oiNotice 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 .
Status of the Claims
This FINAL action is in response to Applicant’s amendment of 30 June 2026. Claims 1-14 are
pending and have been considered as follows.
Response to Arguments
Applicant’s amendment and/or arguments with respect to the Claim Objections and rejection of claims under 35 USC 101 as set forth in the office action of 31 March 2026 have been considered and are persuasive. Therefore, the Claim Objections and rejection of claims under 35 USC 101 as set forth in the office action of 31 March 2026 have been withdrawn.
Applicant’s amendment and/or arguments with respect to the rejection of Claims 1-14 under
35 USC 103 as set forth in the office action of 31 March 2026 have been considered-
Regarding Applicant’s arguments associated with the amended limitation “a path generation unit configured to generate a global path and a local path using the occupancy map; and a traveling control unit configured to control traveling of the moving object in accordance with the local path, wherein … the path generation unit periodically generates the global path using information on a divided region in which the static obstacle exists in the occupancy map, and periodically generates the local path to follow the global path using information on the divided region in which the static obstacle exists and the first divided region in which the dynamic obstacle exists in the occupancy map, and the traveling control unit controls a speed and an angular velocity of the moving object in accordance with the local path”,
Applicant’s arguments have been considered and are moot because the new ground(s) of rejection
does not rely on any reference applied in the prior rejection of record for any teaching or matter
specifically challenged in the argument. See 35 USC 103 below.
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 (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 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.
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.
Claim(s) 1-7, 9, 10 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Shen (WO 2020118545 A1), in view of Heo (US 20230288568 A1), and further in view of Binney (US 20150253777 A1).
Regarding claim 1, Shen discloses a moving object control system comprising: one or more processors; and a memory storing instructions which, when the instructions are executed by the one or more processors, cause the moving object control system to function as (See at least Figure 7, [0010], [0069]): an acquisition unit configured to acquire a captured image captured by a moving object, and depth information of an environment captured in the captured image ([0013-0014] These probabilities can be updated according to observations from range sensors like LiDARs, radars, sonars or depth cameras. The observation data obtained by performing sensing with the use of the sensing equipment such as a radar is used for estimation of the position and the shape of an object located around the sensor of the autonomous device. The sensors may also determine or estimate whether the object is moving or stationary and compile information correlating each cell in the grid with this information); a map generation unit configured to generate an occupancy map indicating occupancy of an obstacle for each divided region ([0013] Occupancy maps are usually in the form of a grid in either two-or three-dimensional spaces. For each cell of the grid, there is a value indicating the probability of that cell being occupied by an object (or an obstacle). [0045] The environment may be subdivided into a grid of cells. Thus, a grid may have a plurality of cells), obtained by dividing a peripheral region of the moving object, on a basis of the depth information ([0013] These probabilities can be updated according to observations from range sensors like LiDARs, radars, sonars or depth cameras. At each time point in time, only a part of cells (typically a few surrounding cells) in the grid can be observed.), wherein the occupancy map includes a first divided region indicating occupancy of the dynamic obstacle forgotten according to a forgetting rate higher than a forgetting rate of a static obstacle that does not autonomously move ([0017-0019], [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories… [0032] In certain disclosed embodiments, RTU is used to converge occupancy probability after a time interval to an unknown state…Thus, if a cell has not been observed for a very long time, its occupancy probability should converge to a neutral value, which indicates an unknown state. [0056-0057] Regressing Factor- It is worth noting that those factors can be set differently for different cells. For example, a cell that is occupied by a wall is obviously less likely to be freed than a cell occupied by a person) the map generation unit sets divided region in a depth direction ([0046] The step of looping each observed cell means that, in one embodiment, steps 406 to 412 may be applied to all the cells in the map except those outside of the sensor’s view. For depth cameras for example, the observed cells may be the cells containing the location of each pixel point (observed as occupied) and those on the ray from the camera to a pixel point (observed as unoccupied)).
Shen does not explicitly disclose an identification unit configured to identify a dynamic obstacle that is autonomously movable and included in the captured image by using the captured image; the map generation unit sets a divided region in a depth direction that is away from the moving object from a position of the identified dynamic obstacle, as the first divided region indicating the occupancy of the dynamic obstacle. However, Heo teaches an identification unit configured to identify a dynamic obstacle that is autonomously movable and included in the captured image by using the captured image (See at least [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, Fig. 10A/B classifying dynamic objects); the map generation unit sets a divided region in a depth direction that is away from the moving object from a position of the identified dynamic obstacle, as the first divided region indicating the occupancy of the dynamic obstacle (See at least Fig. 2, [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster. [0063-0068] The dynamic occupancy grid map may include a plurality of cells including point data corresponding respectively to a plurality of objects located around the vehicle 1. [Fig. 11A-12B] Predicted path region ahead of object). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches an identification unit configured to identify a dynamic obstacle that is autonomously movable and included in the captured image by using the captured image; the map generation unit sets a divided region in a depth direction that is away from the moving object from a position of the identified dynamic obstacle, as the first divided region indicating the occupancy of the dynamic obstacle since they are both directed to occupancy grid mapping and environment perception for moving objects, and incorporation of the teachings of Heo would improve the accuracy and reliability of the occupancy map by enabling differentiated treatment of dynamic and static obstacles, including appropriate updating or forgetting of obstacle information over time.
Further, Shen as modified by Heo does not explicitly disclose a path generation unit configured to generate a global path and a local path using the occupancy map; and a traveling control unit configured to control traveling of the moving object in accordance with the local path, and the path generation unit periodically generates the global path using information on a divided region in which the static obstacle exists in the occupancy map, and periodically generates the local path to follow the global path using information on the divided region in which the static obstacle exists and the first divided region in which the dynamic obstacle exists in the occupancy map, and the traveling control unit controls a speed and an angular velocity of the moving object in accordance with the local path. However, Binney teaches disclose a path generation unit configured to generate a global path and a local path using the occupancy map (See at least abstract, [0070-0082] A given robot pose 1188, goal pose 1192 and global occupancy grid 1190 can be used by a global planner 1194 to generate a global plan (distance map) 1160. In some embodiments, map data can be used with a global occupancy grid that integrates known positions of objects in the mapped area, and in conjunction with robot pose input and the goal pose, a robot global plan 1160 for navigation can be generated. Referring still to FIG. 11, a local planner 1196 can combine a global plan 1160 with integrated data of local object positions from the local occupancy grid 1156 and the robot pose 1188. With such data, a local planner 1196 can adjust the local plan to avoid or go around blockages or obstructions in the robot path); and a traveling control unit configured to control traveling of the moving object in accordance with the local path (See at least abstract, [0040-0050], [0080-0082] With such data, a local planner 1196 can adjust the local plan to avoid or go around blockages or obstructions in the robot path. Local sensing and re-orientation of the robot (twists) 1158 can verify local paths, and provide control input to the wheels (1198) for partial or full rotation of the robot, or backward or forward movement that avoids objects or people while traveling to a desired destination. A robot (e.g., 100, 300) can have a motor control system of sufficient fidelity for smoothly decelerate multiple motors (wheels) simultaneously. In a particular embodiments, a robot (e.g., 100, 300 can include a high-frequency (e.g. 1000 Hz) motor control loop system. Referring still to FIG. 3, in operation, a robot (e.g., 100, 300) can detect a movable object 326, and plan a new path to avoid the object, or slow down or halt until the object 326 is no longer in the desired movement path 332 of the robot (e.g., 100, 300). Stationary objects 324-0/1 can be avoided according to a local movement plan. Stationary objects 324-0/1 can already be known, and verified by location detection, or newly detected by the robot), and the path generation unit periodically generates the global path using information on a divided region in which the static obstacle exists in the occupancy map (See at least abstract, [0070-0073], [0075-0082] . Environment limits or known objects are shown as 1070-A (walls) and 1070-B (known furniture). These features can be derived from map data, global occupancy grid, or local occupancy grid. A robot 1000 can follow a local plan 1074 derived from a local occupancy grid, map data, and global plan. Environment 1022 also includes a new object 1072. New object 1072 is not included or known from the map data, global plan or local plan. Data from the sensor suite (e.g., 1152) in combination with map data 1158 can be used to arrive at a global occupancy grid 1190. In the embodiment shown, a user interface 1114 can be used to enter/receive data that indicates a destination for the robot. Such data can be used to arrive at a goal pose 1192, which can include the position of a target destination. A given robot pose 1188, goal pose 1192 and global occupancy grid 1190 can be used by a global planner 1194 to generate a global plan (distance map) 1160. In some embodiments, map data can be used with a global occupancy grid that integrates known positions of objects in the mapped area, and in conjunction with robot pose input and the goal pose, a robot global plan 1160 for navigation can be generated), and periodically generates the local path to follow the global path using information on the divided region in which the static obstacle exists and the first divided region in which the dynamic obstacle exists in the occupancy map (See at least abstract, [0066-0070], [0074-0082] Based on data generated by blocks 752 and 754, a local occupancy grid can be derived 756. As one example, upon detecting a new object/obstacle, a local occupancy grid can be updated to include the presence of the object/obstacle, as well as whether such an object/obstacle is in motion or stationary. Referring to FIG. 10C, based on the new local occupancy grid, robot 1000 can generate a new local plan 1074′ that enables it to arrive at the target destination 1034-C, while at the same time avoiding the new object 1072. A local planner 1196 can combine a global plan 1160 with integrated data of local object positions from the local occupancy grid 1156 and the robot pose 1188. With such data, a local planner 1196 can adjust the local plan to avoid or go around blockages or obstructions in the robot path. Examiner notes adjusting the local plan as periodic generation.), and the traveling control unit controls a speed and an angular velocity of the moving object in accordance with the local path (See at least abstract, [0045-0050],[0080-0090] To enable such a response, a robot (e.g., 100, 300) can have a motor control system of sufficient fidelity for smoothly decelerate multiple motors (wheels) simultaneously. In a particular embodiments, a robot (e.g., 100, 300 can include a high-frequency (e.g. 1000 Hz) motor control loop system. Local sensing and re-orientation of the robot (twists) 1158 can verify local paths, and provide control input to the wheels (1198) for partial or full rotation of the robot, or backward or forward movement that avoids objects or people while traveling to a desired destination. Examiner notes that “twists” is combining the combining of linear velocity and angular velocity). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen as modified by Heo to incorporate the teachings of Binney which teaches disclose a path generation unit configured to generate a global path and a local path using the occupancy map; and a traveling control unit configured to control traveling of the moving object in accordance with the local path, and the path generation unit periodically generates the global path using information on a divided region in which the static obstacle exists in the occupancy map, and periodically generates the local path to follow the global path using information on the divided region in which the static obstacle exists and the first divided region in which the dynamic obstacle exists in the occupancy map, and the traveling control unit controls a speed and an angular velocity of the moving object in accordance with the local path since they are both directed to occupancy grid mapping and environment perception for moving objects, and incorporation of the teachings of Binney would improve the accuracy and reliability of the occupancy map by enabling differentiated treatment of dynamic and static obstacles, including appropriate updating or forgetting of obstacle information over time.
Regarding claim 2, Shen discloses wherein the map generation unit (See at least [0013-0015] and [0045]) generates a first occupancy map (See at least [0017-0019] and [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories…).
Shen does not explicitly disclose the first occupancy map including the first divided region indicating the occupancy of the dynamic obstacle, and a second occupancy map including a second divided region indicating occupancy of the static obstacle. However, Heo teaches the first occupancy map including the first divided region indicating the occupancy of the dynamic obstacle, and a second occupancy map including a second divided region indicating occupancy of the static obstacle (See at least Fig. 2, Fig. 11A-12B dynamic object region and static object region, and [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches the first occupancy map including the first divided region indicating the occupancy of the dynamic obstacle, and a second occupancy map including a second divided region indicating occupancy of the static obstacle since they are both directed to occupancy grid mapping and environment perception for moving objects, and incorporation of the teaching of Heo would improve accuracy and reliability of the occupancy map by enabling more precise representation and obstacles of dynamic and static obstacles.
Regarding claim 3, Shen discloses wherein the map generation unit generates a third occupancy map (See at least [0017-0019] and [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories…).
Shen does not explicitly disclose the third occupancy map including the first divided region indicating the occupancy of the dynamic obstacle and a second divided region indicating occupancy of the static obstacle. However, Heo teaches the third occupancy map including the first divided region indicating the occupancy of the dynamic obstacle and a second divided region indicating occupancy of the static obstacle (See at least Fig. 2, Fig. 11A-12B dynamic object region and static object region, and [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster. See at least [0156-0161]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches the third occupancy map including the first divided region indicating the occupancy of the dynamic obstacle and a second divided region indicating occupancy of the static obstacle since they are both directed to occupancy grid mapping and environment perception for moving objects, and incorporation of the teachings of Heo would improve the accuracy and reliability of the occupancy map by enabling unified representation of both dynamic and static obstacles within the same occupancy map.
Regarding claim 4, Shen discloses wherein the map generation unit specifies which divided region is the first divided region indicating the occupancy of the dynamic obstacle (See at least [0013] These probabilities can be updated according to observations from range sensors like LiDARs, radars, sonars or depth cameras and [0017-0019], [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories… [0046] For depth cameras for example, the observed cells may be the cells containing the location of each pixel point (observed as occupied) and those on the ray from the camera to a pixel point (observed as unoccupied)).
Shen does not explicitly disclose specifying which divided region is the first divided region, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information. However, Heo teaches specifying which divided region is the first divided region, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information (See at least Fig. 2, Fig. 11A-12B classifying dynamic object region and static object region, and [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster. See at least [0156-0161]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches specifying which divided region is the first divided region, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information since they are both directed to occupancy grid mapping and environment perception using sensor data, and incorporation of the teachings of Heo would improve the accuracy and reliability of the system by enabling more precise selection of regions corresponding to obstacles based on depth information.
Regarding claim 5, Shen does not explicitly disclose wherein the map generation unit sets, as the first divided region indicating the occupancy of the dynamic obstacle, the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information. However, Heo teaches wherein the map generation unit sets, as the first divided region indicating the occupancy of the dynamic obstacle, the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information (See at least Fig. 2 step 203“determining a cluster corresponding to each plurality of objects on a dynamic grid map”, step 205 “classify an object corresponding to the cluster into a dynamic object” ad Fig. 11A-12B divided regions extending relative to object indicating the occupancy of the obstacle. [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster. See at least [0156-0161]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches wherein the map generation unit sets, as the first divided region indicating the occupancy of the dynamic obstacle, the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information since they are both directed to occupancy grid mapping and environment perception using sensor data, and incorporation of the teaching Heo would improve the accuracy and reliability of the system by enabling more precise spatial positioning of regions corresponding to dynamic obstacles in a depth direction relative to the moving object.
Regarding claim 6, Shen does not explicitly disclose wherein the map generation unit integrates, as the first divided region for the same dynamic obstacle, the divided region adjacent to the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information. However, Heo teaches wherein the map generation unit integrates, as the first divided region for the same dynamic obstacle, the divided region adjacent to the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information (See at least Fig. 2 step 203“determining a cluster corresponding to each plurality of objects on a dynamic grid map”, step 205 “classify an object corresponding to the cluster into a dynamic object”, Fig. 6A-6C cluster of points forming objects, and Fig. 11A-12B divided regions extending relative to object indicating the occupancy of the obstacle. [0015-0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster. [0065-0068] The dynamic occupancy grid map may include a plurality of cells including point data corresponding respectively to a plurality of objects. See at least [0156-0161]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches wherein the map generation unit integrates, as the first divided region for the same dynamic obstacle, the divided region adjacent to the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information since they are both directed to occupancy grid mapping and depth information using sensor data, and incorporation of the teaching of Heo would improve the accuracy and reliability of the system by enabling representation of dynamic obstacles through integration of adjacent regions corresponding to the same object.
Regarding claim 7, Shen does not explicitly disclose wherein the map generation unit does not set, as the first divided region for the dynamic obstacle, a divided region located farther than a threshold from the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information. However, Heo teaches wherein the map generation unit does not set, as the first divided region for the dynamic obstacle, a divided region located farther than a threshold from the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information ([0022] The classifying the object corresponding to the cluster into the static object or the dynamic object may include determining, among the objects, a first object, configured such that a region size of the cluster corresponding to the first object is greater than a first predetermined threshold, as the static object, and determining, among the objects, a second object, configured such that a region size of the cluster corresponding to the second object is equal to or less than the first predetermined threshold, as the static object or the dynamic object. See at least [0082-0084]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches wherein the map generation unit does not set, as the first divided region for the dynamic obstacle, a divided region located farther than a threshold from the first divided region indicating the occupancy of the dynamic obstacle, from among the divided regions indicating the occupancy of the obstacle and specified on a basis of the depth information since they are both directed to occupancy grid mapping and environment perception data, and incorporation of the teachings of Heo would improve the accuracy and reliability of the system by enabling thresholds for regions corresponding to dynamic obstacles.
Regarding claim 9, Shen as modified by Heo and Binney discloses wherein the instructions further cause the moving object control system to function as: an accumulation unit configured to accumulate information on the obstacle detected in the past for each divided region (See at least Shen abstract and [0077] The memory circuitry configured to receive and store probability information from the processor circuitry and to store the probability value and its corresponding time stamp at a probability table; a processor circuitry in communication with the memory circuitry, the processor circuitry configured to: receive occupancy information, the occupancy information defining whether a first of a plurality of cells in the environment is occupied, determine a first probability value that the first cell is occupied at a first point in time, direct the first probability value and its corresponding timestamp to the memory circuitry to store) obtained by dividing the peripheral region of the moving object (See at least Shen [0013] These probabilities can be updated according to observations from range sensors like LiDARs, radars, sonars or depth cameras. At each time point in time, only a part of cells (typically a few surrounding cells) in the grid can be observed.); and a forgetting unit configured to cause the accumulated information on the obstacle to be forgotten according to the forgetting rate assigned for each divided region (see at least Shen [0017-0019], [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories… [0032] In certain disclosed embodiments, RTU is used to converge occupancy probability after a time interval to an unknown state…Thus, if a cell has not been observed for a very long time, its occupancy probability should converge to a neutral value, which indicates an unknown state. [0056-0057] Regressing Factor- It is worth noting that those factors can be set differently for different cells. For example, a cell that is occupied by a wall is obviously less likely to be freed than a cell occupied by a person).
Regarding claim 10, Shen does not explicitly disclose wherein the dynamic obstacle is a vehicle or another traffic participant. However, Heo teaches wherein the dynamic obstacle is a vehicle or another traffic participant (See at least Fig. 10A/10B, Gig. 11A/11B, [0006], and [0007]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches wherein the dynamic obstacle is a vehicle or another traffic participant since they are both directed to occupancy grid mapping and environment perception in vehicle environment, and incorporation of the teachings of Heo would improve the accuracy and reliability of the system by enabling and handling dynamic obstacles encountered in an environment.
Regarding claim 12, Shen does not explicitly disclose wherein the identification unit further identifies a distance from the moving object to the dynamic obstacle and a boundary region of the dynamic obstacle on a basis of the captured image. However, Heo teaches the identification unit (See at least [0015] An embodiment of the present invention provides a method and system for classifying objects around a vehicle which may effectively classify the objects around the vehicle into static objects and/or dynamic object. [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, Fig. 10A/B classifying dynamic objects) further identifies a distance from the moving object to the dynamic obstacle and a boundary region of the dynamic obstacle on a basis of the captured image (See at least [0018] Based on LiDAR data received from LiDAR of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster and velocity vector information included in cells belonging to the cluster. See at least [0099-0101] The velocity vector information of the point data may be calculated based on velocity information acquired based on past and present position information of the point data and elapsed time, and velocity information of the vehicle 1 included in the information related to movement of the vehicle 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen to incorporate the teachings of Heo which teaches wherein the identification unit further identifies a distance from the moving object to the dynamic obstacle and a boundary region of the dynamic obstacle on a basis of the captured image since they are both directed to environment perception and occupancy mapping around a vehicle, and incorporation of the teachings of Heo would improve the accuracy and reliability of the system by enabling more precise localization of detected dynamic obstacles.
Regarding claim 13, Shen discloses a control method for a moving object control system, the control method comprising (See at least Figure 7, [0010], [0069]). The rest of claim 13 is commensurate in scope with claim 1. See rejection of claim 1 above.
Regarding claim 14, Shen discloses a non-transitory computer-readable storage medium of storing a program for causing a computer to function as each unit of a moving object control system (See at least Figure 7, [0010], [0011], [0069]). The rest of claim 14 is commensurate in scope with claim 1. See rejection of claim 1 above.
Claim(s) 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Shen (WO 2020118545 A1), in view of Heo (US 20230288568 A1), and further in view of Binney (20150253777 A1) and further in view of Plaza (EP 3731130 A1); hereinafter Plaza.
Regarding claim 8, Shen as modified by Heo and Binney discloses the divided region in the depth direction from the position of the identified dynamic obstacle (See at least Shen [0013] These probabilities can be updated according to observations from range sensors like LiDARs, radars, sonars or depth cameras and [0017-0019], [0029] In certain embodiments, distinction is made between static and movable (dynamic) objects by assigning different regressing factors (or weights) for different cells upon their object categories… [0046] For depth cameras for example, the observed cells may be the cells containing the location of each pixel point (observed as occupied) and those on the ray from the camera to a pixel point (observed as unoccupied)).
Shen as modified by Heo and Binnney does not explicitly disclose wherein the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle includes a region of which a depth is greater than a depth from the moving object to the identified dynamic obstacle, from among regions defined by boundaries of the identified dynamic obstacle. However, Plaza discloses wherein the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle includes a region of which a depth is greater than a depth from the moving object to the identified dynamic obstacle, from among regions defined by boundaries of the identified dynamic obstacle (See at least [0010 -0011] The area behind the object is unknown. Then, this process is repeated when the object is viewed from a different position and a second free sub-area is determined in the same way as for the first free sub-area, and similarly the front surface of the object is also known and the unknown area is again behind the object. [0033] Again, this is everything behind the object that cannot be seen, and is not actually shown in the figures for the second acquisition, but is in effect as shown in Fig. 2a but for the position of Fig. 2b. See at least Fig. 2a-2d object and region behind it labeled “unknown area”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen as modified by Heo and Binney to incorporate the teachings of Plaza which teaches wherein the divided region in the depth direction that is away from the moving object from the position of the identified dynamic obstacle includes a region of which a depth is greater than a depth from the moving object to the identified dynamic obstacle, from among regions defined by boundaries of the identified dynamic obstacle since they are all directed to environment perception and occupancy mapping of objects using depth perception, and incorporation of the teachings of Plaza would improve the accuracy of the system by enabling more precise localization of regions located beyond detected dynamic obstacles.
Regarding claim 11, Shen as modified by Heo and Binney does not explicitly disclose wherein the acquisition unit acquires the captured image captured by a monocular imaging apparatus, and the depth information obtained from a stereo image captured by a plurality of imaging apparatuses. However, Plaza teaches wherein the acquisition unit acquires the captured image captured by a monocular imaging apparatus, and the depth information obtained from a stereo image captured by a plurality of imaging apparatuses (See at least [0003] Lidars or other depth sensors (ultrasonic, radars, stereo vision, etc.) are used for capturing the surrounding scenario as a point cloud. With consecutive captures, from several points of view (when the sensor is in a mobile platform, such a car, with a location system, such odometry, all the point clouds are accumulated knowing the motion of the mobile platform and merged into just one occupancy 3D map. [0043] In an example, the sensor is one of: a laser sensor, a radar sensor, an ultrasonic sensor, a stereo vision sensor. See at least [0026] Object sub-extents for each of the plurality of datasets acquired and associated plurality of locations.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Shen as modified by Heo and Binney to incorporate the teachings of Plaza which teaches wherein the acquisition unit acquires the captured image captured by a monocular imaging apparatus, and the depth information obtained from a stereo image captured by a plurality of imaging apparatuses since they are all directed to environment perception and object detection using sensor data, and incorporation of the teachings of Plaza would improve the accuracy and reliability of the system by enabling more precise estimation of objects in the surrounding environment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LABIBAH ILMA ALI/Examiner, Art Unit 3667
/SAHAR MOTAZEDI/Primary Examiner, Art Unit 3667
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