DETAILED ACTION
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements submitted on 03/11/2025 and 04/24/2025 have been considered by the Examiner and made of record in the application file.
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 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.
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.
Claim(s) 11, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Natroshvili (US 2018/0182083 A1).
Regarding claims 11, 19 and 20, Natroshvili discloses a method/non-transitory CRM/device for processing image data for application of a machine learning model, comprising the following steps:
[claim 19: A non-transitory computer-readable medium on which is stored a computer program including instructions for processing image data for application of a machine learning model, the instructions, when executed by a computer, causing the computer to perform the following steps: (paragraph 16)]
ascertaining image data, wherein the image data result from image acquisition with a camera; (capturing a wide-angle or fisheye camera image at block 802, see paragraph 54 and figure 8)
transforming the image data into a sight ray representation; and (mapping the camera image onto a unit sphere using camera intrinsic parameters, with projected image points represented by angular coordinates, see paragraphs 32-38 and 54, block 804 in figure 8)
providing an input for the machine learning model based on the image data in the sight ray representation. (applying a CNN convolution layers to the projected image, followed by a fully connected layer, see paragraphs 39 and 54, blocks 806-808 in figure 8)
Claim Rejections - 35 USC § 103
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 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.
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Natroshvili in view of Lang (“PointPillars: Fast Encoders for Object Detection from Point Clouds”).
Regarding claim 12, Natroshvili discloses the claimed invention wherein the image data in the sight ray representation are represented by image points, (pixels mapped onto the spherical surface have two angular coordinates, see paragraphs 37-38) however, Natroshvili fails to specifically disclose providing a grid representation in which a grid includes a plurality of grid cells, wherein the image points are assigned to the grid cells.
In related art, Lang discloses providing a grid representation in which a grid includes a plurality of grid cells, wherein the image points are assigned to the grid cells. (an evenly spaced x-y grid groups points into pillars, see section 2.1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Lang into the teachings of Natroshvili to enable end-to-end training of a 3D object detection network.
Regarding claim 13, Natroshvili, as modified by Lang, discloses the claimed invention wherein the image points are assigned to the grid cells in different numbers, (sampling and padding accommodate unequal pillar occupancies, see Lang, section 2.1) wherein the providing of the input includes the following step for further preprocessing: carrying out a normalization based on the grid representation, by normalizing a distance between a cell center point of each respective grid cell and the image points assigned to the respective grid cell in the grid representation. (xp and yp encode offsets from the pillar center before neural feature encoding, see Lang, section 2.1)
Regarding claim 14, Natroshvili, as modified by Lang, discloses the claimed invention wherein the normalization calculates a feature map, wherein, for each respective grid cell of the grid cells, the feature map includes a feature vector which is calculated from the image points of the respective grid cell. (per pillar encoded features are scattered into a spatial pseudo-image, see Lang, section 2.1)
Regarding claim 15, Natroshvili, as modified by Lang, discloses the claimed invention wherein the normalization is based on an application of a neural network to the image points of the respective grid cell. (a simplified PointNet applies a linear layer, Batch-Norm, ReLU, and pooling to pillar points, see Lang, section 2.1)
Regarding claim 16, Natroshvili, as modified by Lang, discloses the claimed invention wherein the neural network includes at least one convolutional layer. (paragraph 39)
Regarding claim 17, Natroshvili, as modified by Lang, discloses the claimed invention wherein the feature map includes a single feature vector, with at least one channel per grid cell. (the CxP encoding supplies one C-channel vector per encoded pillar, scattered into CxHxW, see Lang, section 2.1)
Claim 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Natroshvili in view of Li (US 2022/0044033 A1).
Regarding claim 18, Natroshvili discloses the claimed invention wherein the machine learning model is used with the provided input (the CNN processes the projected camera image, see paragraphs 39 and 54) but fails to discloses wherein a vehicle is controlled based on the application of the machine learning model, (network outputs support collision avoidance, and the controller implements driving functions through vehicle actuators, see paragraphs 16 and 19) wherein the machine learning model is trained using dropout. (DNN training uses dropout, including exemplary rates of 0.2, 0.4 and 0.6, see paragraphs 9 and 19)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Li into the teachings of Natroshvili to effectively provide distortion compensated camera perception for autonomous driving functions.
Conclusion
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/BOBBAK SAFAIPOUR/ Primary Examiner, Art Unit 2665
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