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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12 January 2023 by the applicant has been considered and is included in the file.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Vorobyov et al. (hereinafter Vorobyov, US 20130096886 A1).
Regarding claim 1, Vorobyov anticipates a system, comprising:
one or more processors ([0049]; Fig. 2(a), processor (22)) configured to:
receive data point information captured using a lidar device ([0046]);
project the data point information onto a horizontal plane perspective ([0054], [0058]; Fig. 3, step (46) where an approximate ground plane among planes is found);
filter the projected data point information ([0058]; Fig. 3, step (48) where data is further used to extract features);
determine a ground plane among one or more planes identified using the filtered projected data point information ([0058]; Fig. 3, step (49) where an accurate ground plane is found);
and provide a parameter associated with the determined ground plane for configuring the lidar device ([0064], where an angle of plane is determined);
and a memory coupled with at least a portion of the one or more processors ([0046]).
Regarding claim 5, Vorobyov anticipates the system of claim 1, wherein
the one or more processors are configured to project the data point information onto the horizontal plane perspective including by being configured to project a collection of points from a three-dimensional space ([0005], [0054] original point cloud is 3-Dimensional).
Claims 19 and 20, regarding a method of operation and a computer program product embodied in a non-transitory computer readable medium, respectively, are similarly rejected to claim 1.
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) 2-4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vorobyov et al. (hereinafter Vorobyov, US 20130096886 A1) and in view of Campbell et al. (hereinafter Campbell, US 20190107623 A1).
Regarding claim 2, Vorobyov teaches the system of claim 1.
Vorobyov does not explicitly describe the collection of the data or scan frames.
Campbell teaches a LIDAR system where received data point information is collected during a first scan frame of the lidar device ([0093]).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Campbell to operate the system during a first scan frame with a reasonable expectation of success. Operating systems for repeated scans to collect multiple point clouds is well known in the art of LIDAR systems, and to one of ordinary skill in the art it is understood that the process as taught by Vorobyov would be repeated for multiple scans as described in Campbell.
Regarding claim 3, Vorobyov as modified above teaches the system of claim 2.
Vorobyov does not explicitly describe the collection of the data or scan frames.
Campbell teaches a LIDAR system where the parameter associated with the determined ground plane is utilized to configure the lidar device for a second scan frame subsequent to the first scan frame ([0093]).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Campbell to operate the system during a second scan frame, subsequent to the first scan, where a parameter is used to modify the second scan with a reasonable expectation of success. Operating systems for repeated scans to collect multiple point clouds is well known in the art of LIDAR systems, and as taught by Campbell, information collected during first scans can be used in the system of Vorobyov with predictable results of modifying parameters for subsequent scans.
Regarding claim 4, Vorobyov teaches the system of claim 1.
Vorobyov does not explicitly describe the collection of the data, scan frames, or detection window timing.
Campbell teaches a LIDAR system where the received data point information is collected during a window of a scan frame of the lidar device that is shorter in time than the scan frame ([0089], where time required to collect a sub-portion of a scan, such as a pixel, is less than the total scan or frame time).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Campbell to operate the system where a detection window makes up a small portion of the total scan window with a reasonable expectation of success. Operating systems for repeated scans, or with detection windows which are sub-sets of total scans is well known in the art of LIDAR systems, and to one of ordinary skill in the art it is understood that the process as taught by Vorobyov would be repeated for multiple individual frames within a larger scan, as described in Campbell.
Regarding claim 18, Vorobyov teaches the system of claim 1.
Vorobyov is silent on the specifics of the parameters associated with a ground plane relating to a scan angle of the device.
Campbell teaches a LIDAR system where the provided parameter is utilized by the lidar device to adjust a scan angle of the lidar device ([0089], where scan angle may be constant or changed between scans).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Campbell to relate a scan angle as the parameter associated with a given ground plane for configuring the lidar device with a reasonable expectation of success. It is well known that scanning LIDAR systems, such as the one taught by Campbell and which outputs point cloud data as utilized by Vorobyov, require the system to have a scan angle associated with a given scan.
Claim(s) 6-12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vorobyov et al. (hereinafter Vorobyov, US 20130096886 A1) and in view of Dunik et al. (hereinafter Dunik, US 20130080111 A1).
Regarding claim 6, Vorobyov teaches the system of claim 1.
Vorobyov is silent on the system determining co-linearity of points.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, wherein the one or more processors are configured to filter the projected data point information including by being configured to determine co-linearity of different data points of the projected data point information ([0082], where linearity or similarity between two sets of data points, such as in two identified planes, is checked).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to verify co-linearity of points with a reasonable expectation of success. As Dunik describes, determining similarity or divergence of planes or subsets of points assists in plane matching, and reduces the chance of false positive detections of planes ([0081]).
Regarding claim 7, Vorobyov teaches the system of claim 1.
Vorobyov does not explicitly describe the iteration process of planarity check on planes, where subsequent planes are sub-planes of the previous plane.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to determine the ground plane including by being configured to perform a planarity check of an initial plane ([0059] - [0063]; Figs. 2, 5A-B, where an initial plane is determined based on highest inlier rates).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to determine a planarity of an initial plane with a reasonable expectation of success. As Dunik describes, data collection and analysis of point clouds is applicable in object tracking and, for example, identifying pathways for traversal in an environment, ([0128] – [0130]) and plane merging or reduction in the amount of data processed reduces the chance of false positive detections of planes which may lead to errors in navigation ([0081]) while decreasing necessary processing power.
Regarding claim 8, Vorobyov as modified above teaches the system of claim 7.
Vorobyov does not explicitly describe the iteration process of planarity check on planes, where subsequent planes are sub-planes of the previous plane.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to determine the ground plane including by being further configured to perform planarity checks of multiple sub-planes of the initial plane ([0059] - [0063]; Figs. 2, 5A-B, where an initial plane is further divided by finding a smaller subset of the original set of inliers).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to determine a planarity of an initial plane with a reasonable expectation of success. As Dunik describes, data collection and analysis of point clouds is applicable in object tracking and, for example, identifying pathways for traversal in an environment, ([0128] – [0130]) and plane merging or reduction in the amount of data processed reduces the chance of false positive detections of planes which may lead to errors in navigation ([0081]) while decreasing necessary processing power.
Regarding claim 9, Vorobyov as modified above teaches the system of claim 8.
Vorobyov does not explicitly describe the iteration process of planarity check on planes, where subsequent planes are sub-planes of the previous plane.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to determine the ground plane including by being further configured to perform planarity checks of multiple smaller sub-planes of the sub-planes of the initial plane ([0059] - [0063]; Figs. 2, 5A-B, where a subset of inliers is further divided by finding a smaller subset of the subset of inliers).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to determine a planarity of an initial plane with a reasonable expectation of success. As Dunik describes, data collection and analysis of point clouds is applicable in object tracking and, for example, identifying pathways for traversal in an environment, ([0128] – [0130]) and plane merging or reduction in the amount of data processed reduces the chance of false positive detections of planes which may lead to errors in navigation ([0081]) while decreasing necessary processing power.
Regarding claim 10, Vorobyov teaches the system of claim 1.
Vorobyov does not explicitly describe the iteration process of planarity check on planes, where subsequent planes are sub-planes of the previous plane.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to determine the ground plane including by being configured to perform a planarity check multiple times and iteratively on successively smaller sub-regions of an initial candidate ground plane ([0059] - [0063]; Figs. 2, 5A-B, where a process of determining a subset of inliers from a larger subset of inliers is iterated until a number of iterations or a metric is met).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to determine a planarity of an initial plane with a reasonable expectation of success. As Dunik describes, data collection and analysis of point clouds is applicable in object tracking and, for example, identifying pathways for traversal in an environment, ([0128] – [0130]) and plane merging or reduction in the amount of data processed reduces the chance of false positive detections of planes which may lead to errors in navigation ([0081]) while decreasing necessary processing power.
Regarding claim 11, Vorobyov as modified above teaches the system of claim 10.
Vorobyov is silent on the system determining planarity via a covariance matrix.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to perform the planarity check including by being configured to determine a covariance matrix ([0085]; a plane envelope is constructed by determining a 3-D covariance matrix covariance matrix).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to utilize a covariance matrix to assess variability within a plane with a reasonable expectation of success. As Dunik notes, 3D covariance matrices are a useful way of describing a plane within a process of plane extraction and detection ([0048]).
Regarding claim 12, Vorobyov as modified above teaches the system of claim 11.
Vorobyov is silent on the system determining planarity via a covariance matrix.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the covariance matrix is a 3x3 matrix ([0085]; a plane envelope is constructed by determining a 3-D covariance matrix covariance matrix).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to utilize a covariance matrix to assess variability within a plane with a reasonable expectation of success. As Dunik notes, 3D covariance matrices are a useful way of describing a plane within a process of plane extraction and detection ([0048]).
Regarding claim 14, Vorobyov as modified above teaches the system of claim 11.
Vorobyov is silent on the system calculating the eigenvalues of a covariance matrix.
Dunik teaches a system and method for determining plane similarity, which may be applied to 3-D point clouds as collected by a LIDAR system, where the one or more processors are configured to perform the planarity check including by being further configured to determine eigenvalues of the covariance matrix ([0085]; covariance matrix eigenvalues are determined).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to utilize a covariance matrix to assess variability within a plane with a reasonable expectation of success. As Dunik notes, 3D covariance matrices are a useful way of describing a plane within a process of plane extraction and detection ([0048]). Determining eigenvalues and eigenvectors is well-known to one of ordinary skill in the art as a way of characterizing a matrix.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vorobyov et al. (hereinafter Vorobyov, US 20130096886 A1), in view of Dunik et al. (hereinafter Dunik, US 20130080111 A1) and further in view of Xing et al. (hereinafter Xing, US 20210241462 A1).
Regarding claim 13, Vorobyov as modified above teaches the system of claim 11.
Vorobyov and Dunik are silent on the system calculating covariance matrix by use of a summed area table.
Xing teaches the one or more processors are configured to determine the covariance matrix including by being configured to determine a summed area table based on areas of the successively smaller sub-regions of the initial candidate ground plane ([0038]; covariance matrix calculation may use a summed-area table (SAT)).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Vorobyov and Dunik to incorporate the teachings of Xing to utilize a summed area table (SAT) to find a covariance matrix to with a reasonable expectation of success. As Xing teaches, use of SATs in a system where a plane is being assessed for regional smoothness will act to accelerate the computation of the covariance matrix ([0038]).
Claim(s) 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vorobyov et al. (hereinafter Vorobyov, US 20130096886 A1), in view of Dunik et al. (hereinafter Dunik, US 20130080111 A1) and further in view of Norimatsu et al. (hereinafter Norimatsu, JP2022176248A).
Regarding claim 15, Vorobyov as modified above teaches the system of claim 14.
Vorobyov and Dunik are silent on determining a ratio of eigenvalues.
Norimatsu teaches a system which analyses point cloud data where the one or more processors are configured to perform a planarity check including by being further configured to determine a ratio of a first eigenvalue of the determined eigenvalues to a second eigenvalue of the determined eigenvalues ([0079], where a ratio between a first eigenvalue and a second or combination of first to third eigenvalues can be taken).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to utilize a covariance matrix to assess variability within a plane with a reasonable expectation of success. As Dunik notes, 3D covariance matrices are a useful way of describing a plane within a process of plane extraction and detection ([0048]). Determining eigenvalues and eigenvectors is well-known to one of ordinary skill in the art as a way of characterizing a matrix.
Regarding claim 16, Vorobyov as modified above teaches the system of claim 15.
Vorobyov and Norimatsu are silent on the relative values of the eigenvalues.
Dunik teaches determining eigenvalues of a covariance matrix, where the first eigenvalue is smaller than all other eigenvalues of the determined eigenvalues ([0085] - [0086]; covariance matrix eigenvalues are determined where a smallest eigenvalue is related to the non-main direction of the plane).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Vorobyov to incorporate the teachings of Dunik to utilize a covariance matrix to assess variability within a plane with a reasonable expectation of success. As Dunik notes, 3D covariance matrices are a useful way of describing a plane within a process of plane extraction and detection ([0048]). Determining eigenvalues and eigenvectors is well-known to one of ordinary skill in the art as a way of characterizing a matrix.
Regarding claim 17, Vorobyov as modified above teaches the system of claim 15.
Vorobyov and Dunik are silent on determining a ratio of eigenvalues or comparing it to a threshold value.
Norimatsu teaches a system which analyses point cloud data where the one or more processors are configured to perform the planarity check including by being further configured to compare the ratio with a specified threshold ([0079], where a ratio of eigenvalues can be compared to a predetermined value).
To one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Vorobyov and Dunik to incorporate the teachings of Norimatsu to determine a ratio of eigenvalues, and to compare the ratio to a predetermined value, with a reasonable expectation of success. Norimatsu notes that a ratio of eigenvalues to a sum of the eigenvalues, or to one another, can yield information on which principal component is most prominent and the predetermined value can be representative of a lower limit of the component which can be taken when an object exists in a plane, such as a cylindrical body, which would contribute to a lower planarity ([0079]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fu et al. (US 20130218472 A1) teaches a method, and system, for segmenting point cloud data, where 3-D point cloud data is projected onto a horizontal plane, and above-ground points are iteratively filtered out.
Mahata et al. (US 20230186647 A1) teaches a process for identifying road surfaces within LIDAR data where the point cloud is iteratively segmented.
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/K.M.R./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645