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 .
Allowable Subject Matter
Claims 5,9,10,15,19,20 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.
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) 1,2,4,11,12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (US-9633483-B1) in view of Stojanovic (Stojanovic, V., et al. "A service-oriented indoor point cloud processing pipeline." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42 (2019): 339-346.).
Regarding claim 1, Xu discloses receiving, from a scanning system, a point cloud associated with a surface, wherein the point cloud includes a discrete set of data points( col.2 lines 26-30 “receiving a three-dimensional (3D) point cloud having a plurality of data points in 3D space; ”);
sorting, using a k-dimensional tree, the discrete set of data points, wherein the sorting includes organizing each data point of the discrete set of data points based on a plurality of nearest neighbors( col.9, lines 65-68, “A solution is to make use of approximate nearest-neighbors queries via convention kd-tree representations. The overall algorithmic steps for object clustering are as follows” :); downsampling, using voxels, the discrete set of data points of the point cloud (col. 8, lines 23-27, “The point cloud can be down-sampled using any suitable down-sampling technique. As a non-limiting example, the point cloud can be down-sampled using the voxelized grid approach where a 3D voxel grid is positioned over the input point cloud data.”); producing a voxel grid geometry (col. 8, lines 41-45, “A 3D voxel grid can be created efficiently through a hierarchical Octree data structure. An Octree data structure is a commonly understood concept to those skilled in the art, where the Octree is a tree data structure in which each Octree node has either eight children or no children.”);
However, Xu does not fully disclose superimposing, using the k-dimensional tree, the voxel grid geometry and the point cloud, the superimposing including a fast lookup of nearest neighbors; producing data by computing normal, curvature, and spherical coordinates using both the point cloud and the voxel grid geometry separately; and determining, using the voxel grid geometry and k-dimensional tree, point-by-point deflection, wherein the point-by-point deflection represents a deviation of the surface from a design plan.
The combination of Xu and Stojanovic do disclose superimposing, using the k-dimensional tree, the voxel grid geometry and the point cloud, the superimposing including a fast lookup of nearest neighbors(Stojanovic in page 342, Section 3.2 para. 4 teaches, “In our case, we evaluate and visualize how close a cluster of points of a given as-is point cloud is to the overlapping voxel element of an voxlized as-designed BIM model in the same 3D space. In turn, the deviation threshold value is used to determine beyond what threshold (measured as distance in Euclidean space), we consider a 3D point to be deviating. This value can be adjusted by the user or based on the required use-case specific parameters. The deviation threshold value allows us to set an acceptable fault tolerance when comparing different geometric and primitive-type representations.” Stojanovic explicitly teaches overlapping a voxelized BIM mesh and an as-is point cloud in the same 3D space, and then determined voxel by voxel, which points fall in which voxels. This is effectively superimposing voxel grid and point cloud deviation analysis. This can be used with the k-d tree taught by Xu.); producing data by computing normal, curvature, and spherical coordinates using both the point cloud and the voxel grid geometry separately(Stojanovic page 342, Section 3.2 “The segmentation tool makes use of the Point Cloud Library framework (Cousins, Rusu, 2011), in order to perform segmentation operations on a given point cloud. The segmentation operations include computation of point normal vectors, segmentation using region growing, and generation of axis-aligned (AABB) and object-oriented (OOBB) bounding boxes generated from the segmented point clusters.” This passage teaches computing point normals and using a surface curvature threshold as part of segmentation and deviation analysis on point clouds. The spherical coordinates are implicitly disclosed through distances and orientations in Euclidean 3D.); and determining, using the voxel grid geometry and k-dimensional tree, point-by-point deflection, wherein the point-by-point deflection represents a deviation of the surface from a design plan(Stojanovic, Page 342, Section 3.2, para. 1 “This voxelized mesh representation can then be aligned and compared against the as-is point cloud representation in order to highlight any spatial deviations.” Stojanovic directly performs deviation analysis between a voxelized as-designed BIM model (design plan) and an as-is point cloud, checking each point/voxel combination to determine whether a point is deviating and marking deviating points. Xu provides the necessary k-d tree nearest neighbor teachings as mentioned above.) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Stojanovic into the teachings of Xu in order to a reduce computation time and memory usage when processing large indoor point clouds.
Regarding claim 2, the combination of Xu and Stojanovic disclose all the elements of claim 1 as discussed above. Xu also discloses wherein the voxel grid geometry is defined by a size, and wherein the downsampling includes computing a plurality of first and second coordinates based on a plurality of two-dimensional centroids(Xu states in col.8, lines 23-40, “The point cloud can be down-sampled using any suitable down-sampling technique. As a non-limiting example, the point cloud can be down-sampled using the voxelized grid approach where a 3D voxel grid is positioned over the input point cloud data. In each voxel (i.e., 3D box), all the points inside will be approximated (i.e., down-sampled) to their centroid. For example, imagine a dense point cloud of a car. The system first partitions the point cloud using voxelized grid as depicted in FIG. 4. Then, within each voxel, the system deletes every point except for the center one. The end result is a much sparser point cloud (of the car) as compared to the initial one. For the purpose of the present invention, the system does not require the full “resolution” of the point clouds. This approach is a little slower than approximating them with the center of the voxel, but it represents the underlying surface more accurately to prevent loss of recognition accuracy. FIG. 4, for example, depicts an example of partitioning of a point cloud of a vehicle with a voxel grid.” The first half of the claim (“voxel grid geometry is defined by a size”) is well supported by Xu’s 3D voxel grid. The voxel grid being defined by a cubic bounding box that encloses all points and a subdivision by a fixed factor. This is directly analogous to a voxel grid whose geometry is parameterized by cell size/resolution, or being “defined by a size.” Xu also states in col.10, lines 47-53 “. The basic idea is to project the 3D blob into multiple 2D image slices at various heights. The 2D slices contain all the 3D shape information of the object if the sample slices are detailed enough (similar to CT/MRI scanned slices). The 2D image slices are regularly spaced images and thus, all the available image processing techniques can be applied to process these image slices.“ This further demonstrates how the 3D blobs are projected into 2D image slices, where each voxel maps to a 2D position in those slices, effectively first and second coordinates in the slice plane.)
Regarding claim 4, the combination of Xu and Stojanovic disclose all the elements of claim 1 as discussed above. Stojanovic also discloses wherein the fast lookup of nearest neighbors is based on a distance parameter and a maximum number of neighboring points(page.342, Section 3.3, para.3 “In our case, we evaluate and visualize how close a cluster of points of a given as-is point cloud is to the overlapping voxel element of an voxlized as-designed BIM model in the same 3D space. In turn, the deviation threshold value is used to determine beyond what threshold (measured as distance in Euclidean space), we consider a 3D point to be deviating” This passage teaches a distance-based parameter that controls when a point is considered deviating. This means when it is “far enough” from its surface. This is aligns with the claim limitation of the “distance parameter.” Further apge 343, section 3.3 para. 1 “Using the web-based client tool, users are able to adjust each of the segmentation, reconstruction and deviation analysis parameters, and interactively view and inspect the generated results sent by the server. This includes setting parameter such as the sub-sampling and scaling factors for the point cloud, region growing segmentation parameters (min/max point sampling, surface curvature threshold, knearest neighbour sampling size), and the desired voxel resolution size for the voxelized version of the as-is OOBB mesh used for deviation analysis comparison;” is effectively a maximum number of neighboring points used for neighborhood operations. This is analogous to the claim limitation on the number of neighbors returned by the fast nearest-neighbor search.) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Stojanovic into the teachings of Xu in order to a reduce computation time and memory usage when processing large indoor point clouds.
Claim 11, which is similar in scope to claim 1, thus rejected under the same rationale.
Claim 12, which is similar in scope to claim 2, thus rejected under the same rationale.
Claim 14, which is similar in scope to claim 4, thus rejected under the same rationale.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu as modified by Stojanovic as applied to claim 2 above, and further in view of Tourapis(US-20200314435-A1).
Regarding claim 3, the combination of Xu and Stojanovic discloses all the elements of claim 2 as discussed above. However, the combination does not disclose wherein the downsampling comprises computing a plurality of third coordinates, and wherein computing a third coordinate of the plurality of third coordinates comprises calculating an average of nearest neighbor coordinate values of the third coordinate.
Tourapis does disclose wherein the downsampling comprises computing a plurality of third coordinates, and wherein computing a third coordinate of the plurality of third coordinates comprises calculating an average of nearest neighbor coordinate values of the third coordinate. (para. [694-697], discusses PC1 and PC2 where PC2 is a re-sampled version of PC1. In the resampling step each point in PC2 is associated with a local neighborhood of points in PC1 and is assigned representative values derived from that neighborhood, effectively being a downsampled representation where each output point’s data is derived from a group of neighbors. Further para. [0379-396] teaches computing per-point values for downsampled point cloud by averaging per-component values over a nearest-neighbor set of original points, which is structurally the same as computing a third coordinate of the plurality of third coordinates by calculating an average of nearest neighbor coordinate values of that third coordinate.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Stojanovic into the teachings of Xu in order to a reduce computation time and memory usage when processing large indoor point clouds.
Claim 13, which is similar in scope to claim 3, thus rejected under the same rationale.
Claim(s) 6 and 16, is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu as modified by Stojanovic as applied to claim 1 above, and further in view of Li (Li, Fangxin, et al. "Laser scanning based surface flatness measurement using flat mirrors for enhancing scan coverage range." Remote Sensing 13.4 (2021): 714.)
Regarding claim 6, the combination of Xu and Stojanovic disclose all the elements of claim 1 as discussed above. However, the combination does not disclose wherein the surface is a concrete surface
Li does disclose wherein the surface is a concrete surface(Li in the abstract states, “Thanks to its speed and accuracy, terrestrial laser scanning (TLS) has been popularly used for surface flatness inspection of concrete slabs”. The entire study described in the paper is explicitly a study about concrete surfaces.) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Li into the combination of teachings of Xu and Stojanovic in order to a have a system that can better make determinations on different surfaces.
Claim 16, which is similar in scope to claim 6, thus rejected under the same rationale.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu as modified by Stojanovic as applied to claim 1 above, and further in view of Minear(US-20090232388-A1).
Regarding claim 7, the combination of Xu and Stojanovic discloses all the elements of claim 1 as discussed above. However the combination does not disclose wherein the scanning system includes a LIDAR system. Minear does disclose wherein the scanning system includes a LIDAR system.(para.[0033] “One example of a 3D imaging system that generates one or more frames of 3D point cloud data is a conventional LIDAR imaging system. In general, such LIDAR systems use a high-energy laser, optical detector, and timing circuitry to determine the distance to a target. In a conventional LIDAR system one or more laser pulses is used to illuminate a scene. Each pulse triggers a timing circuit that operates in conjunction with the detector array. In general, the system measures the time for each pixel of a pulse of light to transit a round-trip path from the laser to the target and back to the detector array. The reflected light from a target is detected in the detector array and its round-trip travel time is measured to determine the distance to a point on the target. The calculated range or distance information is obtained for a multitude of points comprising the target, thereby creating a 3D point cloud.”) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Minear into the combination of teachings of Xu and Stojanovic in order to a be able to directly capture accurate 3D geometry with high point density over large areas.
Claim 17, which is similar in scope to claim 7, thus rejected under the same rationale.
Claim(s) 8,18,21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu as modified by Stojanovic as applied to claim 1 above, and further in view of Kumar (Kumar, V. Vinod, G. R. N. Tagore, and A. Venugopal. "Some investigations on geometric conformity analysis of a 3-D freeform objects produced by rapid prototyping (FDM) process." Int J Appl Res Mech Eng 1.2 (2011): 82-86.)
Regarding claim 8, the combination of Xu and Stojanovic disclose all the elements of claim 1 as discussed above. The combination of Xu and Stojanovic also discloses the method further comprising: identifying, using the voxel grid geometry and the k-dimensional tree, a remediation location(Stojanovic , page. 341, col.2 para. 2, “A voxelized representation of an as-built or as-designed mesh can be generated by evaluating the shape and projection properties of the triangular mesh that is used to generate a 3D voxel grid (Eisert, 2005). This voxelized representation of the mesh can be computed, at varying resolutions, most commonly using octrees (Hornung et al., 2013) — and can preserve most of the geometric features of the original polygonal mesh, making it useful for approximating deviations of non-rectified or curved geometry.” In this reference “remediation locations” correspond to voxels whose derivations exceed a threshold when comparing the as-is point cloud to the voxelized as designed BIM. These values can be used with the k-dimensional tree taught in Xu to fully disclose the claim element.); It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Stojanovic into the teachings of Xu in order to a allow for more efficient computations when processing large point clouds.
However, the combination of Xu and Stojonavic alone do not fully disclose determining a first and second coordinate associated with the remediation location; determining an amount of deflection associated with the remediation location; and transmitting, to a remediation resource, the first and second coordinate and the amount of deflection.
The combination of Xu, Stojonavic and Kumar do disclose determining a first and second coordinate associated with the remediation location(Kumar in Table 1, explicitly outputs X and Y coordinates for each spot where deviation is computed. The X/Y entries are “first and second coordinates” associated with “each deviation location on the surface. This is essentially “determining a first and second coordinate associated with the remediation location”); determining an amount of deflection associated with the remediation location(Kumar In Tables 1–3 , gives for each (X, Y) location a numerical “Deviation (mm)” value. For example, Table 1 shows deviations such as 0.561 mm, 0.543 mm, 0.224 mm at given coordinates; Table 2 shows deviations after uniform compensation; Table 3 shows deviations after non-uniform compensation. These are described explicitly as “Deviation values of freeform surface at different cross section” For each coordinate pair, they compute and list a scalar deviation value in millimiters. This directly aligns with the claim element.). ; and transmitting, to a remediation resource, the first and second coordinate and the amount of deflection(Stojanovic in Section 5, discloses “Additionally, with Web3D-based visualization, the users can interactively inspect and annotate the point cloud results for various processing and analysis stages” Stojanovic discloses a pipeline that transmits deviation information to users who are in effect the remediation resource. The Web3D client must receive all the data necessary to communicate the necessary information of location and deflection.) It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the teachings of Kumar into the combination of teachings of Xu and Stojanovic in order to have a system that accurately determines deflection amount and is able to transmit those coordinates properly.
Claims 18 and 21, which are similar in scope to claim 8, thus rejected under the same rationale.
Conclusion
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/CHRIS ALEJANDRO PUNTIER/Examiner, Art Unit 2616
/DANIEL F HAJNIK/Supervisory Patent Examiner, Art Unit 2616