Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C. 101 have been fully considered and are persuasive. The eligibility rejection of record has been withdrawn.
Applicant’s arguments with respect to 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 are 1-5, 7, 9-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20210241026 by Deng et al. (hereinafter “Deng”), further in view of US20250237762 by Weikersdorfer et al. (hereinafter “Weikersdorfer”).
Regarding claim 1, Deng teaches A method for generating an occupancy grid, comprising: obtaining detection information from a plurality of heterogeneous sensors by making a plurality of detections by the plurality of heterogeneous sensors, the detection information comprising a plurality of sensor measurement see for example paragraphs [0123]-[0124] and Fig. 10, where the system fuses raw sensor data from a plurality of sensors (camera, radar, and lidar) into a fused raw data map.
generating a single combined measurement see again paragraphs [0123]-[0124] and Fig. 10, as well as paragraphs [0130]-[0132], where the raw sensor data is fused into a single layer (Fig. 10 output of block 1044, Fig. 12 step 1212) prior to any occupancy determination or other processing.
determining occupancy probabilities for a plurality of see paragraphs [0123]-[0124] and [0130]-[0132], Figs. 10 and 12, as well as paragraphs [0125]-[0126], where the system uses the fused raw data map to classify objects and determine object presence, dimensions, velocity, etc.
and outputting the occupancy See for example paragraphs [0047]-[0048], where the sensor processor 340 outputs its information to the vehicle (where the sensor processor 340 is what transforms the sensor data, see paragraph [0123]).
Deng does not explicitly teach performing these calculations in terms of a sensor measurement grid having a plurality of cells.
However, however, Weikersdorfer teaches using “a sensor measurement grid” “having a plurality of cells.” See for example Fig. 1, where disparate sensor data types are fed into the neural network to produce a map. This process is described in [0033]-[0034], stating that the system can input the data raw rather than processed, or may be processed into a top-down or bird’s eye view (BEV) image or map, for input into the neural network. See also paragraphs [0026]-[0027], where the system can output an occupancy map having cells. Similarly, see paragraphs [0049]-[0050] describing inputting a fused grid map of sensor data to the neural network to generate an occupancy map.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor fusion system of Deng with the grid map methods of Weikersdorfer with a reasonable expectation of success. Doing so allows the system to fuse individual sensors into a grid format, which can improve efficiency and accuracy of the system and its ability to recognize important features.
Claims 10 and 19 have similar limitations to claim 1 above, and are therefore rejected using a similar rationale.
Regarding claim 2, Deng teaches wherein generating the single combined measurement grid includes combining the detection information in a single data buffer. See for example paragraph [0047], where the system stores sensor data in memory 344, and paragraphs [0123]-[024], where the system combines the sensor data into a single output.
Claims 11 and 20 have similar limitations to claim 2 above, and are therefore rejected using a similar rationale.
Regarding claim 3, Deng teaches wherein the plurality of heterogeneous sensors includes a first radar configured to detect targets in a first area, and a second radar configured to detect targets in a second area that is different from the first area. See for example paragraphs [0123]-[0124] and Figs. 2 and 10, where the sensors include multiple radar sensors (116B) (and their areas of detection are at least partially different, even if they overlap).
Claim 12 has similar limitations to claim 3 above, and is therefore rejected using a similar rationale.
Regarding claim 4, Deng teaches wherein the plurality of heterogeneous sensors includes a lidar. See again paragraphs [0123]-[0124] and Figs. 2 and 10, where the sensors include a lidar 112.
Claim 13 has similar limitations to claim 4 above, and is therefore rejected using a similar rationale.
Regarding claim 5, Deng teaches wherein the plurality of heterogeneous sensors includes a camera. See again paragraph [0123]-[0124] and Figs. 2 and 10, where the sensors include a camera (116A, 116F).
Claim 14 has similar limitations to claim 5 above, and is therefore rejected using a similar rationale.
Regarding claim 7, Deng teaches wherein the occupancy probabilities include an occupied probability and a free probability for each of the . See paragraphs [0124]-[0126], where the neural network layers are used to identify and classify objects in space.
Deng does not explicitly teach performing these calculations in terms of a plurality of cells.
However, however, Weikersdorfer teaches using a sensor measurement grid having a plurality of cells. See again paragraphs [0049]-[0050] describing inputting a fused grid map of sensor data to the neural network to generate an occupancy map. See also paragraphs [0077] and [0081]-[0084], where the occupancy maps include “free” and “occupied” spaces.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor fusion system of Deng with the grid map methods of Weikersdorfer with a reasonable expectation of success. Doing so allows the system to fuse individual sensors into a grid format, which can improve efficiency and accuracy of the system and its ability to recognize important features.
Claim 16 has similar limitations to claim 7 above, and is therefore rejected using a similar rationale.
Regarding claim 9, Deng does not explicitly teach, but Das teaches wherein the occupancy grid includes a grid cell state with a velocity indication for at least one of the plurality of cells. See for example paragraphs [0123]-[0125] where the system classifies objects in the fused grid map into static or dynamic objects. See also paragraphs [0100]-[0107] where the system measures the velocities of objects in the grid map.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor fusion system of Deng with the grid map methods of Weikersdorfer with a reasonable expectation of success. Doing so allows the system to fuse individual sensors into a grid format, which can improve efficiency and accuracy of the system and its ability to recognize important features.
Claim 18 has similar limitations to claim 9 above, and is therefore rejected using a similar rationale.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Weikersdorfer as applied to claims 1 and 10 above, and further in view of US20230095384 by Sharma Banjade et al. (hereinafter “Sharma Banjade”).
Regarding claim 6, Deng does not explicitly teach, but Sharma Banjade teaches wherein obtaining the detection information includes receiving remote sensor detection information via a network interface. See for example paragraphs [0040], [0076], [0079], and [0053], where occupancy maps are used for path planning, and “VAMs” (Vehicle Awareness Messages) are transmitted between entities, including the single occupancy grid map.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor fusion system of Deng, modified by the grid map methods of Weikersdorfer, with the remote detections of Sharma Banjade with a reasonable expectation of success. Doing so allows the system to fuse remote sensors into a the vehicle grid, improving safety of the vehicle and reducing blind spots.
Claim 15 has similar limitations to claim 6 above, and is therefore rejected using a similar rationale.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Weikersdorfer as applied to claims 1 and 10 above, and further in view of US20220214444 by Das et al. (hereinafter “Das”).
Regarding claim 8, Deng does not explicitly teach, but Das teaches wherein the occupancy probabilities include a dynamic probability and a static probability for each of the plurality of cells. See for example paragraphs [0123]-[0125] where the system classifies objects in the fused grid map into static or dynamic objects. See also paragraphs [0100], [0107], [0110]-[0112], and [0118] where the system determines occupancy based on sensor confidence and probability.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor fusion system of Deng, modified by the grid map methods of Weikersdorfer, with the dynamic and static cell probabilities of Das with a reasonable expectation of success. Doing so allows the system to determine the likelihood of cell occupancy based on the cell’s velocity.
Claim 17 has similar limitations to claim 8 above, and is therefore rejected using a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20210348944 by Lehmann et al. teaching merging of raw environmental maps between vehicles.
US20210088624 by Puglielli et al. teaching combining raw radar data into a single grid.
US20220194412 by Zhang et al. teaching combining raw sensor data from different types of sensors into a single map.
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/JORDAN T SMITH/ Examiner, Art Unit 3666
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