DETAILED ACTION
Responsive to the claims filed October 1, 2024. Claims 1-20 are presented. 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
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al (US 2021/0146963).
As per claim 1, Li et al teach a method for motion detection, comprising:
performing a first prediction to predict voxel occupancy based on a sequence of input point clouds including a current point cloud and a set of previous point clouds; performing a second prediction to predict voxel occupancy for the sequence of input point clouds using predicted voxel occupancy between the input point clouds (see at least paragraph [0027]); voxel representations are generated from LIDAR sweeps, for each sweep the point cloud is voxelized into a 3D occupancy grid with each sweep producing a finer grade voxel representation of the object detected);
performing motion detection based on the completed voxel occupancy; and performing an action responsive to a detected motion (see at least paragraphs [0022-0024, 0033, 0085, 0097]; detecting motion and trajectories of the voxelized objects and controlling the vehicle based on this information).
As per claim 2, Li et al teach performing flow prediction based on the motion detection (see at least figs 3A, 3B).
As per claim 3, Li et al teach detecting objects within the sequence of input point clouds (see at least paragraphs [0027, 0097]).
As per claim 4, Li et al teach determining whether the detected objects are static or in motion using an encoder-decoder model (see at least paragraph [0025]).
As per claim 5, Li et al teach filtering out background objects and objects that are moving with a speed greater than a threshold value (see at least paragraphs [0081-0083]).
As per claim 6, Li et al teach wherein completing the voxel occupancy includes completing occupancy for only those surfaces which are visible (see at least paragraphs [0027-0028]).
As per claim 7, Li et al teach wherein warping the predicted voxel occupancy includes setting occupied voxel from the set of previous point clouds as occupied in the current point cloud (see at least paragraphs [0030, 0101]).
As per claim 8, Li et al teach wherein warping the predicted voxel occupancy includes setting voxels along a line of sight to the occupied voxel as unoccupied in the current point cloud (see at least paragraphs [0030, 0101]).
As per claim 9, Li et al teach wherein the action controls an autonomous vehicle to avoid an object that is moving (see at least paragraph [0083]).
As per claim 10, Li et al teach wherein controlling the autonomous vehicle includes performing a steering, accelerating, or braking action (see at least paragraphs [0022, 0067]).
Claims 11-20 contain similar limitations as the claims above and therefore are rejected under similar rationale.
Conclusion
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RAMSEY REFAI
Primary Examiner
Art Unit 3664
/RAMSEY REFAI/Primary Examiner, Art Unit 3664