (for example, units, modules, etc.) of each block diagram are implemented as respective hardware or software pieces, or multiple constitutional elements may be implemented as an integrated hardware or software piece. The embodiment described above may be implemented in the form of programming instructions that can be executed by various computing elements and recorded on a computer-readable recording medium. The computer-readable recording medium may contain programming instructions, data files, data structures… and dedicated hardware devices configured to store and execute programming instructions, for example, ROM, RAM, and flash memory. The hardware device can be configured as one or more software modules for performing processing according to the present invention and vice versa ) comprising: calculating overlapping areas of a plurality of original images (Par. [0006-9]: provide a method and apparatus for identifying an overlapping region in two neighboring source images… image processing method includes: receiving a first source image and a second source image from multiple image sources; receiving geometric information required for stitching the first source image and the source image; and synthesizing an image from the first source image and the second source image on the basis of the geometric information. The geometric information may include seam information for specifying the overlapping region in the first source image and the second source image… the seam information may include seam point information indicating coordinates of linear lines constituting a boundary seam line of the overlapping region ; Par. [0062-63]: identify an overlapping region in two neighboring source images and to align the positions of two neighboring source images… corresponding areas (i.e., overlapping region) in the two neighboring source images are determined ; Par. [0084-86]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images… In the example shown in FIG. 6, the seam line consists of a plurality of linear lines. In this example, the overlapping region is specified on the basis of the coordinate information of the plurality of linear lines. Specifically, the overlapping region may be specified on the basis of position information including a start point and an end point of each linear line and a cross point of the linear lines constituting the seam line ; calculating overlapping areas of a plurality of original images (e.g. method and apparatus for identifying overlapping region(s) in two neighboring source (i.e. input, original, etc.) images (i.e. calculating overlapping areas of a plurality of original images), as indicated above), for example); using the calculated overlapping areas to provide masked images (Par. [0006-11]: provide a method and apparatus for identifying an overlapping region in two neighboring source images… image processing method includes: receiving a first source image and a second source image from multiple image sources; receiving geometric information required for stitching the first source image and the source image; and synthesizing an image from the first source image and the second source image on the basis of the geometric information. The geometric information may include seam information for specifying the overlapping region in the first source image and the second source image… the seam information may include seam point information indicating coordinates of linear lines constituting a boundary seam line of the overlapping region… the seam information may include mask image information for distinguishing the first source image and the second source image from each other ; Par. [0084-89]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images… In the example shown in FIG. 6, the seam line consists of a plurality of linear lines. In this example, the overlapping region is specified on the basis of the coordinate information of the plurality of linear lines. Specifically, the overlapping region may be specified on the basis of position information including a start point and an end point of each linear line and a cross point of the linear lines constituting the seam line… the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1) ; using the calculated overlapping areas to provide masked images (e.g. method and apparatus for identifying overlapping region(s) in two neighboring source (i.e. input, original, etc.) images (i.e. calculated overlapping areas), for example, including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, including seam lines that are defined on the basis of mask image(s) that show an overlapping region between the two images (i.e. using the calculated overlapping areas to provide masked images), as indicated above), for example); extracting features from the masked images (Par. [0062-63]: In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images and to align the positions of two neighboring source image… based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points ; Par. [0084-89]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images… In the example shown in FIG. 6, the seam line consists of a plurality of linear lines. In this example, the overlapping region is specified on the basis of the coordinate information of the plurality of linear lines. Specifically, the overlapping region may be specified on the basis of position information including a start point and an end point of each linear line and a cross point of the linear lines constituting the seam line… the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1) ; extracting features from the masked images (e.g. method and apparatus for identifying overlapping region(s) in two neighboring source (i.e. input, original, etc.) images (i.e. calculated overlapping areas), for example, including extracting feature points (i.e. extracting features) of two neighboring source images, for example, including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, including seam lines that are defined on the basis of mask image(s) that show an overlapping region between the two images (i.e. extracting features from the masked images), as indicated above), for example); calculating a transformation of the original images; transforming the original images, using the calculated transformation; and stitching the transformed original images to output a stitched image (Par. [0006-12]: provide a method and apparatus for identifying an overlapping region in two neighboring source images… image processing method includes: receiving a first source image and a second source image from multiple image sources; receiving geometric information required for stitching the first source image and the source image; and synthesizing an image from the first source image and the second source image on the basis of the geometric information. The geometric information may include seam information for specifying the overlapping region in the first source image and the second source image … In the image processing method according to the present invention, the seam information may include seam point information indicating coordinates of linear lines constituting a boundary seam line of the overlapping region… the seam information may include mask image information for distinguishing the first source image and the second source image from each other… the seam information may include seam type information. The seam type information may indicate which of the seam point information and the seam mask information is used to specify the overlapping region ; Par. [0042-70]: media processing apparatus 100 receives sources images from the media source 10, defines a stitching-related workflow for the source images, and stitches the source images to synthesize a 360-degree image… In order to synthesize a 360-degree image from a plurality of source images, the extraction of geometric information on the plurality of source images needs to be performed through the geometric information extraction task or step. The extracted geometric information is used for at least one processing operation among lens distortion compensation for projection and image stitching, image alignment based on camera parameters, blending, and post-processing… FIG. 3 illustrates the geometric information extraction step shown in FIG. 2 in detail, and FIG. 4 illustrates the image stitching step shown in FIG. 2 in detail… The geometric information extraction step (or task) S12 includes at least one step (or task) among a step (or task) S12-1 of extracting feature points… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images and to align the positions of two neighboring source images… In the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points. In this step (or task), intrinsic and extrinsic parameters of cameras are extracted. At least either the intrinsic parameters or the extrinsic parameters of the cameras include a nomography matrix for image alignment… In the seam information extraction step (or task) S12-3, when an object suddenly appears in front, seam information is newly extracted or the previously extracted seam information is updated. Specifically, in this step (or task), when information that a new object appears when two source images are compared is input, new seam information is extracted or the initial (previous) extracted seam information is updated. Table 2 is a chart showing the input and output of each step… Task Input Output Extraction Source images Features of source images of features Extraction Features of source Relationship between of feature images extracted features, and based camera intrinsic/extrinsic camera parameters parameter Extraction (initial(previous) Seam information (updated of seam extracted seam information information), object occurrence information, and source images… FIG. 5 is a diagram illustrating a geometric information extraction step and an image stitching step using the extracted geometric information, which are performed in a network-based media processing apparatus ; Par. [0084-93]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images. The seam line consists of at least one linear line or at least one curve… the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1)… The seam mask information is used to specify an overlapping region in two neighboring images… The seam type information indicates which of the seam point information and the seam mask information defines the overlapping region. For example, when the seam type information is “seam point”, the seam point information is used to specify an overlapping region between source images constituting a 360-degree image. On the other hand, when the seam type information is “seam mask”, the seam mask information is used to specify an overlapping region between source images constituting a 360-degree image… The coordinate type information indicates a coordinate system of points constituting the seam line defining an overlapping region between two neighboring source images ; calculating a transformation of the original images; transforming the original images, using the calculated transformation; and stitching the transformed original images to output a stitched image (e.g. method and apparatus for identifying overlapping region(s) in two neighboring source (i.e. input, original, etc.) images (i.e. calculated overlapping areas), for example, including extracting feature points (i.e. extracting features) of two neighboring source images, for example, including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, including seam lines that are defined on the basis of mask image(s) that show an overlapping region between the two images, for example, and including extracting information required for stitching the first source image and the source image, including extraction of geometric information of a plurality of source images, such as the first source image and the source image, for example, in which the extracted information is used for at least one processing operation (i.e. transformation) among lens distortion compensation for projection and image stitching, image alignment based on camera parameters, blending, and post-processing (i.e. calculating a transformation of the original images; transforming the original images, using the calculated transformation), for example, in order to synthesize a 360-degree image (i.e. output a stitched image) from a plurality of source images (i.e. and stitching the transformed original images to output a stitched image), as indicated above), for example) but fails to teach the following as further recited in claim 1. However, Miyashita teaches masking areas of the original images in which the original images do not overlap each other; extracting features of the original images; calculating a homography for transformation of the original images, using the extracted features; transforming the original images, using the calculated homography (Par. [0045-46]: transformation parameter generation unit 30 estimates a homography matrix having the set of feature points from each angle of view as inputs in accordance with a method represented by a RANSAC algorithm. The homography matrix is a parameter to be used for projective transformation such that a certain image is superimposed on another image, and corresponds to the first transformation parameter… In the estimation of the homography matrix, the transformation parameter generation unit searches for the feature points matching each other by comparing the set of feature points ; (Par. [0075-95]: FIG. 9 is a block diagram illustrating a configuration example of a panoramic video composition apparatus… In a panoramic video composition apparatus 200 illustrated in FIG. 9, an action of an afterimage generation unit 210 differs from the action of the afterimage generation unit 10 of the panoramic video composition apparatus 100… The afterimage generation unit 210 performs the projective transformation based on the first transformation parameter on the frame images constituting the video for each of the videos at the different angles of view, acquires mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions and non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame image… the afterimage generation unit 210 returns the mismatching regions to the original frame images by using an inverse matrix of the first transformation parameter, and generates an afterimage ZG by superimposing the images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked and extracted… FIG. 11 illustrates the afterimage ZG extracted from the mismatching regions returned to the original frame images… afterimage generation unit 210 performs the projective transformation on the frame images constituting the video for each of the videos from the different angles of view based on the first transformation parameter, acquires the mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs the projective transformation for returning the mismatching regions to the original frame images, and generates the afterimage by superimposing images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame images. Thus, the image of the portion with the large misalignment between the angles of view may be intensively corrected ; masking areas in which the original images do not overlap each other; extracting features of the original images; calculating a homography for transformation of the original images, using the extracted features; transforming the original images, using the calculated homography (e.g. panoramic video composition method and apparatus acquires mismatching regions between angles of view of original frame images (i.e. the original images) on which a projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions (i.e. areas in which the original images do not overlap each other) and non-moving regions in the mismatching regions returned to the original frame images are masked (i.e. masking areas in which the original images do not overlap each other) and extracted (i.e. extracting features of the original images), for example, and estimates a homography matrix having the set of extracted feature points from each angle of view as inputs (i.e. calculating a homography for transformation of the original images, using the extracted features) in accordance with a method represented by a RANSAC algorithm, for example, and the homography matrix is a parameter to be used for projective transformation such that a certain image is superimposed on another image, such as each of the original frame images, and corresponds to the first transformation parameter (i.e. transforming the original images, using the calculated homography), as indicated above), for example). UM and Miyashita are considered to be analogous art because they pertain to image processing applications. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with masking an area in which the original images do not overlap each other, extracting features of the original images, calculating a homography for transformation of the original images, using the extracted features, and transforming the original images, using the calculated homography (as taught by Miyashita, Abstract, Par. [0045-46, 75-95, ]) to improve image quality of a panoramic image (Miyashita, Abstract, Par. [0003-6, 34]). Regarding claim 2 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images (UM, Par. [0088-92]: the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1)… The seam mask information is used to specify an overlapping region in two neighboring images… The mask image information includes information on the configuration of the mask image (that is, information on pixel values of the mask image) or path information of the mask image. Here, the path information of the mask image indicates a storage path of the mask image composed of black and white pixels, which is used to specify an overlapping region between two neighboring images… The seam type information indicates which of the seam point information and the seam mask information defines the overlapping region. For example, when the seam type information is “seam point”, the seam point information is used to specify an overlapping region between source images constituting a 360-degree image. On the other hand, when the seam type information is “seam mask”, the seam mask information is used to specify an overlapping region between source images constituting a 360-degree image ; wherein the calculating of the overlapping areas comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images (e.g. method and apparatus for identifying an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including a mask image that shows an overlapping region between the two images including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, and the mask image is composed of black and white pixels (i.e. units of pixels), which are used to specify (i.e. determine, calculate, etc.) an overlapping region between two neighboring images (i.e. wherein the calculating of the overlapping area comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images), as indicated above), for example). Regarding claim 4 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, outputting a result of calculating the overlapping areas without change (UM, Par. [0061-63]: extracting feature points, a step (or task) S12-2 of extracting camera parameters according to the feature points, and a step (or task) S12-3 of extracting seam information… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images… the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points ; wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, outputting a result of calculating the overlapping areas without change (e.g. method and apparatus for identifying (i.e. outputting) an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including extracted feature points that are used to identify an overlapping region in two neighboring source images by determining corresponding (i.e. matching, no information about an error, without change, etc.) areas or regions of the overlapping region in the two neighboring source images on the basis of the extracted feature points (i.e. wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, outputting a result of calculating the overlapping areas without change), as indicated above), for example). Regarding claim 5 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping area by including or removing additional marginal areas in or from a first calculated overlapping areas (UM, Par. [0061-63]: extracting feature points, a step (or task) S12-2 of extracting camera parameters according to the feature points, and a step (or task) S12-3 of extracting seam information… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images… the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points… In the blending and post-processing step (or task) S14-3, filtering for color difference correction and noise removal for the overlapping region in the aligned two source images is performed ; wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping areas by including or removing additional marginal areas in or from first calculated overlapping areas (e.g. method and apparatus for identifying (i.e. outputting) an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including extracted feature points that are used to identify an overlapping region in two neighboring source images by determining corresponding (i.e. matching, no information about an error, etc.) areas or regions of the overlapping region in the two neighboring source images on the basis of the extracted feature points (i.e. in the case of no information about an error in the calculating of the overlapping areas with respect to the original images), for example, including performing noise (i.e. error) removal for the overlapping region in the two source images (i.e. wherein the calculating of the overlapping areas comprises, in the case of no information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping areas by including or removing additional marginal areas in or from a first calculated overlapping area), for example). Regarding claim 6 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises, in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images, correcting the overlapping areas considering the error (UM, Par. [0061-63]: extracting feature points, a step (or task) S12-2 of extracting camera parameters according to the feature points, and a step (or task) S12-3 of extracting seam information… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images… the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points… In the blending and post-processing step (or task) S14-3, filtering for color difference correction and noise removal for the overlapping region in the aligned two source images is performed ; wherein the calculating of the overlapping areas comprises, in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images, correcting the overlapping areas considering the error (e.g. method and apparatus for identifying (i.e. outputting) an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including extracted feature points that are used to identify an overlapping region in two neighboring source images by determining corresponding areas or regions of the overlapping region in the two neighboring source images on the basis of the extracted feature points, for example, including performing noise (i.e. in response to receiving information about an error in the calculating of the overlapping area with respect to the original images) removal for the overlapping region in the two source images (i.e. wherein the calculating of the overlapping area comprises, in response to receiving information about an error in the calculating of the overlapping area with respect to the original images, correcting the overlapping area considering the error), for example). Regarding claim 7 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises, in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping area by including or removing the error and additional marginal areas in or from first calculated overlapping areas (UM, Par. [0061-63]: extracting feature points, a step (or task) S12-2 of extracting camera parameters according to the feature points, and a step (or task) S12-3 of extracting seam information… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images… the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points… In the blending and post-processing step (or task) S14-3, filtering for color difference correction and noise removal for the overlapping region in the aligned two source images is performed ; wherein the calculating of the overlapping areas comprises, in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping areas by including or removing the error and additional marginal areas in or from first calculated overlapping areas (e.g. method and apparatus for identifying (i.e. outputting) an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including extracted feature points that are used to identify an overlapping region in two neighboring source images by determining corresponding areas or regions of the overlapping region in the two neighboring source images on the basis of the extracted feature points, for example, including performing noise (i.e. in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images) removal for the overlapping region in the two source images (i.e. wherein the calculating of the overlapping areas comprises, in response to receiving information about an error in the calculating of the overlapping areas with respect to the original images, determining the overlapping areas by including or removing the error and additional marginal areas in or from first calculated overlapping areas), for example). Regarding claim 8 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original image (Miyashita, Par. [0045-46]: transformation parameter generation unit 30 estimates a homography matrix having the set of feature points from each angle of view as inputs in accordance with a method represented by a RANSAC algorithm. The homography matrix is a parameter to be used for projective transformation such that a certain image is superimposed on another image, and corresponds to the first transformation parameter… In the estimation of the homography matrix, the transformation parameter generation unit searches for the feature points matching each other by comparing the set of feature points ; (Par. [0075-95]: FIG. 9 is a block diagram illustrating a configuration example of a panoramic video composition apparatus… In a panoramic video composition apparatus 200 illustrated in FIG. 9, an action of an afterimage generation unit 210 differs from the action of the afterimage generation unit 10 of the panoramic video composition apparatus 100… The afterimage generation unit 210 performs the projective transformation based on the first transformation parameter on the frame images constituting the video for each of the videos at the different angles of view, acquires mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions and non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame image… the afterimage generation unit 210 returns the mismatching regions to the original frame images by using an inverse matrix of the first transformation parameter, and generates an afterimage ZG by superimposing the images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked and extracted… FIG. 11 illustrates the afterimage ZG extracted from the mismatching regions returned to the original frame images… afterimage generation unit 210 performs the projective transformation on the frame images constituting the video for each of the videos from the different angles of view based on the first transformation parameter, acquires the mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs the projective transformation for returning the mismatching regions to the original frame images, and generates the afterimage by superimposing images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame images. Thus, the image of the portion with the large misalignment between the angles of view may be intensively corrected ; wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original images (e.g. panoramic video composition method and apparatus acquires mismatching regions between angles of view of original frame images (i.e. the original images) on which a projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions (i.e. areas other than the overlapping area calculated with respect to the original images) and non-moving regions in the mismatching regions returned to the original frame images are masked (i.e. wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original images), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. Regarding claim 13 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises extracting features from the original images and calculating areas in which features found in common between the original images are distributed as the overlapping areas (UM, Par. [0006-12]: provide a method and apparatus for identifying an overlapping region in two neighboring source images… image processing method includes: receiving a first source image and a second source image from multiple image sources; receiving geometric information required for stitching the first source image and the source image; and synthesizing an image from the first source image and the second source image on the basis of the geometric information. The geometric information may include seam information for specifying the overlapping region in the first source image and the second source image … In the image processing method according to the present invention, the seam information may include seam point information indicating coordinates of linear lines constituting a boundary seam line of the overlapping region… the seam information may include mask image information for distinguishing the first source image and the second source image from each other… the seam information may include seam type information. The seam type information may indicate which of the seam point information and the seam mask information is used to specify the overlapping region ; Par. [0042-70]: media processing apparatus 100 receives sources images from the media source 10, defines a stitching-related workflow for the source images, and stitches the source images to synthesize a 360-degree image… In order to synthesize a 360-degree image from a plurality of source images, the extraction of geometric information on the plurality of source images needs to be performed through the geometric information extraction task or step. The extracted geometric information is used for at least one processing operation among lens distortion compensation for projection and image stitching, image alignment based on camera parameters, blending, and post-processing… FIG. 3 illustrates the geometric information extraction step shown in FIG. 2 in detail, and FIG. 4 illustrates the image stitching step shown in FIG. 2 in detail… The geometric information extraction step (or task) S12 includes at least one step (or task) among a step (or task) S12-1 of extracting feature points… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images and to align the positions of two neighboring source images… In the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points. In this step (or task), intrinsic and extrinsic parameters of cameras are extracted. At least either the intrinsic parameters or the extrinsic parameters of the cameras include a nomography matrix for image alignment… In the seam information extraction step (or task) S12-3, when an object suddenly appears in front, seam information is newly extracted or the previously extracted seam information is updated. Specifically, in this step (or task), when information that a new object appears when two source images are compared is input, new seam information is extracted or the initial (previous) extracted seam information is updated. Table 2 is a chart showing the input and output of each step… Task Input Output Extraction Source images Features of source images of features Extraction Features of source Relationship between of feature images extracted features, and based camera intrinsic/extrinsic camera parameters parameter Extraction (initial(previous) Seam information (updated of seam extracted seam information information), object occurrence information, and source images… FIG. 5 is a diagram illustrating a geometric information extraction step and an image stitching step using the extracted geometric information, which are performed in a network-based media processing apparatus ; Par. [0084-93]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images. The seam line consists of at least one linear line or at least one curve… the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1)… The seam mask information is used to specify an overlapping region in two neighboring images… The seam type information indicates which of the seam point information and the seam mask information defines the overlapping region. For example, when the seam type information is “seam point”, the seam point information is used to specify an overlapping region between source images constituting a 360-degree image. On the other hand, when the seam type information is “seam mask”, the seam mask information is used to specify an overlapping region between source images constituting a 360-degree image… The coordinate type information indicates a coordinate system of points constituting the seam line defining an overlapping region between two neighboring source images ; wherein the calculating of the overlapping areas comprises extracting features from the original images and calculating areas in which features found in common between the original images are distributed as the overlapping areas (e.g. method and apparatus for identifying an overlapping region in two neighboring source (i.e. input, original, etc.) images (i.e. the calculating of the overlapping areas), for example, including a mask image that shows an overlapping region between the two images (i.e. calculating areas in which features found in common), including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, including extracting information required for stitching the first source image and the source image (i.e. extracting features from the original images), including extracted feature points of two neighboring source images (i.e. wherein the calculating of the overlapping areas comprises extracting features from the original images and calculating areas in which features found in common between the original images are distributed as the overlapping areas), as indicated above), for example). Regarding claim 18 , is a corresponding apparatus claim rejected as applied to the method claim 1 above. Regarding claim 20 , claim 18 is incorporated and the combination of UM and Miyashita, as a whole, teaches the apparatus (UM, Par. [0004]), wherein the overlapping area calculation unit is configured to extract features from the original images and calculate areas in which features found in common between the original images are distributed as the overlapping areas (UM, Par. [0006-12]: provide a method and apparatus for identifying an overlapping region in two neighboring source images… image processing method includes: receiving a first source image and a second source image from multiple image sources; receiving geometric information required for stitching the first source image and the source image; and synthesizing an image from the first source image and the second source image on the basis of the geometric information. The geometric information may include seam information for specifying the overlapping region in the first source image and the second source image … In the image processing method according to the present invention, the seam information may include seam point information indicating coordinates of linear lines constituting a boundary seam line of the overlapping region… the seam information may include mask image information for distinguishing the first source image and the second source image from each other… the seam information may include seam type information. The seam type information may indicate which of the seam point information and the seam mask information is used to specify the overlapping region ; Par. [0042-70]: media processing apparatus 100 receives sources images from the media source 10, defines a stitching-related workflow for the source images, and stitches the source images to synthesize a 360-degree image… In order to synthesize a 360-degree image from a plurality of source images, the extraction of geometric information on the plurality of source images needs to be performed through the geometric information extraction task or step. The extracted geometric information is used for at least one processing operation among lens distortion compensation for projection and image stitching, image alignment based on camera parameters, blending, and post-processing… FIG. 3 illustrates the geometric information extraction step shown in FIG. 2 in detail, and FIG. 4 illustrates the image stitching step shown in FIG. 2 in detail… The geometric information extraction step (or task) S12 includes at least one step (or task) among a step (or task) S12-1 of extracting feature points… In the feature point extraction step (or task) S12-1, feature points of two neighboring source images are extracted. The feature points are used to identify an overlapping region in two neighboring source images and to align the positions of two neighboring source images… In the camera parameter extraction step (or task) S12-2 based on the feature points, corresponding areas (i.e., overlapping region) in the two neighboring source images are determined on the basis of the extracted feature points. In this step (or task), intrinsic and extrinsic parameters of cameras are extracted. At least either the intrinsic parameters or the extrinsic parameters of the cameras include a nomography matrix for image alignment… In the seam information extraction step (or task) S12-3, when an object suddenly appears in front, seam information is newly extracted or the previously extracted seam information is updated. Specifically, in this step (or task), when information that a new object appears when two source images are compared is input, new seam information is extracted or the initial (previous) extracted seam information is updated. Table 2 is a chart showing the input and output of each step… Task Input Output Extraction Source images Features of source images of features Extraction Features of source Relationship between of feature images extracted features, and based camera intrinsic/extrinsic camera parameters parameter Extraction (initial(previous) Seam information (updated of seam extracted seam information information), object occurrence information, and source images… FIG. 5 is a diagram illustrating a geometric information extraction step and an image stitching step using the extracted geometric information, which are performed in a network-based media processing apparatus ; Par. [0084-93]: FIG. 6 is a diagram illustrating an example of specifying an overlapping region in two neighboring images… A seam line represents the boundary of an overlapping region in the two neighboring images. The seam line consists of at least one linear line or at least one curve… the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1)… The seam mask information is used to specify an overlapping region in two neighboring images… The seam type information indicates which of the seam point information and the seam mask information defines the overlapping region. For example, when the seam type information is “seam point”, the seam point information is used to specify an overlapping region between source images constituting a 360-degree image. On the other hand, when the seam type information is “seam mask”, the seam mask information is used to specify an overlapping region between source images constituting a 360-degree image… The coordinate type information indicates a coordinate system of points constituting the seam line defining an overlapping region between two neighboring source images ; wherein the overlapping area calculation unit is configured to extract features from the original images and calculate areas in which features found in common between the original images are distributed as the overlapping areas (e.g. method and apparatus for identifying an overlapping region in two neighboring source (i.e. input, original, etc.) images (i.e. the calculating of the overlapping area), for example, including a mask image that shows an overlapping region between the two images (i.e. calculate areas in which features found in common between the original images), including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region, for example, including extracting information required for stitching the first source image and the source image (i.e. extract features from the original images), including extracted feature points of two neighboring source images (i.e. wherein the calculating of the overlapping areas comprises extracting features from the original images and calculating areas in which features found in common between the original images are distributed as the overlapping areas), as indicated above), for example) . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over UM, in view of, as applied to claim 1 above, in further view of LI et al. (Chinese Pat. Application No. 109961444 A), hereafter referred to as LI . Regarding claim 3 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the calculating of the overlapping areas comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images (UM, Par. [0088-92]: the seam line is defined on the basis of a mask image. The mask image shows an overlapping region between the two images. The information on the mask image for specifying the overlapping region is defined as the seam mask information… The mask image is a black-and-white image consisting of 0's and 1's. For example, the value of each of the pixels constituting the overlapping region is set to 1 (or 0), and the value of each of the pixels constituting the other regions is set to 0 (or 1)… The seam mask information is used to specify an overlapping region in two neighboring images… The mask image information includes information on the configuration of the mask image (that is, information on pixel values of the mask image) or path information of the mask image. Here, the path information of the mask image indicates a storage path of the mask image composed of black and white pixels, which is used to specify an overlapping region between two neighboring images… The seam type information indicates which of the seam point information and the seam mask information defines the overlapping region. For example, when the seam type information is “seam point”, the seam point information is used to specify an overlapping region between source images constituting a 360-degree image. On the other hand, when the seam type information is “seam mask”, the seam mask information is used to specify an overlapping region between source images constituting a 360-degree image ; wherein the calculating of the overlapping areas comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images (e.g. method and apparatus for identifying an overlapping region in two neighboring source (i.e. input, original, etc.) images, for example, including a mask image that shows an overlapping region between the two images including seam point information indicating coordinates of lines constituting a boundary seam line of each overlapping region (i.e. overlapping areas), for example, and the mask image is composed of black and white pixels (i.e. units of pixels), which are used to specify (i.e. determine, calculate, etc.) an overlapping region between two neighboring images (i.e. wherein the calculating of the overlapping areas comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images), as indicated above), for example). The combination of UM and Miyashita, as a whole, teaches calculating of the overlapping area comprises calculating presence or absence of an overlap in units of pixels with respect to each of the original images, as indicated above, but fails to teach super-pixels as recited in claim 3. However, LI teaches super-pixels (Pg. 1: an image processing method is provided, including: obtaining an image to be processed; generating superpixels of the to-be-processed image ; Pg. 2: generating a superpixel of the adversarial sample image… generate a superpixel of the to-be-processed image ; Pg. 3: generate superpixels of the image after receiving the image ; Pg. 4-5: generating a superpixel of the image to be processed… the to-be-processed image is decomposed into a plurality of superpixels, and each superpixel may be considered to correspond to a "segment"… the image to be processed is divided into non-overlapping regions to form a plurality of superpixels ; super-pixels (e.g. image processing method is provided, including obtaining an image to be processed and generating superpixels (i.e. super-pixels) of the to-be-processed image by decomposing the to-be-processed image into a plurality of superpixels, as indicated above), for example). UM, Miyashita, and LI are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of UM, Miyashita, and LI, as a whole, would have rendered obvious the invention recited in claim 3 with a reasonable expectation of success in order to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with super-pixels (as taught by LI, Abstract, Pg. 1-5) by dividing a to-be-processed image into non-overlapping regions to form a plurality of superpixels (LI, Abstract, Pg. 1-5) . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over UM, in view of, as applied to claim 1 above, in further view of LEE et al (US PG Pub. No. 2019/0332625 A1), hereafter referred to as LEE . Regarding claim 9 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original images (Miyashita, Par. [0045-46]: transformation parameter generation unit 30 estimates a homography matrix having the set of feature points from each angle of view as inputs in accordance with a method represented by a RANSAC algorithm. The homography matrix is a parameter to be used for projective transformation such that a certain image is superimposed on another image, and corresponds to the first transformation parameter… In the estimation of the homography matrix, the transformation parameter generation unit searches for the feature points matching each other by comparing the set of feature points ; (Par. [0075-95]: FIG. 9 is a block diagram illustrating a configuration example of a panoramic video composition apparatus… In a panoramic video composition apparatus 200 illustrated in FIG. 9, an action of an afterimage generation unit 210 differs from the action of the afterimage generation unit 10 of the panoramic video composition apparatus 100… The afterimage generation unit 210 performs the projective transformation based on the first transformation parameter on the frame images constituting the video for each of the videos at the different angles of view, acquires mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions and non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame image… the afterimage generation unit 210 returns the mismatching regions to the original frame images by using an inverse matrix of the first transformation parameter, and generates an afterimage ZG by superimposing the images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked and extracted… FIG. 11 illustrates the afterimage ZG extracted from the mismatching regions returned to the original frame images… afterimage generation unit 210 performs the projective transformation on the frame images constituting the video for each of the videos from the different angles of view based on the first transformation parameter, acquires the mismatching regions between the angles of view of the frame images on which the projective transformation is performed, performs the projective transformation for returning the mismatching regions to the original frame images, and generates the afterimage by superimposing images on which the non-overlap regions and the non-moving regions in the mismatching regions returned to the original frame images are masked in order of the frame images. Thus, the image of the portion with the large misalignment between the angles of view may be intensively corrected ; wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original images (e.g. panoramic video composition method and apparatus acquires mismatching regions between angles of view of original frame images (i.e. the original images) on which a projective transformation is performed, performs projective transformation for returning the mismatching regions to the original frame images, and generates an afterimage by superimposing images on which non-overlap regions (i.e. areas other than the overlapping area calculated with respect to the original images) and non-moving regions in the mismatching regions returned to the original frame images are masked (i.e. wherein the masking comprises masking areas other than the overlapping areas calculated with respect to the original images), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 1. However, LEE teaches and a surrounding area of the overlapping area (Par. [0013-16]: extracting of the features may comprise selecting keypoints overlapping the building mask images from among the detected keypoints; and extracting features of the selected keypoints… The selecting of the keypoints may comprise selecting keypoints whose surrounding regions overlap the building mask images from among the detected keypoints… an operation of selecting keypoints whose surrounding regions overlap regions of the selected buildings in the building mask images ; Par. [0059-60]: provides panoramic images… a panoramic image and used as a building image of the present invention, and a mask image of the building may be generated using the generated perspective image and information obtained from a depth map (normal vectors, distances from a camera, and pixel-specific plane indices)… generate a building image and a building mask image using an image (or a panoramic image) and the 3D model information ; Par. [109-124]: operation of extracting features (S230) may include an operation of selecting keypoints overlapping the building mask images from among the detected keypoints and an operation of extracting features of the selected keypoints… In the operation of selecting keypoints, keypoints whose preset surrounding regions overlap the building mask images may be selected from among the detected keypoints… an operation of selecting keypoints whose surrounding regions overlap regions of the selected buildings in the building mask images… keypoints whose preset surrounding regions overlap the building mask images may be selected from among the detected keypoints… an operation of selecting keypoints whose surrounding regions overlap regions of the selected buildings in the building mask images ; and a surrounding area of the overlapping area (e.g. generate building images and building mask images by using an image, or a panoramic image, for example, in which detected keypoints whose surrounding regions overlap the building mask images (i.e. and a surrounding area of the overlapping area) are selected from among the detected keypoints, as indicated above), for example). UM, Miyashita, and LEE are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of UM, Miyashita, and LEE, as a whole, would have rendered obvious the invention recited in claim 9 with a reasonable expectation of success in order to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with and a surrounding area of the overlapping area (as taught by LEE, Abstract, Par. [0013-16, 59-60, 109-124]) to generate building images and building mask images by using an image, or a panoramic image, for example (LEE, Abstract, Par. [0013-16, 59-60, 109-124]) . 07-21-aia AIA Claim s 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over UM, in view of, as applied to claim 1 above, in further view of JAISWAL et al (US PG. Pub. No. 2017/0269585 A1), hereafter referred to as JAISWAL . Regarding claim 10 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), but fails to teach the following as further recited in claim 10. However, JAISWAL teaches wherein the calculating of the overlapping areas comprises using Global Positioning System (GPS) information of the original images (Par. [0011-37]: system for managing stations and vehicles comprising: a server operable to receive a signal from a user device, create a command based on the signal … initially processes the data and sends the processed data to the server, and the server integrates the received data into the representation of the area… the server includes a cloud… the signal includes GPS coordinates, and the cloud validates the GPS coordinates… the cloud collects the imagery data and maps the area that the vehicle has surveyed using image tagging and image stitching… image stitching includes collating the imagery data based on the GPS coordinates ; Par. [0051-76]: the cloud collects the imagery data and maps the area that the vehicle has surveyed using image tagging and image stitching… the image stitching includes collating the imagery data based on the GPS coordinates… the server includes a cloud… the signal includes GPS coordinates, and the service management module validates the GPS coordinates ; Par. [0106-129]: cloud 100 collects all the imagery data captured by the vehicle 300 and maps the entire area that the vehicle 300 has surveyed using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm. Alternatively, the cloud 100 collects all the imagery data collected by a plurality of vehicles, processes the data and makes a comprehensible data to the user using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm… Specifically, the image stitching algorithm includes collating the imagery data based on the GPS coordinates. The GPS coordinates are location information where the imagery data was captured. Firstly, the cloud 100 selects one or more imagery data among all the imagery data based on the analysis… cloud 100 combines the selected imagery data with overlapping at least a part of the imagery data to produce a segmented panorama or high-resolution imagery data. The cloud 100 conducts the overlapping between the imagery data based on the GPS coordinates in order to map the area that the vehicle 300 has surveyed… The imagery data is stitched together on the service management module 130 and analysed for useful information in order to report to the user. The service management module 130 collects all the imagery data and maps the entire area that the vehicle 300 has surveyed using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm… The image stitching algorithm includes collating the imagery data based on the GPS coordinates… The various learning algorithm includes at least one of obstacle avoidance models, image processing models, image stitching and object classification ; wherein the calculating of the overlapping areas comprises using Global Positioning System (GPS) information of the original images (e.g. system for managing stations and vehicles includes image stitching algorithm collates the imagery data captured (i.e. the original images) based on GPS coordinates, which include location information where the imagery data was captured (i.e. using Global Positioning System (GPS) information of the original images), for example, and conducts overlapping between the imagery data based on the GPS coordinates (i.e. wherein the calculating of the overlapping areas comprises using GPS information of the original images) to produce a segmented panorama or high-resolution imagery data, as indicated above), for example). UM, Miyashita, and JAISWAL are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of UM, Miyashita, and JAISWAL, as a whole, would have rendered obvious the invention recited in claim 10 with a reasonable expectation of success in order to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with wherein the calculating of the overlapping area comprises using GPS information of the original images (as taught by JAISWAL, Abstract, Par. [0011-37, 51-76, 106-129]) to produce a segmented panorama or high-resolution imagery data (JAISWAL, Abstract, Par. [0011-37, 51-76, 106-129]). Regarding claim 19 , claim 18 is incorporated and the combination of UM and Miyashita, as a whole, teaches the apparatus (UM, Par. [0004]), but fails to teach the following as further recited in claim 19 However, JAISWAL teaches wherein the overlapping area calculation unit is configured to use Global Positioning System (GPS) information in calculating the overlapping areas of the original images (Par. [0011-37]: system for managing stations and vehicles comprising: a server operable to receive a signal from a user device, create a command based on the signal … initially processes the data and sends the processed data to the server, and the server integrates the received data into the representation of the area… the server includes a cloud… the signal includes GPS coordinates, and the cloud validates the GPS coordinates… the cloud collects the imagery data and maps the area that the vehicle has surveyed using image tagging and image stitching… image stitching includes collating the imagery data based on the GPS coordinates ; Par. [0051-76]: the cloud collects the imagery data and maps the area that the vehicle has surveyed using image tagging and image stitching… the image stitching includes collating the imagery data based on the GPS coordinates… the server includes a cloud… the signal includes GPS coordinates, and the service management module validates the GPS coordinates ; Par. [0106-129]: cloud 100 collects all the imagery data captured by the vehicle 300 and maps the entire area that the vehicle 300 has surveyed using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm. Alternatively, the cloud 100 collects all the imagery data collected by a plurality of vehicles, processes the data and makes a comprehensible data to the user using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm… Specifically, the image stitching algorithm includes collating the imagery data based on the GPS coordinates. The GPS coordinates are location information where the imagery data was captured. Firstly, the cloud 100 selects one or more imagery data among all the imagery data based on the analysis… cloud 100 combines the selected imagery data with overlapping at least a part of the imagery data to produce a segmented panorama or high-resolution imagery data. The cloud 100 conducts the overlapping between the imagery data based on the GPS coordinates in order to map the area that the vehicle 300 has surveyed… The imagery data is stitched together on the service management module 130 and analysed for useful information in order to report to the user. The service management module 130 collects all the imagery data and maps the entire area that the vehicle 300 has surveyed using various machine learning methods, e.g. image tagging algorithm and image stitching algorithm… The image stitching algorithm includes collating the imagery data based on the GPS coordinates… The various learning algorithm includes at least one of obstacle avoidance models, image processing models, image stitching and object classification ; wherein the overlapping area calculation unit is configured to use Global Positioning System (GPS) information in calculating the overlapping areas of the original images (e.g. system for managing stations and vehicles includes image stitching algorithm collates the imagery data captured (i.e. the original images) based on GPS coordinates, which include location information where the imagery data was captured (i.e. use Global Positioning System (GPS) information), for example, and conducts overlapping between the imagery data based on the GPS coordinates (i.e. wherein the overlapping area calculation unit is configured to use GPS information in calculating the overlapping areas of the original images) to produce a segmented panorama or high-resolution imagery data, as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 10 . 07-21-aia AIA Claim s 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over UM, in view of, as applied to claim 1 above, in further view of Youmans et al (US PG Pub. No. 2020/0324898 A1), hereafter referred to as Youmans . Regarding claim 11 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), but fails to teach the following as further recited in claim 11. However, Youmans teaches wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated without considering a GPS error (Par. [0014]: Geo-tagging specific landmarks, objects, and or objects that can best be recognized by object detection algorithms such that the positioning information may be used by autonomous flight vehicles to determine positioning or location without a GPS signal. These methods include geo-tagged standard or 2D images in a database, where cameras or sensors on a vehicle scan its surroundings, and when they find an exact object of scene within real world that matches an image or scene or images or scenes in the database, it can infer its actual GPS position ; Par. [0023]: the vehicle may be in an area with a denied, degraded, or otherwise lacking GPS signal ; Par. [0043-50]: GPS signals may be degraded in any terrain, including dense urban terrain, from the air and or at street level where GPS signal is week or fully blocked, as well as any other terrain or areas where GPS signal does not exist or is blocked or hindered, including mountainous areas, plains, forest, jungle, ocean, open areas, lakes, ice-cap areas, on Earth or around Mars or other planets where there is no global or planetary positioning system, underground or underwater where there is no signal, or otherwise… images taken from many angles can be stitched together to create precise building or area geometries… Embedding GPS coordinates within spatial geometry reconstruction enables 3D Area Maps that aid in autonomous flight in GPS denied environments… Merging 3D area maps and or 3D GPS or position marking encoded 3D area maps augments existing autonomous flight and autonomous flight in GPS denied and communication degraded areas in many ways, including many areas of overlap ; Par. [0067]: Position and timing are often highly intertwined within autonomous systems, and by informing positioning without GPS through methods of the present invention, an embodiment of the present invention enables timing solutions and or informing or providing timing to a vehicle despite spotty, denied, degraded or otherwise non-optimal GPS and or signal and or timing and or communication environments ; Par. [0097]: using system context to create prior and posterior distributions of obstacles of a flight area that may be without communication or GPS ; wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated without considering a GPS error (e.g. invention enables timing solutions and or informing or providing timing to a vehicle despite spotty, denied, degraded (i.e. erroneous, noisy, etc.) or otherwise non-optimal GPS, for example, including positioning information used by autonomous flight vehicles to determine positioning or location without a GPS signal (i.e. without considering a GPS error), for example, in which images taken from many angles are stitched together to create precise building or area geometries by merging 3D area maps and or 3D GPS or position marking encoded 3D area maps to augment existing autonomous flight and autonomous flight in GPS denied and communication degraded areas in many ways, including many areas of overlap (i.e. wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated without considering a GPS error), as indicated above), for example). UM, Miyashita, and Youmans are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of UM, Miyashita, and Youmans, as a whole, would have rendered obvious the invention recited in claim 11 with a reasonable expectation of success in order to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated without considering a GPS error (as taught by Youmans, Abstract, Par. [0014, 49-50, 67, 97]) to provide timing to a vehicle despite spotty, denied, degraded or otherwise non-optimal GPS signal (Youmans, Abstract, Par. [0014, 49-50, 67, 97]). Regarding claim 12 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), but fails to teach the following as further recited in claim 12. However, Youmans teaches wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated by considering a GPS error (Par. [0014-15]: Geo-tagging specific landmarks, objects, and or objects that can best be recognized by object detection algorithms such that the positioning information may be used by autonomous flight vehicles to determine positioning or location without a GPS signal. These methods include geo-tagged standard or 2D images in a database, where cameras or sensors on a vehicle scan its surroundings, and when they find an exact object of scene within real world that matches an image or scene or images or scenes in the database, it can infer its actual GPS position… A 3D area map may include GPS or location information. A 3D area map may be loaded onto a vehicle, so that a vehicle may determine its location even in GPS degraded environment, by identifying where it is on the 3D area map, by using image recognition, distance sensors, IMU/INS, flight path integration ; Par. [0023]: the vehicle may be in an area with a denied, degraded, or otherwise lacking GPS signal ; Par. [0043-50]: GPS signals may be degraded in any terrain, including dense urban terrain, from the air and or at street level where GPS signal is week… images taken from many angles can be stitched together to create precise building or area geometries… Embedding GPS coordinates within spatial geometry reconstruction enables 3D Area Maps that aid in autonomous flight in GPS denied environments… Merging 3D area maps and or 3D GPS or position marking encoded 3D area maps augments existing autonomous flight and autonomous flight in GPS denied and communication degraded areas in many ways, including many areas of overlap ; Par. [0063]: if the drone determines that its sensors are seeing an exact object that is in one of the photos in the database, it can know its location, by using the image that contains the object it has detected, and it loads the position coordinates of that image, from where it was taken, and then determines a difference between location from where it saw the object and the size, positioning, difference of where the image of the object was taken, and so adjusts its expected position from that of where the image was taken, slightly ; wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated by considering a GPS error (e.g. invention enables timing solutions and or informing or providing timing to a vehicle despite spotty, denied, degraded (i.e. erroneous, noisy, etc.) or otherwise non-optimal GPS, for example, including positioning information used by autonomous flight vehicles to determine positioning or location, including GPS signals degraded (i.e. by considering a GPS error) in any terrain, including dense urban terrain, from the air and or at street level where GPS signal is week, for example, in which images taken from many angles are stitched together to create precise building or area geometries by merging 3D area maps and or 3D GPS or position marking encoded 3D area maps to augment existing autonomous flight and autonomous flight in GPS denied and communication degraded areas in many ways, including many areas of overlap (i.e. wherein, in calculating the overlapping areas using the GPS information, the overlapping areas are calculated by considering a GPS error), as indicated above), for example). The same motivation to combine above-mentioned teachings applies, as previously indicated in claim 11 . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over UM, in view of, as applied to claim 1 above, in further view of KIM et al (US PG Pub. No. 2022/0139073 A1), hereafter referred to as KIM . Regarding claim 14 , claim 1 is incorporated and the combination of UM and Miyashita, as a whole, teaches the method (UM, Par. [0004]), but fails to teach the following as further recited in claim 14. However, KIM teaches wherein the calculating of the overlapping areas comprises calculating the overlapping areas using an artificial neural network (Par. [0004-19]: in order to connect different images, a method of detecting feature points from two images, including a common space, and transforming and connecting the images through a transformation function (transformation matrix) that overlaps the detected feature points is used to minimize the error… mapping in this specification may be matching (stitching) two images when the two images can be connected (matched or stitched)… an automatic topology mapping processing method including the steps of: acquiring, by an automatic topology mapping processing system, a plurality of images, wherein at least two of the plurality of images include a common area in which a common space is captured; extracting, by the automatic topology mapping processing system, features of the images from each of the images through a feature extractor using a neural network; and determining, by the automatic topology mapping processing system, mapping images of the images based on the features extracted from each of the images… The neural network may be a network trained to output a transformation relation so that points corresponding to each other extracted from the overlapping common area of the divided images divided from a predetermined image to have an overlapping area may optimally match… The images may be 360-degree images photographed at different positions ; Par. [0080]: the neural network 20 may be a neural network that is trained to derive an optimal transformation relation (e.g., minimizing the error) so that points corresponding to each other, which are extracted from the overlapping common area of the divided images, may match. The divided images can be acquired by dividing any one image to have an overlapping area ; wherein the calculating of the overlapping areas comprises calculating the overlapping areas using an artificial neural network (e.g. automatic topology mapping processing method including the steps of: acquiring, by an automatic topology mapping processing system, a plurality of images (i.e. the original images), by using a neural network (i.e. using an artificial neural network) trained to output a transformation relation so that points corresponding to each other extracted from an overlapping common area of divided images divided from a predetermined image (i.e. any one image of the plurality of images) to have an overlapping area (i.e. wherein the calculating of the overlapping areas comprises calculating the overlapping areas using an artificial neural network), which are optimally match based on features extracted from each of the plurality images as indicated above), for example). UM, Miyashita, and KIM are considered to be analogous art because they pertain to image processing applications. Therefore, the combined teachings of UM, Miyashita, and KIM, as a whole, would have rendered obvious the invention recited in claim 14 with a reasonable expectation of success in order to modify the method and apparatus for identifying an overlapping region in two neighboring source images, including mask image that shows an overlapping region between the two images, and extracting information required for stitching the first source image and the source image, in which the extracted information is used for at least one processing operation, in order to synthesize a 360-degree image from a plurality of source images (as disclosed by UM) with wherein the calculating of the overlapping area comprises calculating the overlapping area using an artificial neural network (as taught by KIM, Abstract, Par. [0004-19, 80]) in order to connect different images (KIM, Abstract, Par. [0004-19, 80]) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 15 is 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 07-40 AIA Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Contact Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUILLERMO RIVERA-MARTINEZ whose telephone number is 571-272-4979. The examiner can normally be reached on Monday-Friday (8am - 5pm Eastern Time). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on 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 an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677 Application/Control Number: 18/339,444 Page 2 Art Unit: 2677 Application/Control Number: 18/339,444 Page 3 Art Unit: 2677 Application/Control Number: 18/339,444 Page 4 Art Unit: 2677 Application/Control Number: 18/339,444 Page 5 Art Unit: 2677 Application/Control Number: 18/339,444 Page 6 Art Unit: 2677 Application/Control Number: 18/339,444 Page 7 Art Unit: 2677 Application/Control Number: 18/339,444 Page 8 Art Unit: 2677 Application/Control Number: 18/339,444 Page 9 Art Unit: 2677 Application/Control Number: 18/339,444 Page 10 Art Unit: 2677 Application/Control Number: 18/339,444 Page 11 Art Unit: 2677 Application/Control Number: 18/339,444 Page 12 Art Unit: 2677 Application/Control Number: 18/339,444 Page 13 Art Unit: 2677 Application/Control Number: 18/339,444 Page 14 Art Unit: 2677 Application/Control Number: 18/339,444 Page 15 Art Unit: 2677 Application/Control Number: 18/339,444 Page 16 Art Unit: 2677 Application/Control Number: 18/339,444 Page 17 Art Unit: 2677 Application/Control Number: 18/339,444 Page 18 Art Unit: 2677 Application/Control Number: 18/339,444 Page 19 Art Unit: 2677 Application/Control Number: 18/339,444 Page 20 Art Unit: 2677 Application/Control Number: 18/339,444 Page 21 Art Unit: 2677 Application/Control Number: 18/339,444 Page 22 Art Unit: 2677 Application/Control Number: 18/339,444 Page 23 Art Unit: 2677 Application/Control Number: 18/339,444 Page 24 Art Unit: 2677 Application/Control Number: 18/339,444 Page 25 Art Unit: 2677 Application/Control Number: 18/339,444 Page 26 Art Unit: 2677 Application/Control Number: 18/339,444 Page 27 Art Unit: 2677 Application/Control Number: 18/339,444 Page 28 Art Unit: 2677 Application/Control Number: 18/339,444 Page 29 Art Unit: 2677 Application/Control Number: 18/339,444 Page 30 Art Unit: 2677 Application/Control Number: 18/339,444 Page 31 Art Unit: 2677 Application/Control Number: 18/339,444 Page 32 Art Unit: 2677 Application/Control Number: 18/339,444 Page 33 Art Unit: 2677 Application/Control Number: 18/339,444 Page 34 Art Unit: 2677 Application/Control Number: 18/339,444 Page 35 Art Unit: 2677 Application/Control Number: 18/339,444 Page 36 Art Unit: 2677 Application/Control Number: 18/339,444 Page 37 Art Unit: 2677 Application/Control Number: 18/339,444 Page 38 Art Unit: 2677 Application/Control Number: 18/339,444 Page 39 Art Unit: 2677 Application/Control Number: 18/339,444 Page 40 Art Unit: 2677 Application/Control Number: 18/339,444 Page 41 Art Unit: 2677 Application/Control Number: 18/339,444 Page 42 Art Unit: 2677 Application/Control Number: 18/339,444 Page 43 Art Unit: 2677 Application/Control Number: 18/339,444 Page 44 Art Unit: 2677 Application/Control Number: 18/339,444 Page 45 Art Unit: 2677 Application/Control Number: 18/339,444 Page 46 Art Unit: 2677 Application/Control Number: 18/339,444 Page 47 Art Unit: 2677 Application/Control Number: 18/339,444 Page 48 Art Unit: 2677 Application/Control Number: 18/339,444 Page 49 Art Unit: 2677 Application/Control Number: 18/339,444 Page 50 Art Unit: 2677 Application/Control Number: 18/339,444 Page 51 Art Unit: 2677
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