DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1-17 are pending
Claims 1-4, 6-9, 11, 13, and 17 are amended.
Claims 18-20 are canceled
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, 5, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), and Jeong et al. (KR 20190059395).
Regarding claim 1.
Gallaway teaches:
A method for 3D geospatial mapping comprising: providing 2D satellite imagery related to an area of interest for geospatial mapping (Gallaway [0008] FIG. 1 is a simplified block diagram depicting clustering of images from airborne or spaceborne (e.g., satellite) platforms. As shown, a plurality of platforms 102, such as satellites, capture images of a target 103 from different view angles.);
analyzing the satellite imagery to generate a digital elevation model (DEM), wherein the DEM is a bare surface profile of the area of interest without protrusions (Gallaway [0042] Optionally, if available, read a digital elevation map (DEM) for the geographic region covered by these images.);
generating a point cloud of the area of interest (Gallaway [0006] A point cloud is a set of points in a three-dimensional coordinate system. In general, a point cloud is a three-dimensional model of a scene on the earth. In geographic information systems, for example, point clouds are used to make digital elevation models of the terrain, or to generate three-dimensional models of, for instance, an urban environment. Point clouds can be formed by combining two-dimensional images captured from two or more perspectives.);
generating a 3D geographical information system (GIS) map (Gallaway [0089] In block 312, the sparse, high-confidence point clouds are analyzed to generate a map representing a low and a high “Z” value for each “X/Y” location of each point cloud. The original dense raw points are then filtered based on whether they lie within this “Z” range. The following figure demonstrates an approach for generating an exemplary map.);
computing a geometry of the buildings from the point cloud (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like. [0089] In block 312, the sparse, high-confidence point clouds are analyzed to generate a map representing a low and a high “Z” value for each “X/Y” location of each point cloud. The original dense raw points are then filtered based on whether they lie within this “Z” range. The following figure demonstrates an approach for generating an exemplary map. );
and texturing the GIS map (Gallaway [0102] The optional process of colorization adds image intensities to the point cloud. Without image intensities, a point cloud is simply a collection of 3D (X,Y,Z) points representing the locations of structural surfaces. However, with image intensities, the point cloud can be viewed as an image, where each point has a “color” based on what type of structure it is representing. For example, a point on grass would be colored green and a point on asphalt would be colored dark gray. This extra attribution makes each 3D point a 3D+ attribute point (e.g., [X,Y,Z]+[R,G,B]) and makes it more interpretable for both a human analyst or to a downstream further processing.).
Gallaway fails to teach:
identifying a road network in the point cloud, identifying people and cars from the point cloud, and removing the people and cars from the point cloud (Babahajiani [0003] Three-dimensional (3D) object recognition systems using laser scanning, such as Light Detection And Ranging (LiDAR), provide an output of 3D point clouds. 3D point clouds can be used for a number of applications, such as rendering appealing visual effect based on the physical properties of 3D structures and cleaning of raw input 3D point clouds e.g. by removing moving objects (car, bike, person).);
by layering the DEM to form a layered 3D GIS dimensional map
and layering the buildings on the DEM with the road network;
Babahajiani teaches:
identifying a road network in the point cloud, identifying people and cars from the point cloud, and removing the people and cars from the point cloud (Babahajiani [0003] Three-dimensional (3D) object recognition systems using laser scanning, such as Light Detection And Ranging (LiDAR), provide an output of 3D point clouds. 3D point clouds can be used for a number of applications, such as rendering appealing visual effect based on the physical properties of 3D structures and cleaning of raw input 3D point clouds e.g. by removing moving objects (car, bike, person).);
Chiba teaches:
layering the road network onto the bare surface profile of the DEM, wherein the road network is void of people and cars (Chiba [0079] That is, the elevation value allocated to each mesh of the digital elevation model (DEM) is increased several times (emphasized 5 times) per mesh, each mesh is set as a focused point, a certain range is defined per focused point, and an aboveground opening, an underground opening, and an inclination are derived, to generate a second red relief image GP (emphasized 5 times) in which a brighter color is allocated to a part having a higher aboveground opening, in which a darker color is allocated to a part having a higher underground opening, and in which a red-emphasized color is allocated to a part having a higher inclination.[0080] The first feature height comparison image generating unit 158 synthesizes the first building height comparison image GM (first DHM height gradient-tinted image: refer to FIG. 10) in the first building height comparison image storing unit 153 with the second red relief image GP (emphasized 5 times) in the second red relief image storing unit 161 to generate a first feature height comparison image GEC, in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, in the first feature height comparison image storing unit 160. [0081] The first terrain/feature height-based colored image generating unit 162 synthesizes the second red relief image GP (emphasized 5 times: refer to FIG. 16) in the second red relief image storing unit 161, the first gradient-tinted image GD (refer to FIG. 9) in the first gradient-tinted image storing unit 149, and the first red relief image (emphasized 0.2 times) in the first red relief image storing unit 148 with each other to generate a first terrain/feature height-based colored image GHC (also referred to as first Super Cool Map), in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, and in which a feature (a building, a tree, or the like) is expressed in color in accordance with a height and an inclination thereof, in the first terrain/feature height-based colored image storing unit 164.);
Jeong teaches:
by layering the DEM to form a layered 3D GIS dimensional map (Jeong [Pg 3 Par 4] The GIS layer refers to a multi-layered structure of GIS information, consisting of a terrain aerial photo layer, a city and county administrative boundary layer, a highway layer, and a building layer. The lower layer is covered by the upper layer, and ON / OFF of a specific layer and extraction of a specific layer are also possible. [Pg 3 Par 14] A numerical elevation model is a generic term for digital terrain or depth survey data. It is also called DEM and represents elevation values only for terrain not including vegetation and artifacts. The scale of the extracted elevation model may be, for example, 1: 1000, 1: 2500, or 1: 5000, and the scale is not limited here. Each GIS layer is extracted with the same scale as the numerical elevation model.)
and layering the buildings on the DEM with the road network (Jeong [Pg 7 Par 3] Referring to FIG. 7, the 3D terrain generation unit 140 generates a three-dimensional terrain (GIS) from the GIS information to a GIS layer for extracting property information about boundaries, buildings, and roads, A height extracting unit 142 for extracting a height map from the GIS information, a merging unit 143 for merging the GIS layer into the height map, and a GIS layer for the building, And a white box building generation unit 144 for finally generating a three-dimensional CCTV control image by generating a white box type building.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba and Jeong. Detecting people and cars and having a DEM without the cars and people, as in Babahajiani, Chiba and Jeong, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people, to yield predictable results.
Regarding claim 2.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 1
further comprises generating a digital surface model (DSM) from the satellite imagery, wherein the DSM is a surface profile of the area of interest (Gallaway [0008] FIG. 1 is a simplified block diagram depicting clustering of images from airborne or spaceborne (e.g., satellite) platforms. As shown, a plurality of platforms 102, such as satellites, capture images of a target 103 from different view angles. [0093] In summary, the point cloud generation process generates a set of unconstrained 3D points from a given set of images, or further refines those 3D points into a constrained (raster) model such as a DSM.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba and Jeong. Detecting people and cars and having a DEM without the cars and people, as in Babahajiani, Chiba and Jeong, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people, to yield predictable results.
Regarding claim 5.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 1
wherein the DEM comprises a DEM point cloud defining a ground surface profile of the area of interest without protrusions (Chiba [0056] The first computer main body unit 100 includes an oblique camera image storing unit 102, a 3D city model generating unit 107, a 3D city model storing unit 109, a first point cloud LAS filing unit 121, a first LAS data storing unit 123, a first DSM generating unit 125 (for example, 20 cm DEM), a first DSM data storing unit 124, a first differencing unit 127, a first differencing data storing unit 129, and a ground DEM storing unit 131 having stored therein a 5 m mesh ground DEM (Geospatial Information Authority of Japan).).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba and Jeong. Detecting people and cars and having a DEM without the cars and people, as in Babahajiani, Chiba and Jeong, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people, to yield predictable results.
Regarding claim 10.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 9
wherein computing the geometry of the buildings comprises computing the geometry of the buildings from the 3D models of the buildings in the area of interest (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like. [0089] In block 312, the sparse, high-confidence point clouds are analyzed to generate a map representing a low and a high “Z” value for each “X/Y” location of each point cloud. The original dense raw points are then filtered based on whether they lie within this “Z” range. The following figure demonstrates an approach for generating an exemplary map. ).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba and Jeong. Detecting people and cars and having a DEM without the cars and people, as in Babahajiani, Chiba and Jeong, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people, to yield predictable results.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395) and Ahmed et al. (US 20200250428).
Regarding claim 3.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 1
and generating 3D models of buildings in the area of interest (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like).
Gallaway, Babahajiani, Chiba and Jeong fail to teach:
comprises preprocessing the satellite imagery which includes generating the point cloud of the area of interest and further comprises: removing atmospheric clouds from the point cloud (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.);
removing shadows from the point cloud (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.);
Ahmed teaches:
comprises preprocessing the satellite imagery which includes generating the point cloud of the area of interest and further comprises: removing atmospheric clouds from the point cloud (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.);
removing shadows from the point cloud (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba and Jeong with Ahmed. Removing clouds and shadows, as in Ahmed, would benefit the Gallaway, Babahajiani, Chiba and Jeong teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, removing shadows from the point cloud, to yield predictable results.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395), Shao et al. (CN 201811340566), Yu et al. (US 20210201570), and Xu et al. (US 20180075319).
Regarding claim 4.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 1 wherein generating the 3D models of the buildings in the area of interest comprises:
enhancing the 2D satellite imagery to produce enhanced satellite imagery (Gallaway [0072] The output of the bundle adjustment block 306 are the corrected geometry parameters with minimized relative error, which allows the subsequent processing to make certain assumptions to improve both quality and timelines for the final 3D point clouds.);
refining shapes of the buildings (Gallaway [0074] The imaging geometry used for building the line of site (LOS) vector grid are the new, bundle-adjustment-corrected geometry parameters, rather than the original parameters.);
Gallaway, Babahajiani, Chiba and Jeong fail to teach:
detecting shadows in the enhanced satellite imagery to identify shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
identifying footprints of the buildings based on information of the shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
estimating heights of the buildings (Yu [0013] In order to accomplish the above objects, a method for generating a digital surface model using satellite imagery according to an embodiment of the present invention includes correcting a geometric error in input satellite images, generating one or more depth maps using a stereo matching method based on the satellite images, the geometric error of which is corrected, combining the generated depth maps, estimating a height for each location on a ground surface, and generating a digital surface model.);
and generating the 3D models of the buildings to produce generated 3D building models of the buildings (Shao [Pg 6 Par 2] In the embodiment of the invention, by shielding the short of the object and display the shape of the building and virtual object within a predetermined range of the current position is adjusted so that the shorter of objects in the simulated environment can be accurately displayed, not being shielded, solves the map scene in the related art short object position displaying the technical problem of inaccurate and reach the technical effect of the accuracy of the display position.).
Xu teaches:
detecting shadows in the enhanced satellite imagery to identify shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
identifying footprints of the buildings based on information of the shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
Yu teaches:
estimating heights of the buildings (Yu [0013] In order to accomplish the above objects, a method for generating a digital surface model using satellite imagery according to an embodiment of the present invention includes correcting a geometric error in input satellite images, generating one or more depth maps using a stereo matching method based on the satellite images, the geometric error of which is corrected, combining the generated depth maps, estimating a height for each location on a ground surface, and generating a digital surface model.);
Shao teaches:
and generating the 3D models of the buildings to produce generated 3D building models of the buildings (Shao [Pg 6 Par 2] In the embodiment of the invention, by shielding the short of the object and display the shape of the building and virtual object within a predetermined range of the current position is adjusted so that the shorter of objects in the simulated environment can be accurately displayed, not being shielded, solves the map scene in the related art short object position displaying the technical problem of inaccurate and reach the technical effect of the accuracy of the display position.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba and Jeong with Shao, Yu, and Xu. Detecting shadows and creating 3d models of buildings, as in Shao, Yu, and Xu, would benefit the Gallaway, Babahajiani, Chiba and Jeong teachings by being able to create models of each building. Additionally, this is the application of a known technique, detecting shadows to identify a building with height estimations, to yield predictable results.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395) and Yu et al. (US 20210201570).
Regarding claim 6.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 2
Gallaway, Babahajiani, Chiba and Jeong fail to teach:
wherein the DSM comprises a DSM point cloud defining a surface profile of the area of interest with protrusions (Yu [0002] The present invention relates generally to a method and apparatus for generating a digital surface model using satellite images, and more particularly to technology for receiving optical satellite images and estimating a height of a ground surface through image processing, thereby generating a Digital Surface Model (DSM).).
Yu teaches:
wherein the DSM comprises a DSM point cloud defining a surface profile of the area of interest with protrusions (Yu [0002] The present invention relates generally to a method and apparatus for generating a digital surface model using satellite images, and more particularly to technology for receiving optical satellite images and estimating a height of a ground surface through image processing, thereby generating a Digital Surface Model (DSM).).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba and Jeong with Yu. Generating a DSM with ground height, as in Yu, would benefit the Gallaway, Babahajiani, Chiba and Jeong teachings by being able to make an accurate image. Additionally, this is the application of a known technique, creating a DSM based on the ground surface, to yield predictable results.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395) and Deng et al. (CN 108257212).
Regarding claim 7.
Gallaway, Babahajiani, Chiba and Jeong teach:
The method of claim 1
Gallaway, Babahajiani, Chiba and Jeong fail to teach:
wherein generating the 3D GIS map comprises generating a 3D GIS map with multiple levels of details (LODs) (Deng [Pg 1 Par 6] 3D geographic information system (GIS) model is more and more used for planning and analysis of city level. generally needs to define 3D city GIS model of different level of detail (LoD) so as to more effectively browse and process large models. When traversing 3 D-city model in multiple LoD graphical user interface, distance of the object will change, it is necessary to correspondingly modifying the LoD representation of object. Therefore, to automatically and quickly change needs to 3D city GIS model of LoD.).
Deng teaches:
wherein generating the 3D GIS map comprises generating a 3D GIS map with multiple levels of details (LODs) (Deng [Pg 1 Par 6] 3D geographic information system (GIS) model is more and more used for planning and analysis of city level. generally needs to define 3D city GIS model of different level of detail (LoD) so as to more effectively browse and process large models. When traversing 3 D-city model in multiple LoD graphical user interface, distance of the object will change, it is necessary to correspondingly modifying the LoD representation of object. Therefore, to automatically and quickly change needs to 3D city GIS model of LoD.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba and Jeong with Deng. Generating a GIS with multiple LODS, as in Deng, would benefit the Gallaway, Babahajiani, Chiba and Jeong teachings by having multiple levels of details. Additionally, this is the application of a known technique, generating a GIS with multiple LODS, to yield predictable results.
Regarding claim 8.
Gallaway, Babahajiani, Chiba and Jeong and Deng teach:
The method of claim 7
wherein texturing the GIS comprises repeating layering the road network, computing the geometry of the building and texturing for each LOD of the 3D GIS map (Chiba [0079] That is, the elevation value allocated to each mesh of the digital elevation model (DEM) is increased several times (emphasized 5 times) per mesh, each mesh is set as a focused point, a certain range is defined per focused point, and an aboveground opening, an underground opening, and an inclination are derived, to generate a second red relief image GP (emphasized 5 times) in which a brighter color is allocated to a part having a higher aboveground opening, in which a darker color is allocated to a part having a higher underground opening, and in which a red-emphasized color is allocated to a part having a higher inclination.[0080] The first feature height comparison image generating unit 158 synthesizes the first building height comparison image GM (first DHM height gradient-tinted image: refer to FIG. 10) in the first building height comparison image storing unit 153 with the second red relief image GP (emphasized 5 times) in the second red relief image storing unit 161 to generate a first feature height comparison image GEC, in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, in the first feature height comparison image storing unit 160. [0081] The first terrain/feature height-based colored image generating unit 162 synthesizes the second red relief image GP (emphasized 5 times: refer to FIG. 16) in the second red relief image storing unit 161, the first gradient-tinted image GD (refer to FIG. 9) in the first gradient-tinted image storing unit 149, and the first red relief image (emphasized 0.2 times) in the first red relief image storing unit 148 with each other to generate a first terrain/feature height-based colored image GHC (also referred to as first Super Cool Map), in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, and in which a feature (a building, a tree, or the like) is expressed in color in accordance with a height and an inclination thereof, in the first terrain/feature height-based colored image storing unit 164.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, and Chiba with Deng. Generating a GIS with multiple LODS, as in Deng, would benefit the Gallaway, Babahajiani, and Chiba teachings by having multiple levels of details. Additionally, this is the application of a known technique, generating a GIS with multiple LODS, to yield predictable results.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Deng et al. (CN 108257212), Jeong et al. (KR 20190059395) and Guo et al. (US 20200126232).
Regarding claim 9.
Gallaway, Babahajiani, Chiba and Jeong, and Deng teach:
The method of claim 7
removing shadows from the point cloud (Babahajiani [0045] Each client device 106 may operate a variety of different applications that may be used, for instance, to view high-elevation digital images that are processed using techniques described herein to remove transient obstructions such as clouds, shadows (e.g., cast by clouds), snow, manmade items (e.g., tarps draped over crops), etc. For example, a first client device 1061 operates an image viewing client 107 (e.g., which may be standalone or part of another application, such as part of a web browser). Another client device 106N may operate a crop prediction application 109 that allows a user to initiate and/or study agricultural predictions and/or recommendations provided by, for example, crop yield and diagnosis system 144.);
and generating 3D models of buildings in the area of interest (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like).
Gallaway, Babahajiani, Chiba and Jeong, and Deng fail to teach:
comprises preprocessing the satellite imagery which includes generating the point cloud of the area of interest and further comprises: removing atmospheric clouds from the point cloud (Guo [0045] Each client device 106 may operate a variety of different applications that may be used, for instance, to view high-elevation digital images that are processed using techniques described herein to remove transient obstructions such as clouds, shadows (e.g., cast by clouds), snow, manmade items (e.g., tarps draped over crops), etc. For example, a first client device 1061 operates an image viewing client 107 (e.g., which may be standalone or part of another application, such as part of a web browser). Another client device 106N may operate a crop prediction application 109 that allows a user to initiate and/or study agricultural predictions and/or recommendations provided by, for example, crop yield and diagnosis system 144.);
Guo teaches:
comprises preprocessing the satellite imagery which includes generating the point cloud of the area of interest and further comprises: removing atmospheric clouds from the point cloud (Guo [0045] Each client device 106 may operate a variety of different applications that may be used, for instance, to view high-elevation digital images that are processed using techniques described herein to remove transient obstructions such as clouds, shadows (e.g., cast by clouds), snow, manmade items (e.g., tarps draped over crops), etc. For example, a first client device 1061 operates an image viewing client 107 (e.g., which may be standalone or part of another application, such as part of a web browser). Another client device 106N may operate a crop prediction application 109 that allows a user to initiate and/or study agricultural predictions and/or recommendations provided by, for example, crop yield and diagnosis system 144.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba, Jeong and Deng with Guo. Removing clouds, as in Guo, would benefit the Gallaway, Babahajiani, Chiba, Jeong and Deng teachings by not having obstacles. Additionally, this is the application of a known technique, removing atmospheric clouds, to yield predictable results.
Claim(s) 11, 12, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395) and Ahmed et al. (US 20200250428).
Regarding claim 11.
Gallaway teaches:
A system for geospatial mapping comprising:
an input module, the input module is configured to receiving 2D satellite imagery of an area of interest (Gallaway [0008] FIG. 1 is a simplified block diagram depicting clustering of images from airborne or spaceborne (e.g., satellite) platforms. As shown, a plurality of platforms 102, such as satellites, capture images of a target 103 from different view angles.),
a processing module, wherein the processing module is configured to generate a DEM of the area of interest, wherein the DEM is a bare surface profile of the area of interest without protrusions (Gallaway [0042] Optionally, if available, read a digital elevation map (DEM) for the geographic region covered by these images.),
generate 3D models of buildings in the area of interest (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like),
and an output module, wherein the output module is configured to output a textured GIS map (Gallaway [0102] The optional process of colorization adds image intensities to the point cloud. Without image intensities, a point cloud is simply a collection of 3D (X,Y,Z) points representing the locations of structural surfaces. However, with image intensities, the point cloud can be viewed as an image, where each point has a “color” based on what type of structure it is representing. For example, a point on grass would be colored green and a point on asphalt would be colored dark gray. This extra attribution makes each 3D point a 3D+ attribute point (e.g., [X,Y,Z]+[R,G,B]) and makes it more interpretable for both a human analyst or to a downstream further processing.).
Gallaway fails to teach:
remove atmospheric clouds, remove shadows (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.),
and remove people and cars (Babahajiani [0003] Three-dimensional (3D) object recognition systems using laser scanning, such as Light Detection And Ranging (LiDAR), provide an output of 3D point clouds. 3D point clouds can be used for a number of applications, such as rendering appealing visual effect based on the physical properties of 3D structures and cleaning of raw input 3D point clouds e.g. by removing moving objects (car, bike, person).);
a road module, wherein the road module is configured to layer a road network onto the DEM without people and cars (Chiba [0079] That is, the elevation value allocated to each mesh of the digital elevation model (DEM) is increased several times (emphasized 5 times) per mesh, each mesh is set as a focused point, a certain range is defined per focused point, and an aboveground opening, an underground opening, and an inclination are derived, to generate a second red relief image GP (emphasized 5 times) in which a brighter color is allocated to a part having a higher aboveground opening, in which a darker color is allocated to a part having a higher underground opening, and in which a red-emphasized color is allocated to a part having a higher inclination.[0080] The first feature height comparison image generating unit 158 synthesizes the first building height comparison image GM (first DHM height gradient-tinted image: refer to FIG. 10) in the first building height comparison image storing unit 153 with the second red relief image GP (emphasized 5 times) in the second red relief image storing unit 161 to generate a first feature height comparison image GEC, in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, in the first feature height comparison image storing unit 160. [0081] The first terrain/feature height-based colored image generating unit 162 synthesizes the second red relief image GP (emphasized 5 times: refer to FIG. 16) in the second red relief image storing unit 161, the first gradient-tinted image GD (refer to FIG. 9) in the first gradient-tinted image storing unit 149, and the first red relief image (emphasized 0.2 times) in the first red relief image storing unit 148 with each other to generate a first terrain/feature height-based colored image GHC (also referred to as first Super Cool Map), in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, and in which a feature (a building, a tree, or the like) is expressed in color in accordance with a height and an inclination thereof, in the first terrain/feature height-based colored image storing unit 164.);
layering the 3D models of buildings in the bare DEM with the road network (Jeong [Pg 3 Par 4] The GIS layer refers to a multi-layered structure of GIS information, consisting of a terrain aerial photo layer, a city and county administrative boundary layer, a highway layer, and a building layer. The lower layer is covered by the upper layer, and ON / OFF of a specific layer and extraction of a specific layer are also possible. [Pg 3 Par 14] A numerical elevation model is a generic term for digital terrain or depth survey data. It is also called DEM and represents elevation values only for terrain not including vegetation and artifacts. The scale of the extracted elevation model may be, for example, 1: 1000, 1: 2500, or 1: 5000, and the scale is not limited here. Each GIS layer is extracted with the same scale as the numerical elevation model.) (Jeong [Pg 7 Par 3] Referring to FIG. 7, the 3D terrain generation unit 140 generates a three-dimensional terrain (GIS) from the GIS information to a GIS layer for extracting property information about boundaries, buildings, and roads, A height extracting unit 142 for extracting a height map from the GIS information, a merging unit 143 for merging the GIS layer into the height map, and a GIS layer for the building, And a white box building generation unit 144 for finally generating a three-dimensional CCTV control image by generating a white box type building.);
Ahmed teaches:
remove atmospheric clouds, remove shadows (Ahmed [0032] As such, the cloud map may be used as a mask for the observed image. In other words, pixels that are identified as clouds or shadows may be removed from an observed image before using the observed image to generate a yield prediction for the agricultural field.),
Babahajiani teaches:
and remove people and cars (Babahajiani [0003] Three-dimensional (3D) object recognition systems using laser scanning, such as Light Detection And Ranging (LiDAR), provide an output of 3D point clouds. 3D point clouds can be used for a number of applications, such as rendering appealing visual effect based on the physical properties of 3D structures and cleaning of raw input 3D point clouds e.g. by removing moving objects (car, bike, person).);
Chiba teaches:
a road module, wherein the road module is configured to layer a road network onto the bare DEM, the road network is without people and cars (Chiba [0079] That is, the elevation value allocated to each mesh of the digital elevation model (DEM) is increased several times (emphasized 5 times) per mesh, each mesh is set as a focused point, a certain range is defined per focused point, and an aboveground opening, an underground opening, and an inclination are derived, to generate a second red relief image GP (emphasized 5 times) in which a brighter color is allocated to a part having a higher aboveground opening, in which a darker color is allocated to a part having a higher underground opening, and in which a red-emphasized color is allocated to a part having a higher inclination.[0080] The first feature height comparison image generating unit 158 synthesizes the first building height comparison image GM (first DHM height gradient-tinted image: refer to FIG. 10) in the first building height comparison image storing unit 153 with the second red relief image GP (emphasized 5 times) in the second red relief image storing unit 161 to generate a first feature height comparison image GEC, in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, in the first feature height comparison image storing unit 160. [0081] The first terrain/feature height-based colored image generating unit 162 synthesizes the second red relief image GP (emphasized 5 times: refer to FIG. 16) in the second red relief image storing unit 161, the first gradient-tinted image GD (refer to FIG. 9) in the first gradient-tinted image storing unit 149, and the first red relief image (emphasized 0.2 times) in the first red relief image storing unit 148 with each other to generate a first terrain/feature height-based colored image GHC (also referred to as first Super Cool Map), in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, and in which a feature (a building, a tree, or the like) is expressed in color in accordance with a height and an inclination thereof, in the first terrain/feature height-based colored image storing unit 164.);
Jeong teaches:
layering the 3D models of buildings in the bare DEM with the road network (Jeong [Pg 3 Par 4] The GIS layer refers to a multi-layered structure of GIS information, consisting of a terrain aerial photo layer, a city and county administrative boundary layer, a highway layer, and a building layer. The lower layer is covered by the upper layer, and ON / OFF of a specific layer and extraction of a specific layer are also possible. [Pg 3 Par 14] A numerical elevation model is a generic term for digital terrain or depth survey data. It is also called DEM and represents elevation values only for terrain not including vegetation and artifacts. The scale of the extracted elevation model may be, for example, 1: 1000, 1: 2500, or 1: 5000, and the scale is not limited here. Each GIS layer is extracted with the same scale as the numerical elevation model.) (Jeong [Pg 7 Par 3] Referring to FIG. 7, the 3D terrain generation unit 140 generates a three-dimensional terrain (GIS) from the GIS information to a GIS layer for extracting property information about boundaries, buildings, and roads, A height extracting unit 142 for extracting a height map from the GIS information, a merging unit 143 for merging the GIS layer into the height map, and a GIS layer for the building, And a white box building generation unit 144 for finally generating a three-dimensional CCTV control image by generating a white box type building.);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba, Jeong and Ahmed. Detecting people and cars and having a DEM without the cars and people and clouds, as in Babahajiani, Chiba, Jeong and Ahmed, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people and clouds, to yield predictable results.
Regarding claim 12.
Gallaway, Babahajiani, Chiba and Jeong and Ahmed teach:
The system of claim 11
wherein the processing module further generates a DSM, the DSM is a surface profile of the area of interest (Gallaway [0008] FIG. 1 is a simplified block diagram depicting clustering of images from airborne or spaceborne (e.g., satellite) platforms. As shown, a plurality of platforms 102, such as satellites, capture images of a target 103 from different view angles. [0093] In summary, the point cloud generation process generates a set of unconstrained 3D points from a given set of images, or further refines those 3D points into a constrained (raster) model such as a DSM.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba, Jeong and Ahmed. Detecting people and cars and having a DEM without the cars and people and clouds, as in Babahajiani, Chiba, Jeong and Ahmed, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people and clouds, to yield predictable results.
Regarding claim 13.
Gallaway, Babahajiani, Chiba and Jeong and Ahmed teach:
The system of claim 12
wherein: the DSM comprises a DSM point cloud defining the surface profile of the area of interest (Gallaway [0008] FIG. 1 is a simplified block diagram depicting clustering of images from airborne or spaceborne (e.g., satellite) platforms. As shown, a plurality of platforms 102, such as satellites, capture images of a target 103 from different view angles. [0093] In summary, the point cloud generation process generates a set of unconstrained 3D points from a given set of images, or further refines those 3D points into a constrained (raster) model such as a DSM.);
and the DEM comprises a DEM point cloud defining a ground surface profile of the area of interest without protrusions (Chiba [0056] The first computer main body unit 100 includes an oblique camera image storing unit 102, a 3D city model generating unit 107, a 3D city model storing unit 109, a first point cloud LAS filing unit 121, a first LAS data storing unit 123, a first DSM generating unit 125 (for example, 20 cm DEM), a first DSM data storing unit 124, a first differencing unit 127, a first differencing data storing unit 129, and a ground DEM storing unit 131 having stored therein a 5 m mesh ground DEM (Geospatial Information Authority of Japan).).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba, Jeong and Ahmed. Detecting people and cars and having a DEM without the cars and people and clouds, as in Babahajiani, Chiba, Jeong and Ahmed, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people and clouds, to yield predictable results.
Regarding claim 14.
Gallaway, Babahajiani, Chiba and Jeong and Ahmed teach:
The system of claim 11
is further configured to compute a geometry of the buildings from the point cloud, wherein the geometry of the buildings is computed from the 3D models of the buildings (Gallaway [0031] The resulting 3D point cloud can be used for any analytics that require a knowledge of a 3D scene, such as any enemy threat determination, flood modeling, aircraft landing zone suitability, or detection of 3D objects, such as buildings, structures, human activity (scene changes over time), and the like. [0089] In block 312, the sparse, high-confidence point clouds are analyzed to generate a map representing a low and a high “Z” value for each “X/Y” location of each point cloud. The original dense raw points are then filtered based on whether they lie within this “Z” range. The following figure demonstrates an approach for generating an exemplary map. ).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway with Babahajiani, Chiba, Jeong and Ahmed. Detecting people and cars and having a DEM without the cars and people and clouds, as in Babahajiani, Chiba, Jeong and Ahmed, would benefit the Gallaway teachings by being able to remove unneeded things. Additionally, this is the application of a known technique, detecting people and cars and having a DEM without the cars and people and clouds, to yield predictable results.
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395), Ahmed et al. (US 20200250428), and Deng et al (CN 108257212).
Regarding claim 15.
Gallaway, Babahajiani, Chiba and Jeong and Ahmed teach:
The system of claim 13
Gallaway, Babahajiani, Chiba and Jeong and Ahmed fail to teach:
wherein the textured GIS map comprises multiple LODs (Deng [Pg 1 Par 6] 3D geographic information system (GIS) model is more and more used for planning and analysis of city level. generally needs to define 3D city GIS model of different level of detail (LoD) so as to more effectively browse and process large models. When traversing 3 D-city model in multiple LoD graphical user interface, distance of the object will change, it is necessary to correspondingly modifying the LoD representation of object. Therefore, to automatically and quickly change needs to 3D city GIS model of LoD.).
Deng teaches:
wherein the textured GIS map comprises multiple LODs (Deng [Pg 1 Par 6] 3D geographic information system (GIS) model is more and more used for planning and analysis of city level. generally needs to define 3D city GIS model of different level of detail (LoD) so as to more effectively browse and process large models. When traversing 3 D-city model in multiple LoD graphical user interface, distance of the object will change, it is necessary to correspondingly modifying the LoD representation of object. Therefore, to automatically and quickly change needs to 3D city GIS model of LoD.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba, Jeong, and Ahmed with Deng. Generating a GIS with multiple LODS, as in Deng, would benefit the Gallaway, Babahajiani, Chiba, Jeong, and Ahmed teachings by having multiple levels of details. Additionally, this is the application of a known technique, generating a GIS with multiple LODS, to yield predictable results.
Regarding claim 16.
Gallaway, Babahajiani, Chiba, Jeong, Ahmed, and Deng teach:
The system of claim 15
wherein the output module is configured to repeatedly layer the road network, compute the geometry of the buildings and texture for each LOD (Chiba [0079] That is, the elevation value allocated to each mesh of the digital elevation model (DEM) is increased several times (emphasized 5 times) per mesh, each mesh is set as a focused point, a certain range is defined per focused point, and an aboveground opening, an underground opening, and an inclination are derived, to generate a second red relief image GP (emphasized 5 times) in which a brighter color is allocated to a part having a higher aboveground opening, in which a darker color is allocated to a part having a higher underground opening, and in which a red-emphasized color is allocated to a part having a higher inclination.[0080] The first feature height comparison image generating unit 158 synthesizes the first building height comparison image GM (first DHM height gradient-tinted image: refer to FIG. 10) in the first building height comparison image storing unit 153 with the second red relief image GP (emphasized 5 times) in the second red relief image storing unit 161 to generate a first feature height comparison image GEC, in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, in the first feature height comparison image storing unit 160. [0081] The first terrain/feature height-based colored image generating unit 162 synthesizes the second red relief image GP (emphasized 5 times: refer to FIG. 16) in the second red relief image storing unit 161, the first gradient-tinted image GD (refer to FIG. 9) in the first gradient-tinted image storing unit 149, and the first red relief image (emphasized 0.2 times) in the first red relief image storing unit 148 with each other to generate a first terrain/feature height-based colored image GHC (also referred to as first Super Cool Map), in which a terrain (a road or a slope) is expressed in color in accordance with a height and an inclination thereof, and in which a feature (a building, a tree, or the like) is expressed in color in accordance with a height and an inclination thereof, in the first terrain/feature height-based colored image storing unit 164.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba, Jeong, and Ahmed with Deng. Generating a GIS with multiple LODS, as in Deng, would benefit the Gallaway, Babahajiani, Chiba, Jeong, and Ahmed teachings by having multiple levels of details. Additionally, this is the application of a known technique, generating a GIS with multiple LODS, to yield predictable results.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallaway et al. (US 20210019937) in view of Babahajiani et al. (US 20170116781), Chiba et al. (US 20210199433), Jeong et al. (KR 20190059395) Ahmed et al. (US 20200250428), Xu et al (US 20180075319), and Yu et al. (US 20210201570).
Regarding claim 17.
Gallaway, Babahajiani, Chiba, Jeong, and Ahmed teach:
The system of claim 11
wherein the processing module, when it generates the 3D models of the builds comprises: enhancing the 2D satellite imagery to produce enhanced satellite imagery (Gallaway [0072] The output of the bundle adjustment block 306 are the corrected geometry parameters with minimized relative error, which allows the subsequent processing to make certain assumptions to improve both quality and timelines for the final 3D point clouds.);
refining shapes of the buildings (Gallaway [0074] The imaging geometry used for building the line of site (LOS) vector grid are the new, bundle-adjustment-corrected geometry parameters, rather than the original parameters.);
Gallaway, Ahmed, Babahajiani, and Chiba, fail to teach:
detecting shadows in the enhanced satellite imagery to identify shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
identifying footprints of the buildings based on information of the shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
and estimating heights of the buildings (Yu [0013] In order to accomplish the above objects, a method for generating a digital surface model using satellite imagery according to an embodiment of the present invention includes correcting a geometric error in input satellite images, generating one or more depth maps using a stereo matching method based on the satellite images, the geometric error of which is corrected, combining the generated depth maps, estimating a height for each location on a ground surface, and generating a digital surface model.).
Xu teaches:
detecting shadows in the enhanced satellite imagery to identify shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
identifying footprints of the buildings based on information of the shadow regions (Xu [0061] The use of either height or shadow may still have some limitations in detecting actual building footprints in complex urban scenarios.);
Yu teaches:
and estimating heights of the buildings (Yu [0013] In order to accomplish the above objects, a method for generating a digital surface model using satellite imagery according to an embodiment of the present invention includes correcting a geometric error in input satellite images, generating one or more depth maps using a stereo matching method based on the satellite images, the geometric error of which is corrected, combining the generated depth maps, estimating a height for each location on a ground surface, and generating a digital surface model.).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gallaway, Babahajiani, Chiba, Jeong, and Ahmed with Xu and Yu. Detecting shadows and creating 3d models of buildings, as in Xu and Yu, would benefit the Gallaway, Babahajiani, Chiba, Jeong, and Ahmed teachings by being able to create models of each building. Additionally, this is the application of a known technique, detecting shadows to identify a building with height estimations, to yield predictable results.
Response to Arguments
Applicant's arguments filed 1/13/2026 have been fully considered but they are not persuasive.
Applicant argues:
“Independent claim 1 is directed to a method for 3D geospatial mapping based on 2D satellite imagery related to an area of interest for geospatial mapping. The satellite imagery is analyzed to generate a bare digital elevation model (DEM). The bare DEM is a bare surface profile of the area of interest without protrusions. A point cloud of the area of interest is generated. A road network is identified as well as people and cars. The people and cars are removed. A 3D geographical information system (GIS) dimensional map is generated by layering the bare DEM to form a layered GIS dimensional map. This includes layering the road network onto the bare surface profile of the bare DEM. The building geometry of buildings is computed. The buildings are layered onto the DEM with the road network. The layered GIS map with the road network and buildings is textured.
In rejecting the claims based on Gallway, the Examiner directs Applicant to paragraph [0042]. See, e.g.,Action, page 3. The directed portion only describes that, "if available, read a digital elevation map (DEM) for the geographic region covered by these images." See, e.g., Galloway, paragraph [0042]. This, however, is not generating a DEM from the satellite imagery or a bare DEM from satellite imagery. Galloway only discusses obtaining knowledge of the elevation from reading a DEM (not a bare DEM but a DEM that includes buildings) or provided by the user. See, e.g., Gal/away, paragraph [0043]. Applicant submits that Gallaway nowhere teaches or suggests generating a bare DEM from the satellite imagery, as required by the present independent claims.
Furthermore, Gallaway also fails to teach or suggest generating a layered GISmap by layering a road network onto the bare DEM and buildings onto the DEM with the road network after calculating the geometry of the buildings from the satellite imagery, as also required by the present independent claims. This fact is acknowledged by the Examiner.
To compensate for the defects of Gallaway, the Examiner relies on Babahajiani. Applicant submits that there is no motivation to combine the references. For example, Babahajiani discusses using LiDAR scanning to obtain point clouds. Applicant submits that generating point clouds by LiDAR is very different from generating point clouds from 2D satellite imagery.Unlike using 2D imagery, the type of objects and their dimensions are already known when using LiDAR scanning. Even if the references were combined, they still fail to render the present independent claims unpatentable. See, e.g., Action, page 5.”
Babahajani shows a method for generating 3d point clouds based on an image. In figure 6a-6b it shows an image from above showing that the input is a satellite type image.
Gallaway speaks about a dem in [0042] and [0043] and Chiba adds multiple mentions of generating a DEM in the abstract and [0057], [0069] [0109], Gallaway also mentions ([0006] A point cloud is a set of points in a three-dimensional coordinate system. In general, a point cloud is a three-dimensional model of a scene on the earth. In geographic information systems, for example, point clouds are used to make digital elevation models of the terrain, or to generate three-dimensional models of, for instance, an urban environment. Point clouds can be formed by combining two-dimensional images captured from two or more perspectives.) where he speaks about using these point clouds to make elevation models.
In regards to the amendments, Jeong is added (Jeong [Pg 3 Par 4] The GIS layer refers to a multi-layered structure of GIS information, consisting of a terrain aerial photo layer, a city and county administrative boundary layer, a highway layer, and a building layer. The lower layer is covered by the upper layer, and ON / OFF of a specific layer and extraction of a specific layer are also possible. [Pg 3 Par 14] A numerical elevation model is a generic term for digital terrain or depth survey data. It is also called DEM and represents elevation values only for terrain not including vegetation and artifacts. The scale of the extracted elevation model may be, for example, 1: 1000, 1: 2500, or 1: 5000, and the scale is not limited here. Each GIS layer is extracted with the same scale as the numerical elevation model.) The GIS can be contained of multiple layers, and each layer is extracted. This also mentions the DEM and other parts of the claims.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/DENIS VASILIY MINKO/Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612
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