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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6, 13-14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nehmadi et al. (U.S. Pub No. 20220398851).
Regarding claims 1, 13-14, Nehmadi et al. disclose a computer-implemented method of generating a trajectory for an autonomous vehicle, AV (See paragraph 0001, 0004), using an autonomy stack, the autonomy stack including an end-to-end network trained to generate a trajectory for the AV from sensor inputs (See paragraph 0054; Fig. 1A), a tracking module and a planning module, the computer-implemented method comprising:
receiving, by the tracking module, a plurality of objects identified based on sensor inputs (See paragraph 0054, Fig. 1A, 2, 4A, 6);
fusing, by the tracking module, the plurality of objects (See paragraph 0081; Fig. 13); and generating, using the planning module, a further trajectory for the AV based on the fused plurality of objects and the trajectory generated by the end-to-end network (See paragraph 0054, 0126).
Regarding claim 2, Nehmadi et al. disclose wherein the receiving the plurality of objects comprises: generating, using an object identification portion of the end-to-end network, a plurality of occupancy grids from sensor inputs; extracting, from a hidden layer at an end of the object identification portion of the end-to-end network, the plurality of occupancy grids; and extracting the plurality of objects from the plurality of occupancy grids [(See paragraph 0056, 0059, 0068-0069; as the object detection from the sensor data is disclosed by Nehmadi et al. and the detector modules in Fig. 1A, 2, 4A 6); and the “occupancy grids” are a well-known format of the sensor data in autonomous vehicles, representing the vehicle’s surroundings in a discrete grid (See paragraph 0124)].
Regarding claim 3, Nehmadi et al. disclose wherein the generating, using the object identification portion of the end-to-end network, the plurality of occupancy grids, comprises: populating each segment of a plurality of segments of each occupancy grid with a state, wherein the state is selected from a list of states including: obstacle, occlusion, an object including its semantic class and its velocity, road, and pavement (See paragraph 0056; Nehmadi et al. disclose object detection and classification of detected objects in relevant for the autonomous driving classes, such as road, vehicles, occupied areas, etc.).
Regarding claim 4, Nehmadi et al. disclose marking, using the object identification portion of the end-to-end network, a lane boundary and/or a bounding box around the object (See paragraph 0056, Fig.29 for the feature of “lane boundaries” and [0066] and Fig. 3, 5 for the feature of “bounding box”).
Regarding claim 6, Nehmadi et al. disclose: fusing, using a temporal fusion portion of the end-to-end network, the first, second, and third, occupancy grids; and extracting a plurality of objects from the occupancy grid fused by the temporal fusion portion, wherein the fusing, by the tracking module, the plurality of objects further comprises fusing, by the tracking module, the plurality of objects extracted from the plurality of occupancy grids, and the plurality of objects extracted from the occupancy grid fused using the temporal fusion portion of the end-to-end network (See paragraph 0081, 0125; Nehmadi et al. discloses two fusion functions, as raw sensor data fusion and as object fusion, corresponding to the temporal fusion and the fusion of the tracking module as claimed “raw data fusion” and “detection merger” modules in Fig. 1A and 13).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 5, is/are rejected under 35 U.S.C. 103 as being unpatentable over Nehmadi et al. (U.S. Pub No. 20220398851).
Regarding claim 5, Nehmadi et al. disclose wherein the object identification portion of the end-to-end network comprises a vision portion, a LiDAR portion, and a RADAR portion, and wherein the sensor inputs include images, laser point cloud, and a LiDAR point cloud, wherein the generating, using the object identification portion of the end-to-end network, the plurality of occupancy grids comprises: identifying, using the vision portion, a first occupancy grid of the plurality of occupancy grids from the images; identifying, using the LiDAR portion, a second occupancy grid of the plurality of occupancy grids from the LiDAR point cloud; and identifying, using the RADAR portion, a third occupancy grid of the plurality of occupancy grids from the RADAR point cloud (See paragraph 0056, 0060, 0067; Figs. 1A, 3, 5, 7). the “occupancy grids” are a well-known format of the sensor data in autonomous vehicles, representing the vehicle’s surroundings in a discrete grid (See paragraph 0124)].
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nehmadi et al. (U.S. Pub No. 20220398851) in view of Ghorai Prasenjit et al (“State Estimation and motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving : A Survey”, vol. 23, no. 10. 1 October 2022 (2022-10-01), pages 16983-17002)
Regarding claims 7-8, Nehmadi et al. disclose all wherein the autonomy stack includes a prediction module, wherein the computer-implemented method further comprises: generating, using the prediction module, future states of the plurality of objects based on the fused objects from the tracking module, wherein the generating, using the planning module, a further trajectory for the AV based on the fused plurality of objects and the trajectory generated by the end-to-end network comprises :generating, using the planning module, the further trajectory for the AV using the trajectory and the future states of the plurality of objects from the prediction module, wherein the future states include future object position and future object velocity;
and generating, using a prediction portion of the end-to-end network, future states of the fused occupancy grids from the object identification portion; extracting future states of a plurality of objects from the future states of the fused occupancy grids; and generating, using a planning portion of the end-to-end network, the trajectory from the future states of the fused occupancy grids, wherein the generating, using the prediction module, future states of the plurality of objects from the tracking module comprises: generating, using the prediction module, future states of the plurality of objects from the plurality of objects fused by the tracking module and the future states of the plurality of objects extracted from the future states of the plurality of objects from the prediction portion of the end-to-end network. (See Section III of State Estimation and Motion Prediction of Ghorai; as it defines well established functional features of a prediction and path planning module in autonomous driving and do not involve inventive step).
Allowable Subject Matter
Claims 9-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art does not specifically disclose wherein the prediction module is a rules-based model and/or the tracking module is a rules based model, and/or the planning module is a rules-based model;
nor does the prior art disclose wherein the autonomy stack further comprises a control module, the computer-implemented method further comprising: generating, using the control module, a control command based on the further trajectory, the control command configured to operate one or more actuators of the AV for moving the AV; nor does the prior art disclose further comprising: generating, using an odometry module, a relative position of the AV, wherein, the fusing, by the tracking module, the plurality of objects is based on the relative position of the AV, wherein the generating, using the prediction module, future states of the plurality of objects is based on the relative position of the AV, wherein the generating, using the planning module, the further trajectory for the AV is based on the relative position of the AV. Nor does the prior art disclose comprising: generating, using a localization module, a position of the AV, wherein, the fusing, by the tracking module, the plurality of objects is based on the position of the AV, wherein the generating, using the prediction module, future states of the plurality of objects is based on the position of the AV, wherein the generating, using the planning module, the further trajectory for the AV is based on the position of the AV.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barrera (U.S. Pub No. 20240416949) discloses an embodiment related to a system, wherein the system is operable to determine a road surface condition, wherein the road surface condition is at least one of an ice, wet, and snow; adjust, a safe distance value of the host vehicle based on the road surface condition; detect a vehicle type, a speed, and a visible roof area of a target vehicle; determine an uphill road that the host vehicle is approaching; determine that the target vehicle is slowing down based on a change in speed of the target vehicle in real-time; and determine, a collision avoidance action for the host vehicle to avoid a collision with the target vehicle.
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/GERTRUDE ARTHUR JEANGLAUDE/Primary Examiner, Art Unit 3661
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