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 .
Response to Amendment
This communication is in response to the action filed on 05/26/2026.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments filed on 05/26/2026 on pages 8-15, under REMARKS with respect to 35
U.S.C. 102 and 103 claim rejections to claims 1-20 have been fully considered and are persuasive. The rejections to the claims have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 2018/0262685 A1.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-3, 5-10, 12-17, 19-20 are rejected under 35 § U.S.C. 103 as being obvious over WO 2020/139377 A1 to ZHANG et al. (hereinafter “ZHANG”) in view of US 2018/0262685 A1 to MELER et al. (hereinafter “MELER”).
As per claim 1, ZHANG discloses a method, comprising: deriving, by one or more computing devices (a computing system comprising computing components such as a memory and processor to execute stored instructions and perform a corresponding method for image processing; abstract; paragraphs [0066], [0093]), a query point cloud from a sweep of a light detection and ranging (lidar) sensor device of a vehicle and a reference point cloud from a high- definition (HD) map (the method for image processing comprising using a LIDAR system to acquire a plurality of point clouds for comparison (used as a reference) to a high definition map generated point cloud using a semi manual matching process; paragraphs [0056-0058], [0086], [0091-0093], [0095]; fig 7); extracting, by the one or more computing devices, a first set of features from the query point cloud and a second set of features from the reference point cloud determining, by the one or more computing devices (the method performed by the computing system further adapted to allow the user to alter 3D point cloud data and associated pose graph data which both comprise features of the lidar image data and the generated HD map point cloud data used as a reference; paragraphs [0056-0058], [0060-0062]), a coarse alignment with a plurality of uniformly sampled initial vehicle headings (each object/vehicle detected is assigned a motion vector value acting as the vehicle/object heading and further allows the system to measuring the distance between two points from maps or point clouds of the object having the assigned motion vector, and tuning parts of a map to better align or match two or more point clouds; paragraphs [0044], [0056-0058]), wherein the generating is based on a plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud (the provided semi manual user interface allows for the user to adjust the alignment and registration of two existing point clouds in the real point cloud data and the generated map point cloud data and identifies misalignments in order to align them along the identified path of travel for the vehicle; paragraphs [0019], [0062], [0111]); and estimating, by the one or more computing devices, a position-orientation pose of the vehicle by refining the coarse alignment using an iterative closest point (ICP) algorithm (the method performed by the computing system further adapted to use a iterative closest point algorithm in order to determine differences between neighboring point clouds, which assists in object/vehicle/pedestrian detection in relation to the vehicle position and chosen path of travel; paragraphs [0026], [0040], [0052], [0062], [0079], [0096]; CLAIM 14). ZHANG fails to disclose by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings.
MELER discloses by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization (the computing system is adapted to generate transformations such as a warp transformations using a cost function related to non-convex response and is adapted to perform it using successive graduated feature selection or graduated non-convex refinement to determine pixel displacement in images captured form autonomous vehicles using lidar detection methods and graduated nonconvexity warping transformations; abstract; paragraphs [0107-0111], [0206]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings of MELER reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce processing computational load and / or processing time, refinements at larger scales may be performed on a reduced grid compared to refinement at smaller scales as suggested by paragraph [0109] of MELER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MELER with ZHANG to obtain the invention as specified in claim 1.
As per claim 2, ZHANG in view of MELER discloses the method of claim 1. Modified ZHANG further discloses wherein the deriving the reference point cloud from the HD map further comprises: selecting, by the one or more computing devices and based on geolocation data obtained from one or more geolocation sensor devices of the vehicle at commencement of an activation sequence of the vehicle (the computing system is adapted to provide geographic location data related to sensors resident on the vehicle various radar sensors and allow for real time position /location data relating to the vehicle in relation to the generated map and travel path; paragraphs [0028], [0032], [0082], [0091]; Claim 15), a map tile comprising a predefined geographical area that includes an initial location of the vehicle from a plurality of map tiles in the HD map (the computing system is adapted to provide geographic location data related to map data 154 which allows for generation of a 3D map which depicts and shows various street elements including layout of streets, buildings, road details and information, land marks, intersections, bridges, and various other map points of interest; paragraphs [0028], [0082], [0091]; Claim 15); and obtaining, by the one or more computing devices, the reference point cloud from the selected map tile in the HD map (the computing device in communication with server 130 can transmit the map data 154 to a vehicle 120 for storage therein in the on vehicle data store 129; paragraphs [0032-0033], [0056-0061]).
As per claim 3, ZHANG in view of MELER discloses the method of claim 1. Modified ZHANG further discloses further comprising: down sampling, by the one or more computing devices, voxels in the query point cloud and the reference point cloud (the computing system provides a user interface via which the user may down scale/ zoom in a 2D map projection of a 3D point cloud rendering if the zoom scale meets a predefined threshold; paragraphs [0072], [0082-0084]; figures 4 and 8); and deriving, by the one or more computing devices, a surface normal for each of the query point cloud and the reference point cloud (data processing system 123 provides processed data 162 to the vehicle control system 126 to respond to point-to-point activity in the surroundings of the vehicle 120, processed data 162 is comparisons between the raw sensor data 161 representing an operational environment of the vehicle 120, which is continuously monitored via sensor array 121-and the map data stored in the data store 129, further the data processing system 123 is programmed with machine learning or other artificial intelligence capabilities to enable the vehicle 120 to identify and respond to conditions, events, and/or potential hazards on a surface such as a road while driving in traffic; paragraphs [0052]).
As per claim 5, ZHANG in view of MELER discloses the method of claim 1. Modified ZHANG further discloses further comprising: generating, by the one or more computing devices, the plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud by pairing each point in the query point cloud with a closest neighboring point in the reference point cloud (the computing system is adapted to provide over said user interfaces for viewing HD maps at different levels, exploring 3D point clouds of a certain part of an HD map, measuring the distance between two points from maps or point clouds from related positions, and tuning parts of a map to better align or match (pair) two or more point clouds; paragraphs [0056-0058]).
As per claim 6, ZHANG in view of MELER discloses the method of claim 1. Modified ZHANG further discloses wherein the determining the coarse alignment further comprises: determining, by the one or more computing devices, a number of inlier feature matches for each transformation solution in the plurality of transformation solutions (the computing devices are adapted to further provide options, via the user interface, to select a command and an associated scale for the command, wherein the scale represents a numeric amount of at least one of: movement, yaw, pitch or roll and further the suggested commands can include movement along each of an x axis, y axis and z axis; paragraphs [0102-0105]); and identifying, by the one or more computing devices, the transformation solution with the highest number of inlier feature matches as the coarse alignment (the user interface operations comprise automatically determining a suggested spatial manipulation of the first set of point cloud data to better match at least the first subset of points with the second subset of points by automatically applying the suggested spatial manipulation within the 3D rendering displayed in the user interface, pending user approval of the suggested spatial manipulation the manipulation is determined based at least in part on a determination that the first set of point cloud data and the second set of point cloud data are misaligned by less than a threshold, wherein the threshold represents at least one of a distance or an angle feature; paragraphs [0102-0105]).
As per claim 7, ZHANG in view of MELER discloses the method of claim 6. Modified ZHANG further discloses wherein each inlier feature match comprises a feature correspondence pair having a first point in the query point cloud and a second point in the reference point cloud that are within a predetermined distance threshold of each other (the computing system is further adapted to provide the system may identify two or more point clouds that have edges that are misaligned by less than a threshold distance wherein the threshold is adjustable for example to, 0.1, 0.5, 1, etc. in the x axis, y axis, and/or z axis; paragraphs [0056-0058], [0085]).
As per claim 8, ZHANG discloses a non-transitory computer readable medium storing instructions that, when executed by one or more processors, causes the one or more processors to perform operations comprising (a computing system comprising computing components such as a memory and processor to execute stored instructions and perform a corresponding operation for image processing; abstract; paragraphs [0066], [0093]): deriving a query point cloud from a sweep of a light detection and ranging (lidar) sensor device of a vehicle and a reference point cloud from a high-definition (HD) map (the operation for image processing comprising using a LIDAR system to acquire a plurality of point clouds for comparison (used as a reference) to a high definition map generated point cloud using a semi manual matching process; paragraphs [0056-0058], [0086], [0091-0093], [0095]; fig 7); extracting a first set of features from the query point cloud and a second set of features from the reference point cloud (the method performed by the computing system further adapted to allow the user to alter 3D point cloud data and associated pose graph data which both comprise features of the lidar image data and the generated HD map point cloud data used as a reference; paragraphs [0056-0058], [0060-0062]); determining a coarse alignment (each object/vehicle detected is assigned a motion vector value acting as the vehicle/object heading and further allows the system to measuring the distance between two points from maps or point clouds of the object having the assigned motion vector, and tuning parts of a map to better align or match two or more point clouds; paragraphs [0044], [0056-0058]), wherein the generating is based on a plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud (the provided semi manual user interface allows for the user to adjust the alignment and registration of two existing point clouds in the real point cloud data and the generated map point cloud data and identifies misalignments in order to align them along the identified path of travel for the vehicle; paragraphs [0019], [0062], [0111]); and estimating a position-orientation pose of the vehicle by refining the coarse alignment using an iterative closest point (ICP) algorithm (the method performed by the computing system further adapted to use a iterative closest point algorithm in order to determine differences between neighboring point clouds, which assists in object/vehicle/pedestrian detection in relation to the vehicle position and chosen path of travel; paragraphs [0026], [0040], [0052], [0062], [0079], [0096]; CLAIM 14). ZHANG fails to disclose by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings.
MELER discloses by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings (the computing system is adapted to generate transformations such as a warp transformations using a cost function related to non-convex response and is adapted to perform it using successive graduated feature selection or graduated non-convex refinement to determine pixel displacement in images captured form autonomous vehicles using lidar detection methods and graduated nonconvexity warping transformations; abstract; paragraphs [0107-0111], [0206]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings of MELER reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce processing computational load and / or processing time, refinements at larger scales may be performed on a reduced grid compared to refinement at smaller scales as suggested by paragraph [0109] of MELER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MELER with ZHANG to obtain the invention as specified in claim 8.
As per claim 9, ZHANG in view of MELER discloses the non-transitory computer readable medium of claim 8. Modified ZHANG further discloses wherein the operations further comprise: selecting, based on geolocation data obtained from one or more geolocation sensor devices of the vehicle at commencement of an activation sequence of the vehicle (the computing system is adapted to provide geographic location data related to sensors resident on the vehicle various radar sensors and allow for real time position /location data relating to the vehicle in relation to the generated map and travel path; paragraphs [0028], [0032], [0082], [0091]; Claim 15), a map tile comprising a predefined geographical area that includes an initial location of the vehicle from a plurality of map tiles in the HD map (the computing system is adapted to provide geographic location data related to map data 154 which allows for generation of a 3D map which depicts and shows various street elements including layout of streets, buildings, road details and information, land marks, intersections, bridges, and various other map points of interest; paragraphs [0028], [0082], [0091]; Claim 15); and obtaining the reference point cloud from the selected map tile in the HD map (the computing device in communication with server 130 can transmit the map data 154 to a vehicle 120 for storage therein in the on vehicle data store 129; paragraphs [0032-0033], [0056-0061]).
As per claim 10, ZHANG in view of MELER discloses the non-transitory computer-readable medium of claim 8. Modified ZHANG further discloses wherein the operations further comprise: down sampling voxels in the query point cloud and the reference point cloud (the computing system provides a user interface via which the user may down scale/ zoom in a 2D map projection of a 3D point cloud rendering if the zoom scale meets a predefined threshold; paragraphs [0072], [0082-0084]; figures 4 and 8); and deriving a surface normal for each of the query point cloud and the reference point cloud (data processing system 123 provides processed data 162 to the vehicle control system 126 to respond to point-to-point activity in the surroundings of the vehicle 120, processed data 162 is comparisons between the raw sensor data 161 representing an operational environment of the vehicle 120, which is continuously monitored via sensor array 121-and the map data stored in the data store 129, further the data processing system 123 is programmed with machine learning or other artificial intelligence capabilities to enable the vehicle 120 to identify and respond to conditions, events, and/or potential hazards on a surface such as a road while driving in traffic; paragraphs [0052]).
As per claim 12, ZHANG in view of MELER discloses the non-transitory computer-readable medium of claim 8. Modified ZHANG further discloses wherein the operations further comprise: generating the plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud by pairing each point in the query point cloud with a closest neighboring point in the reference point cloud (the computing system is adapted to provide over said user interfaces for viewing HD maps at different levels, exploring 3D point clouds of a certain part of an HD map, measuring the distance between two points from maps or point clouds from related positions, and tuning parts of a map to better align or match (pair) two or more point clouds; paragraphs [0056-0058]).
As per claim 13, ZHANG in view of MELER discloses the non-transitory computer-readable medium of claim 8. Modified ZHANG further discloses wherein the determining the coarse alignment further comprises: determining a number of inlier feature matches for each transformation solution in the plurality of transformation solutions (the computing devices are adapted to further provide options, via the user interface, to select a command and an associated scale for the command, wherein the scale represents a numeric amount of at least one of: movement, yaw, pitch or roll and further the suggested commands can include movement along each of an x axis, y axis and z axis; paragraphs [0102-0105]); and identifying the transformation solution with the highest number of inlier feature matches as the coarse alignment (the user interface operations comprise automatically determining a suggested spatial manipulation of the first set of point cloud data to better match at least the first subset of points with the second subset of points by automatically applying the suggested spatial manipulation within the 3D rendering displayed in the user interface, pending user approval of the suggested spatial manipulation the manipulation is determined based at least in part on a determination that the first set of point cloud data and the second set of point cloud data are misaligned by less than a threshold, wherein the threshold represents at least one of a distance or an angle feature; paragraphs [0102-0105]).
As per claim 14, ZHANG in view of MELER discloses the non-transitory computer-readable medium of claim 13. Modified ZHANG further discloses wherein each inlier feature match comprises a feature correspondence pair having a first point in the query point cloud and a second point in the reference point cloud that are within a predetermined distance threshold of each other (the computing system is further adapted to provide the system may identify two or more point clouds that have edges that are misaligned by less than a threshold distance wherein the threshold is adjustable for example to, 0.1, 0.5, 1, etc. in the x axis, y axis, and/or z axis; paragraphs [0056-0058], [0085]).
As per claim 15, ZHANG discloses a system, comprising: one or more processors (a computing system comprising computing components such as a memory and processor to execute stored instructions and perform a corresponding operation for image processing; abstract; paragraphs [0066], [0093]); and a memory communicatively coupled to the one or more processors, wherein the memory stores instructions that, when executed by the one or more processors (a computing system comprising computing components such as a memory and processor to execute stored instructions and perform a corresponding operation for image processing; abstract; paragraphs [0066], [0093]), cause the one or more processors to perform operations comprising: deriving a query point cloud from a sweep of a light detection and ranging (lidar) sensor device of a vehicle and a reference point cloud from a high-definition (HD) map (the operation for image processing comprising using a LIDAR system to acquire a plurality of point clouds for comparison (used as a reference) to a high definition map generated point cloud using a semi manual matching process; paragraphs [0056-0058], [0086], [0091-0093], [0095]; fig 7); extracting a first set of features from the query point cloud and a second set of features from the reference point cloud (the method performed by the computing system further adapted to allow the user to alter 3D point cloud data and associated pose graph data which both comprise features of the lidar image data and the generated HD map point cloud data used as a reference; paragraphs [0056-0058], [0060-0062]) determining a coarse alignment (each object/vehicle detected is assigned a motion vector value acting as the vehicle/object heading and further allows the system to measuring the distance between two points from maps or point clouds of the object having the assigned motion vector, and tuning parts of a map to better align or match two or more point clouds; paragraphs [0044], [0056-0058]), wherein the generating is based on a plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud (the provided semi manual user interface allows for the user to adjust the alignment and registration of two existing point clouds in the real point cloud data and the generated map point cloud data and identifies misalignments in order to align them along the identified path of travel for the vehicle; paragraphs [0019], [0062], [0111]); and estimating a position-orientation pose of the vehicle by refining the coarse alignment using an iterative closest point (ICP) algorithm (the method performed by the computing system further adapted to use a iterative closest point algorithm in order to determine differences between neighboring point clouds, which assists in object/vehicle/pedestrian detection in relation to the vehicle position and chosen path of travel; paragraphs [0026], [0040], [0052], [0062], [0079], [0096]; CLAIM 14). ZHANG fails to disclose by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings.
MELER discloses by generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings (the computing system is adapted to generate transformations such as a warp transformations using a cost function related to non-convex response and is adapted to perform it using successive graduated feature selection or graduated non-convex refinement to determine pixel displacement in images captured form autonomous vehicles using lidar detection methods and graduated nonconvexity warping transformations; abstract; paragraphs [0107-0111], [0206]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have generating a plurality of transformation solutions through graduated non-convexity (GNC) optimization with a plurality of uniformly sampled initial vehicle headings of MELER reference. The Suggestion/motivation for doing so would have been to provide the ability to reduce processing computational load and / or processing time, refinements at larger scales may be performed on a reduced grid compared to refinement at smaller scales as suggested by paragraph [0109] of MELER. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MELER with ZHANG to obtain the invention as specified in claim 15.
As per claim 16, ZHANG in view of MELER discloses the system of claim 15. Modified ZHANG further discloses wherein the operations further comprise: selecting, based on geolocation data obtained from one or more geolocation sensor devices of the vehicle at commencement of an activation sequence of the vehicle (the computing system is adapted to provide geographic location data related to sensors resident on the vehicle various radar sensors and allow for real time position /location data relating to the vehicle in relation to the generated map and travel path; paragraphs [0028], [0032], [0082], [0091]; Claim 15), a map tile comprising a predefined geographical area that includes an initial location of the vehicle from a plurality of map tiles in the HD map (the computing system is adapted to provide geographic location data related to map data 154 which allows for generation of a 3D map which depicts and shows various street elements including layout of streets, buildings, road details and information, land marks, intersections, bridges, and various other map points of interest; paragraphs [0028], [0082], [0091]; Claim 15); and obtaining the reference point cloud from the selected map tile in the HD map (the computing device in communication with server 130 can transmit the map data 154 to a vehicle 120 for storage therein in the on vehicle data store 129; paragraphs [0032-0033], [0056-0061]).
As per claim 17, ZHANG in view of MELER discloses the system of claim 15. Modified ZHANG further discloses wherein the operations further comprise: down sampling voxels in the query point cloud and the reference point cloud (the computing system provides a user interface via which the user may down scale/ zoom in a 2D map projection of a 3D point cloud rendering if the zoom scale meets a predefined threshold; paragraphs [0072], [0082-0084]; figures 4 and 8); and deriving a surface normal for each of the query point cloud and the reference point cloud (data processing system 123 provides processed data 162 to the vehicle control system 126 to respond to point-to-point activity in the surroundings of the vehicle 120, processed data 162 is comparisons between the raw sensor data 161 representing an operational environment of the vehicle 120, which is continuously monitored via sensor array 121-and the map data stored in the data store 129, further the data processing system 123 is programmed with machine learning or other artificial intelligence capabilities to enable the vehicle 120 to identify and respond to conditions, events, and/or potential hazards on a surface such as a road while driving in traffic; paragraphs [0052]).
As per claim 19, ZHANG in view of MELER discloses the system of claim 15. Modified ZHANG further discloses wherein the operations further comprise: generating the plurality of matches between the first set of features from the query point cloud and the second set of features from the reference point cloud by pairing each point in the query point cloud with a closest neighboring point in the reference point cloud (the computing system is adapted to provide over said user interfaces for viewing HD maps at different levels, exploring 3D point clouds of a certain part of an HD map, measuring the distance between two points from maps or point clouds from related positions, and tuning parts of a map to better align or match (pair) two or more point clouds; paragraphs [0056-0058]).
As per claim 20, ZHANG in view of MELER discloses the system of claim 15. Modified ZHANG further discloses wherein the determining the coarse alignment further comprises: determining a number of inlier feature matches for each transformation solution in the plurality of transformation solutions, wherein each inlier feature match comprises a feature correspondence pair having a first point in the query point cloud and a second point in the reference point cloud that are within a predetermined distance threshold of each other (the computing devices are adapted to further provide options, via the user interface, to select a command and an associated scale for the command, wherein the scale represents a numeric amount of at least one of: movement, yaw, pitch or roll and further the suggested commands can include movement along each of an x axis, y axis and z axis; paragraphs [0102-0105]); and identifying the transformation solution with the highest number of inlier feature matches as the coarse alignment (the user interface operations comprise automatically determining a suggested spatial manipulation of the first set of point cloud data to better match at least the first subset of points with the second subset of points by automatically applying the suggested spatial manipulation within the 3D rendering displayed in the user interface, pending user approval of the suggested spatial manipulation the manipulation is determined based at least in part on a determination that the first set of point cloud data and the second set of point cloud data are misaligned by less than a threshold, wherein the threshold represents at least one of a distance or an angle feature; paragraphs [0102-0105]).
Claims 4, 11, 18 are rejected under 35 § U.S.C. 103 as being obvious over WO 2020/139377 A1 to ZHANG et al. (hereinafter “ZHANG”) in view of US 2018/0262685 A1 to MELER et al. (hereinafter “MELER”) in view of US 2023/0204363 A1 to TIIRA (hereinafter “TIIRA”).
As per claim 4, ZHANG in view of MELER discloses the method of claim 1. ZHANG fails to disclose wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving, by the one or more computing devices, at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud.
TIIRA discloses wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving, by the one or more computing devices, at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud (the features may be extracted from the point clouds of interest by generating histograms related to the obtained lidar data, wherein the histogram provides a fast and accurate scan registration through minimization of the distance between compact 3D NDT representations, of points in the point clouds; abstract; fig 2; paragraphs [0046-0048], [0062]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have deriving, by the one or more computing devices, at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud of TIIRA reference. The Suggestion/motivation for doing so would have been to provide the ability to identify common upward pointing linear shaped features such trees and poles are clustered in a single histogram bin relating to point clouds obtained via LIDAR as suggested by TIIRA at paragraph [0048]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine TIIRA with ZHANG to obtain the invention as specified in claim 4.
As per claim 11, ZHANG in view of MELER discloses the non-transitory computer-readable medium of claim 8. ZHANG fails to disclose wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud.
TIIRA discloses wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud (the features may be extracted from the point clouds of interest by generating histograms related to the obtained lidar data, wherein the histogram provides a fast and accurate scan registration through minimization of the distance between compact 3D NDT representations, of points in the point clouds; abstract; fig 2; paragraphs [0046-0048], [0062]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud of TIIRA reference. The Suggestion/motivation for doing so would have been to provide the ability to identify common upward pointing linear shaped features such trees and poles are clustered in a single histogram bin relating to point clouds obtained via LIDAR as suggested by TIIRA at paragraph [0048]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine TIIRA with ZHANG to obtain the invention as specified in claim 11.
As per claim 18, ZHANG in view of MELER discloses the system of claim 15. ZHANG fails to disclose wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud.
TIIRA discloses wherein the extracting the first set of features from the query point cloud and the second set of features from the reference point cloud further comprises: deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud (the features may be extracted from the point clouds of interest by generating histograms related to the obtained lidar data, wherein the histogram provides a fast and accurate scan registration through minimization of the distance between compact 3D NDT representations, of points in the point clouds; abstract; fig 2; paragraphs [0046-0048], [0062]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify ZHANG to have deriving at least one fast point feature histogram (FPFH) descriptor for every point in the query point cloud and the reference point cloud of TIIRA reference. The Suggestion/motivation for doing so would have been to provide the ability to identify common upward pointing linear shaped features such trees and poles are clustered in a single histogram bin relating to point clouds obtained via LIDAR as suggested by TIIRA at paragraph [0048]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine TIIRA with ZHANG to obtain the invention as specified in claim 18.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800- 786-9199 (IN USA OR CANADA) or 571-272-1000.
/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights