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 § 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 1, 3-4 and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Muro et al. (Moving‑object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction, Artificial Life and Robotics, 2021) in view of Xiang et al. (Data-Driven 3D Voxel Patterns for Object Category Recognition, IEEE, 2015) in view of Hornung et al. (OctoMap: an efficient probabilistic 3D mapping framework based on octrees, Auton Robot, 2013) in view of Nowak et al. (Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM, Institute of Robotics and Machine Intelligence, 2021) in view of Kocamaz et al. (US 2023/0099494).
Regarding claim 1, Muro et al. (hereinafter Muro) discloses a recognition system (Muro, 2 Experimental system) comprising:
recognize, in a scan space, a target moving object that is movable in the scan space by scanning the target moving object using a scanning device mounted on a host moving object (Muro, Fig. 2 moving-target tracking. Fig. 1 illustrates a scanning device mounted on a host moving object), configured to:
acquire three-dimensional scan data representing a scan point group generated by scanning of the scan space (Muro, 2 Experimental system, [0001], “LiDAR acquires 384 measurements (the 3D position of the object and reflection intensity) every 0.55 ms”);
read, a three-dimensional dynamic map representing a mapping point group generated by mapping of an object present in the scan space (Muro, 3 NDT-based SLAM, [0003], “the scan data obtained prior to the current time are called the local environment map. The pose X is calculated by matching the current scan data with the local environment map”. The environment map reads on a three-dimensional dynamic map representing a mapping point group generated by mapping of an object present in the scan space);
though Muro teaches the scan point group; Muro does not expressly disclose “cluster the scan point group of multiple three-dimensional voxels into which the scan space is divided and generate recognition data by recognizing the target moving object”;
Xiang et al. (hereinafter Xiang) discloses cluster group of multiple three-dimensional voxels into which a space is divided and generate recognition data by recognizing the target moving object (Xiang, 3.1 3D Voxel Exemplars from data, [0002], “The 3D voxel representation has several good properties. First, by encoding the 3D voxel space into empty or occupied voxels, 3D voxel exemplars can capture the 3D shape of objects… As a result, 3D voxel exemplars are able to encode information about 3D shape, viewpoint, truncation and occlusion in a uniform 3D space”. Fig. 5 illustrates examples of 3D clusters. In addition, Fig. 1 illustrates recognition framework is able to not only detect objects in images, but also segment the detected objects from the background, estimate the 3D poses and 3D shapes, localize them in the 3D space, and even infer the occlusion relationship among them).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the moving object detection system of Muro to incorporate the voxel-based clustering and object recognition function of Xiang. The motivation for doing so would have been enabling more accurate identification of objects in LIDAR sensor data.
Muro as modified by Xiang does not expressly disclose “identifying a state of the scan space in each of multiple three-dimensional voxels into which the scan space is divided”;
Hornung et al. (hereinafter Hornung) discloses identifying a state of the scan space in each of multiple three-dimensional voxels into which the scan space is divided (Hornung, 3.2 Probabilistic sensor fusion, [0004], “log-odds values can be converted into probabilities and vice versa and we therefore store this value for each voxel instead of the occupancy probability”. Fig. 4 illustrates visualization of the octree showing occupied voxels (dark) and free voxels (white). In addition, in 3.1 Octrees, [0002], “An illustration of an octree containing free and occupied nodes from real laser sensor data can be seen in Fig. 4”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the voxel-based processing of Muro as modified by Xiang by incorporating the occupancy map that indicates the state of each voxels as taught by Hornung. The motivation for doing so would have been improving accuracy of voxel state estimation.
In addition, Muro as modified by Xiang and Hornung does not expressly disclose “the target moving object present between multiple stationary objects which are spaced apart from each other”;
Nowak et al. (hereinafter Nowak) discloses target moving object present between multiple stationary objects which are spaced apart from each other (Nowak, Fig. 7 illustrates moving object present between multiple stationary objects which are spaced apart from each other).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the object recognition system of Muro by incorporating the function of identifying moving objects among stationary objects as taught by Nowak. The motivation for doing so would have been providing ability to accurately identify and classify objects of interest within a scene.
Muro as modified by Xiang, Hornung and Nowak does not expressly disclose “front-rear relation along a scan direction of the scanning device in the scan space”;
Kocamaz et al. (hereinafter Kocamaz) discloses front-rear relation along a scan direction of a scanning device in a scan space (Kocamaz, [0159], “the LIDAR sensor(s) 864 may be capable of providing a list of objects and their distances for a 360-degree field of view”);
a computer-readable non-transitory storage medium (Kocamaz, [0194], “The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types”); and
a processor, by executing a program stored in the computer-readable non-transitory storage (Kocamaz, [0196], “The CPU(s) 906 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 900”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the object recognition system of Muro by incorporating the front-rear relation along a scan direction of a scanning device as taught by Kocamaz. The motivation for doing so would have been enabling interpretation of spatial relationships between objects.
Regarding claim 3, Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses multiple target moving objects overlapped with one another in the scan space when viewed in the scan direction (Kocamaz, [0026], “the sensor data 102 may include other types of sensor data used for lane assignments, such as LiDAR data”. In addition, in paragraph [0034], “FIGS. 2A-2D illustrate example annotations applied to sensor data and GT mask(s) 116 for use in the ground truth generator 108 for training a machine learning model to assign objects to lanes”. Figs. 2A-D illustrate target moving objects, which are overlapped with one another in the scan space when viewed in a scan direction of the scanning device).
Regarding claim 4, Muro et al. (hereinafter Muro) discloses a recognition system (Muro, 2 Experimental system) comprising:
Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses recognizing the target moving objects, which are overlapped with one another in the scan space when viewed in a scan direction of the scanning device (Kocamaz, [0026], “the sensor data 102 may include other types of sensor data used for lane assignments, such as LiDAR data”. In addition, in paragraph [0034], “FIGS. 2A-2D illustrate example annotations applied to sensor data and GT mask(s) 116 for use in the ground truth generator 108 for training a machine learning model to assign objects to lanes”. Figs. 2A-D illustrate target moving objects, which are overlapped with one another in the scan space when viewed in a scan direction of the scanning device).
The remaining limitations recite in claim 4 are similar in scope to the functions recited in claim 1 and therefore are rejected under the same rationale.
Regarding claim 12, Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses store the generated recognition data in the storage medium (Kocamaz, [0194], “The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types”).
Regarding claim 13, Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses control display of the generated recognition data (Kocamaz, [0088], “provide outputs (e.g., represented by output data, display data, etc.) via a human-machine interface (HMI) display 834… the HMI display 834 may display information about the presence of one or more objects (e.g., a street sign, caution sign, traffic light changing, etc.), and/or information about driving maneuvers the vehicle has made, is making, or will make (e.g., changing lanes now, taking exit 34B in two miles, etc.)r”).
Regarding claim 14, Muro discloses a recognition device mountable on a host moving object (Muro, Fig. 1).
The limitations recite in claim 14 are similar in scope to the functions recited in claim 1 and therefore are rejected under the same rationale.
Regarding claim 15, Muro discloses a recognition device mountable on a host moving object (Muro, Fig. 1).
The limitations recite in claim 15 are similar in scope to the functions recited in claim 4 and therefore are rejected under the same rationale.
Regarding claim 16, Muro discloses a recognition method (Muro, Fig. 2).
The limitations recite in claim 16 are similar in scope to the functions recited in claim 1 and therefore are rejected under the same rationale.
Regarding claim 17, Muro discloses a recognition method (Muro, Fig. 2).
The limitations recite in claim 17 are similar in scope to the functions recited in claim 4 and therefore are rejected under the same rationale.
Regarding claim 18, Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses a recognition program product stored in a computer-readable non-transitory storage medium, the recognition program product comprising instructions to be executed by a processor (Kocamaz, [0004], “determine the object classification label and the lane identifier for objects detected using sensor data generated using one or more sensors of an autonomous or semi-autonomous machine”. In addition, in paragraph [0051], “a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory”).
The limitations recite in claim 18 are similar in scope to the functions recited in claim 1 and therefore are rejected under the same rationale.
Regarding claim 19, Muro as modified by Xiang, Hornung and Nowak and Kocamaz with the same motivation from claim 1 discloses a recognition program product stored in a computer-readable non-transitory storage medium, the recognition program product comprising instructions to be executed by a processor (Kocamaz, [0004], “determine the object classification label and the lane identifier for objects detected using sensor data generated using one or more sensors of an autonomous or semi-autonomous machine”. In addition, in paragraph [0051], “a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory”).
The limitations recite in claim 19 are similar in scope to the functions recited in claim 4 and therefore are rejected under the same rationale.
Allowable Subject Matter
Claims 2 and 5-11 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 KYLE ZHAI whose telephone number is (571)270-3740. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ke Xiao can be reached at (571) 272 - 7776. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE ZHAI/Primary Examiner, Art Unit 2611
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