DETAILED ACTION
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 8th, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim recites the limitation “a computer readable storage medium” in lines 2-3. Under broadest reasonable interpretation, this limitation encompasses transitory forms of computer-readable storage media, such as signals per se, which is considered non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Independent claims 1, 15, and 16 are directed to a method, product, and system, respectively, for steering automated vehicles. Therefore, claims 1, 15 and 16 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous independent claims are rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
A computer-implemented method of steering an automated vehicle on a ground of a designated area using a set of one or more offboard sensors,
each being a 3D laser scanning Lidar, the method comprising repeatedly executing algorithmic iterations, wherein each iteration of the algorithmic iterations comprises:
obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor, by
accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays emitted by said each sensor, the characteristics including hit points of the rays, as well as projections of the hit points and the rays on a plane corresponding to the ground, and
determining a state of each cell of the cells based on the identified characteristics, whereby, given a first height above the plane and a second height above the first height,
a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and
a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell; and
determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the steps of repeatedly executing algorithmic iterations, obtaining a grid by processing the dataset, determining a state, and determining a trajectory in the context of this claim encompasses a person looking at data collected (received, accessed, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented method of steering an automated vehicle on a ground of a designated area using a set of one or more offboard sensors,
each being a 3D laser scanning Lidar, the method comprising repeatedly executing algorithmic iterations, wherein each iteration of the algorithmic iterations comprises:
obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor, by
accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays emitted by said each sensor, the characteristics including hit points of the rays, as well as projections of the hit points and the rays on a plane corresponding to the ground, and
determining a state of each cell of the cells based on the identified characteristics, whereby, given a first height above the plane and a second height above the first height,
a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and
a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell; and
determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the step of accessing a dataset is recited at a high level of generality (i.e. as a general means of receiving information and casting rays to detect information for use in the determining and other steps), and amounts to no more than mere data gathering necessary to perform the abstract idea, which is a form of insignificant extra-solution activity. The step of forwarding the trajectory is additionally recited at a high level of generality and amounts to no more than mere post-solution output. ***In order to expedite prosecution, Examiner also notes that the mere recitation of “forwarding the determined trajectory to a drive-by-wire system of the automated vehicle” in the independent claims is not significant enough to integrate the judicial exception into a practical application since the claims do not include a positive recitation of the automated vehicle automatically driving based on the computed trajectories (if supported by the specification, such limitation is an example of a significant enough limitation to integrate the judicial exception into a practical application).
Lastly, claims 1, 15, and 16 further recite offboard sensors comprising 3D laser scanning Lidars, a computer-readable storage medium, and a computerized system. These limitations merely describe how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 9 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processing system to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of accessing a dataset and forwarding the trajectory are well-understood, routine and conventional activities because the specification does not provide any indication that the processor is anything other than a conventional computer, nor that the sensors are anything other than conventional Lidar sensors. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Dependent claims 2-14 and 17-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-14 and 17-20 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1-2, 4, 11-13, 15-16, and 17-18 rejected under 35 U.S.C. 103 as being unpatentable over US 20230134717 A1, filed November 3rd, 2022, hereinafter “Xu”, in view of US 20180300560 A1, with an earliest priority date of April 13th, 2017, hereinafter “Westerhoff”.
Regarding claim 1, Xu teaches A computer-implemented method of steering an automated vehicle on a ground of a designated area using a set of one or more offboard sensors, each being a 3D laser scanning Lidar. See at least [0063]-[0067], [0075]-[0076], and figures 1a-1d, wherein LiDAR sensors are installed at different roadside locations to monitor designated areas, such as an intersection. The LiDAR sensors scan their environment to provide 3D point data.
the method comprising repeatedly executing algorithmic iterations. See at least [0076], [0121], [0129], and [0141], wherein the method is repeatedly performed for each frame (timestep) of collected LiDAR data.
wherein each iteration of the algorithmic iterations comprises: obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor. See at least [0107] and figure 7B, step 756, wherein a 2D channel-azimuth structure is obtained for a roadside LiDAR sensor system. See at least [0126]-[0129] and figures 10-11, wherein the 2D structure represents a grid of cells and the associated points present in the cells.
by accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays emitted by said each sensor, the characteristics including hit points of the rays. See at least [0102], [0106]-[0107], figure 6, and figures 7A-B, wherein the 2D channel-azimuth structure is obtained by accessing point cloud data from a LiDAR sensor and processing the point cloud to identify hit points.
and determining a state of each cell of the cells based on the identified characteristics. See at least [0107] and figure 7B, step 760, wherein the data points in the 2D structure are compared to a point threshold, which classifies the state of the cell.
Xu remains silent on as well as projections of the hit points and the rays on a plane corresponding to the ground; whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell; and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle.
Westerhoff teaches as well as projections of the hit points and the rays on a plane corresponding to the ground. See at least [0061]-[0067] and figures 3A-B, wherein the projection of hit points (41) and rays (42) corresponding to the ground plane Mxy are used to determine an associated point’s occupancy state. The states include hit, pass (free), or covered (occluded/unknown).
whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height. See at least [0066]-[0067], [0078]-[0079], figures 3A-B, and figure 4, wherein a map point is determined to be a hit (occupied) if a projection of a hit point relative to the ground if above a first height, e.g. 25cm. The first height is defined as the height where ϕTh returns a value of 0.5.
and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell. See at least [0066]-[0067], [0080]-[0083], figures 3A-B, and figure 4, wherein a map point is determined to be a pass (free) if a projection of a ray passing over (or overhanging) the point is at or below a second height. The second height is defined as the height where ϕDh returns a value of 0.5, and is within a range 50. As seen in figure 4, range 50 includes height values that are larger than the first height.
and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle. See at least [0022] and [0058], wherein the determined occupancy map is output to a driver assistance or autonomous driving system for path planning. The autonomous steering and braking systems are then controlled based on the path planning.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s technique of using projections of hit points and rays with the ground to classify occupancy states, classifying points into a free or occupied state based on a comparison between a second and first height, respectively, to the projections, and using the obtained occupancy map to determine a path for the autonomous vehicle and control automated driving systems of the vehicle. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 2, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches wherein, at said each iteration, the grid obtained for each sensor is defined according to a polar coordinate system, a pole of which corresponds to a location of said each sensor, whereby said each cell is defined by a given radius and a given azimuth. See at least [0076]-[0078], [0102], figure 6, figure 7A, and figure 11, wherein the 2D grid for the sensor is defined according to a coordinate system comprising azimuth, elevation angle, and distance (radius) coordinates. The coordinate system is centered on the location of the sensor.
and at determining the states of the cells, a further condition is applied to said each cell for it to be in a free state, should this cell be an outer cell on a same azimuth and at a larger radius than an inner cell that is matched by a projection of an inner hit point of a reference ray. See at least [0102] and figure 6, wherein map point 602 is on the same azimuth as target point 606, but as a larger distance/radius. Target point 606 is a projection of an inner hit point of reference ray 601A. Whether or not map point 602 corresponds to a ground point (free) or a target point (occupied) depends on the inner hit point B at target point 606.
Xu remains silent on whereby no overhanging ray is allowed to set the outer cell in a free state if this overhanging ray has already passed below the inner hit point when passing over the outer cell.
Westerhoff teaches whereby no overhanging ray is allowed to set the outer cell in a free state if this overhanging ray has already passed below the inner hit point when passing over the outer cell. See at least [0081], wherein a ray is not allowed to set a map point as unoccupied if the passing ray has already hit an object before reaching the outer cell.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s technique of disallowing overhanging rays to set outer cells in a free state based on the overhanging ray passing below a hit point. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 4, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches obtaining the grid for each sensor further comprises initializing each of the cells to the unknown state, determining the state of each cell comprises iterating over each ray of the rays identified as parts of the identified characteristics to infer a state of each of the cells that is impacted by said each ray. See at least [0096] and [0107], wherein a 2D data structure for the grid is initialized with every cell being blank, or unknown. The 2D data structure is then updated as the LiDAR data is processed based on the classified states of analyzed points and associated rays.
Xu remains silent on wherein the state of each of the cells can be a free state, an occupied state, or an unknown state.
Westerhoff teaches wherein the state of each of the cells can be a free state, an occupied state, or an unknown state. See at least [0061]-[0067] and figures 3A-B, wherein the projection of hit points (41) and rays (42) corresponding to the ground plane Mxy are used to determine an associated point’s occupancy state. The states include hit, pass (free), or covered (occluded/unknown).
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s cell states comprising free (pass) states, occupied (hit) states, and unknown (covered) states. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 11, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu remains silent on wherein the first height is between 3 and 8 cm, while the second height is between 12 and 30 cm, and each of the first height and the second height is measured with respect to said plane.
Westerhoff teaches wherein the first height is between 3 and 8 cm, while the second height is between 12 and 30 cm, and each of the first height and the second height is measured with respect to said plane. See at least [0079]-[0083] and figure 4, wherein the second height is defined as the height where ϕDh returns a value of 0.5, and is within a range 50. As seen in figure 4, range 50 includes values between 12-30cm. The first height is defined as the height where ϕTh returns a value of 0.5, which includes -25cm to 25 cm. Both heights are measured with respect to the ground plane h=0.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s first and second heights. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 12, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches wherein each of said sensors is configured to scan its surroundings by emitting rays at constant angles, the latter separated by at most one degree of angle in an elevation plane transverse to said plane. See at least [0161], wherein the LiDAR is configured to scan its surrounding by emitting rays at constant vertical angles of 0.33°.
Regarding claim 13, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches wherein said several algorithmic iterations are executed at an average frequency that is between 5 and 20 hertz. See at least [0119] and [0121], wherein the frame iterations are executed at 10Hz.
Regarding claim 15, Xu teaches A computer program product for steering an automated vehicle in a designated area, the computer program product comprising a computer readable storage medium having program instructions embodied therewith. See at least [0215]-[0216].
the program instructions executable by processing means of a computerized system, to cause the computerized system to repeatedly execute several algorithmic iterations. See at least [0076], [0121], [0129], and [0141], wherein the method is repeatedly performed for each frame (timestep) of collected LiDAR data.
each comprising: obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor. See at least [0107] and figure 7B, step 756, wherein a 2D channel-azimuth structure is obtained for a roadside LiDAR sensor system. See at least [0126]-[0129] and figures 10-11, wherein the 2D structure represents a grid of cells and the associated points present in the cells.
by accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays emitted by said each sensor, the characteristics including hit points of the rays. See at least [0102], [0106]-[0107], figure 6, and figures 7A-B, wherein the 2D channel-azimuth structure is obtained by accessing point cloud data from a LiDAR sensor and processing the point cloud to identify hit points.
and determining a state of each cell of the cells based on the identified characteristics. See at least [0107] and figure 7B, step 760, wherein the data points in the 2D structure are compared to a point threshold, which classifies the state of the cell.
Xu remains silent on as well as projections of the hit points and the rays on a plane corresponding to the ground; whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell; and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle.
Westerhoff teaches as well as projections of the hit points and the rays on a plane corresponding to the ground. See at least [0061]-[0067] and figures 3A-B, wherein the projection of hit points (41) and rays (42) corresponding to the ground plane Mxy are used to determine an associated point’s occupancy state. The states include hit, pass (free), or covered (occluded/unknown).
whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height. See at least [0066]-[0067], [0078]-[0079], figures 3A-B, and figure 4, wherein a map point is determined to be a hit (occupied) if a projection of a hit point relative to the ground if above a first height, e.g. 25cm. The first height is defined as the height where ϕTh returns a value of 0.5.
and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell. See at least [0066]-[0067], [0080]-[0083], figures 3A-B, and figure 4, wherein a map point is determined to be a pass (free) if a projection of a ray passing over (or overhanging) the point is at or below a second height. The second height is defined as the height where ϕDh returns a value of 0.5, and is within a range 50. As seen in figure 4, range 50 includes height values that are larger than the first height.
and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle. See at least [0022] and [0058], wherein the determined occupancy map is output to a driver assistance or autonomous driving system for path planning. The autonomous steering and braking systems are then controlled based on the path planning.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s technique of using projections of hit points and rays with the ground to classify occupancy states, classifying points into a free or occupied state based on a comparison between a second and first height, respectively, to the projections, and using the obtained occupancy map to determine a path for the autonomous vehicle and control automated driving systems of the vehicle. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 16, Xu teaches A system for steering an automated vehicle in a designated area, wherein the system comprises a set of one or more offboard sensors, each being a 3D laser scanning Lidar. See at least [0063]-[0067], [0075]-[0076], and figures 1a-1d, wherein LiDAR sensors are installed at different roadside locations to monitor designated areas, such as an intersection. The LiDAR sensors scan their environment to provide 3D point data.
and one or more processing subsystems, the latter configured to repeatedly execute algorithmic iterations. See at least [0076], [0121], [0129], and [0141], wherein the method is repeatedly performed for each frame (timestep) of collected LiDAR data.
wherein, in operation, each iteration of the algorithmic iterations comprises: obtaining, for each sensor of the one or more offboard sensors, a grid, which is a 2D occupancy grid of cells reflecting a perception of said each sensor. See at least [0107] and figure 7B, step 756, wherein a 2D channel-azimuth structure is obtained for a roadside LiDAR sensor system. See at least [0126]-[0129] and figures 10-11, wherein the 2D structure represents a grid of cells and the associated points present in the cells.
by accessing a dataset capturing a point cloud model of an environment of said each sensor and processing the dataset to identify characteristics of rays emitted by said each sensor, the characteristics including hit points of the rays. See at least [0102], [0106]-[0107], figure 6, and figures 7A-B, wherein the 2D channel-azimuth structure is obtained by accessing point cloud data from a LiDAR sensor and processing the point cloud to identify hit points.
and determining a state of each cell of the cells based on the identified characteristics. See at least [0107] and figure 7B, step 760, wherein the data points in the 2D structure are compared to a point threshold, which classifies the state of the cell.
Xu remains silent on as well as projections of the hit points and the rays on a plane corresponding to the ground; whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height, and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell; and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle.
Westerhoff teaches as well as projections of the hit points and the rays on a plane corresponding to the ground. See at least [0061]-[0067] and figures 3A-B, wherein the projection of hit points (41) and rays (42) corresponding to the ground plane Mxy are used to determine an associated point’s occupancy state. The states include hit, pass (free), or covered (occluded/unknown).
whereby, given a first height above the plane and a second height above the first height, a necessary and sufficient condition for said each cell to be in an occupied state is to be matched by a projection of a hit point located above the first height. See at least [0066]-[0067], [0078]-[0079], figures 3A-B, and figure 4, wherein a map point is determined to be a hit (occupied) if a projection of a hit point relative to the ground if above a first height, e.g. 25cm. The first height is defined as the height where ϕTh returns a value of 0.5.
and a necessary condition for said each cell to be in a free state is to be crossed by a projection of an overhanging ray that has dropped below the second height when passing over said each cell. See at least [0066]-[0067], [0080]-[0083], figures 3A-B, and figure 4, wherein a map point is determined to be a pass (free) if a projection of a ray passing over (or overhanging) the point is at or below a second height. The second height is defined as the height where ϕDh returns a value of 0.5, and is within a range 50. As seen in figure 4, range 50 includes height values that are larger than the first height.
and determining, based on the grid obtained for said each sensor, a trajectory for the automated vehicle and forwarding the determined trajectory to a drive-by-wire system of the automated vehicle. See at least [0022] and [0058], wherein the determined occupancy map is output to a driver assistance or autonomous driving system for path planning. The autonomous steering and braking systems are then controlled based on the path planning.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s technique of using projections of hit points and rays with the ground to classify occupancy states, classifying points into a free or occupied state based on a comparison between a second and first height, respectively, to the projections, and using the obtained occupancy map to determine a path for the autonomous vehicle and control automated driving systems of the vehicle. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 17, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu remains silent on wherein the first height is equal to 5 cm, and the second height is equal to 20 cm.
Westerhoff teaches wherein the first height is equal to 5 cm, and the second height is equal to 20 cm. See at least [0079]-[0083] and figure 4, wherein the second height is defined as the height where ϕDh returns a value of 0.5, and is within a range 50. As seen in figure 4, range 50 includes 20cm. The first height is defined as the height where ϕTh returns a value of 0.5, which includes -25cm to 25 cm. Both heights are measured with respect to the ground plane h=0.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Xu with Westerhoff’s first and second heights. It would have been obvious to modify because doing so provides a low effort method of providing vehicles with occupancy maps, as recognized by Westerhoff (see at least [0002]-[0004]).
Regarding claim 18, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches wherein said constant angles span a range of at least 30 degrees of angle in said elevation plane. See at least [0161], wherein the LiDAR is configured to scan its surrounding by emitting rays at constant vertical angles of 0.33° for a total vertical FOV of 40°, from -15° to 25°.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Xu and Westerhoff as applied to claims above, and further in view of US 20050184987 A1, filed March 1st, 2005, hereinafter “Vincent”.
Regarding claim 3, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu additionally teaches wherein the identified characteristics further include elevation angles of the rays, and said further condition is enforced by comparing an elevation angle of said overhanging ray with an elevation angle of said reference ray. See at least [0076], [0102], and figure 6, wherein an elevation angle is determined for the rays, and a map point’s state is further dependent on the elevation angle.
Xu remains silent on whereby the elevation angle of the overhanging ray must be larger than the elevation angle of the reference ray to be allowed to set the outer cell in a free state.
Vincent teaches whereby the elevation angle of the overhanging ray must be larger than the elevation angle of the reference ray to be allowed to set the outer cell in a free state. See at least [0061]-[0065] and figure 8, wherein rays with a larger elevation angle (810-816) than reference ray 820 can override an occluded state. The occluded state is defined by a reference ray 820 with an inner hit point R5.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Vincent’s technique of allowing overhanging rays with higher elevation angles to set outer cells in free, un-occluded states. It would have been obvious to modify because doing so enables sensor systems to better represent occluded and occupied regions, as recognized by Vincent (see at least [0003]-[0007]).
Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Xu and Westerhoff as applied to claims above, and further in view of US 20250026371 A1, with an earliest priority date of July 19th, 2023, hereinafter “Andert”.
Regarding claim 5, Xu and Westerhoff in combination teach all of the limitations of claim 1 as discussed above, and Xu remains silent on wherein the set of one or more offboard sensors comprises N sensors N>2, located at distinct positions in the designated area, such that N grids are obtained, which overlap at least partly, and determining said trajectory further comprises fusing data from the N grids obtained, whereby the trajectory is determined based on the fused data.
Andert teaches wherein the set of one or more offboard sensors comprises N sensors N>2, located at distinct positions in the designated area, such that N grids are obtained, which overlap at least partly. See at least [0051] and figure 7, wherein sensors 0-N (with at least 2 sensors) provide sensor data, and the sensor observations are associated with the same tracks, i.e., they overlap. See at least [0047]-[0048] and figure 8A, wherein the sensors are associated with objects including fixed objects 202A-C distributed at distinct positions in the area.
and determining said trajectory further comprises fusing data from the N grids obtained, whereby the trajectory is determined based on the fused data. See at least [0048] and figures 8A-B, wherein data from the sensors are fused. See at least [0022], wherein cooperative sensor fusion is used in localization and perception systems of autonomous vehicles.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Andert’s technique of fusing overlapping grids from multiple sensors distributed in the area, and controlling autonomous vehicles based on the fusion. It would have been obvious to modify because doing so enables decreased errors when performing cooperative sensing fusion, as recognized by Andert (see at least [0021]).
Regarding claim 6, Xu, Westerhoff, and Andert in combination teach all of the limitations of claim 5 as discussed above, and Xu additionally teaches wherein the method further comprises, prior to fusing said data, converting each of the N grids into a cartesian grid, which is defined in a cartesian coordinate system and has rectangular cells, so that at least some of the rectangular cells of any one of the N grids coincide with cells of a distinct one of the N grids. See at least [0076]-[0078], [0107], and figure 7B, wherein, prior to combining sensor data to obtain a final data structure, the sensor data is converted into 3D LiDAR point cloud data at step 770. The 3D LiDAR point cloud data is in a Cartesian coordinate system, represented by (X, Y, Z) coordinates. The locations in the Cartesian 3D point cloud represent the same locations found in the 2D data structure.
Regarding claim 7, Xu, Westerhoff, and Andert in combination teach all of the limitations of claim 5 as discussed above, and Xu remains silent on wherein the N grids are obtained by dispatching sensor data to K processing systems, whereby each processing system k of the K processing systems receives Nk datasets of the sensor data as obtained from Nk respective sensors of the set of N offboard sensors, where k = 1 to K, K ≥ 2, Nk ≥ 2 ∀ k, and N = ∑k Nk, and processing, at said each processing system k, the N datasets received to obtain Mk occupancy grids corresponding to perceptions from Mk respective sensors of the offboard sensors, respectively, Nk ≥ Mk ≥ 1, wherein the Mk occupancy grids overlap at least partly.
Andert teaches wherein the N grids are obtained by dispatching sensor data to K processing systems, whereby each processing system k of the K processing systems receives Nk datasets of the sensor data as obtained from Nk respective sensors of the set of N offboard sensors, where k = 1 to K, K ≥ 2, Nk ≥ 2 ∀ k, and N = ∑k Nk. See at least [0047]-[0048] and figures 8A-B, wherein each processing system 202A-c and 204A-C receives 2 datasets of the sensor data as obtained from 2 respective sensors of the set of 12 offboard sensors. K = 6 processing systems, Nk = 2 for every processing system, and N = 12 sensors = ∑ 2 sensors for every processing system.
and processing, at said each processing system k, the Nk datasets received to obtain Mk occupancy grids corresponding to perceptions from Mk respective sensors of the offboard sensors, respectively, Nk ≥ Mk ≥ 1, wherein the Mk occupancy grids overlap at least partly. See at least [0047]-[0049] and figures 8A-B, wherein each processing system processes the 2 received datasets to obtain 1 occupancy grid for each sensor using recognition pipelines. Mk = 1. See at least [0051] and figure 7, wherein sensors 0-N (with at least 2 sensors) provide sensor data, and the sensor observations are associated with the same tracks, i.e., they overlap.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Andert’s technique of fusing dispatching sensor data to 6 processing systems, wherein each processing system receives 2 datasets as obtained from 2 sensors of the set of 12 sensors, and processing the datasets to receive 1 occupancy grid, at least partly overlapping with other occupancy grids. It would have been obvious to modify because doing so enables decreased errors when performing cooperative sensing fusion, as recognized by Andert (see at least [0021]).
Regarding claim 8, Xu, Westerhoff, and Andert in combination teach all of the limitations of claim 7 as discussed above, and Xu remains silent on wherein fusing the data from the N grids comprises: fusing, at said each processing system k, data from the Mk occupancy grids obtained to form a fused occupancy grid, whereby K fused occupancy grids are formed by the K processing systems, respectively; forwarding the K fused occupancy grids to a further processing system; and merging, at the further processing system, the K fused occupancy grids to obtain a global occupancy grid for the designated area.
Andert teaches wherein fusing the data from the N grids comprises: fusing, at said each processing system k, data from the Mk occupancy grids obtained to form a fused occupancy grid, whereby K fused occupancy grids are formed by the K processing systems, respectively. see at least [0047]-[0049], wherein data from the 1 occupancy grid per sensor is fused, at each processing system, to form a fused local sensor measurement, whereby 12 fused occupancy grids are formed by the 12 processing systems.
forwarding the K fused occupancy grids to a further processing system; and merging, at the further processing system, the K fused occupancy grids to obtain a global occupancy grid for the designated area. See at least [0050], wherein each processing system communicates their local fused occupancy grid to a further processing system 206 to obtain global fused measurement data.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Andert’s technique of fusing the data from the obtained occupancy grids to form a fused occupancy grid, and forwarding the fused occupancy grids to an external system to obtain a global occupancy grid. It would have been obvious to modify because doing so enables decreased errors when performing cooperative sensing fusion, as recognized by Andert (see at least [0021]).
Claim 9 rejected under 35 U.S.C. 103 as being unpatentable over Xu, Westerhoff, and Andert as applied to claims above, and further in view of US 20200104289 A1, filed September 18th, 2019, hereinafter “Premawardena”.
Regarding claim 9, Xu, Westerhoff, and Andert in combination teach all of the limitations of claim 8 as discussed above, and Xu additionally teaches wherein the N datasets received at said each iteration by said each processing system k are respectively associated with N first timestamps. See at least [0076], wherein the LiDAR datasets are associated with timestamp data.
Xu remains silent on and said each iteration further comprises: assigning K second timestamps to the K fused occupancy grids, where each of the K second timestamps is equal to an oldest of the N first timestamps associated with the N datasets as processed at said each processing system k; and assigning a global timestamp to the global occupancy grid, where the global timestamp is obtained as an oldest of the K second timestamps, and said trajectory is determined in accordance with the global timestamp.
Premawardena teaches said each iteration further comprises: assigning K second timestamps to the K fused occupancy grids, where each of the K second timestamps is equal to an oldest of the N first timestamps associated with the N datasets as processed at said each processing system k, and assigning a global timestamp to the global occupancy grid, where the global timestamp is obtained as an oldest of the K second timestamps, and said trajectory is determined in accordance with the global timestamp. See at least [0125], wherein occupancy grids are assigned timestamp data after being updated by a processing system. See at least [0124], wherein, during data processing, occupancy grids are assigned the oldest timestamps out of the timestamps associated with the scene grid. See at least [0096], wherein data processing steps can be applied to sensor data before or after fusion. In combination with Andert’s teaching, discussed above in claim 8, of global occupancy data, this limitation is taught in its entirety.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Premawardena’s technique of assigning an oldest timestamp of associated timestamps to an occupancy grid, either before or after performing sensor fusion, and determining a trajectory in view of the timestamp. It would have been obvious to modify because doing so enables increased accuracy when sharing maps among autonomous vehicles, as recognized by Premawardena (see at least [0002]-[0006] and [0037]-[0042]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Xu, Westerhoff, Andert, and Premawardena as applied to claims above, and further in view of US 20210072390 A1, filed September 5th, 2019, hereinafter “Baek”.
Regarding claim 10, Xu, Westerhoff, Andert, and Premawardena in combination teach all of the limitations of claim 9 as discussed above, and Xu remains silent on wherein processing the N datasets at said each processing system k further comprises discarding any of the Nk datasets that is older than a reference time for the N datasets by more than a predefined time period, whereby Mk is at most equal to Nk, and the reference time is computed as an average of the N timestamps.
Premawardena teaches wherein processing the N datasets at said each processing system k further comprises discarding any of the Nk datasets that is older than a reference time for the N datasets by more than a predefined time period, whereby Mk is at most equal to Nk. See at least [0124], wherein scene descriptions that have a timestamp older than an age threshold are excluded from the occupancy grid. See at least [0005] and [0038], wherein the exclusion is based on a staleness of the dataset
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Premawardena’s technique of discarding datasets older than a threshold age. It would have been obvious to modify because doing so enables increased accuracy when sharing maps among autonomous vehicles, as recognized by Premawardena (see at least [0002]-[0006] and [0037]-[0042]).
Baek teaches the reference time is computed as an average of the N timestamps. See at least [0042], wherein a reference timestamp is determined, based on a mean data acquisition time.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Baek’s technique of computing a reference time as an average of data acquisition times. It would have been obvious to modify because doing so enables sensor fusion capable of synchronizing acquisition time points without interference, as recognized by Baek (see at least [0002]-[0004]).
Claims 14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu and Westerhoff as applied to claims above, and further in view of US 20190195990 A1, filed December 12th, 2022, hereinafter “Shand”.
Regarding claim 14, Xu and Westerhoff in combination each all of the limitations of claim 1 as discussed above, and Xu remains silent on wherein at least one of said sensors is located at a height that is between 1.9 m and 5 m, this height measured with respect to said plane.
Shand teaches wherein at least one of said sensors is located at a height that is between 1.9 m and 5 m, this height measured with respect to said plane. See at least [0024], wherein a LiDAR system is installed at a height of 2 meters with respect to a principal plane.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Shand’s sensor height. It would have been obvious to modify because doing so enables vehicles to receive increased resolution sensor data from LIDAR systems, as recognized by Shand (see at least [0024]-[0029]).
Regarding claim 19, Xu, Westerhoff, and Shand in combination each all of the limitations of claim 14 as discussed above, and Xu remains silent on wherein each of said sensors is located at a height that is between 1.9 m and 5 m.
Shand teaches wherein each of said sensors is located at a height that is between 1.9 m and 5 m. See at least [0024], wherein each emitter of the LiDAR system is installed at a height of 2 meters with respect to a principal plane.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Shand’s sensor height. It would have been obvious to modify because doing so enables vehicles to receive increased resolution sensor data from LIDAR systems, as recognized by Shand (see at least [0024]-[0029]).
Regarding claim 20, Xu, Westerhoff, and Shand in combination each all of the limitations of claim 14 as discussed above, and Xu remains silent on wherein said height that is between 1.9 m and 3 m.
Shand teaches wherein said height that is between 1.9 m and 3 m. See at least [0024], wherein each emitter of the LiDAR system is installed at a height of 2 meters with respect to a principal plane.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Xu with Shand’s sensor height. It would have been obvious to modify because doing so enables vehicles to receive increased resolution sensor data from LIDAR systems, as recognized by Shand (see at least [0024]-[0029]).
Conclusion
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/S.M.J./Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
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