DETAILED ACTION
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
This Office Action is in response to the application filed on 11/25/2024. Claim(s) 1 - 20 are presently pending and are examined in this first action on the merits (FAOM).
Priority
Examiner acknowledges Applicant’s claim to priority based on Application KR10-2024-0095876 filed 07/19/2024
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/21/2023 has been 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.
Claims 1, 9-11 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kohira Hiromatsu JP7634796B1 (“Hiromatsu”).
As per Claim 1 and 11,
Hiromatsu discloses,
A robot control apparatus comprising:
at least one sensor (see at least [0022] The robot device 2 is a wheeled robot that receives commands remotely from the control device 1 and moves through space in accordance with the received commands. The robot device 2 includes a communication unit 21, a storage unit 22, a three-dimensional point cloud sensor 23, and [0025] The three-dimensional point cloud sensor 23 is, for example, a sensor capable of acquiring the surrounding environment as three-dimensional point cloud data, such as a 3D LiDAR (3-Dimensions Light Detection and Ranging) or a stereo camera. The acquired three-dimensional local point cloud data corresponding to the surrounding environment of the robot device 2 is used in the control program 27 for self-position estimation, surrounding environment detection)
a processor (see at least [0036] As shown in the figure, the control device 1 is a computer including hardware such as a processor 101)
wherein the processor is configured to:
obtain, via the at least one sensor and from an area within a designated distance from a robot, data points associated with one or more target objects around the robot, (see at least [0032] the surrounding environment detection unit 273 extracts a three-dimensional point cloud corresponding to the determined contradiction point cloud and a certain range around the determined contradiction point cloud from the three-dimensional global point cloud data as a corresponding point cloud, and compares the average height between the contradiction point cloud and the corresponding point cloud. Next, if the average height of the contradiction point cloud is higher than the average height of the corresponding point cloud, the surrounding environment detection unit 273 determines that a temporary obstacle exists in the range corresponding to the contradiction point cloud)
wherein the area is segmented, based on a designated angle and a designated length, into a plurality of segmented areas;(see at least Fig. 3 step S330 and S340, [0007] when an environmental map consisting of a three-dimensional point cloud measured in a target area is divided into a plurality of grids such that a plane included in the target area is divided, each grid of the plurality of grids is set as a target grid, and when it is determined that a three-dimensional point cloud corresponding to a planar area exists in the target grid, the path generation device extracts a planar area from the target grid; when a planar area is extracted from each grid from which the planar area is extracted, the path generation device extracts the planar area as a target plane, and when a three-dimensional point cloud exists in a grid including the target plane between a height of the target plane and a height obtained by adding the height of the target plane to a movable height threshold that is equal to or greater than the height of a target moving body, in a height direction relative to the target plane, [0009] FIG. 5 is a diagram explaining a lattice division method according to the first embodiment. FIG. 6 is a diagram explaining a plane extraction method according to the first embodiment, and [0050] In FIG. 5, the three-dimensional global point cloud data 31 is divided into square grids with a side length of L)
determine, among the data points, a first group of data points that belong to a first segmented area of the plurality of segmented areas; determine, among the data points, a second group of data points that belong to one or more second segmented areas of the plurality of segmented areas, wherein the one or more second segmented areas are adjacent to the first segmented area; (see at least [0054] First, the map creation unit 142 creates a histogram from the number of points included in each rectangular parallelepiped obtained by dividing the target grid by a certain width in the height direction. Next, the map creation unit 142 determines each bin in the created histogram in which the number of bins is equal to or greater than a threshold as a plane candidate, and calculates the average value and standard deviation value of the height of the three-dimensional point cloud included in each bin determined to be a plane candidate)
determine, based on an angle difference between the first group and the second group and based on a height difference between the first group and the second group, whether the first segmented area corresponds to a ground; (see at least [0054] First, the map creation unit 142 creates a histogram from the number of points included in each rectangular parallelepiped obtained by dividing the target grid by a certain width in the height direction, [0054] Thereafter, the map creation unit 142 determines, among the plane candidates, a plane candidate whose corresponding standard deviation value of height is equal to or less than a threshold as a plane, and transitions to step S370, [0054] The bins determined to be plane candidates in the grid 32 only contain road surface reflection point clouds, so the standard deviation is relatively small. On the other hand, the bins determined to be plane candidates in the grid 33 contain road surface reflection point clouds and tree reflection point clouds, so the standard deviation is relatively large, and [0055] the map creation unit 142 may use an algorithm such as RANSAC (RANdom SAMPLE Consensus) to detect a plane from the points included in each grid, and extract the detected plane as a plane that the robot device 2 can enter, provided that the angle between the detected plane and the horizontal plane is equal to or less than a threshold)
control, based on the determination of whether the first segmented area corresponds to the ground, movement of the robot (see at least [0007] a path creation unit that creates a path for the target moving body from a movement start position on the planar area not classified as an obstacle plane shown in the planar map to a movement target position on the planar area not classified as an obstacle plane shown in the planar map, based on the planar areas not classified as obstacle planes shown in the planar map, [0022] The robot device 2 is a wheeled robot that receives commands remotely from the control device 1 and moves through space in accordance with the received commands, [0096] it is also possible to control the robot apparatus 2 with respect to the route using only the coordinate information of the route , and [0106] The path creation unit 144 according to the present embodiment regards a plurality of consecutive planar regions passing through on the created path as a target continuous plane, determines the road surface condition of the target continuous plane based on the height difference between the planar regions of the target continuous plane, and assigns a speed according to the determined road surface condition of the target continuous plane as the speed of the target moving body on the target continuous plane)
Hiromatsu fails to explicitly teach segmenting an area within a distance from a robot into a plurality of segmented areas “based on a designated angle and a designated length” but Hiromatsu does disclose creating a rectangular “grid” based on a “designated length” in the horizontal plane (Fig. 5), and creating “bins” within the rectangular parallelepiped based on a “designated height” of the bin (Fig. 6). This is equivalent to the segmented areas or unit areas (see specification [0113]). Hiromatsu discloses determining based on the data points in the various bins and the standard deviation of the points within the bins the ground, presence of an obstacle and the height of the obstacle.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective data of the claimed invention to modify the invention of Hiromatsu, with a reasonable expectation of success, to enable the autonomous mobile robot to move indoors, under bridge girders, under tree branches, and the like (0008) and regard the point cloud data classified into pedestrians, bicycles, or the like as data corresponding to a temporary obstacle (0032).
As per Claim 9 and 19,
Hiromatsu discloses,
wherein the processor is further configured to:
determine, based on a first representative point of the first group and a second representative point of the second group, at least one of the angle difference or the height difference (see at least [0054] the map creation unit 142 creates a histogram from the number of points included in each rectangular parallelepiped obtained by dividing the target grid by a certain width in the height direction, [0054 It is preferable that the width of the bin is set to be equal to or less than the upper limit of the height of the step that the robot device 2 can traverse, and [0055] the map creation unit 142 may use an algorithm such as RANSAC (RANdom SAMPLE Consensus) to detect a plane from the points included in each grid, and extract the detected plane as a plane that the robot device 2 can enter, provided that the angle between the detected plane and the horizontal plane is equal to or less than a threshold. The detected plane is also called a detected plane. The detected plane may not be a flat plane. In the example shown in Figure 6, only one plane is extracted from one grid, but in cases where a step or a three-dimensional intersection is included within one grid, the map creation unit 142 may extract multiple planes from one grid).
As per Claim 10 and 20,
Hiromatsu discloses,
wherein the processor is further configured to:
assign, based on the designated angle and the designated distance, an identifier to each of the plurality of segmented areas (see at least [0050] In FIG. 5, coordinate information is assigned to each grid in order along each axis direction. Specifically, as coordinate information, (k, l) is assigned to the grid located at the bottom left, (k+1, l) and (k+2, l) are assigned to the grids adjacent to the grid located at the bottom left in the right direction in order, and (k, l+1) and (k, l+2) are assigned to the grids adjacent to the grid located at the bottom left in the depth direction in order, and [0064] 9 and 10 are schematic diagrams showing an example of a method for registering a candidate plane and an obstacle plane in the plane list 41 in list format. The plane list 41 corresponds to a planar map. First, the map creation unit 142 assigns i+4 as an integer ID (Identifier) that does not overlap with other planes to the grid 33 that was determined to have an obstacle in step S380.the height of the three-dimensional point cloud included in each bin determined to be a plane candidate)
Claims 2-3, 5, 12-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Hiromatsu in view of Chang Hyeon Lee et. al KR20180105984A (“Lee”)
As per Claim 2 and 12,
Hiromatsu discloses,
wherein the processor is further configured to:
determine, based on the first segmented area being determined to not correspond to the ground and based on a grid map, that the first segmented area corresponds to at least one of a static object, a dynamic object, or a mixed object (see at least [0032] when no planar region is detected from the inconsistent point cloud, the surrounding environment detection unit 273 determines that a temporary obstacle exists in the range corresponding to the inconsistent point cloud. Note that the surrounding environment detection unit 273 may classify at least a part of the three-dimensional local point cloud data into pedestrians, bicycles, or the like using a deep learning method or the like, and may regard the point cloud data classified into pedestrians, bicycles, or the like as data corresponding to a temporary obstacle, and [0054] It is preferable that the width of the bin is set to be equal to or less than the upper limit of the height of the step that the robot device 2 can traverse. This is because, in the obstacle extraction process described later, when a bin above a bin from which a plane is extracted contains three-dimensional point cloud data, the bin above the bin is determined to be an obstacle)
Hiromatsu does not disclose,
The obstacle being at least one of a static object, a dynamic object, or a mixed object
Lee teaches,
The obstacle being at least one of a static object, a dynamic object, or a mixed object (see at least [0022] various sensing units of the robot are used to discriminate fixed objects and moving objects in the current space to generate an aurial map, compares it with a total map to grasp a situation in which moving objects are arranged in a space, So that the previously set travel route can be changed, and [0048] The sensing unit 110 may use the sensed data to determine the fixed object and the dynamic objects (moving obstacle) to generate the surround map, and may change or newly generate the route based on the generated surround map
Thus, Hiromatsu teaches classifying at least a part of the three-dimensional local point cloud data into pedestrians, bicycles, or the like using a deep learning method or the like, and may regard the point cloud data classified into pedestrians, bicycles, or the like as data corresponding to a temporary obstacle and Lee teaches determining fixed objects (fixed obstacles) and moving objects (moving obstacles).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hiromatsu with detection of fixed or moving obstructions as taught by Lee, with a reasonable expectation of success, to generate the surround map, and may change or newly generate the route based on the generated surround map (0048).
As per Claim 3 and 13,
Hiromatsu discloses,
wherein the processor is further configured to:
determine, based on the first segmented area corresponding to the mixed object and based on the grid map, a partial dynamic object included in the first segmented area; and output at least one of the static object, the dynamic object, the mixed object, or the partial dynamic object. (see at least [0032] the surrounding environment detection unit 273 determines that a temporary obstacle exists in the range corresponding to the contradiction point cloud, and [0125] The map creation unit 142 compares the completed grid map with the grid corresponding to the position of the contradiction point group. When the grid corresponding to the movement candidate plane is continuously determined to be a temporary obstacle, the map creation unit 142 changes the classification of the planar area corresponding to the grid to an obstacle plane.)
As per Claim 5 and 15,
Hiromatsu discloses,
match, based on cropping the grid map to a designated size, the plurality of segmented areas with the grid map (see at least [0050] FIG. 5 is a schematic diagram showing an example of a grid division method. In FIG. 5, the three-dimensional global point cloud data 31 is divided into square grids with a side length of L. In FIG. 5, coordinate information is assigned to each grid in order along each axis direction. Specifically, as coordinate information, (k, l) is assigned to the grid located at the bottom left, (k+1, l) and (k+2, l) are assigned to the grids adjacent to the grid located at the bottom left in the right direction in order, and (k, l+1) and (k, l+2) are assigned to the grids adjacent to the grid located at the bottom left in the depth direction in order, [0054] the map creation unit 142 creates a histogram from the number of points included in each rectangular parallelepiped obtained by dividing the target grid by a certain width in the height direction. Next, the map creation unit 142 determines each bin in the created histogram in which the number of bins is equal to or greater than a threshold as a plane candidate, and calculates the average value and standard deviation value of the height of the three-dimensional point cloud included in each bin determined to be a plane candidate. Thereafter, the map creation unit 142 determines, among the plane candidates, a plane candidate whose corresponding standard deviation value of height is equal to or less than a threshold as a plane, and transitions to step S370, and [0054] in the obstacle extraction process described later, when a bin above a bin from which a plane is extracted contains three-dimensional point cloud data, the bin above the bin is determined to be an obstacle)
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hiromatsu in view Lee as applied to Claim 3 and 13 and further in view of Ali Ebrahimi Afrouzi et. al. US11435192B1(“Afrouzi”)
As per Claim 4 and 14
Hiromatsu does not disclose,
determine, based on transforming a first coordinate system centered around the robot into a second coordinate system representing the grid map, the at least one of the static object, the dynamic object, the mixed object, or the partial dynamic object (see at least
Afrouzi teaches,
determine, based on transforming a first coordinate system centered around the robot into a second coordinate system representing the grid map, the at least one of the static object, the dynamic object, the mixed object, or the partial dynamic object (see at least [0399] the processor may add newly discovered obstacles (e.g., static and dynamic obstacles) and/or cliffs to the map when unexpectedly (or expectedly) encountered during coverage, [0423] local sensing may be superimposed over the global map and may create a dynamic and constantly evolving map. In some embodiments, the processor updates the global map as the global sensors provide additional information throughout operation. For example, FIG. 113E illustrates that data sensed by global sensors are integrated into the global map 11307. As the robot approaches obstacles, they may fall within the range of range sensor and the processor may gradually add the obstacles to the map, [0431] the processor may mark the locations of obstacles (e.g., static and dynamic) encountered in the map. and [0620] FIG. 126 illustrates an example of the subsystems of the robot described herein, wherein global and local mapping may be used in localization of the robot and vice versa, global and local mapping may be used in map filling, map filling may be used in determining cell properties of the map, cell properties may be used in establishing zones, creating subzones, and evaluating traversability, and subzones and traversability may be used for polymorphic path planning.)
Thus, Hiromatsu teaches creating a rectangular “grid” based on a “designated length” in the horizontal plane, and further creating “bins” within the rectangular parallelepiped based on a “designated height” of the bin and Afrouzi teaches map transformation and determining fixed objects (fixed obstacles) and moving objects (moving obstacles).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Hiromatsu with detection using map transformation of fixed or moving obstructions as taught by Afrouzi, with a reasonable expectation of success, to devise intelligent path plans and task plans for efficient navigation and task completion (0004).
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hiromatsu in view of Afrouzi
As per Claim 6 and 16
Hiromatsu does not disclose,
wherein the processor is further configured to:
generate, based on the data points and while the robot is operating, a local map.
Afrouzi teaches,
wherein the processor is further configured to:
generate, based on the data points and while the robot is operating, a local map (see at least [0158] the robot may generate a global and local map of the environment using data collected by sensors of the robot)
Thus, Hiromatsu teaches creating a rectangular “grid” based on a “designated length” in the horizontal plane, and further creating “bins” within the rectangular parallelepiped based on a “designated height” of the bin and Afrouzi teaches map transformation and determining fixed objects (fixed obstacles) and moving objects (moving obstacles).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Hiromatsu with detection using map transformation of fixed or moving obstructions as taught by Afrouzi, with a reasonable expectation of success, to devise intelligent path plans and task plans for efficient navigation and task completion (0004).
As per Claim 7 and 17
Hiromatsu does not disclose,
wherein the processor is further configured to:
update the local map by performing, based on a movement of the robot, a delta transform on the local map.
Afrouzi teaches,
wherein the processor is further configured to:
update the local map by performing, based on a movement of the robot, a delta transform on the local map (see at least [0261] the data collected by one type of sensor may be used in generating or updating a local map while data from the other type of sensor may be used for generating or updating a global map, [0158] the processor may adjust previous data to account for a measured movement of the robot as it moves from observing one field of view to the next (e.g., differing from one another due to a difference in sensor pose), and [0158] the processor may stitch the new data with the previous data at overlapping points to generate or update the map)
Thus, Hiromatsu teaches creating a rectangular “grid” based on a “designated length” in the horizontal plane, and further creating “bins” within the rectangular parallelepiped based on a “designated height” of the bin and Afrouzi teaches map transformation and determining fixed objects (fixed obstacles) and moving objects (moving obstacles).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Hiromatsu with detection using map transformation of fixed or moving obstructions as taught by Afrouzi, with a reasonable expectation of success, to devise intelligent path plans and task plans for efficient navigation and task completion (0004).
As per Claim 8 and 18
Hiromatsu discloses,
wherein the processor is further configured to:
exclude, based on a speed of an external object being greater than or equal to a designated speed or a type of the external object being identified as a designated type, the external object from the local map (see at least [0008] a map creation unit creates a planar map showing planar areas that are not classified as obstacle planes, [0125] When the grid corresponding to the movement candidate plane is continuously determined to be a temporary obstacle, the map creation unit 142 changes the classification of the planar area corresponding to the grid to an obstacle plane. When the grid corresponding to the obstacle plane is continuously determined to be a temporary plane, the map creation unit 142 changes the classification of the planar area corresponding to the grid to a movement candidate plane.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicants should take note of the prior art in the PTO-892.
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/A.P./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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