DETAILED ACTION
Status of Claims
This action is in response to the application No. 19/229208 filed on 6/5/2025. Claims 1-20 are pending for examination.
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.
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) 1, 6-10, 15-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kroeger US 2020/0005489 (“Kroeger”).
As to claims 1, 10, and 17, Kroeger discloses a system and method such as a machine comprising:
an image sensor (see at least Fig 4; [0053]: the vehicle 402 may include additional sensor assemblies. In some instances, the sensor assemblies may include, but are not limited to, one or more LIDAR sensors, radar sensors, image sensors); and
a LiDAR sensor (see at least Fig 4; [0053]: the vehicle 402 may include additional sensor assemblies. In some instances, the sensor assemblies may include, but are not limited to, one or more LIDAR sensors, radar sensors, image sensors),
wherein the machine is to perform one or more planning, control, or navigation operations after calibrating the image sensor with respect to the LiDAR sensor, wherein the image sensor is calibrated with respect to the LiDAR sensor based at least on relating one or more first points of one or more images represented by image data obtained using the image sensor to one or more second points of a point cloud corresponding to LiDAR data obtained using the LiDAR sensor (see at least [0099]: the process 600 generates a second correction function. For example, the operation 618 uses the error exemplified by the distance between points in the point cloud and the image edges to determine a correction function (e.g., as a calibration matrix) to constrain the cameras capturing the image data relative to the vehicle. Thus, comparing lidar data to the camera images as described herein may constrain the sixth-degree of freedom of the cameras, to provide a complete calibration function for the array of cameras. Implementations of this disclosure may assume that the lidar data is well-calibrated, e.g., that the lidar sensor is correctly calibrated relative to the vehicle).
As to claims 6, 15, and 19, Kroeger discloses wherein the one or more values of the one or more parameters related to calibrating the image sensor with respect to the LiDAR sensor are further determined, at least in part, by determining one or more distances associated with at least a portion of the first points (see at least [0099]: the operation 618 uses the error exemplified by the distance between points in the point cloud and the image edges to determine a correction function).
As to claims 7 and 16, Kroeger discloses wherein the machine is further to: determine that at least a portion of the one or more first points are associated with a feature located within the environment, wherein the relating the one or more first points of the one or more images to the one or more second points of the point cloud is further based at least on the one or more first points being associated with the feature (see at least [0095]: the operation 612 may determine object edges for images in the image data. For instance, for each image (including each of the images analyzed at operation 604) an edge detection algorithm, such as a Canny edge detection algorithm, may be applied to determine edges in the images. Edge detection may also include applying a distance transform to pixels in the image, e.g., to quantify for each pixel in the image a straight-line distance (i.e. Euclidian distance) to the nearest edge pixel).
As to claims 8, Kroeger discloses wherein the one or more parameters include at least one of one or more translations associated with the image sensor or one or more rotations associated with the image sensor (see at least [0028]: the process can include generating a correction function to correct misalignment (e.g., physical misalignment) between the cameras. Because the example described herein uses epipolar geometry to determine alignment between images, the calibration function may only constrain the cameras relative to each other and may contain a scale ambiguity. For example, because the process 100 does not estimate 3D geometry and/or depth, only five of the six degrees of freedom (i.e., x-translation, y-translation, z-translation, roll, pitch, yaw) are fully constrained for each camera. Optimizing both cameras 106a, 106b with these constraints will yield a self-consistent alignment of the cameras, with undetermined scale, position and rotation, and one translation degree of freedom for each camera).
As to claim 9 and 20, Kroeger discloses wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (see at least Abstract: This disclosure is directed to calibrating sensors mounted on an autonomous vehicle).
Allowable Subject Matter
Claims 2-5, 11-14, and 18 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS P INGRAM whose telephone number is (571)272-7864. The examiner can normally be reached M-F 10-6 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Thomas Ingram/Primary Examiner, Art Unit 3668
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