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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/21/2026 has been entered.
Claim Status
Claims 1 and 12have been amended. No claim has been added or cancelled. Claims 1-20 are pending.
Compact Prosecution
With respect to Claim Interpretation, the Examiner has provided some notes regarding “[BRI on the record]” throughout the Office Action, so that the record is clear about the scope of the claimed invention, and the record is also clear about the basis for the Examiner’s analyses. A clear record of the claim interpretation could expedite the examination by creating the condition to allow the examination to focus on Applicant’s inventive concept and its comparison with related prior art.
If there are disagreements, Applicant may present an alternative interpretation based on MPEP 2111. The Examiner will adopt Applicant’s interpretation on the record, if Applicant’s interpretation is reasonable and/or arguments are persuasive.
Applicant may amend claims relying on the Examiner’s claim interpretation provided on the record.
Response to Arguments
Applicant’s arguments (Remarks p. 7) are moot in view of the Examiner’s new ground of rejections with an added reference.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
Claims 1-3, 6, 9, 12-14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yadav et al. (“Rural Road Surface Extraction Using Mobile LiDAR Point Cloud Data”) in view of Yu et al. (“3D reconstruction of road surfaces using an integrated multi-sensory approach.”), Ding et al. (CN 110443770 A), and Ninja (“Average height of a point based on nearby points”).
Regarding Claim 1, Yadav teaches A method of generating surface feature data, the method comprising:
receiving a data set comprising a plurality of data points (
“Rural Road Surface Extraction Using Mobile LiDAR Point Cloud Data.” Yadav Title.
The received “data set” is mapped to disclosed “Point Cloud Data.”);
extracting input layers from the data set (
[BRI on the record] with respect to “input layers from the data set,” the Examiner is reading the limitation to mean: input data points from the data set and placed into layers according to data points’ coordinates. The interpretation is in light of the specification, which states:
[0043] In embodiments, the input layers 306 and, thus, the roadway surface 304, may also be auto-generated by the computing device 102. For example, as depicted in FIG. 2 and discussed hereinabove, the memory component 260 may include the LiDAR processing logic 262 for receiving and processing LiDAR data from the sensor 104c of the vehicle 104. The LiDAR processing logic 262 may automatically distinguish data points 300 associated with the roadway 304 and data points 300 associated with objects other than the roadway (such as trees, buildings, or cars). The input layers 306 generated from the data points 300 are depicted in FIG. 3B. The input layers 306 may be two-dimensional in nature and, thus, may not accurately reflect heights and/or imperfections on the roadway surface 304. As such, the input layers 306 may be dilated, eroded to planar surfaces 500, and heights may be assigned on each of the planar surfaces 500 to more accurately reflect the roadway surface 304, as described further herein.
Spec. ¶ 43.
[Mapping Analysis]
Yadav p. 533 right col.:
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The claimed “input layer” is mapped to the layers of ground points.
If an “input layer” is 2D plane without height, it may be mapped to each ground tile of ground points in the X-Y plane, when the group points are projected on to the plane.
If an “input layer” is 3D, it may be mapped to the ground points between the layer (Zminimum) and layer (Zminimum +hground)
If an “input layer” is a 3D surface, it may be mapped to ground points in the surface defined by the 4 corner points of each grid, and the 4 corner points have x, y, and z coordinate values.);
dilating and unionizing the input layers (
[BRI on the record] with respect to “dilating,” the Examiner is reading the limitation to mean: enlarging. The interpretation is in light of the specification, which states:
[0045] The input layers 306 may be dilated and unionized by the system 100, such as to generate the unionized input layer 400, as depicted in FIG. 4. The input layers 306 may be dilated until each of the adjacent input layers 306 are overlapping. The input layers 306 may then be unionized, to generate the single, unionized input layer 400, as depicted in FIG. 4. The unionized input layer 400 is a single planar surface, combining the individual input layers 306 into the unionized input layer 400.
Spec. ¶ 45.
[Mapping Analyses]
Yadav discloses, “The 2D roughly classified ground points lying within square grids are further divided into partial overlapping circles of radius Rradius (Yadav et al. 2016). 2D circles are converted into 3D discs when Z value of each 2D point is considered.” Yadav p. 533 right col.
Here, the ground points of layers are dilated/enlarged, because (ii) the area represented by a point has been dilated to a circle, (ii) the circles become overlapping which bridges neighboring layers.
Yadav discloses unionizing layers by unionizing group-surface points satisfying certain requirements, stating “Data points of all such discs from each grid are filtered out and grouped together as planar ground surface,” and “In this section all on-ground objects, shrubs and clusters of fluctuating ground points in their vicinity in terms of their elevations are removed from input data set P and rest is data set Pplanarground, which represents planar ground surface.” Yadav p. 534 left col.);
eroding
[BRI on the record] with respect to “eroding,” the Examiner is reading the limitation to mean: shrinking. The interpretation is in light of the specification, which states:
[0046] The logic may further cause the system 100 to erode and triangulate the input layers 306 (e.g., the unionized input layer 400) to generate a plurality of planar surfaces 500, as depicted in FIG. 5. The dilation described above may be undone/reversed in order to have a more accurate representation of the roadway surface 304, while maintaining the unionization of the input layers 306. The input layers 306 may be eroded to a geometry that reflects boundaries of the input layers 306 before the input layers 306 were dilated, such as to more accurately reflect dimensions of the roadway surface 304.
Spec. ¶ 46.
[Mapping Analyses]
Yadav discloses eroding away non-road points throughout a seeding process, stating “The new seed points classified as road surface points are further chosen and their neighbours are considered as next seed points. Above discussed steps are again repeated using these next seed points and the process continues till neighbours satisfy the criteria to be a road surface point. Data point once acted as the seed point does not considered again as seed point. In this way the seed points which qualify as road surface points in a sequence are connected together to form a cluster. The largest cluster of connected road points qualifies as final refined road surface and represented by new data set Prefinedroad.” Yadav pp. 534-535.);
assigning heights to
The refined road surfaces represented by data points set Prefinedroad < Pplanarground. Because data points have Z values, and the refined road surfaces represented by the data points also have assigned heights.)
However, Yadav does not explicitly disclose
triangulating refined road data to generate a plurality of planar surfaces;
for each vertex of the plurality of planar surfaces, calculating an average height of data points surrounding the vertex, wherein a first data point nearer the vertex is assigned a higher weight than a second data point farther from the vertex when calculating the average height of data points surrounding the vertex;
assigning heights to each vertex of the plurality of planar surfaces based on the average height of data points surrounding each vertex;
detecting incomplete features in the plurality of planar surfaces; and
filling the incomplete features in the plurality of planar surfaces.
Yu teaches
triangulating refined road data to generate a plurality of planar surfaces (
Yu discloses, “Our purpose is to reconstruct accurate surface information from the scattered coarse point cloud data. We are inspired by the work of Saaban [31] to construct local quadratic polynomials to compute surface normals, and further to use a popular 2D Delaunay triangulation method for surface reconstruction. We explain this interpolation scheme in the following paragraphs.” Yu et al. p. 813 left col.);
assigning heights to each vertex of the plurality of planar surfaces (After Yadav’s planar surfaces have been triangulated, according to Yu, to be represented by vertices, and the vertices inherit the height values of Yadav’s planar surfaces.);
detecting incomplete features in the plurality of planar surfaces (
[BRI on the record] with respect to “incomplete features,” the Examiner is reading the limitation to mean: features with missing information compared to equivalent features acquired through the same process. The interpretation is in light of the specification, which states:
[0055] The logic may further cause the system 100 to detect incomplete features 502 in the plurality of planar surfaces 500, as depicted in FIG. 7A. Incomplete features 502 may include incomplete data points 300 and, thus, the failure of the system 100 to assign heights to vertices of vertices of the planar surfaces 500. The incomplete features 502 may be an area in which the LiDAR sensor 104c did not capture any data points 300. The incomplete features 502 may be due to a pothole in the roadway surface 304, an area in which a car was parked while the LiDAR sensor was detecting points on the roadway surface 304, or any other object/imperfection on the roadway surface 304 that may result in incomplete features on the plurality of planar surfaces 500.
Spec. ¶ 55.
[Mapping Analyses]
“The result after registering range points is non-uniformly and irregularly distributed. Furthermore, with water puddles or glass objects on the road surface at the time of scanning, range data may be missing in these areas. Our solution for visualizing scattered point clouds of road surface data is to first grid the surface using interpolation methods and then fill holes in areas missing range measurements.” Yu p. 812 right col.); and
filling the incomplete features in the plurality of planar surfaces (Id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s (a) tessellation and (b) hole filling with Yadav. One of ordinary skill in the art would be motivated to (a) preserve details on the road surface model and (b) provide an estimate when information is incomplete. The model created will provide more information and more useful to a user.
Yadav in view of Yu does not explicitly disclose
for each vertex of the plurality of planar surfaces, calculating an average height of data points surrounding the vertex, wherein a first data point nearer the vertex is assigned a higher weight than a second data point farther from the vertex when calculating the average height of data points surrounding the vertex;
assigning heights to each vertex based on the average height of data points surrounding each vertex.
Ding teaches
for each vertex of the plurality of planar surfaces, calculating an average height of data points surrounding the vertex
assigning heights to each vertex of the plurality of planar surfaces based on the average height of data points surrounding each vertex (
“. . . constructing the discrete point cloud TIN model according to the discrete point cloud TIN model. obtaining the one ring neighborhood of each vertex in the model, bicyclic adjacent domain, . . . calculating the bicyclic neighbourhood height average value of each point and the bicyclic neighbourhood height standard deviation; . . . .” Ding Abstract.
“h (v) is the height of vertex v, Eh (v) bicyclic adjacent domain average TIN elevation model bicyclic adjacent domain point corresponding to the vertex v.” Ding p. 4.
After Yadav in view of Yu is combined with Ding, road surface points go through Ding’s method.
Yadav discloses eroding away non-road points throughout a seeding process, stating “The new seed points classified as road surface points are further chosen and their neighbours are considered as next seed points. Above discussed steps are again repeated using these next seed points and the process continues till neighbours satisfy the criteria to be a road surface point. Data point once acted as the seed point does not considered again as seed point. In this way the seed points which qualify as road surface points in a sequence are connected together to form a cluster. The largest cluster of connected road points qualifies as final refined road surface and represented by new data set Prefinedroad.” Yadav pp. 534-535. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ding’s height approximation for a vertex with Yadav in view of Yu. One of ordinary skill in the art would be motivated to simplify the triangulation representation and reasonably represent height based on point cloud.
However, Yadav in view of Yu and Ding does not explicitly disclose:
wherein a first data point nearer the vertex is assigned a higher weight than a second data point farther from the vertex when calculating the average height of data points surrounding the vertex.
Ninja teaches
wherein a first data point nearer the vertex is assigned a higher weight than a second data point farther from the vertex when calculating the average height of data points surrounding the vertex (
“I have a bunch of peaks, whose position and elevation i know. I have a point which is surrounded by all these peaks. I know its position. I am trying to calculate the elevation of this point.
I would like to calculate the height of this point, based on how close/far it is to each of these peaks, and the elevation of each of these peaks.
Example: Peak 1 is at (0,0), with an elevation of 500 Peak 2 is at (100,100), with an elevation of 1000 Peak 3 is at (0,100), with an elevation of 750
If my point is at (99,99), i want the elevation of this point to be as close to 1000.” Ninja p. 1.
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Here, the weight (Sum of all distances – distance from Pi)/(Sum of all distances) for a point is higher when the point is nearer to the vertex, the height of which is to be estimated.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ninja’s averaging strategy with Yadav in view of Yu and Ding. One of ordinary skill in the art would be motivated to more accurately estimate the height for many situations. Attributes of a vertex and a neighboring point is more likely to be similar if they are closer. For example, the climate attributes for NYC and Philadelphia are more likely to be similar compared to those of NYC and Miami.
Claim 12 is substantially similar to Claim 1, and the rejection analyses for Claim 1 are applied to Claim 12. In addition, Claim 12 recites, “A system for generating surface feature data, the system comprising a computing device comprising a processor and a memory component, wherein the memory component stores logic that, when executed by the processor, causes the system to perform” (Yadav and Yu disclose computer graphics techniques).
Regarding Claim 2, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 1, wherein the data set includes the plurality of data points from LiDAR sensor data (“Rural Road Surface Extraction Using Mobile LiDAR Point Cloud Data” Yadav Title.).
Claim 13 is substantially similar to Claim 2, and the rejection analyses for Claim 2 are applied to Claim 13.
Regarding Claim 3, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 1, wherein the input layers represent a roadway surface (
The claimed “input layer” is mapped to the layers of ground points that represent unrefined roadway surface.
Yadav states, “In this way the seed points which qualify as road surface points in a sequence are connected together to form a cluster. The largest cluster of connected road points qualifies as final refined road surface and represented by new data set Prefinedroad.” Yadav p. 535.).
Claim 16 is substantially similar to Claim 3, and the rejection analyses for Claim 3 are applied to Claim 16.
Regarding Claim 6, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 1, wherein each of the plurality of data points includes Cartesian coordinates, intensity values, and timestamp values (
“Data set P contain detail geometric and radiometric information of roadway scene in terms of 3D coordinate and intensity of MLS data point.” Yadav p. 533 right col. In the same paragraph, Yadav mentions x, y, and z coordinates.
Yadav teaches GPS time as “timestamp values,” stating “Therefore, we propose a method for extracting rural roads, which works on raw unstructured MLS data. The unstructured ,data is independent of scanning geometry and neighbourhood structure (e.g. data containing scan information) in the input data file. Additional information like GPS time, navigation data, returned pulse width, proximity to the vehicle and vehicle mounted GPS antenna height, which are used in the existing literature, are not required in the proposed method.” Yadav p. 532 right col.
Here, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yadav’s (a) GPS time. One of ordinary skill in the art would be motivated to preserve additional dimension of data, so that the data could support more types of data processing in the future. Further, Yadav’s image processing method could be integrated with processing methods that uses GPS time to provide a stronger model adapted to a wide range of scenarios.).
Claim 18 is substantially similar to Claim 6, and the rejection analyses for Claim 6 are applied to Claim 18.
Regarding Claim 9, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 6, wherein the heights are assigned to each vertex of the plurality of planar surfaces through the Cartesian coordinates of three-dimensional points in the data set (
Yadav teaches, before triangulation, the heights are assigned to . . . the plurality of planar surfaces through the Cartesian coordinates of three-dimensional points in the data set.
“Data set P contain detail geometric and radiometric information of roadway scene in terms of 3D coordinate and intensity of MLS data point.” Yadav p. 533 right col. In the same paragraph, Yadav mentions x, y, and z coordinates.
Yadav teaches creating 3D road surfaces based on above mentioned MLS data points, stating “Therefore the proper maintenance and road safety analysis of rural roads are recommended activity, which could be addressed using detailed 3D road surface information.” Yadav Abstract. Later in the abstract, the author discloses that its method achieves the goal. Therefore, the planar surfaces generated have height values derived from the 3D coordinates of the data points.
After Yadav’s planar surfaces have been triangulated, according to Yu, to be represented by vertices, and the vertices inherit the height values of Yadav’s planar surfaces.
Yu discloses, “Our purpose is to reconstruct accurate surface information from the scattered coarse point cloud data. We are inspired by the work of Saaban [31] to construct local quadratic polynomials to compute surface normals, and further to use a popular 2D Delaunay triangulation method for surface reconstruction. We explain this interpolation scheme in the following paragraphs.” Yu et al. p. 813 left col.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s (a) tessellation with Yadav. One of ordinary skill in the art would be motivated to (a) preserve details on the road surface model.).
Regarding Claim 14, Yadav in view of Yu, Ding, and Ninja teaches The system of claim 12, further comprising a graphical user interface communicatively coupled to the processor, wherein the system further displays a three-dimensional model of the surface feature data on the graphical user interface (
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s model displaying with Yadav in view of Yu and Ding. One of ordinary skill in the art would be motivated to allow a user to review a model created, so that the user may see the information the model creates and/or the user may provide feedback to the modeling process.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claim 3, in further view of Dolan et al. (US 20210365610 A1).
Regarding Claim 4, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 3.
Yadav in view of Yu, Ding, and Ninja does not explicitly disclose wherein a distance between vertices of the plurality of planar surfaces are smaller on a roadway surface edge.
Dolan teaches wherein a distance between vertices of the plurality of planar surfaces are smaller on a roadway surface edge (
Dolan fig. 2A
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Note, a) the density of the mesh is higher at the roadway surface edge in comparison with roadway surface center; and b) during the turns, the mesh density is even higher at the roadway surface edges.
Yadav in view of Yu, Ding, and Ninja already teaches a triangulated mesh system. Once Yadav in view of Yu, Ding, and Ninja is combined with Dolan, the density of triangulated mesh is denser at certain edges, and therefore, a distance between vertices at the edge is smaller.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dolan’s with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to sufficiently capture the characteristics of a road surface when it makes a turn or at the edge of the road. The road surface undergoes significantly more changes compared to the road surface that is going perfectly straight. Further, the precise borders of a road surface is important to a driver, and it has impact on safety.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claims 3 and 12, in further view of Wu et al. (“Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors”).
Regarding Claim 5, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 3.
Yu further teaches wherein the incomplete features in the plurality of planar surfaces are roadway imperfections
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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yu’s road-surface modeling with Yadav. One of ordinary skill in the art would be motivated to improve road safety and maintenance. Yadav states “Therefore the proper maintenance and road safety analysis of rural roads are recommended activity, which could be addressed using detailed 3D road surface information.” Yadav Abstract.
Yadav in view of Yu, Ding, and Ninja does not explicitly disclose wherein the incomplete features in the plurality of planar surfaces are potholes in the roadway surface.
Wu teaches wherein the incomplete features in the plurality of planar surfaces are potholes in the roadway surface (“Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors.” Wu Title.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu’s pothole detection with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to enhance road safety and maintenance.
Claim 17 is substantially similar to Claim 5, and the rejection analyses for Claim 5 are applied to Claim 17.
Claims 7-8 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claims 6 and 18, in further view of Guo et al. (“Automatic reconstruction of road surface features by using terrestrial mobile lidar”).
Regarding Claim 7, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 6.
However, Yadav in view of Yu, Ding, and Ninja does not explicitly disclose further comprising detecting surface color features through the intensity values of the plurality of data points.
Guo teaches further comprising detecting surface color features through the intensity values of the plurality of data points (
“The images were produced using the reflective intensity of point clouds. Image-processing techniques, including image binarization, morphological image processing, and component labeling, were then used for identifying candidate objects of road surface features.” Guo p. 167 right col. Fig. 5 shows the road surface features:
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s road sign detection with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to enhance road safety and maintenance. Guo Abstract.
Claim 19 is substantially similar to Claim 7, and the rejection analyses for Claim 7 are applied to Claim 19.
Regarding Claim 8, Yadav in view of Yu, Ding, Ninja, and Guo teaches The method of claim 7, wherein the surface color features are road paint features of a roadway (
Guo:
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s road sign detection with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to enhance road safety and maintenance. Guo Abstract.
Claim 20 is substantially similar to Claim 8, and the rejection analyses for Claim 8 are applied to Claim 20.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claim 1, in further view of Guo et al. (“Automatic reconstruction of road surface features by using terrestrial mobile lidar”) and Musabji et al. (US 20120059720 A1).
Regarding Claim 10, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 1.
Yadav in view of Yu, Ding, and Ninja does not explicitly disclose further comprising:
generating a two-dimensional model of the surface feature data based on the data set; and generating a three-dimensional model of the surface feature data based on the data set.
Guo teaches generating a two-dimensional model of the surface feature data based on the data set (“The images were produced using the reflective intensity of point clouds. Image-processing techniques, including image binarization, morphological image processing, and component labeling, were then used for identifying candidate objects of road surface features.” Guo p. 167 right col. Fig. 5 shows the road surface features:
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s road sign detection with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to enhance road safety and maintenance. Guo Abstract.
Yadav in view of Yu, Ding, Ninja, and Guo does not explicitly disclose generating a three-dimensional model of the surface feature data based on the data set.
Musabji teaches generating a three-dimensional model of the surface feature data based on the data set (“The icons or decals 2102 are textures, such as a two-dimensional image of a 35 MPH speed limit road sign, projected on to the road surface ground plane 2106 shown in the image 2100.” Musabji ¶ 109.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Musabji’s integration of 2D signs into a 3D model with Yadav in view of Yu, Ding, Ninja, and Guo. One of ordinary skill in the art would be motivated to enhance road safety and maintenance and to make the model more informative.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claim 1, in further view of Raymond et al. (US 20130187910 A1).
Regarding Claim 11, Yadav in view of Yu, Ding, and Ninja teaches The method of claim 1.
However, Yadav in view of Yu, Ding, and Ninja does not explicitly disclose further comprising filling the incomplete features in the plurality of planar surfaces with an algorithm utilizing a Laplace operator.
Raymond teaches further comprising filling the incomplete features in the plurality of planar surfaces with an algorithm utilizing a Laplace operator (
Ryamond states, “To address this issue, the conversion program may fill these holes or gaps using linear interpolation or Poisson blending (e.g., solving Laplace's equation to minimize gradients in the hole regions). . . . These methods can produce plausible or useful results, but many of these methods may produce unnatural and/or undesirable artifacts near occlusion boundaries and inside disoccluded regions.” Ryamond ¶ 53.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ryamond’s hole filling technique with Yadav in view of Yu, Ding, and Ninja. One of ordinary skill in the art would be motivated to provide a plausible estimate when information/data are incomplete. The model created will provide more information and will be more useful to a user.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Yadav in view of Yu, Ding, and Ninja as applied to Claim 12, in further view of Guenter et al. (US 20070002043 A1).
Regarding Claim 15, Yadav in view of Yu, Ding, and Ninja teaches The system of claim 12.
Yadav in view of Yu and Ding does not explicitly disclose wherein a distance between vertices of the plurality of planar surfaces may be adjusted.
Guenter teaches wherein a distance between vertices of the plurality of planar surfaces may be adjusted (“Once static triangulation is complete, the coarse triangulated mesh (e.g., 1400) is further refined at runtime to generate a more refined mesh.” Guenter ¶ 64.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guenter’s triangulated-mesh refinement process with Yadav in view of Yu , Ding, and Ninja. One of ordinary skill in the art would be motivated to provide more a detailed model iteratively or optionally. A user may be presented with a coarse model first, and if needed, the system may produce a more detailed model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Izzat et al. (US 20180059666 A1) AUTOMATED VEHICLE ROAD MODEL DEFINITION SYSTEM, related to the subject matter (road model construction) of the instant application.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHENGXI LIU whose telephone number is (571)270-7509. The examiner can normally be reached M-F 9 AM - 5 PM.
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/ZHENGXI LIU/Primary Examiner, Art Unit 2611