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 the Claims
This office action is in response to the amendment filed 12/29/2025. Claims 2-4 have been cancelled. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-11, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190370691 A1 hereinafter Chae in view of JP 2020004342 A hereinafter Fujii.
Regarding claim 1, Chae teaches a robotic cleaner, comprising: (A robot cleaner is an artificial intelligence (AI) device or an AI robot which automatically cleans by voluntarily driving in an area to clean without any manipulation of a user and simultaneously sucking foreign materials such as dust and the like from a floor surface. Paragraph [0003])
a traveling unit for moving a body in a traveling region; (The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air. Paragraph [0044])
a distance measuring sensor for acquiring distance sensing information about a distance to an object outside the body; and (The depth sensor may sense that light irradiated from a light emitting unit (not shown) is reflected from an object and is returned. The depth sensor may measure a distance from the object, based on a difference of a time to sense the returned light, an amount of the returned light and the like. Paragraph [0155])
a control unit (The processor 180 may determine at least one executable operation of the AI apparatus 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor 180 may control the components of the AI apparatus 100 to execute the determined operation. Paragraph [0070]) which generates a grid map about the traveling region from the distance sensing information, performs, when dividing the traveling region into a plurality of sub-areas, (At this time, the processor 180 may map the acquired sensor data on an around map as a map around the AI robot 100. The around map is a map showing an around space within a predetermined radius from the AI robot 100, and, for example, may be a map for an around space within a 30 cm radius from the AI robot 100. Paragraphs [0204-0205] The sensor data is acquired continuously, and the sensor data acquired at a time may include sensor values corresponding to specific viewpoints, and the complex area determination model may determine the complex area in the specific viewpoints by using the sensor data. For example, if it is assumed that the sensor data acquired at some time includes sensor values corresponding to a first area, a second area, a third area and a fourth area, the complex area determination model may output whether each of a first area, a second area, a third area and a fourth area is the complex area. Herein, the first to fourth areas are only one example, and the sizes of each area may have a predetermined size (e.g., 5 cm×5 cm), and may be a pixel or voxel unit. Paragraphs [0220-0221])
wherein the distance measuring sensor comprises a LiDAR sensor that irradiates light to an object outside the body and calculates the distance sensing information by reflected light, (Examples of the sensors included in the sensing unit 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar. Paragraph [0066] The depth sensor may sense that light irradiated from a light emitting unit (not shown) is reflected from an object and is returned. The depth sensor may measure a distance from the object, based on a difference of a time to sense the returned light, an amount of the returned light and the like. Paragraph [0155])
wherein the sub-area is divided into a surface area when the robotic cleaner travels the travel region for a predetermined time or by a predetermined distance, and (The sensor data is acquired continuously, and the sensor data acquired at a time may include sensor values corresponding to specific viewpoints, and the complex area determination model may determine the complex area in the specific viewpoints by using the sensor data. For example, if it is assumed that the sensor data acquired at some time includes sensor values corresponding to a first area, a second area, a third area and a fourth area, the complex area determination model may output whether each of a first area, a second area, a third area and a fourth area is the complex area. Herein, the first to fourth areas are only one example, and the sizes of each area may have a predetermined size (e.g., 5 cm×5 cm), and may be a pixel or voxel unit. Paragraphs [0220-0221])
Chae does not teach ray casting on a plurality of traveling nodes on a path of the grid map with respect to each sub- area to search for an open space, and sets an open node for the open space to calculate a topology graph between the traveling nodes and the open node, and
wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces.
However, Fujii teaches ray casting on a plurality of traveling nodes on a path of the grid map with respect to each sub- area to search for an open space, and sets an open node for the open space to calculate a topology graph between the traveling nodes and the open node, and (In step 108, via step 105 or step 107, the dynamic node unit 65 generates a dynamic node Nd. In step 109, the dynamic edge unit 66 generates a first dynamic edge Ed1, a second dynamic edge Ed2, a third dynamic edge Ed3, and a fourth dynamic edge Ed4. Thereafter, the process proceeds to step 201. In step 201, while the moving unit 20 is moving on the fourth dynamic edge Ed4, the environment recognition unit 30 determines whether or not the third obstacle 13 on the set edge Es has been detected. When the third obstacle 13 on the set edge Es is detected, the process proceeds to step 203 . When the third obstacle 13 on the set edge Es is not detected, the process proceeds to step 202. Paragraphs [0069-0070])
wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces. (The dynamic node unit 65 generates dynamic nodes Nd, which are a plurality of points set randomly on the map M. In the figure, the dynamic nodes Nd are indicated by black circles. If the dynamic node Nd is generated on an obstacle or the like on the map M, it is rejected. The generated dynamic node Nd is output to the dynamic edge unit 66. Paragraph [0045] Examiner notes that the control unit controls a plurality of traveling nodes in the map. An open node is then set for each open space.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein the control unit utilizes ray casting on a plurality of traveling nodes on a path of the grid map with respect to each sub- area to search for an open space, and sets an open node for the open space to calculate a topology graph between the traveling nodes and the open node and wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to perform ray casting on a plurality of traveling nodes of the robotic cleaner which would improve path planning and accuracy in unknown environments along with facilitating open spaces as suggested by Fujii in Paragraphs [0045 and 0069-0070])
Regarding claim 5, the combination of Chae and Fujii teaches the robotic cleaner according to claim 1. Chae does not teach wherein the control unit sets the open node in a central area of a width of the open space.
However, Fujii teaches wherein the control unit sets the open node in a central area of a width of the open space. (The set node corrector 63 corrects the set node Ns based on the first margin distance Lo1, the second margin distance Lo2, the moving body speed Vm, the moving body angle θm, and the moving portion width Wm. The set node correcting unit 63 may automatically correct each set node Ns. The set node corrector 63 may also manually correct each set node Ns. The corrected set node Ns is set as a corrected node Nc. The correction node Nc includes the same information as the setting node Ns. Paragraph [0040])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein the control unit sets the open node in a central area of a width of the open space of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to set the open node in the central area of an open space which would improve identification of traversable regions as suggested by Fujii in paragraph [0040])
Regarding claim 6, the combination of Chae and Fujii teaches the robotic cleaner according to claim 5. Chae does not teach wherein, when the open space is formed between two obstacles that are spaced apart without a step, the control unit sets the open node in the central area of a separation distance between the two obstacles.
However, Fujii teaches wherein, when the open space is formed between two obstacles that are spaced apart without a step, (The obstacle distance calculation unit 60 is capable of calculating an obstacle distance Lf, which is the distance from the first obstacle 11 to the second obstacle 12 on the map M.In addition, the obstacle distance calculation unit 60 can calculate the area distance Lb, which
is the distance from the first approachable area B1 to the second approachable area B2, based on the first margin distance Lo1, the second margin distance Lo2, and the obstacle distance Lf. The calculated obstacle-to-obstacle distance Lf and area-to-area distance Lb are output to the route planning unit 67. Paragraph [0035]) the control unit sets the open node in the central area of a separation distance between the two obstacles. (The setting node unit 61 can set a plurality of setting nodes Ns, which are candidate points on the map M through which the moving unit 20 passes. Paragraph [0036] Examiner notes that Fujii discloses calculating margin distances from each obstacle and a total separation distance. The system then uses those distances to determine Node positions which are derived from mid point calculations between the two obstacles.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein, when the open space is formed between two obstacles that are spaced apart without a step, the control unit sets the open node in the central area of a separation distance between the two obstacles of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to identify the center point between two obstacles which would improve path planning and accuracy in unknown environments as suggested by Fujii in paragraphs [0035-0036])
Regarding claim 7, the combination of Chae and Fujii teaches the robotic cleaner according to claim 5. Chae does not teach wherein, when the open space is formed between two obstacles spaced apart by a step, the control unit sets the open node at an intersection of a center line between the two obstacles and a vertical line of the traveling node.
However, Fujii teaches wherein, when the open space is formed between two obstacles spaced apart by a step, the control unit sets the open node at an intersection of a center line between the two obstacles and a vertical line of the traveling node. (As shown in FIG. 6, the set node corrector 63 adjusts the first approachable area B1 so that the first margin distance Lo1 decreases, for example. Furthermore, the set node corrector 63 adjusts the second approachable area B2 so that the second margin distance Lo2 decreases. At this time, the setting node correction unit 63 corrects the setting node Ns based on the adjusted first approachable area B1, second approachable area B2 and moving unit width Wm so that the moving unit 20 does not come into contact with the first obstacle 11 or the second obstacle 12. In FIG. 6, the first approachable area B1 and the second approachable area B2 before adjustment are indicated by two-dot chain lines. The set node Ns is depicted by a white circle with a dashed line. The set edge Es is depicted by a dashed line. The correction nodes Nc are indicated by solid white circles. As shown in Figure 7, the setting node correction unit 63 may correct the setting node Ns so that the setting node Ns is located on a center line Ob located in the center between the adjusted first accessible area B1 and the second accessible area B2. Paragraphs [0041-0042] Examiner notes that Fujii teaches correcting and positioning nodes on a center line between two obstacles ensuring alignment with the travel path.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein, when the open space is formed between two obstacles spaced apart by a step, the control unit sets the open node at an intersection of a center line between the two obstacles and a vertical line of the traveling node of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to position nodes along a center line between two obstacles which would improve path planning and accuracy in unknown environments as suggested by Fujii in Paragraphs [0041-0042])
Regarding claim 8, Chae and Fujii teach the robotic cleaner according to claim 5. Chae does not teach wherein, when the topology graph for the sub-area is generated, the control unit moves to one of the open nodes of a last traveling node, changes the moved open node to a closed node, and then moves to the remaining open nodes in the topology graph and partitions another sub-area.
However, Fujii teaches wherein when the topology graph for the sub-area is generated, (The setting node unit 61 can set a plurality of setting nodes Ns, which are candidate points on the map M through which the moving unit 20 passes. Paragraph [0036], The set edge Es is output to the set edge corrector 64 and the path planner 67. Paragraph [0039] Examiner notes that this is generating a node and edge structure which corresponds to a topology graph of the sub area) the control unit moves to one of the open nodes of a last traveling node, changes the moved open node to a closed node, (At this time, the setting node correction unit 63 corrects the setting node Ns based on the adjusted first approachable area B1, second approachable area B2 Paragraph [0041]) and then moves to the remaining open nodes in the topology graph and partitions another sub-area. (As shown in Figure 7, the setting node correction unit 63 may correct the setting node Ns so that the setting node Ns is located on a center line Ob located in the center between the adjusted first accessible area B1 and the second accessible area B2. Similarly, in FIG. 7, the setting nodes Ns are depicted as white circles with dashed lines. The set edge Es is depicted by a dashed line. The correction nodes Nc are indicated by solid white circles. Paragraph [0042] Examiner notes that Fujii discloses that nodes are generated, used for travel, corrected after use (moving from open to close), and traversal continues through the remaining nodes to divide the map into sub areas)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include when the topology graph for the sub-area is generated, the control unit moves to one of the open nodes of a last traveling node, changes the moved open node to a closed node, and then moves to the remaining open nodes in the topology graph and partitions another sub-area of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to ensure proper cleaning by closing nodes that have been visited as suggested by Fujii in Paragraphs [0041-0042])
Regarding claim 9, the combination of Chae and Fujii teach the robotic cleaner according to claim 8. Chae does not teach wherein the control unit moves to the open node with the largest width among a plurality of open nodes of the last traveling node.
However, Fujii teaches wherein the control unit moves to the open node with the largest width among a plurality of open nodes of the last traveling node. (The dynamic node unit 65 generates dynamic nodes Nd, which are a plurality of points Paragraph [0045] Furthermore, the dynamic edge unit 66 generates the first dynamic edge Ed1, the second dynamic edge Ed2 and the third dynamic edge Ed3, respectively, so that the first dynamic distance Ld1, the second dynamic distance Ld2, the third dynamic distance Ld3 and the fourth dynamic distance Ld4 are less than the distance threshold Ld_th. The distance threshold Ld_th is set arbitrarily and is set through experiments or simulations. Furthermore, the distance threshold Ld_th may be set to a different value for each of the first dynamic distance Ld1, the second dynamic distance Ld2, the third dynamic distance Ld3, and the fourth dynamic distance Ld4. Paragraph [0049] Examiner notest that Fujii teaches generating multiple candidate nodes and compares the width/distances (Ld1-Ld4) between them. The system then picks the node that corresponds with the largest width based on the distance threshold requirement)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein the control unit moves to the open node with the largest width among a plurality of open nodes of the last traveling node of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to select open nodes based on distance thresholds and widths which would improve control by enabling more accurate node selection as suggested by Fujii in Paragraphs [0045] and [0049])
Regarding claim 10, the combination of Chae and Fujii teaches the robotic cleaner according to claim 8. Chae does not teach wherein the control unit calculates that the robotic cleaner is able to travel by the ray casting from the traveling node and that a space in which the robotic cleaner has not previously travelled is the open space.
However, Fujii teaches wherein the control unit calculates that the robotic cleaner is able to travel by the ray casting from the traveling node (The dynamic edge unit 66 generates a first dynamic edge Ed1, a second dynamic edge Ed2, a third dynamic edge Ed3 and a fourth dynamic edge Ed4 based on the moving body position Pm, the setting node Ns, the correction node Nc, the dynamic node Nd and the moving body destination Gm. Paragraph [0047] Furthermore, the dynamic edge unit 66 generates the first dynamic edge Ed1, the second dynamic edge Ed2, the third dynamic edge Ed3, and the fourth dynamic edge Ed4 using, for example, RRT or PRM. Paragraph [0048] Examiner notes that an RRT or PRM method are ray-casting/path expansion algorithms) and that a space in which the robotic cleaner has not previously travelled is the open space. (Furthermore, the dynamic edge unit 66 generates the first dynamic edge Ed1, the second dynamic edge Ed2, the third dynamic edge Ed3, and the fourth dynamic edge Ed4 using, for example, RRT or PRM. RRT stands for Rapidly-exploring Random Tree. PRM is an abbreviation for Probabilistic Roadmap Method. The length of the first dynamic edge Ed1 is defined as a first dynamic distance Ld1. The length of the second dynamic edge Ed2 is defined as a second dynamic distance Ld2. The length of the third dynamic edge Ed3 is defined as a third dynamic distance Ld3. The length of the fourth dynamic edge Ed4 is defined as a fourth dynamic distance Ld4. Paragraph [0048] Examiner notes that Fujii explicitly discloses the use of RRT/PRM algorithms to explore from the traveling node which determines untraveled regions as an open space)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein the control unit calculates that the robotic cleaner is able to travel by the ray casting from the traveling node and that a space in which the robotic cleaner has not previously travelled is the open space of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to determine untraveled regions as open spaces through ray casting as suggested by Fujii in Paragraphs [0047-0048])
Regarding claim 11, the combination of Chae and Fujii teach the robotic cleaner according to claim 10. Chae additionally teaches wherein the control unit performs 360-degree ray casting around the traveling node. (The sensing unit 140 may include at least one of a depth sensor (not shown), an RGB sensor (not shown), a collision sensing sensor (not shown), and a precipice sensor (not shown), and may acquire image data around the AI apparatus 100 The depth sensor may sense that light irradiated from a light emitting unit (not shown) is reflected from an object and is returned. The depth sensor may measure a distance from the object, based on a difference of a time to sense the returned light, an amount of the returned light and the like. The depth sensor may acquire two-dimensional image information or three-dimensional image information around the AI robot 100, based on the distance between measured objects Paragraphs [0154-0156])
Regarding claim 17, the combination of Chae and Fujii teaches the robotic cleaner according to claim 1. Chae additionally teaches wherein the control unit processes the grid map as an image to calculate the final map. (The depth sensor may acquire two-dimensional image information or three-dimensional image information around the AI robot 100, based on the distance between measured objects. The RGB sensor may acquire color image information for an object or a user around the AI robot 100. The color image information may be an imaging image of the object. The RGB sensor camera may be referred to as an RGB camera. Paragraphs [0156-0157] At this time, the processor 180 may map the acquired sensor data on an around map as a map around the AI robot 100. Paragraph [0204])
Regarding claim 18, Chae teaches a method for controlling a robotic cleaner, the method comprising:
obtaining distance sensing information about a distance to an object outside a body while moving in an unknown traveling region by a LiDAR sensor; (Examples of the sensors included in the sensing unit 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar. Paragraph [0066] The depth sensor may sense that light irradiated from a light emitting unit (not shown) is reflected from an object and is returned. The depth sensor may measure a distance from the object, based on a difference of a time to sense the returned light, an amount of the returned light and the like. Paragraph [0155]))
generating a grid map for the traveling region from the distance sensing information; (At this time, the processor 180 may map the acquired sensor data on an around map as a map around the AI robot 100. The around map is a map showing an around space within a predetermined radius from the AI robot 100, and, for example, may be a map for an around space within a 30 cm radius from the AI robot 100. Paragraphs [0204-0205] The sensor data is acquired continuously, and the sensor data acquired at a time may include sensor values corresponding to specific viewpoints, and the complex area determination model may determine the complex area in the specific viewpoints by using the sensor data. For example, if it is assumed that the sensor data acquired at some time includes sensor values corresponding to a first area, a second area, a third area and a fourth area, the complex area determination model may output whether each of a first area, a second area, a third area and a fourth area is the complex area. Herein, the first to fourth areas are only one example, and the sizes of each area may have a predetermined size (e.g., 5 cm×5 cm), and may be a pixel or voxel unit. Paragraphs [0220-0221])
generating a final topology graph for the traveling region by connecting the topology graphs for the plurality of sub-areas, (The around map is a map showing an around space within a predetermined radius from the AI robot 100, and, for example, may be a map for an around space within a 30 cm radius from the AI robot 100. An SLAM (Simultaneous Localization And Mapping) map is a global map for an overall space in which the AI robot 100 operates, and the around map is a local map for the around space of the AI robot 100. In terms of these matters, the around map is different from the SLAM map. Further, the processor 180 may map the acquired sensor data on the SLAM map. Examiner notes that Chae teaches a hierarchical mapping approach where local maps are generated and then integrated into a global SLAM map (final topology graph))
wherein the sub-area is divided into a surface area when the robotic cleaner travels the travel region for a predetermined time or by a predetermined distance, and (The sensor data is acquired continuously, and the sensor data acquired at a time may include sensor values corresponding to specific viewpoints, and the complex area determination model may determine the complex area in the specific viewpoints by using the sensor data. For example, if it is assumed that the sensor data acquired at some time includes sensor values corresponding to a first area, a second area, a third area and a fourth area, the complex area determination model may output whether each of a first area, a second area, a third area and a fourth area is the complex area. Herein, the first to fourth areas are only one example, and the sizes of each area may have a predetermined size (e.g., 5 cm×5 cm), and may be a pixel or voxel unit. Paragraphs [0220-0221])
Chae does not teach dividing the traveling region into a plurality of sub-areas and performing ray casting on a plurality of traveling nodes on a path of the grid map with respect to each of the sub-areas to search for open space;
setting an open node for the open space to generate a topology graph between the traveling node and the open node for the sub-area; and
wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces.
However, Fujii teaches dividing the traveling region into a plurality of sub-areas and performing ray casting on a plurality of traveling nodes on a path of the grid map with respect to each of the sub-areas to search for open space; (In step 108, via step 105 or step 107, the dynamic node unit 65 generates a dynamic node Nd. In step 109, the dynamic edge unit 66 generates a first dynamic edge Ed1, a second dynamic edge Ed2, a third dynamic edge Ed3, and a fourth dynamic edge Ed4. Thereafter, the process proceeds to step 201. In step 201, while the moving unit 20 is moving on the fourth dynamic edge Ed4, the environment recognition unit 30 determines whether or not the third obstacle 13 on the set edge Es has been detected. When the third obstacle 13 on the set edge Es is detected, the process proceeds to step 203 . When the third obstacle 13 on the set edge Es is not detected, the process proceeds to step 202. Paragraphs [0069-0070]
setting an open node for the open space to generate a topology graph between the traveling node and the open node for the sub-area; and (In step 108, via step 105 or step 107, the dynamic node unit 65 generates a dynamic node Nd. In step 109, the dynamic edge unit 66 generates a first dynamic edge Ed1, a second dynamic edge Ed2, a third dynamic edge Ed3, and a fourth dynamic edge Ed4. Thereafter, the process proceeds to step 201. In step 201, while the moving unit 20 is moving on the fourth dynamic edge Ed4, the environment recognition unit 30 determines whether or not the third obstacle 13 on the set edge Es has been detected. When the third obstacle 13 on the set edge Es is detected, the process proceeds to step 203 . When the third obstacle 13 on the set edge Es is not detected, the process proceeds to step 202. Paragraphs [0069-0070])
wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces. (The dynamic node unit 65 generates dynamic nodes Nd, which are a plurality of points set randomly on the map M. In the figure, the dynamic nodes Nd are indicated by black circles. If the dynamic node Nd is generated on an obstacle or the like on the map M, it is rejected. The generated dynamic node Nd is output to the dynamic edge unit 66. Paragraph [0045] Examiner notes that the control unit controls a plurality of traveling nodes in the map. An open node is then set for each open space.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include wherein the control unit searches for an open space by executing the ray casting for each of the plurality of traveling nodes within the grid map with respect to each of the sub-areas, and sets the open node for each of the open spaces of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to perform ray casting on a plurality of traveling nodes of the robotic cleaner which would improve path planning and accuracy in unknown environments as suggested by Fujii in Paragraphs [0069-0070])
Regarding claim 19, the combination of Chae and Fujii teaches the method according to claim 18. Chae does not teach wherein in the generation of the topology graph:
when the open space is formed between two obstacles that are spaced apart without a step, the open node is set in a central area of a separation distance between the two obstacles; and
when the open space is formed between two obstacles spaced apart by a step, the open node is set at an intersection of a center line between the two obstacles and a vertical line of the traveling node.
However, Fujii teaches wherein in the generation of the topology graph:
when the open space is formed between two obstacles that are spaced apart without a step, (The obstacle distance calculation unit 60 is capable of calculating an obstacle distance Lf, which is the distance from the first obstacle 11 to the second obstacle 12 on the map M.In addition, the obstacle distance calculation unit 60 can calculate the area distance Lb, which
is the distance from the first approachable area B1 to the second approachable area B2, based on the first margin distance Lo1, the second margin distance Lo2, and the obstacle distance Lf. The calculated obstacle-to-obstacle distance Lf and area-to-area distance Lb are output to the route planning unit 67. Paragraph [0035]) the control unit sets the open node in the central area of a separation distance between the two obstacles. (The setting node unit 61 can set a plurality of setting nodes Ns, which are candidate points on the map M through which the moving unit 20 passes. Paragraph [0036] Examiner notes that Fujii discloses calculating margin distances from each obstacle and a total separation distance. The system then uses those distances to determine Node positions which are derived from mid point calculations between the two obstacles.) ; and
when the open space is formed between two obstacles spaced apart by a step, the open node is set at an intersection of a center line between the two obstacles and a vertical line of the traveling node. (As shown in FIG. 6, the set node corrector 63 adjusts the first approachable area B1 so that the first margin distance Lo1 decreases, for example. Furthermore, the set node corrector 63 adjusts the second approachable area B2 so that the second margin distance Lo2 decreases. At this time, the setting node correction unit 63 corrects the setting node Ns based on the adjusted first approachable area B1, second approachable area B2 and moving unit width Wm so that the moving unit 20 does not come into contact with the first obstacle 11 or the second obstacle 12. In FIG. 6, the first approachable area B1 and the second approachable area B2 before adjustment are indicated by two-dot chain lines. The set node Ns is depicted by a white circle with a dashed line. The set edge Es is depicted by a dashed line. The correction nodes Nc are indicated by solid white circles. As shown in Figure 7, the setting node correction unit 63 may correct the setting node Ns so that the setting node Ns is located on a center line Ob located in the center between the adjusted first accessible area B1 and the second accessible area B2. Paragraphs [0041-0042] Examiner notes that Fujii teaches correcting and positioning nodes on a center line between two obstacles ensuring alignment with the travel path.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control unit of the robotic cleaner disclosed by Chae to include when the open space is formed between two obstacles that are spaced apart without a step, the open node is set in a central area of a separation distance between the two obstacles; and when the open space is formed between two obstacles spaced apart by a step, the open node is set at an intersection of a center line between the two obstacles and a vertical line of the traveling nod of Fujii. One of ordinary skill in the art would have been motivated to make this modification because it would enable the method to position nodes along a center line between two obstacles which would ensure alignment with the travel path suggested by Fujii in paragraphs [0041-0042] and [0069-0070])
Claims 12-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chae in view of Fujii and further in view of Mobile Robot Exploration in Indoor Environment Using Topological Structure with Invisible Barcode hereinafter Huh.
Regarding claim 12, the combination of Chae and Fujii teaches the robotic cleaner according to claim 5. Chae and Fujii do not teach wherein the control unit sets a first open node for one open space searched for a first traveling node, and sets a second open node for another open space searched for a second traveling node that is different from the first traveling node; and
when it is calculated that the first open node and the second open node overlap, one of the overlapping first and second open nodes is deleted.
However, Huh teaches wherein the control unit sets a first open node for one open space searched for a first traveling node, and sets a second open node for another open space searched for a second traveling node that is different from the first traveling node; and (According to properties of the node, the region of node is not overlapped with other node. When a robot finds new node, it examines whether new node is matched with previous nodes. If new node is matched previous node, it is not added to the topological map, and current edge is also merged into the same edge which is existed in previous node. Page 5269)
when it is calculated that the first open node and the second open node overlap, one of the overlapping first and second open nodes is deleted. (According to properties of the node, the region of node is not overlapped with other node. When a robot finds new node, it examines whether new node is matched with previous nodes. If new node is matched previous node, it is not added to the topological map, and current edge is also merged into the same edge which is existed in previous node. Page 5269)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robotic cleaner as disclosed by Chae to include not mapping where a first and second open node interlap of Huh. One of ordinary skill in the art would have been motivated to make this modification because it would enable the robotic cleaner to ensure that the same area is not travelled twice thus increasing the performance of the robotic cleaner as suggested by Huh on page 5269.
Regarding claim 13, the combination of Chae, Fujii, and Huh teach the robotic cleaner according to claim 12. Chae and Fujii do not teach wherein the control unit calculates whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more.
However, Huh teaches wherein the control unit calculates whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more. (According to properties of the node, the region of node is not overlapped with other node. When a robot finds new node, it examines whether new node is matched with previous nodes. If new node is matched previous node, it is not added to the topological map, and current edge is also merged into the same edge which is existed in previous node. Page 5269 The width W between static obstacle: 1.5R<W <L (R : Radius of Robot, L : Door width) Page 5268 Examiner notes that Huh teaches overlap determination by determining if the region of a node is not overlapped with another node. Huh further evaluates overlap using widths defined between static obstacles (W), the robot radius (R), and a fixed door width (L). This constitutes a predetermined range for evaluating when regions overlap.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robotic cleaner disclosed by Chae to include wherein the control unit calculates whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more of Huh. One of ordinary skill in the art would have been motivated to make this modification because it would enable the robotic cleaner to prevent duplicate nodes and redundant paths, thus ensuring the final map is accurate and efficient as suggested by Huh on page 5269.
Regarding claim 14, the combination of Chae, Fujii and Huh teach the robotic cleaner according to claim 13. Chae and Fuji do not teach wherein the control unit generates circles of the same diameter centered on the first open node and the second open node, and calculates that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more.
However, Huh teaches wherein the control unit generates circles of the same diameter centered on the first open node and the second open node, (Fig. 3 depicts nodes as circles with radius R, all circles using the same radius means they have the same diameter Page 5268) and calculates that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more. (Examiner notes Huh compares the circle radius R and against the widths (W, L). When the circles overlap, the nodes are treated as the same Page 5268)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robotic cleaner disclosed by Chae to include wherein the control unit generates circles of the same diameter centered on the first open node and the second open node, and calculates that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more of Huh. One of ordinary skill in the art would have been motivated to make this modification because defining node regions as circles of equal diameter would enable the robotic cleaner to avoid creating duplicate nodes, thereby improving mapping accuracy and navigation efficiency as suggested by Huh on page 5268.
Regarding claim 15, the combination of Chae, Fujii, and Huh teach the robotic cleaner according to claim 13. Chae and Fujii do not teach wherein, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located is left, and the other open node is deleted.
However, Huh teaches wherein, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located is left, and the other open node is deleted. (We define these edges as fusion edges, which should be eliminated. Fusion edges are one of a pair, and nodes linked to fusion edge are same Page 5268 Fig. 5 Eliminate this edge step”. Examiner notes that Huh teaches that duplicate / fusion edges are removed so that only one node/edge remains in the topological structure)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robotic cleaner disclosed by Chae to include wherein, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located is left, and the other open node is deleted of Huh. One of ordinary skill in the art would have been motivated to make this modification because it would enable the system to delete nodes that overlap in a central area as suggested by Huh on page 5268.
Regarding claim 16, the combination of Chae, Fujii and Huh teach the robotic cleaner according to claim 13. Chae additionally teaches wherein the control unit calculates a final topology graph for the traveling region by connecting the topology graphs for the plurality of sub areas. (The around map is a map showing an around space within a predetermined radius from the AI robot 100, and, for example, may be a map for an around space within a 30 cm radius from the AI robot 100. An SLAM (Simultaneous Localization And Mapping) map is a global map for an overall space in which the AI robot 100 operates, and the around map is a local map for the around space of the AI robot 100. In terms of these matters, the around map is different from the SLAM map. Further, the processor 180 may map the acquired sensor data on the SLAM map. Examiner notes that Chae teaches a hierarchical mapping approach where local maps are generated and then integrated into a global SLAM map (final topology graph))
Regarding claim 20. The combination of Chae and Fujii teaches the method according to claim 19. Chae and Fujii do not teach wherein the generation of the topology graph comprises:
setting, by a first open node, one open space searched for a first traveling node, and setting, by a second open node, another open space searched for a second traveling node that is different from the first traveling node;
calculating whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more;
generating circles of the same diameter centered on the first open node and the second open node, and calculating that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more; and
leaving, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located and deleting the other open node.
However, Huh teaches setting, by a first open node, one open space searched for a first traveling node, and setting, by a second open node, another open space searched for a second traveling node that is different from the first traveling node; (According to properties of the node, the region of node is not overlapped with other node. When a robot finds new node, it examines whether new node is matched with previous nodes. If new node is matched previous node, it is not added to the topological map, and current edge is also merged into the same edge which is existed in previous node. Page 5269)
calculating whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more; (According to properties of the node, the region of node is not overlapped with other node. When a robot finds new node, it examines whether new node is matched with previous nodes. If new node is matched previous node, it is not added to the topological map, and current edge is also merged into the same edge which is existed in previous node. Page 5269 The width W between static obstacle: 1.5R<W <L (R : Radius of Robot, L : Door width) Page 5268 Examiner notes that Huh teaches overlap determination by determining if the region of a node is not overlapped with another node. Huh further evaluates overlap using widths defined between static obstacles (W), the robot radius (R), and a fixed door width (L). This constitutes a predetermined range for evaluating when regions overlap.)
generating circles of the same diameter centered on the first open node and the second open node, and calculating that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more; and (Fig. 3 depicts nodes as circles with radius R, all circles using the same radius means they have the same diameter Page 5268 Examiner notes Huh compares the circle radius R and against the widths (W, L). When the circles overlap, the nodes are treated as the same Page 5268)
leaving, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located and deleting the other open node. (We define these edges as fusion edges, which should be eliminated. Fusion edges are one of a pair, and nodes linked to fusion edge are same Page 5268 Fig. 5 Eliminate this edge step”. Examiner notes that Huh teaches that duplicate / fusion edges are removed so that only one node/edge remains in the topological structure)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robotic cleaning method disclosed by Choi to include wherein the generation of the topology graph comprises: setting, by a first open node, one open space searched for a first traveling node, and setting, by a second open node, another open space searched for a second traveling node that is different from the first traveling node; calculating whether the first open node and the second open node overlap when widths of the one open space and the another open space overlap by a predetermined range or more; generating circles of the same diameter centered on the first open node and the second open node, and calculating that the first open node and the second open node are related to the same open node when each circle overlaps by a predetermined range or more; and leaving, among the first open node and the second open node, one open node adjacent to the central area of the open area where the first and second open nodes are located and deleting the other open node of Huh. One of ordinary skill in the art would have been motivated to make this modification because implementing overlap determination into the robotic cleaner method would increase mapping accuracy by ensuring avoidance of redundant nodes as suggested by Huh on page 5268.
Response to Arguments
Applicants’ arguments filed 12/29/2025 have been fully considered.
Applicants’’ amendments overcome the 112(f) interpretation.
Applicants’ amendments overcome the 101 rejection.
Applicant’s argument with regards to the 103 rejection beginning on page 5 of the remarks document and spanning to page 10 of the remarks document filed 12/29/2025 have been fully considered but are not persuasive.
Applicant specifically has amended independent claims 1 and 18 to incorporate limitations previously recited in the dependent claims, including limitation directed to a LiDAR based distance measuring sensor, dividing a traveling region into sub areas based on time or distance and performing ray casting for a plurality of traveling nodes within in each sub area to identify open spaces and set corresponding open nodes.
With respect to the added limitations, the prior O.A relied on Chae for teaching a robotic cleaner including a distance measuring sensor and generation of map data representing the surrounding environment. Chae further teaches representing the environment as discrete spatial regions (e.g., pixels or voxels), which reasonably corresponds to dividing a traveling region into sub areas.
Fujii on the other hand was relied upon for teaching node based exploration and path planning using dynamically generated nodes and edges. Fujii describes expanding from nodes into surrounding space to evaluate traversable regions and construct navigation paths.
Applicant asserts that the cited references do not teach dividing a grid map into sub areas and performing ray casting for each traveling node within each sub area. In this regard, while Fujii does not explicitly use the term ray casting. The disclosed exploration from nodes into surrounding space to determine traversable regions constitutes an equivalent spatial exploration technique that identifies open space from a given node. Therefore, the above noted limitation has been met.
Further, applying Fujii’s node based exploration within the subdivided spatial regions described by Chae would have represented a predictable use of known techniques to improve localized navigation and mapping resolution. Performing exploration on a per region basis allows for more efficient evaluation of traversable space and improved obstacle avoidance.
With respect to the recitation of setting an open node for each identified open space, Fujii’s generation of multiple candidate nodes corresponding to traversable regions reasonable corresponds to associating nodes with identified open spaces within the environment.
Based on the above analysis, the applied references collectively teach the amended limitation according to claims 1 and 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20170131721 A1 discloses a robotic cleaner and a method for controlling the robot which utilizes sensors, grid maps, and nodes.
THIS ACTION IS MADE FINAL. 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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/J.J.P./ Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/23/26