DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
2. Claims 1-6 are objected to because of the following informalities: Both claims 1 and 4 at line 1 recites an orthoimage and both claims 1 and 4 at line 13 recites the ortho-image. Appropriate correction is required.
3. Claim 7 at line 1 recites the ground model which lacks proper antecedent basis.
4. Claim 8 at line 1 recites the ground model which lacks proper antecedent basis.
5. Claim 8 at line 7 recites the point cloud data which lacks proper antecedent basis.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 1-4 recite the limitation the point group data acquired by the point group data acquisition step in claim 1 at line 12. There is insufficient antecedent basis for this limitation in the claim. Claim 1 at line 7 recites a point group data acquisition step for acquiring point cloud data and there is no mention of acquiring a “point group data”. Therefore, the point group data lacks antecedent basis.
Claim Rejections - 35 USC § 101
8. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
9. Claims 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because claims 7 or 8 are not directed to a process, machine, manufacture or composition of matter. Claim 7 and 8 both begin by reciting A ground model which is neither a process, machine, manufacture, nor composition of matter.
Claim Interpretation
10. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
11. The claims 4-6 in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 4-6 recite orthoimage creation means and ground model creation means which are interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 103
12. 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.
13. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
14. Claims 1-2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Klomp et al. (US Patent Application Publication No. 2011/0090337 A1) in view of Sasakawa et al (US Patent Application Publication No. 2009/0067725 A1) in view of Serna et al. (US Patent Application Publication No. 2018/0247447 A1) and further in view of Morczinek et al (US Patent Application Publication No. 2019/0155973 A1).
15. Regarding Claim 1, Klomp discloses A ground model creation method comprising: (paragraph [0072] reciting “The present invention presents a novel way to solve the 3D viewing problem, i.e. a method of projecting a three-dimensional scene onto a two-dimensional image for display, wherein the scene look like its been taken by an orthographic camera, i.e. all lines should appear to be parallel from the image plane to the ground surface (if that is the target view) and preserving distances.” The ground surface is a combination of a 3D scene projected onto a 2D image.) based on three-dimensional coordinates of a plurality of feature points (paragraph [0110] reciting “The following section is related to the geometry and matrix calculation necessary to extract three-dimensional (3D) information from two-dimensional (2D) images. Firstly, relationships between the cameras and feature points drawn from stationary 3D scenes are established. These scenes are built by putting two viewers (or cameras) in two different positions overlooking the same set of 3D points. For now, it may be assumed that the images indeed see the same 3D points. Two images can be matched together even if they view different 3D points. A probabilistic model, which is described below, may be used to separate false matches from correct ones. The step from feature detection to feature matching is a step from 2D image formation to three dimensions. The relationship between the 3D coordinates of a point and the camera frame can be expressed by a rigid-body transformation.” Feature points are used to convert 2D images into 3D information. Therefore, the feature points also become 3D points.) and a plurality of captured images taken by a photographing device such that each of the plurality of feature points is included in at least two captured images; (paragraph [0005] reciting “… This is done by finding corresponding features defined by so called feature points. To correctly orient two images one needs at least four corresponding feature points. Feature selection is today mainly done manually by letting a user click on the two images and select feature points. …” Image are oriented based on features points that are shared at least between 2 or more images being oriented together.) While not explicitly disclosed by Klomp, Sasakawa discloses an orthoimage creation step for creating an orthoimage of a predetermined region (paragraph [0006] reciting “FIG. 13 is a flow chart for describing the method of comparing the feature point of the ortho-image with the building polygon data. In the above-mentioned method, firstly, an aerial image is ortho-corrected so as to produce an ortho-image. While an aerial image photographed using a frame sensor type digital camera corresponds to a central projected image, the central projected image has distortion that becomes larger toward a peripheral area of the photographed image. The ortho-correction corrects this distortion so as to produce an ortho-image which corresponds to such an ortho projected image that a ground surface is viewed from right above this ground surface. Subsequently, feature point extraction processing and the like are performed with respect to the produced ortho-image so as to extract a contour of a building (or a portion of this building), which presently appears on the ortho-image. …” Ortho-corrected ortho-image is created based on feature point comparisons.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Klomp with Sasakawa so that the 3D orthoimages from Sasakawa are used to create the 3D image model in Klomp. This is an obviously beneficial modification as Klomp discloses generating 3D image from 2D image and Sasakawa provides this 3D orthorectified image.
While the combination of Klomp and Sasakawa does not explicitly disclose, Serna discloses a point group data acquisition step for acquiring point cloud data converted into three-dimensional coordinates for each point in the predetermined region by laser light irradiated from a three-dimensional scanning device; (paragraph [0059] reciting “The method 200 includes, at 202, inputting a 3D point cloud. The 3D point cloud may include X, Y, and Z coordinates of a set of points representing surfaces of one or more objects. The 3D point cloud may also include additional attributes associated with the set of points, such as color, intensity, normals, thermic information, global navigation satellite system (GNSS) data (e.g., global positioning system (GPS) data), and the like. The 3D point cloud may be acquired, for example, by one or more of the following 3D imaging devices: terrestrial laser scanning, aerial laser scanning, mobile laser scanning, hand-held sensors, terrestrial photogrammetry, aerial photogrammetry, and the like.”)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Klomp and Sasakawa with Serna so that the 3D points in Klomp can be obtained using the teachings of Serna. The feature points in Klomp can be matched to the point cloud to generate proper 3D surface of the 2D images.
While the combination of Klomp, Sasakawa, and Serna does not explicitly disclose, Morczinek discloses a ground model creation step of creating a ground model of the predetermined region by supplementing the point group data acquired by the point group data acquisition step with color information of the ortho-image. (paragraph [0093] reciting “In some instances, at least a portion of the data in the point cloud 154 can be used to verify corresponding data in the orthomosaic image 152. For example, as described above, each data point (e.g., pixel) in the orthomosaic image 152 can include data defining that data point's position defined by its X and Y coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,R,G,B]) and each data point (e.g., pixel) in the point cloud 154 can include data defining that data point's position defined by its X, Y, and Z coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,Z,R,G,B]). Accordingly, in some instances, the X, Y, R, G, and B value or data for a data point in the point cloud 154 can be used to verify the X, Y, R, G, and B value or data for a corresponding data point in the orthomosaic image 152 (or vice versa).” Therefore, each point in the 3D orthomosaic image can be defined by the color value of its point cloud.)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Klomp, Sasakawa, and Serna with Morczinek so the orthomosaic aerial image mosaic can be modified with color for its 3D points. This is an obviously beneficial modification since it allows the generated 3D ground model image to have the color of the 3D point cloud which matches the natural colors of the terrain.
16. Regarding Claim 2, the limitation The ground model creation method according to claim 1, wherein the photographing device is an unmanned aerial vehicle or a model aerial vehicle that flies at an altitude of 20 meters or less above the ground is obvious to try in view of Klomp. Klomp discloses using unmanned aerial vehicles (UAV). It is obvious that the UAV can fly above 20 meter or below 20 meters. This is obvious to try because flying lower obtains more details where as flying higher obtains less details but broader scanned area. Therefore, flying lower is obvious to try to obtain higher detailed aerial photos for higher detailed orthonormal mosaic images.
17. Regarding Claim 4, Klomp discloses A ground model creation system comprising: (paragraph [0070] reciting “One implementation of the present invention is related to image mosaic generation using tactical reconnaissance platforms, and in particular unmanned aircraft systems, UAS, and unmanned aerial vehicles, UAV. …”) based on three-dimensional coordinates of a plurality of feature points (paragraph [0110] reciting “The following section is related to the geometry and matrix calculation necessary to extract three-dimensional (3D) information from two-dimensional (2D) images. Firstly, relationships between the cameras and feature points drawn from stationary 3D scenes are established. These scenes are built by putting two viewers (or cameras) in two different positions overlooking the same set of 3D points. For now, it may be assumed that the images indeed see the same 3D points. Two images can be matched together even if they view different 3D points. A probabilistic model, which is described below, may be used to separate false matches from correct ones. The step from feature detection to feature matching is a step from 2D image formation to three dimensions. The relationship between the 3D coordinates of a point and the camera frame can be expressed by a rigid-body transformation.” Feature points are used to convert 2D images into 3D information. Therefore, the feature points also become 3D points.) and a plurality of captured images taken by a photographing device such that each of the plurality of feature points is included in at least two captured images; (paragraph [0005] reciting “… This is done by finding corresponding features defined by so called feature points. To correctly orient two images one needs at least four corresponding feature points. Feature selection is today mainly done manually by letting a user click on the two images and select feature points. …” Image are oriented based on features points that are shared at least between 2 or more images being oriented together.) While not explicitly disclosed by Klomp, Sasakawa discloses an orthoimage creation means that creates an orthoimage of a predetermined region (paragraph [0006] reciting “FIG. 13 is a flow chart for describing the method of comparing the feature point of the ortho-image with the building polygon data. In the above-mentioned method, firstly, an aerial image is ortho-corrected so as to produce an ortho-image. While an aerial image photographed using a frame sensor type digital camera corresponds to a central projected image, the central projected image has distortion that becomes larger toward a peripheral area of the photographed image. The ortho-correction corrects this distortion so as to produce an ortho-image which corresponds to such an ortho projected image that a ground surface is viewed from right above this ground surface. Subsequently, feature point extraction processing and the like are performed with respect to the produced ortho-image so as to extract a contour of a building (or a portion of this building), which presently appears on the ortho-image. …” Ortho-corrected ortho-image is created based on feature point comparisons. Orthoimage creation means corresponds to the cameras that capture and processes the images into orthoimages.)
a point group data storage means for storing (paragraph [0050] reciting “A GPS/IMU data correction unit 82 executes correction processing with employment of the differential information acquired by the reference station 8 with respect to the GPS/IMU data stored in the storage unit.” Storage unit corresponds to a point group storage means.)
It would have been obvious to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Klomp with Sasakawa so that the 3D orthoimages from Sasakawa are used to create the 3D image model in Klomp. This is an obviously beneficial modification as Klomp discloses generating 3D image from 2D image and Sasakawa provides this 3D orthorectified image.
While the combination of Klomp and Sasakawa does not explicitly disclose, Serna discloses a point group data storage means for storing point group data converted into three-dimensional coordinates obtained for each point in the predetermined region by a laser beam irradiated from a three-dimensional scanning device; (paragraph [0059] reciting “The method 200 includes, at 202, inputting a 3D point cloud. The 3D point cloud may include X, Y, and Z coordinates of a set of points representing surfaces of one or more objects. The 3D point cloud may also include additional attributes associated with the set of points, such as color, intensity, normals, thermic information, global navigation satellite system (GNSS) data (e.g., global positioning system (GPS) data), and the like. The 3D point cloud may be acquired, for example, by one or more of the following 3D imaging devices: terrestrial laser scanning, aerial laser scanning, mobile laser scanning, hand-held sensors, terrestrial photogrammetry, aerial photogrammetry, and the like.”)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Klomp and Sasakawa with Serna so that the 3D points in Klomp can be obtained using the teachings of Serna. The feature points in Klomp can be matched to the point cloud to generate proper 3D surface of the 2D images.
While the combination of Klomp, Sasakawa, and Serna does not explicitly disclose, Morczinek discloses a ground model creation means for (paragraph [0036] reciting “The memory 120 of the host device 110 can be, for example, a random access memory (RAM), a memory buffer, a hard drive, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and/or the like. The memory 120 can be configured to store, for example, one or more software modules and/or code that can include instructions that can cause the processor 112 to perform one or more processes, …”) creating a ground model of the predetermined region by supplementing the point group data stored in the point group data storage means with color information of the ortho-image. (paragraph [0093] reciting “In some instances, at least a portion of the data in the point cloud 154 can be used to verify corresponding data in the orthomosaic image 152. For example, as described above, each data point (e.g., pixel) in the orthomosaic image 152 can include data defining that data point's position defined by its X and Y coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,R,G,B]) and each data point (e.g., pixel) in the point cloud 154 can include data defining that data point's position defined by its X, Y, and Z coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,Z,R,G,B]). Accordingly, in some instances, the X, Y, R, G, and B value or data for a data point in the point cloud 154 can be used to verify the X, Y, R, G, and B value or data for a corresponding data point in the orthomosaic image 152 (or vice versa).” Therefore, each point in the 3D orthomosaic image can be defined by the color value of its point cloud. )
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Klomp, Sasakawa, and Serna with Morczinek so the orthomosaic aerial image mosaic can be modified with color for its 3D points. This is an obviously beneficial modification since it allows the generated 3D ground model image to have the color of the 3D point cloud which matches the natural colors of the terrain.
18. Regarding Claim 5, the limitation A ground model creation system according to claim 4, wherein the photographing device is an unmanned aerial vehicle or a model aerial vehicle that flies at an altitude of 20 meters or less above the ground is obvious to try in view of Klomp. Klomp discloses using unmanned aerial vehicles (UAV). It is obvious that the UAV can fly above 20 meter or below 20 meters. This is obvious to try because flying lower obtains more details where as flying higher obtains less details but broader scanned area. Therefore, flying lower is obvious to try to obtain higher detailed aerial photos for higher detailed orthonormal mosaic images.
19. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Serna in view of Morczinek.
20. Regarding Claim 7, Serna discloses A ground model characterized in that the ground model is point group data in which each point in a predetermined region is converted into three-dimensional coordinates obtained by laser light irradiated from a three-dimensional scanning device, (paragraph [0059] reciting “The
method 200 includes, at 202, inputting a 3D point cloud. The 3D point cloud may include X, Y, and Z coordinates of a set of points representing surfaces of one or more objects. The 3D point cloud may also include additional attributes associated with the set of points, such as color, intensity, normals, thermic information, global navigation satellite system (GNSS) data (e.g., global positioning system (GPS) data), and the like. The 3D point cloud may be acquired, for example, by one or more of the following 3D imaging devices: terrestrial laser scanning, aerial laser scanning, mobile laser scanning, hand-held sensors, terrestrial photogrammetry, aerial photogrammetry, and the like.”)
While Serna does not explicitly disclose, Morczinek discloses and each point included in the point ground data is supplemented with color information of an orthoimage for the predetermined region. (paragraph [0093] reciting “In some instances, at least a portion of the data in the point cloud 154 can be used to verify corresponding data in the orthomosaic image 152. For example, as described above, each data point (e.g., pixel) in the orthomosaic image 152 can include data defining that data point's position defined by its X and Y coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,R,G,B]) and each data point (e.g., pixel) in the point cloud 154 can include data defining that data point's position defined by its X, Y, and Z coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,Z,R,G,B]). Accordingly, in some instances, the X, Y, R, G, and B value or data for a data point in the point cloud 154 can be used to verify the X, Y, R, G, and B value or data for a corresponding data point in the orthomosaic image 152 (or vice versa).” Therefore, each point in the 3D orthomosaic image can be defined by the color value of its point cloud.)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Serna with Morczinek so the orthomosaic point cloud in Serna can be modified with color from as disclose in Morczinek. This is a beneficial modification since the properly colored 3D orthoimage reflects accurately the real terrain.
21. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Klomp in view of Serna and further in view of Morczinek.
22. Regarding Claim 8, Klomp discloses created based on three-dimensional coordinates of a plurality of feature points(paragraph [0110] reciting “The following section is related to the geometry and matrix calculation necessary to extract three-dimensional (3D) information from two-dimensional (2D) images. Firstly, relationships between the cameras and feature points drawn from stationary 3D scenes are established. These scenes are built by putting two viewers (or cameras) in two different positions overlooking the same set of 3D points. For now, it may be assumed that the images indeed see the same 3D points. Two images can be matched together even if they view different 3D points. A probabilistic model, which is described below, may be used to separate false matches from correct ones. The step from feature detection to feature matching is a step from 2D image formation to three dimensions. The relationship between the 3D coordinates of a point and the camera frame can be expressed by a rigid-body transformation.” Feature points are used to convert 2D images into 3D information. Therefore, the feature points also become 3D points.) and a plurality of images captured by an imaging device so that each of the plurality of feature points is included in at least two images. (paragraph [0005] reciting “… This is done by finding corresponding features defined by so called feature points. To correctly orient two images one needs at least four corresponding feature points. Feature selection is today mainly done manually by letting a user click on the two images and select feature points. …” Image are oriented based on features points that are shared at least between 2 or more images being oriented together.) While Klomp does not explicitly disclose, Serna discloses for each point included in the point cloud data and A ground model characterized in that the ground model is point group data in which each point in a predetermined region is converted into three-dimensional coordinates obtained by laser light irradiated from a three-dimensional scanning device, (paragraph [0059] reciting “The method 200 includes, at 202, inputting a 3D point cloud. The 3D point cloud may include X, Y, and Z coordinates of a set of points representing surfaces of one or more objects. The 3D point cloud may also include additional attributes associated with the set of points, such as color, intensity, normals, thermic information, global navigation satellite system (GNSS) data (e.g., global positioning system (GPS) data), and the like. The 3D point cloud may be acquired, for example, by one or more of the following 3D imaging devices: terrestrial laser scanning, aerial laser scanning, mobile laser scanning, hand-held sensors, terrestrial photogrammetry, aerial photogrammetry, and the like.”)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify Klomp with Serna so that the 3D points in Klomp can be obtained using the teachings of Serna. The feature points in Klomp can be matched to the point cloud to generate proper 3D surface of the 2D images.
While the combination of Klomp and Serna does not explicitly disclose, Morczinek discloses and each point included in the point ground data is supplemented with color information of an orthoimage for the predetermined region (paragraph [0093] reciting “In some instances, at least a portion of the data in the point cloud 154 can be used to verify corresponding data in the orthomosaic image 152. For example, as described above, each data point (e.g., pixel) in the orthomosaic image 152 can include data defining that data point's position defined by its X and Y coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,R,G,B]) and each data point (e.g., pixel) in the point cloud 154 can include data defining that data point's position defined by its X, Y, and Z coordinates and its color value (e.g., a data vector defined and/or described as [X,Y,Z,R,G,B]). Accordingly, in some instances, the X, Y, R, G, and B value or data for a data point in the point cloud 154 can be used to verify the X, Y, R, G, and B value or data for a corresponding data point in the orthomosaic image 152 (or vice versa).” Therefore, each point in the 3D orthomosaic image can be defined by the color value of its point cloud.)
It would have been obvious to a person to a person of ordinary skills in the art before the effective filing date of the claimed invention to modify the combination of Klomp and Serna with Morczinek so the orthomosaic aerial image mosaic can be modified with color for its 3D points. This is an obviously beneficial modification since it allows the generated 3D ground model image to have the color of the 3D point cloud which matches the natural colors of the terrain.
Allowable Subject Matter
23. Claims 3 and 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
24. The following is a statement of reasons for the indication of allowable subject matter: Claim 3 recites the limitation wherein the feature point is an anti-aircraft sign installed on the ground at the time of photographing, the three-dimensional coordinates of the anti-aircraft sign are obtained by a total station, a satellite-based positioning system, or a three-dimensional scanning device which is not disclosed by the cited references, either singly or in combination. While Klomp disclose orthonormal mosaic aerial images, Klomp fails to disclose this limitation. Similarly, Sasakawa, Serna, and Morczinek all fail to disclose this limitation.
25. Claim 6 recites the limitation wherein the feature point is an anti-aircraft sign installed on the ground at the time of photographing by the photographing device, and the three-dimensional coordinates of the anti-aircraft sign are obtained by a total station, a satellite-based positioning system, or a three-dimensional scanning device which is not disclosed by the cited references, either singly or in combination. While Klomp disclose orthonormal mosaic aerial images, Klomp fails to disclose this limitation. Similarly, Sasakawa, Serna, and Morczinek all fail to disclose this limitation.
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/FRANK S CHEN/Primary Examiner, Art Unit 2611
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