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 .
Status of claims
The following claims have been rejected or allowed for the following reasons:
Claim(s) 1-20 is rejected under 35 USC § 103
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2024-0177287, filed on 12/3/25.
Information Disclosure Statement
The information disclosure statement/statements (IDS) were filed on 3/3/26 and 11/17/25. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The factual inquiries 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.
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) 1-4, 7, 10-15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Lee (US 20240094740 A1), in further view of Holson (US 20190337154 A1).
Regarding claim 1 Lee teaches A robot comprising: a sensor; a driver; memory storing instructions; and at least one processor comprising processing circuitry, wherein the instructions, when executed by the at least one processor individually or collectively, cause the robot to: (Lee [0006] reads “According to an aspect of the disclosure, a driving robot includes: a camera including a depth camera; and at least one processor configured to: control the camera to acquire depth data in one or more areas where the driving robot moves, identify, from the acquired depth data, a plurality of scan data sets corresponding to a plurality of predetermined height levels, identify, based on the plurality of scan data sets, a plurality of feature scores corresponding to the plurality of scan data sets, and generate at least one area map corresponding to at least one scan data set among the plurality of scan data sets, wherein a feature score, among the plurality of feature scores, corresponding to the at least one scan data set is greater than or equal to a predetermined critical value.”);
obtain three-dimensional (3D) point cloud information based on second sensing data obtained through the sensor, (Lee [0062] reads “The sensor 150 may detect the surrounding environment. The sensor 150 may detect the depth data of the surrounding environment. Alternatively, the sensor 150 may detect a road surface condition, unevenness on a floor, an obstacle, and the like. For example, the sensor 150 may include an angle sensor, an acceleration sensor, a gravity sensor, a gyro sensor, a geomagnetic sensor, a direction sensor, an infrared sensor, an ultrasonic sensor, a time-of-flight (ToF) sensor, a light detection and ranging (LiDAR) sensor, a laser sensor, a motion recognition sensor, a heat sensor, an image sensor, a tracking sensor, a proximity sensor, an illuminance sensor, a voltmeter, an ammeter, a barometer, a hygrometer, a thermometer, a touch sensor, or the like.”);
based on identifying that the robot is unable to navigate in the obstacle-dense area, divide the 3D point cloud information into a plurality of planes based on a height of the robot, (Lee [0049] reads “The processor 120 may identify scan data of a plurality of predetermined height levels from the acquired depth data. The camera 110 may acquire all the depth data within a range of a field of view of the camera 110. The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. The processor 120 may then scan each depth data corresponding to the set height level, and identify scan data of the height level of 0.2 m, scan data of the height level of 0.4 m, and scan data of the height level of 0.6 m.”);
Lee does not teach based on identifying an obstacle-dense area within a forward path based on first sensing data obtained through the sensor while driving in a space, and identify one or more escape routes for each of the plurality of planes, identify at least one final escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes, and control the driver to drive based on the at least one final escape route.
Holson in analogous art, teaches based on identifying an obstacle-dense area within a forward path based on first sensing data obtained through the sensor while driving in a space, (Holson [0028] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
and identify one or more escape routes for each of the plurality of planes, (Holson [0060] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
identify at least one final escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes, (Holson [0061] reads “In some implementations, the generation of the collision-free route resembles a tree structure, where the 2D planner 142 prunes those routes or segments that result in a potential collision. If more than one collision-free route is identified, the 2D planner 142 can select a particular collision-free path based on any of various criteria (e.g., shortest distance, least deviation from the global path, most clearance from any detected obstacle or object). After the 2D planner 142 has selected a collision-free path, it generates path data 148 describing the selected path.”);
and control the driver to drive based on the at least one final escape route. (Holson [0074] reads “The planning module 140 provides the path data 148 and configuration data 149 to the navigation module 150. The navigation module 150 then generates motor commands 152 that are sent to the robot's motor control 180 to produce the locomotive forces necessary to move the robot 110 along the path described by the path data 148 and in the configuration described by the configuration data.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee with that of Holson to include a method for determining and selecting a given route from multiple created routes. Doing so would constitute the combination of known methods to yield predictable results since both using Lidar to make horizontal scans of a given area and the creation of multiple candidate paths would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of robotic system that could move along a selected candidate path based of sensed lidar data.
Regarding claim 2 Lee/Holson teaches The robot of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: equally divide the height of the robot by a specific interval, and divide the 3D point cloud information into the plurality of planes corresponding to the equally divided heights. (Lee [0049] reads “The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. “);
Regarding claim 3 Lee/Holson teaches The robot of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: based on identifying that the robot is unable to navigate in the obstacle-dense area, obtain the second sensing data through the sensor (Holson [0014] reads “With higher responsiveness and quicker planning, the robot may be able to move at a greater speed while dynamically adjusting its path to account for obstacles. By switching to 3D path planning when no collision-free path is identified by the 2D planning, the robot can leverage the accuracy of 3D analysis to navigate through confined spaces. The 3D analysis also increases the range of options for navigation, allowing the robot to plan paths that would have appeared blocked by 2D analysis alone”);
by performing a primitive motion, (Lee [0041] reads “The driving robot 100 may perform localization based on an area map of an area where the driving robot 100 is positioned while moving based on the set movement plan. The localization may include a process of the driving robot 100 identifying its exact position and direction. The driving robot 100 may identify its exact position and direction based on the entire map and the driving map, and may move to a destination based on the movement plan.”);
and obtain the 3D point cloud information based on the second sensing data. (Holson [0093] reads “Using 3D analysis techniques, the 3D planner evaluates the particular candidate path segment to determine whether the segment is, in fact, collision-free (316). For example, as described previously, based on sensor data received from sensors of the robot (e.g., cameras, LIDAR, radar, etc.), the 3D planner can generate 3D profiles of objects detected in the robot's local environment. The 3D planner also generates a 3D profile of the robot in one or more configuration (e.g., with arms elevated, turned sideways).”);
Regarding claim 4 Lee/Holson teaches The robot of claim 3, wherein the primitive motion comprises at least one of a rotating-in-place motion or a forward-backward moving motion. (Lee [0053] reads “ For example, the processor 120 may control the camera 110 to acquire the depth data at 360 degrees while rotating the driving robot 100 in the area where the driving robot is initially positioned in a horizontal direction.”);
Regarding claim 7 Lee/Holson teaches The robot of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: based on identifying the at least one final escape route, identify a priority of each of the at least one final escape route based on at least one of a location of a target point, an escape distance, or an escape time, and control the driver to drive according to the at least one final escape route with the highest identified priority. (Holson [0071] reads “If multiple collision-free paths are identified, the 3D planner 144 can select one path based on any of various criteria (e.g., shortest distance, least deviation from the global path, most clearance from any detected obstacle or object, most preferred robot configuration).”);
Regarding claim 10 Lee/Holson teaches The robot of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: initialize the accumulated 3D point cloud information after escaping the obstacle-dense area. (Lee [0065] reads “The memory 165 may store data, algorithms, or the like that perform functions of the driving robot 100, and may store programs, instructions, or the like driven by the driving robot 100. Alternatively, the memory 165 may store the data on one or more specific height levels to be virtually scanned, the depth data, the entire map, the area map, the weight data, or the like. The algorithm or data stored in the memory 165 may be loaded into the processor 120 under control of the processor 120 to perform the map generation process or the localization process. For example, the memory 165 may be implemented as a type of a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid state drive (SDD), a memory card, or the like.”);
Regarding claim 11 lee teaches A method of controlling a robot, the method comprising: based on identifying an obstacle-dense area within a forward path based on first sensing data obtained while driving in a space, (Lee [0006] reads “According to an aspect of the disclosure, a driving robot includes: a camera including a depth camera; and at least one processor configured to: control the camera to acquire depth data in one or more areas where the driving robot moves, identify, from the acquired depth data, a plurality of scan data sets corresponding to a plurality of predetermined height levels, identify, based on the plurality of scan data sets, a plurality of feature scores corresponding to the plurality of scan data sets, and generate at least one area map corresponding to at least one scan data set among the plurality of scan data sets, wherein a feature score, among the plurality of feature scores, corresponding to the at least one scan data set is greater than or equal to a predetermined critical value.”);
obtaining three-dimensional (3D) point cloud information based on second sensing data; (Lee [0062] reads “The sensor 150 may detect the surrounding environment. The sensor 150 may detect the depth data of the surrounding environment. Alternatively, the sensor 150 may detect a road surface condition, unevenness on a floor, an obstacle, and the like. For example, the sensor 150 may include an angle sensor, an acceleration sensor, a gravity sensor, a gyro sensor, a geomagnetic sensor, a direction sensor, an infrared sensor, an ultrasonic sensor, a time-of-flight (ToF) sensor, a light detection and ranging (LiDAR) sensor, a laser sensor, a motion recognition sensor, a heat sensor, an image sensor, a tracking sensor, a proximity sensor, an illuminance sensor, a voltmeter, an ammeter, a barometer, a hygrometer, a thermometer, a touch sensor, or the like.”);
dividing the 3D point cloud information into a plurality of planes based on a height of the robot, (Lee [0049] reads “The processor 120 may identify scan data of a plurality of predetermined height levels from the acquired depth data. The camera 110 may acquire all the depth data within a range of a field of view of the camera 110. The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. The processor 120 may then scan each depth data corresponding to the set height level, and identify scan data of the height level of 0.2 m, scan data of the height level of 0.4 m, and scan data of the height level of 0.6 m.”);
Lee does not teach based on identifying that the robot is unable to navigate in the obstacle-dense area, and identifying one or more escape routes for each of the plurality of planes; identifying at least one final escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes; and controlling a driver of the robot to drive based on the at least one final escape route.
Holson in analogous art, teaches based on identifying that the robot is unable to navigate in the obstacle-dense area, (Holson [0028] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
and identifying one or more escape routes for each of the plurality of planes; (Holson [0060] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
identifying at least one final escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes; (Holson [0061] reads “In some implementations, the generation of the collision-free route resembles a tree structure, where the 2D planner 142 prunes those routes or segments that result in a potential collision. If more than one collision-free route is identified, the 2D planner 142 can select a particular collision-free path based on any of various criteria (e.g., shortest distance, least deviation from the global path, most clearance from any detected obstacle or object). After the 2D planner 142 has selected a collision-free path, it generates path data 148 describing the selected path.”);
and controlling a driver of the robot to drive based on the at least one final escape route. (Holson [0074] reads “The planning module 140 provides the path data 148 and configuration data 149 to the navigation module 150. The navigation module 150 then generates motor commands 152 that are sent to the robot's motor control 180 to produce the locomotive forces necessary to move the robot 110 along the path described by the path data 148 and in the configuration described by the configuration data.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee with that of Holson to include a method for determining and selecting a given route from multiple created routes. Doing so would constitute the combination of known methods to yield predictable results since both using Lidar to make horizontal scans of a given area and the creation of multiple candidate paths would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of robotic system that could move along a selected candidate path based of sensed lidar data.
Regarding claim 12 Lee/Holson teaches The method of claim 11, wherein the identifying the one or more escape routes comprises: equally dividing the height of the robot by a specific interval; and dividing the 3D point cloud information into the plurality of planes corresponding to the equally divided heights. (Lee [0049] reads “The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. “);
Regarding claim 13 Lee/Holson teaches The method of claim 11, further comprising: based on identifying that the robot is unable to navigate in the obstacle-dense area, (Holson [0014] reads “With higher responsiveness and quicker planning, the robot may be able to move at a greater speed while dynamically adjusting its path to account for obstacles. By switching to 3D path planning when no collision-free path is identified by the 2D planning, the robot can leverage the accuracy of 3D analysis to navigate through confined spaces. The 3D analysis also increases the range of options for navigation, allowing the robot to plan paths that would have appeared blocked by 2D analysis alone”);
obtaining the second sensing data by performing a primitive motion; (Lee [0041] reads “The driving robot 100 may perform localization based on an area map of an area where the driving robot 100 is positioned while moving based on the set movement plan. The localization may include a process of the driving robot 100 identifying its exact position and direction. The driving robot 100 may identify its exact position and direction based on the entire map and the driving map, and may move to a destination based on the movement plan.”);
and obtaining the 3D point cloud information based on the second sensing data. (Holson [0093] reads “Using 3D analysis techniques, the 3D planner evaluates the particular candidate path segment to determine whether the segment is, in fact, collision-free (316). For example, as described previously, based on sensor data received from sensors of the robot (e.g., cameras, LIDAR, radar, etc.), the 3D planner can generate 3D profiles of objects detected in the robot's local environment. The 3D planner also generates a 3D profile of the robot in one or more configuration (e.g., with arms elevated, turned sideways).”);
Regarding claim 14 Lee/Holson teaches The method of claim 13, wherein the primitive motion comprises at least one of a rotating-in-place motion or a forward-backward moving motion. (Lee [0053] reads “ For example, the processor 120 may control the camera 110 to acquire the depth data at 360 degrees while rotating the driving robot 100 in the area where the driving robot is initially positioned in a horizontal direction.”);
Regarding claim 15 Lee/Holson teaches The method of claim 11, further comprising: based on identifying the at least one final escape route, identifying a priority of each of the at least one final escape route based on at least one of a location of a target point, an escape distance, or an escape time, and controlling the driver to drive according to the at least one final escape route with the highest identified priority. (Holson [0071] reads “If multiple collision-free paths are identified, the 3D planner 144 can select one path based on any of various criteria (e.g., shortest distance, least deviation from the global path, most clearance from any detected obstacle or object, most preferred robot configuration).”);
Regarding claim 17 Lee/Holson teaches The method of claim 11, wherein the obtaining the 3D point cloud information comprises: starting accumulating 3D point cloud information in the memory on the basis of second sensing data. (Lee [0065] reads “The memory 165 may store data, algorithms, or the like that perform functions of the driving robot 100, and may store programs, instructions, or the like driven by the driving robot 100. Alternatively, the memory 165 may store the data on one or more specific height levels to be virtually scanned, the depth data, the entire map, the area map, the weight data, or the like. The algorithm or data stored in the memory 165 may be loaded into the processor 120 under control of the processor 120 to perform the map generation process or the localization process. For example, the memory 165 may be implemented as a type of a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid state drive (SDD), a memory card, or the like.”);
Regarding claim 18 Lee/Holson teaches The method of claim 11, further comprising: initializing the accumulated 3D point cloud information after escaping the obstacle-dense area. (Lee [0065] reads “The memory 165 may store data, algorithms, or the like that perform functions of the driving robot 100, and may store programs, instructions, or the like driven by the driving robot 100. Alternatively, the memory 165 may store the data on one or more specific height levels to be virtually scanned, the depth data, the entire map, the area map, the weight data, or the like. The algorithm or data stored in the memory 165 may be loaded into the processor 120 under control of the processor 120 to perform the map generation process or the localization process. For example, the memory 165 may be implemented as a type of a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid state drive (SDD), a memory card, or the like.”);
Claim(s) 5, 6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Lee/Holson, in further view of Li (NPL | Universal path planning for an indoor drone | 2018).
Regarding claim 5 Lee/Holson teaches The robot of claim 1, and updating the at least one first escape route by identifying at least one second escape route on a plane directly above the lowermost plane, among the plurality of planes and eliminating from the at least one first escape route an escape route that does not coincide with the at least one second escape route, and wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to control the driver to drive based on the updated first escape route. (Holson [0060] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
Lee/Holson does not teach wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to eliminate escape routes from the one or more escape routes by: identifying at least one first escape route on a lowermost plane among the plurality of planes,
Li in analogous art, teaches wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to eliminate escape routes from the one or more escape routes by: identifying at least one first escape route on a lowermost plane among the plurality of planes, (Li page 280 paragraph 8 reads “Finally, the safe shortest path can be planned based on the dilated building model using the A* algorithm. The least cost path can be planned based on the dilated model and the distance field (Fig. 10b). Fig. 11 shows the path planning results. The safe shortest path tends to go towards the ceiling, but it keeps a minimal distance from the ceiling (40 cm). The least cost path lets the drone fly at a fixed height of 100 cm from the floor and stairs, in which α in Eq. (4) is 1.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Li to include a method that would allow the system to utilize a single plane for the creation of an route. Doing so would constitute the combination of known methods to yield predictable results since both route creation utilizing sensed lidar planes and indoor robotic navigation would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that could navigate dense indoor environments.
Regarding claim 6 Lee/Holson teaches The robot of claim 1, and wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to control the driver to drive based on the updated third escape route. (Holson [0074] reads “The planning module 140 provides the path data 148 and configuration data 149 to the navigation module 150. The navigation module 150 then generates motor commands 152 that are sent to the robot's motor control 180 to produce the locomotive forces necessary to move the robot 110 along the path described by the path data 148 and in the configuration described by the configuration data.”);
Lee/Holson does not teach wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to eliminate escape routes from the one or more escape routes by: identifying at least one third escape route on an uppermost plane among the plurality of planes, and updating the at least one third escape route by identifying at least one fourth escape route on a plane directly below the uppermost plane, among the plurality of planes, and eliminating from the at least one third escape route an escape route that does not coincide with the at least one fourth escape route,
Li in analogous art, teaches wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to eliminate escape routes from the one or more escape routes by: identifying at least one third escape route on an uppermost plane among the plurality of planes, and updating the at least one third escape route by identifying at least one fourth escape route on a plane directly below the uppermost plane, among the plurality of planes, and eliminating from the at least one third escape route an escape route that does not coincide with the at least one fourth escape route, (Li page 280 paragraph 8 reads “Finally, the safe shortest path can be planned based on the dilated building model using the A* algorithm. The least cost path can be planned based on the dilated model and the distance field (Fig. 10b). Fig. 11 shows the path planning results. The safe shortest path tends to go towards the ceiling, but it keeps a minimal distance from the ceiling (40 cm). The least cost path lets the drone fly at a fixed height of 100 cm from the floor and stairs, in which α in Eq. (4) is 1.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Li to include a method that would allow the system to utilize a single plane for the creation of an route. Doing so would constitute the combination of known methods to yield predictable results since both route creation utilizing sensed lidar planes and indoor robotic navigation would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that could navigate dense indoor environments.
Regarding claim 19 Lee/Holson teaches The method of claim 11 and updating the at least one first escape route by identifying at least one second escape route on a plane directly above the lowermost plane, among the plurality of planes and eliminating from the at least one first escape route an escape route that does not coincide with the at least one second escape route, and wherein the controlling the driver of the robot to drive based on the at least one final escape route comprises: controlling the driver to drive based on the updated first escape route. (Holson [0060] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
Lee/Holson does not teach wherein the identifying at least one final escape route comprises: identifying at least one first escape route on a lowermost plane among the plurality of planes;
Li in analogous art, teaches wherein the identifying at least one final escape route comprises: identifying at least one first escape route on a lowermost plane among the plurality of planes; (Li page 280 paragraph 8 reads “Finally, the safe shortest path can be planned based on the dilated building model using the A* algorithm. The least cost path can be planned based on the dilated model and the distance field (Fig. 10b). Fig. 11 shows the path planning results. The safe shortest path tends to go towards the ceiling, but it keeps a minimal distance from the ceiling (40 cm). The least cost path lets the drone fly at a fixed height of 100 cm from the floor and stairs, in which α in Eq. (4) is 1.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Li to include a method that would allow the system to utilize a single plane for the creation of an route. Doing so would constitute the combination of known methods to yield predictable results since both route creation utilizing sensed lidar planes and indoor robotic navigation would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that could navigate dense indoor environments.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied Lee/Holson, in further view of Jun (US 20170332862 A1).
Regarding claim 8 Lee/Holson teaches The robot of claim 1,
Lee/Holson does not teach wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: based on the robot failing to reach a short term target point within a specific time after entering the obstacle-dense area or the robot remaining within a specific area during a predetermined time or longer, identify that the robot is unable to navigate in the obstacle-dense area.
Jun in analogous art, teaches wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: based on the robot failing to reach a short term target point within a specific time after entering the obstacle-dense area or the robot remaining within a specific area during a predetermined time or longer, identify that the robot is unable to navigate in the obstacle-dense area. (Jun [0119] reads “In the case where the mobile robot 1 may not travel due to the plurality of obstacles repeatedly within a predetermined distance, the controller 200 determines that the mobile robot 1 is in a stuck state (i.e., mobile robot 1 is has been a particular area for more than a particular length of time and/or has performed more than a threshold number of avoidance moves within the particular area), and sets an escape mode.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Jun to include a method that would allow the system to understand an amount of time that it has been in a location. Doing so would constitute the combination of known methods to yield predictable results since both robotic path planning systems and mobile robots with timer setups would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that would be able to understand the time it has taken to move.
Regarding claim 16 Lee/Holson teaches The method of claim 11,
Lee/Holson does not teach further comprising: based on the robot failing to reach a short term target point within a specific time after entering the obstacle-dense area or the robot remaining within a specific area during a predetermined time or longer, identifying that the robot is unable to navigate in the obstacle-dense area.
Jun in analogous art, teaches further comprising: based on the robot failing to reach a short term target point within a specific time after entering the obstacle-dense area or the robot remaining within a specific area during a predetermined time or longer, identifying that the robot is unable to navigate in the obstacle-dense area. (Jun [0119] reads “In the case where the mobile robot 1 may not travel due to the plurality of obstacles repeatedly within a predetermined distance, the controller 200 determines that the mobile robot 1 is in a stuck state (i.e., mobile robot 1 is has been a particular area for more than a particular length of time and/or has performed more than a threshold number of avoidance moves within the particular area), and sets an escape mode.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Jun to include a method that would allow the system to understand an amount of time that it has been in a location. Doing so would constitute the combination of known methods to yield predictable results since both robotic path planning systems and mobile robots with timer setups would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that would be able to understand the time it has taken to move.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Lee/Holson, in further view of Williams (US 20160297072 A1).
Regarding claim 9 Lee/Holson teaches The robot of claim 1, wherein the instructions, when executed by the at least one processor individually or collectively, further cause the robot to: after escaping the obstacle-dense area, (Lee [0049] reads “The processor 120 may identify scan data of a plurality of predetermined height levels from the acquired depth data. The camera 110 may acquire all the depth data within a range of a field of view of the camera 110. The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. The processor 120 may then scan each depth data corresponding to the set height level, and identify scan data of the height level of 0.2 m, scan data of the height level of 0.4 m, and scan data of the height level of 0.6 m.”);
Lee/Holson does not teach set the obstacle-dense area as an area where re-entry is prohibited.
Williams in analogous art, teaches set the obstacle-dense area as an area where re-entry is prohibited. (Williams [0063] reads “In order to prevent the robot 400 from returning to the region 430 a, where it has already executed a cleaning operation, the controller 390 can establish a virtual barrier 450 that marks regions that the robot 400 has already cleaned, as shown in the portion 423. For example, the controller 390 can update the occupancy grid 420 to identify a location or boundary of the previously cleaned area to prohibit the robot 400 from returning to the area.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Williams to include location mapping of problematic areas. Doing so would constitute the combination of known methods to yield predictable results since both Simultaneous Localization and Mapping (SLAM) and route planning for robotic systems would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of a robotic system that could avoid a given area that it has previously had issues navigating.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over as applied to Lee (US 20240094740 A1 ), in further view of Holson (US 20190337154 A1), in further view of Afrouzi (US 20240310851 A1).
Regarding claim 20 Lee teaches A robot comprising: a sensor; a driver; memory storing instructions; and at least one processor configured to execute the instructions, wherein the instructions, when executed by the at least one processor individually or collectively, cause the robot to: (Lee [0006] reads “According to an aspect of the disclosure, a driving robot includes: a camera including a depth camera; and at least one processor configured to: control the camera to acquire depth data in one or more areas where the driving robot moves, identify, from the acquired depth data, a plurality of scan data sets corresponding to a plurality of predetermined height levels, identify, based on the plurality of scan data sets, a plurality of feature scores corresponding to the plurality of scan data sets, and generate at least one area map corresponding to at least one scan data set among the plurality of scan data sets, wherein a feature score, among the plurality of feature scores, corresponding to the at least one scan data set is greater than or equal to a predetermined critical value.”);
obtain three-dimensional (3D) point cloud information based on second sensing data obtained through the sensor, based on identifying that a maneuverability of the robot within the obstacle-dense area is less than a predetermined threshold, (Lee [0049] reads “The processor 120 may identify scan data of a plurality of predetermined height levels from the acquired depth data. The camera 110 may acquire all the depth data within a range of a field of view of the camera 110. The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. The processor 120 may then scan each depth data corresponding to the set height level, and identify scan data of the height level of 0.2 m, scan data of the height level of 0.4 m, and scan data of the height level of 0.6 m.”);
divide the 3D point cloud information into a plurality of planes based on a height of the robot, (Lee [0049] reads “The processor 120 may identify scan data of a plurality of predetermined height levels from the acquired depth data. The camera 110 may acquire all the depth data within a range of a field of view of the camera 110. The processor 120 may include data on one or more specific height levels to be virtually scanned. The processor 120 may identify the scan data by scanning depth data of the predetermined height level from the acquired depth data. For example, the processor 120 may set a height level of 0.2 m, a height level of 0.4 m, and a height level of 0.6 m as height levels to be scanned. The processor 120 may then scan each depth data corresponding to the set height level, and identify scan data of the height level of 0.2 m, scan data of the height level of 0.4 m, and scan data of the height level of 0.6 m.”);
Lee does not teach identifying an area within a forward path of the robot as an obstacle-dense area based on first sensing data obtained through the sensor while driving in a space,
wherein the area includes a number of obstacles within a predetermined distance of one another and the number of obstacles exceeds a predetermined threshold, based on identifying the obstacle-dense area, and identify one or more escape routes for each of the plurality of planes, identify at least final one escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes, and control the driver to drive based on the at least one final escape route.
Holson in analogous art, teaches identifying an area within a forward path of the robot as an obstacle-dense area based on first sensing data obtained through the sensor while driving in a space, (Holson [0028] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
and identify one or more escape routes for each of the plurality of planes, (Holson [0060] reads “In some implementations, the robot 110 may use 2D floor plan data to determine the global path. In some examples, the floor plan data is mapped to a 2D occupancy grid, which depicts the position of known obstacles (e.g., walls, stairwells, elevators, furniture) in the space. A typical global occupancy grid may have grid spacing on the order of 0.1 meters to 1 meter, where the grid spaces corresponding to obstacles or objects are considered occupied and thus impassable by the robot. Typically, the floor plan or the occupancy grid depicts a projection of obstacles or objects down onto the plane of the floor.”);
identify at least final one escape route, from among the one or more escape routes, by eliminating escape routes from the one or more escape routes that are blocked in any of the plurality of planes, (Holson [0061] reads “In some implementations, the generation of the collision-free route resembles a tree structure, where the 2D planner 142 prunes those routes or segments that result in a potential collision. If more than one collision-free route is identified, the 2D planner 142 can select a particular collision-free path based on any of various criteria (e.g., shortest distance, least deviation from the global path, most clearance from any detected obstacle or object). After the 2D planner 142 has selected a collision-free path, it generates path data 148 describing the selected path.”);
and control the driver to drive based on the at least one final escape route. (Holson [0074] reads “The planning module 140 provides the path data 148 and configuration data 149 to the navigation module 150. The navigation module 150 then generates motor commands 152 that are sent to the robot's motor control 180 to produce the locomotive forces necessary to move the robot 110 along the path described by the path data 148 and in the configuration described by the configuration data.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee with that of Holson to include a method for determining and selecting a given route from multiple created routes. Doing so would constitute the combination of known methods to yield predictable results since both using Lidar to make horizontal scans of a given area and the creation of multiple candidate paths would be known to one with ordinary skill in the art. Similarly, their combination would yield the desired and predictable result of robotic system that could move along a selected candidate path based of sensed lidar data.
Lee/Holson does not teach wherein the area includes a number of obstacles within a predetermined distance of one another and the number of obstacles exceeds a predetermined threshold, based on identifying the obstacle-dense area,
Afrouzi in analogous art, teaches wherein the area includes a number of obstacles within a predetermined distance of one another and the number of obstacles exceeds a predetermined threshold, based on identifying the obstacle-dense area, (Afrouzi [0304] reads “In another example of a multi-dimensional rectangular prism comprising map data, each slice of the rectangular prism comprises a map corresponding to a particular run (i.e., work session) of the robot. The map includes a door and objects (e.g., toys) and the position of the door and objects may vary between runs. In yet another example of a multi-dimensional rectangular prism comprising map data, each slice of the rectangular prism comprises a map corresponding to a particular time stamp t. The map includes debris data, indicating locations with debris accumulation and the position of locations with high accumulation of debris data may vary for each particular time stamp. Depending on sensor observations over some amount of time, the debris data may indicate high debris probability density areas, medium debris probability density areas, and low debris probability density areas, each indicated by a different shade.”);
It would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention to have modified the teachings of Lee/Holson with that of Afrouzi to include a method for analyzing a obstacle dense area. Doing so would allow the robotic system to navigate difficult and non-static environments. (Afrouzi [0004] reads “Autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments. Such robotic devices may include a drone, a robotic vacuum cleaner, a robotic lawn mower, a robotic mop, or other robotic devices. To operate autonomously or with minimal (or less than fully manual) input and/or external control within an environment, methods such as mapping, localization, object recognition, and path planning methods, among others, are required such that robotic devices may autonomously create a map of the environment, subsequently use the map for navigation, and devise intelligent path and task plans for efficient navigation and task completion.”);
Other references not Cited
Throughout examination other references were found that could read onto the prior art. Though these references were not used in this examination they could be used in future examination and could read on the contents of the current disclosure. These references are, Dupuis (US20200338733A1). Dupuis in analogous art, could have been used to teach limitations regarding path planning for a mobile robotic system. This includes analysis of obstacle that could obscure the path of the robotic system. However, Dupuis was not selected for the current rejection of record because it fails to teach many of the needed limitations about the scanning of multiple layers of a given environment.
Conclusion
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/JOHN MARTIN O'MALLEY/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
