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 .
Status of Claims
This is the first Office Action on the merits. Claims 1-16 are currently pending. Claims 4-13 and 15 are currently amended, and claim 16 is new.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2219504.4, filed on 04/10/2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/17/2025 and 03/31/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 2 and 3 are objected to because of the following informalities:
Claim 2 line 6, "0.6" should read "0.6.".
Claim 3 line 7, "0.6." should read "0.6;".
Appropriate correction is required.
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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9 and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Nuss et al. ("A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application") in view of Silva et al. (US20190384309A1), hereinafter Nuss and Silva, respectively.
Regarding claim 1, Nuss teaches of a control system for a vehicle, the control system comprising at least one controller ("Table I shows the parameter set of the DS-PHD/MIB filter. The parallel implementation is tested on an Nvidia GTX980 GPU, supported by a single core of an Intel i7 processor", Pg. 16, "A. Experiment Configuration"), the control system being configured to: receive, from at least one sensor, sensor data indicative of a location of one or more objects detected in an environment of the vehicle ("a measurement grid map with the same dimensions as the grid map is already available and measurement grid cells are stored in the mea", Pg. 13, "B. Implementation Details", "This paper presents a real-time application serving as a fusion layer for laser and radar sensor data…", Abstract, see at least Fig. 6); and modify an occupancy grid stored in a memory accessible to the control system in dependence on the sensor data ("So each grid cell stores a mass for occupied m(O) and a mass for free m(F)", Pg. 10, "A. State Representation", "store_values(rho_b, rho_p, m_free_up, grid_cell_array, j)", Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", line 15, "All particles and grid cells are stored in the particle_array and grid_cell_array arrays, respectively", Pg. 13, "B. Implementation Detail", data being stored in arrays is indicative of memory being used by the sensor), the occupancy grid representing an environment of the vehicle and having a plurality of cells, wherein each cell is associated with at least one value indicative of a likelihood of a corresponding portion of the environment being occupied by one of the one or more objects ("Each cell reads the observed occupancy BBA from the corresponding measurement grid cell and combines it with its predicted occupancy BBA according to (63) to calculate its updated occupancy BBA", Pg. 13, "3) Grid Cell Occupancy Prediction and Update"); and modify the at least one value associated with the determined one or more cells of the occupancy grid to be indicative of an increased uncertainty of the cell being occupied by an object ("In practical applications, a grid map contains a high ration of occluded and therefore unobserved grid cells… One possibility to distinguish between unobserved and occupied cells is to use Dempster-Shafer masses of evidence instead of occupancy probabilities", Pg. 3, "6) Occluded Areas", "the DS-PHD/MIB filter excepts the following information in each measurement grid cell: The observed BBA
m
z
k
+
1
(
c
)
:
2
{
O
,
F
}
-> [0, 1]", Pg. 11, "C. Update", "Each cell reads the observed occupancy BBA from the corresponding measurement grid cell and combined it with its predicted occupancy BBA according to (63) to calculate its updated occupancy BBA", Pg. 11, "Grid Cell Occupancy Prediction and Update").
However, Nuss does not teach of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid where a field of view of the at least one sensor is obstructed by at least one of the one or more objects.
Silva, in the same field of endeavor, teaches of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid where a field of view of the at least one sensor is obstructed by at least one of the one or more objects ("determining, as an occluded region and based at least in part on the sensor data, that at least a portion of the occlusion grid is occluded at a first time", [0135], "The occlusion region component 434 can include functionality to determine a portion of an occlusion grid that is occluded… In some instances, the occlusion region component 434 can dynamically generate an occluded region based on objects in an environment", [0066]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Silva to include determining when a field of view of the sensor is obstructed with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the accuracy of the system by enabling the grid to identify regions that are visible by objects in the environment rather than not classifying it (Silva, [0066], [0049]).
Regarding claim 2, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the at least one value associated with the determined one or more cells of the occupancy grid is an unknown mass value, and the control system is arranged to modify the unknown mass value to a value indicative of the increased uncertainty of the cell being occupied by the object, and optionally, wherein the unknown mass value is given a value of between 0.4 and 0.6 ("The DS-PHD/MIB filter represents the occupancy state of a grid cell with a basic belief assignment (BBA) m :
2
Ω
-> [0, 1]. The frame of discernment Ω contained the events occupied and free: Ω = {O, F}. So each grid cell stored a mass for occupied m(O) and mass for free m(F)", Pg. 10, "A. State Representation").
Regarding claim 3, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the at least one value associated with the determined one or more cells of the occupancy grid is a free mass value and an occupied mass value ("The DS-PHD/MIB filter represents the occupancy state of a grid cell with a basic belief assignment (BBA) m :
2
Ω
-> [0, 1]. The frame of discernment Ω contained the events occupied and free: Ω = {O, F}. So each grid cell stored a mass for occupied m(O) and mass for free m(F)", Pg. 10, "A. State Representation"), and the control system is arranged to modify the free mass value and the occupied mass value to respective values indicative of the increased uncertainty of the cell being occupied by the object, and optionally, wherein the free mass value and the occupied mass are each: given a value between 0.4 and 0.6. given a value less than 0.3; or given a value of substantially 0 (Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", Line 10-11, this algorithm updates both the occupied mass m(O) and free mass m(F) for the cell).
Regarding claim 4, modified Nuss teaches of all limitations of claim 1, as stated above.
However, modified Nuss does not teach of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid within the field of view of the sensor for which the sensor data did not indicate the location of an object; and modify the at least one value associated with the determined one or more cells of the occupancy grid to be indicative of an increased likelihood of the cell being unoccupied by an object.
Silva, in the same field of endeavor, teaches of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid within the field of view of the sensor for which the sensor data did not indicate the location of an object ("LIDAR returns may indicate that a region is occupied or that a LIDAR ray traversed through a region before being received as a reflection by a LIDAR sensor (e.g., the return may indicate that the region is unoccupied)", [0037]); and modify the at least one value associated with the determined one or more cells of the occupancy grid to be indicative of an increased likelihood of the cell being unoccupied by an object ("If a threshold number (or percentage) of returns indicate no return, the occupancy state may be unoccupied. Otherwise, the occupancy state may be indeterminate", [0125], "LIDAR returns may indicate that a region is occupied or that a LIDAR ray traversed through a region before being received as a reflection by a LIDAR sensor (e.g., the return may indicate that the region is unoccupied)", [0037]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Silva to determine the cells in the field of view where no object was detected and reflect that in the likelihood with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the awareness of the vehicle by allowing the system to continuously confirm which areas are clear to travel (Silva, [0037]).
Regarding claim 5, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the at least one value associated with the one or more cells of the occupancy grid for which the sensor data did not indicate the location of an object is a free mass value, and the control system is arranged to modify the free mass value to a value indicative of a likelihood of the cell being unoccupied by the object ("The predicted mass for free is modeled as in a static grid map and given by
m
p
,
+
c
F
k
+
1
=
m
i
n
[
α
T
m
k
c
F
k
,
1
-
m
p
,
+
c
O
k
+
1
]
", Pg. 11, "B. Prediction", "The grid cell additionally stores the posterior mass for free
m
k
+
1
c
(
F
k
+
1
)
as calculated in (63), which completes the posterior state together with the particle set", Pg. 12, "3) Spatial Update", here Free mass = likelihood there was no object detected).
Regarding claim 6, modified Nuss teaches of all limitations of claim 2, as stated above, additionally, wherein the modifying comprises the control system combining a predetermined unknown mass value with a prior unknown mass for the one or more cells of the occupancy grid within the field of view of the sensor where the field of view of the sensor is obstructed by at least one of the one or more objects, and optionally, wherein the combining is according to a Dempster Shafer rule of combination ("The DS-PHD/MIB filter approximates the existence update by simply combining the predicted BBF
m
p
,
+
c
and the observed BBA
m
z
k
+
1
(
c
)
of the corresponding measurement grid cell with the Dempster-Shafer rule of combination (see [4]):
m
k
+
1
c
=
m
p
,
+
c
⊕
m
z
k
+
1
(
c
)
", Pg. 11, "1) Existence Update", Nuss combines a current BBA (basic belief assignment) which includes all the parameters (occupied mass, free mass, and inherently, the unknown mass) with the prior BBA, therefore it is implicit that the result of the combination effectively produces an updated unknown mass within the updated BBA).
Regarding claim 7, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the modifying comprises the control system being configured to: determine the field of view of the sensor in dependence on one or more characteristics associated with the sensor ("The test vehicle is equipped with a Valeo four-layer laser scanner with an opening angle of 120 degrees in the front bumper. Additionally, two short range Delphi single beam mono pulse radars facing to the front left and front right sides cover a similar area", Pg. 16, "A. Experiment Configuration"); and select the one or more cells of the occupancy grid corresponding to the field of view of the sensor ("the assignment of sorted particles to grid cells is straightforward… Each grid cell can store two particles indices. They represent the first and last index of the particle group that has been predicted into the grid cell", Pg. 13, "A. Parallelization Challenges" and "B. 2) Assignment of particles to Grid Cells"; Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", line 1, Algorithm 3 iterates over all grid cells and applies the measurement BBA from meas_cell_array to each. This constitutes selecting all cells within the sensor's measurement coverage for update, i.e., selecting cells corresponding to the FOV).
Regarding claim 9, modified Nuss teaches of all limitations of claim 1, as stated above.
However, modified Nuss does not teach of wherein the control system is configured to: determine the one or more cells of the occupancy grid where the field of view of the sensor is obstructed by at least one of the one or more objects as cells in a shadow of the one of the one or more objects detected in the environment of the vehicle.
Silva, in the same field of endeavor, teaches of wherein the control system is configured to: determine the one or more cells of the occupancy grid where the field of view of the sensor is obstructed by at least one of the one or more objects as cells in a shadow of the one of the one or more objects detected in the environment of the vehicle ("the object 618 can generate a “shadow” with respect to the sensed region of the vehicle 102, illustrated by the occluded region 634", [0102]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Silva to identify the shadow region behind a detected object with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the precision of the system by recognizing that a detected object blocks the sensors view of the area directly behind it (Silva, [0102]).
Regarding claim 12, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, of a system for a vehicle, comprising: the control system of claim 1; and at least one sensor arranged to output sensor data indicative of a location of one or more objects detected in an environment of the vehicle ("The test vehicle is equipped with a Valeo four-layer laser scanner with an opening angle of 120 degrees in the front bumper. Additionally, two short range Delphi single beam mono pulse radars facing to the front left and front right sides cover a similar area", Pg. 16, "A. Experiment Configuration", "This paper presents a real-time application serving as a fusion layer for laser and radar sensor data…", Abstract, see at least Fig. 6).
Regarding claim 13, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, of a vehicle comprising the control system according to claim 1 ("The test vehicle...", Pg. 16, "A. Experiment Configuration").
Regarding claim 14, Nuss teaches of a computer-implemented method, comprising: receiving, from at least one sensor, sensor data indicative of a location of one or more objects detected in an environment of a vehicle ("a measurement grid map with the same dimensions as the grid map is already available and measurement grid cells are stored in the mea", Pg. 13, "B. Implementation Details", "This paper presents a real-time application serving as a fusion layer for laser and radar sensor data…", Abstract, see at least Fig. 6); and modify an occupancy grid stored in a memory in dependence on the sensor data ("So each grid cell stores a mass for occupied m(O) and a mass for free m(F)", Pg. 10, "A. State Representation", "store_values(rho_b, rho_p, m_free_up, grid_cell_array, j)", Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", line 15, "All particles and grid cells are stored in the particle_array and grid_cell_array arrays, respectively", Pg. 13, "B. Implementation Detail", data being stored in arrays is indicative of memory being used by the sensor), the occupancy grid representing an environment of the vehicle and having a plurality of cells, wherein each cell is associated with at least one value indicative of a likelihood of a corresponding portion of the environment being occupied by one of the one or more objects ("Each cell reads the observed occupancy BBA from the corresponding measurement grid cell and combines it with its predicted occupancy BBA according to (63) to calculate its updated occupancy BBA", Pg. 13, "3) Grid Cell Occupancy Prediction and Update"); and modify the at least one value associated with the determined one or more cells of the occupancy grid to be indicative of an increased uncertainty of the cell being occupied by an object ("In practical applications, a grid map contains a high ration of occluded and therefore unobserved grid cells… One possibility to distinguish between unobserved and occupied cells is to use Dempster-Shafer masses of evidence instead of occupancy probabilities", Pg. 3, "6) Occluded Areas", "the DS-PHD/MIB filter excepts the following information in each measurement grid cell: The observed BBA
m
z
k
+
1
(
c
)
:
2
{
O
,
F
}
-> [0, 1]", Pg. 11, "C. Update", "Each cell reads the observed occupancy BBA from the corresponding measurement grid cell and combined it with its predicted occupancy BBA according to (63) to calculate its updated occupancy BBA", Pg. 11, "Grid Cell Occupancy Prediction and Update").
However, Nuss does not teach of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid where a field of view of the at least one sensor is obstructed by at least one of the one or more objects.
Silva, in the same field of endeavor, teaches of wherein the modifying comprises the control system being configured to: determine one or more cells of the occupancy grid where a field of view of the at least one sensor is obstructed by at least one of the one or more objects ("determining, as an occluded region and based at least in part on the sensor data, that at least a portion of the occlusion grid is occluded at a first time", [0135], "The occlusion region component 434 can include functionality to determine a portion of an occlusion grid that is occluded… In some instances, the occlusion region component 434 can dynamically generate an occluded region based on objects in an environment", [0066]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Silva to include determining when a field of view of the sensor is obstructed with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the accuracy of the system by enabling the grid to identify regions that are visible by objects in the environment rather than not classifying it (Silva, [0066], [0049]).
Regarding claim 15, modified Nuss teaches of all limitations of claim 14, as stated above.
However, Nuss does not teach of a computer software which, when executed by a computer, is arranged to perform a method; optionally the computer software is tangibly stored on a non-transitory computer readable storage medium.
Silva, in the same field of endeavor, teaches of Computer software which, when executed by a computer, is arranged to perform a method; optionally the computer software is tangibly stored on a non-transitory computer readable storage medium ("the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations", [0091]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Silva to implement the occupancy grid update method as software on a non-transitory computer-readable storage medium with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the reusability of the system by allowing the method to be stored and executed across different hardware platforms (Silva, [0091]).
Regarding claim 16, modified Nuss teaches of all limitations of claim 12, as stated above, additionally, a vehicle comprising the system according to claim 12 ("The test vehicle...", Pg. 16, "A. Experiment Configuration").
Claims 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Nuss in view of Silva as applied to claim 1 above, and further in view of Gier et al. (US20210278853A1), hereinafter Gier.
Regarding claim 8, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the modifying comprises the control system being configured to: determine a first field of view of the sensor ("The test vehicle is equipped with a Valeo four-layer laser scanner with an opening angle of 120 degrees in the front bumper", Pg. 16, "A. Experiment Configuration"); determine one or more cells of the occupancy grid within the first field of view of the sensor (Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", line 1, "meas_cell_array > This array stores the measurement grid cells (constant size C)"); determine a cell of the occupancy grid corresponding to a location of one of the one or more objects detected in the environment of the vehicle within the first field of view of the sensor ("they are only created in grid cells where the corresponding measurement grid cell reports a mass for occupied:
m
z
k
+
1
(
c
)
O
k
+
1
>
0
", Pg. 12, "3) Spatial Update").
However, modified Nuss does not teach of determine a second field of view of the sensor in dependence on one or more characteristics associated with the sensor and the location of the one of the one or more objects.
Gier, in the same field of endeavor, teaches of determine a second field of view of the sensor in dependence on one or more characteristics associated with the sensor and the location of the one of the one or more objects ("The occlusion occupancy prediction component 632 can use ray casting and/or projection techniques to determine occluded regions associated with an object in addition to information about the particular modality (e.g., range, field of view, etc.)", [0091], "determining, based at least in part on the object and the sensor data, an occluded region in the environment associated with the object", [0131]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Gier to determine a field of view that accounts for the sensor’s characteristics and the location of the detected object with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the accuracy of the occluded region by utilizing both sensor properties and object location (Gier, [0092], [0132]).
Regarding claim 11, modified Nuss teaches of all limitations of claim 1, as stated above, additionally, wherein the control system is arranged to separately update the occupancy grid for each of the plurality of regions ("The remaining part of the algorithm is carried out in parallel for all grid cells", Pg. 13, "3) Grid Cell Occupancy Prediction and Update", Pg. 14, "Algorithm 3 Grid Cell Occupancy Prediction and Update", line 5).
However, modified Nuss does not teach of to divide the environment of the vehicle into a plurality of regions around the vehicle.
Gier, in the same field of endeavor, teaches of to divide the environment of the vehicle into a plurality of regions around the vehicle ("the vehicle 112 can represent an occluded area of the environment using an occlusion grid 114…The occlusion grid 114 can include a plurality of occlusion fields (e.g., the boxes pictured in the occlusion grid 114), which can represent discrete areas of the environment, such as drivable regions", [0026]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Gier to divide the environment around the vehicle with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the efficiency of the system by breaking the environment around the vehicle into zones to be processed independently (Gier, [0025]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nuss in view of Silva as applied to claim 1 above, and further in view of Trojahner (US20230056589A1), hereinafter Trojahner.
Regarding claim 10, modified Nuss teaches of all limitations of claim 1, as stated above.
However, modified Nuss does not teach of the control system being configured to sequentially select cells of the occupancy grid outward from a location of the sensor to determine whether the sensor data indicates the location of an object.
Trojahner, in the same field of endeavor, teaches of the control system being configured to sequentially select cells of the occupancy grid outward from a location of the sensor to determine whether the sensor data indicates the location of an object ("casting rays from the point of view (i.e., sensor location) to different directions and finding intersections with the grid. The first intersection of a ray with the grid yields an occupied cell (impact point). Any other intersection of the same ray is hidden by the first one (i.e., occluded). As such, the method follows each ray from the origin until an intersection has been found or a maximum distance has been reached… the origin is always the sensor origin (i.e., the autonomous vehicle when the sensor is mounted on the autonomous vehicle)", [0043]).
Therefore, one of ordinary skill in the art, before the effective filing date of the claimed invention, would have modified the teachings of modified Nuss with the teaching of Trojahner to process cells in the occupancy grid in an outward direction sequentially with reasonable expectations of success. One of ordinary skill in the art would have been motivated to make this modification in order to improve the accuracy of the system by ensuring it can correctly tell apart cells that are empty versus cells that are blocked from the sensor (Trojahner, [0043]).
Conclusion
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ABIGAIL LEE ESPINOZA
Examiner
Art Unit 3657
/JONATHAN L SAMPLE/Primary Examiner, Art Unit 3657
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