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 action is reply to the Application Number 17/961,537 filed on 11/19/2024
Claims 1 – 10 are currently pending and have been examined
This action is made NON-FINAL
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55
Information Disclosure Statement
The information disclosure statements filed 11/19/2024 have been received and considered.
Claim Objections
Claim 3 is objected to because of the following informalities: lines 5 – 6 state: “recognizing a current congestion level based on information on the number of orders and the number of people received from at least one of the serving robot, a user terminal, and an external server, and the current time”, however it is grammatically incorrect to state “and an external server, and the current time”. For example, it should state “, an external server, and the current time”, removing the first “and”. Appropriate correction is required.
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.
Claim(s) 1 – 2, 5 and 8 – 10 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (US 20230244238 A1), further in view of Knutson et al. (US 20250268443 A1).
Regarding claim 1, Jeong teaches a method of obstacle avoidance control of a serving robot, which is performed by a computing device including at least one processor, the method comprising: (Jeong: Paragraph 0047: “FIG. 1 illustrates a conceptual diagram of a multifunctional autonomous serving robot according to the present invention;”; Paragraph 0154: “In addition, the robot control means (130) further includes a robot driving control unit (136) that controls the driving of the serving robot by operating the power source control unit (133) based on the information loaded, generated, and set from the module deciphering unit (132), the driving space creation unit (134), and the path setting unit (135).”)
generating optimal paths for each of a plurality of tables included in a space in which a serving robot performs serving (Jeong: Paragraphs 0138 – 0139: “The path setting unit (135) includes: a driving path execution module (135a) for performing path planning and path following of the shortest distance according to an input of a target coordinate, to which a probability circle-based spatial search (PCSS) algorithm is applied”: Paragraph 0008: “the present invention intends to provide a serving robot that accurately delivers to a customer table, and to provide a multifunctional indoor autonomous technology-applied serving robot that can be easily converted into a robot capable of performing other functions in addition to the serving function and can work 24 hours a day.”; Paragraph 0141: “a local/global path return setting module (135c) for setting a return of a local path and a global path by creating a local cost-map for obstacle recognition and avoidance by the surrounding environment information acquisition sensing means (140) while the serving robot is driving; and”)
based on map information on the space; (Jeong: Paragraph 0136: “a final driving space map creation module (134d) for creating a driving space map of the serving robot by integrating a result of RTAB-MAP and results of the SLAM execution module (134a), the creation map correction module (134b), and the creation map 2D conversion module (134c) into one 2D map, so that the driving space map of the serving robot can be created”)
when serving for a specific table is set, transmitting a specific optimal path for moving to a target point corresponding to the specific table to the serving robot and controlling the serving robot to move to the target point through the specific optimal path; and (Jeong: Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”)
when the serving robot recognizes an obstacle while moving to the target point and (Jeong: Paragraph 0147: “Accordingly, the present invention performs the detection and tracking of the obstacle using the RGB-D sensor in the obstacle recognition and avoidance method, so as to recognize the location of the 1st-risk cause by contact with a person or other moving component, and manage and detect the 2nd-risk cause of the autonomous driving error in advance.”)
In sum, Jeong teaches a method of obstacle avoidance control of a serving robot, which is performed by a computing device including at least one processor, the method comprising: generating optimal paths for each of a plurality of tables included in a space in which a serving robot performs serving based on map information on the space; when serving for a specific table is set, transmitting a specific optimal path for moving to a target point corresponding to the specific table to the serving robot and controlling the serving robot to move to the target point through the specific optimal path; and when the serving robot recognizes an obstacle while moving to the target point. Jeong however does not teach where the robot stops moving, controlling the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through an avoidance path whereas Knutson does.
Knutson teaches stops moving, controlling the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through an avoidance path. (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. One of ordinary skill in the art would find implementing the waiting method of Knutson with the server robot system of Jeong as combining prior art elements according to known methods to yield predictable results pertaining to server robots better handling obstacles along its optimal route. Jeong teaches a server robot able to autonomously travel to serve different tables with its own method of predicting an obstacle’s movement in path of the robot and able to course-correct its route. The ability of Knutson adds an additional step of waiting a predetermined time for the obstacle to move from its path and once the time is over, to travel around the obstacle. The ability to wait for the obstacle as taught by Knutson can correspond to a the serving robot of Jeong waiting for a customer or another waiter to move out of the way. If the obstacle has not cleared within a predetermined time, the serving robot will now be able to move around the obstacle to travel to their target location. Both methods of Jeong and Knutson identify an obstacle and have their own methods of evaluating the obstacles movement to determine how to adjust their own path, thus both methods yield predictable results.
Regarding claim 2, Jeong, as modified, teaches wherein the generating of the optimal paths for each of the plurality of tables included in the space in which the serving robot performs serving (Jeong: Paragraphs 0138 – 0139: “The path setting unit (135) includes: a driving path execution module (135a) for performing path planning and path following of the shortest distance according to an input of a target coordinate, to which a probability circle-based spatial search (PCSS) algorithm is applied”: Paragraph 0008: “the present invention intends to provide a serving robot that accurately delivers to a customer table, and to provide a multifunctional indoor autonomous technology-applied serving robot that can be easily converted into a robot capable of performing other functions in addition to the serving function and can work 24 hours a day.”; Paragraph 0141: “a local/global path return setting module (135c) for setting a return of a local path and a global path by creating a local cost-map for obstacle recognition and avoidance by the surrounding environment information acquisition sensing means (140) while the serving robot is driving; and”)
based on the map information on the space (Jeong: Paragraph 0136: “a final driving space map creation module (134d) for creating a driving space map of the serving robot by integrating a result of RTAB-MAP and results of the SLAM execution module (134a), the creation map correction module (134b), and the creation map 2D conversion module (134c) into one 2D map, so that the driving space map of the serving robot can be created”)
includes generating one or more optimal paths corresponding to congestion levels for each of the plurality of tables according to one or more congestion levels, and the one or more congestion levels are determined based on at least one of a current time, the number of orders placed in the space, and the number of people who have entered the space. (Jeong: Paragraphs 0145 – 0147: “The local path refers to the generation of a local route such as obstacle avoidance by using information detected while the serving robot is moving. The global path is necessary when the information on all areas of the driving environment is provided and the local path is necessary to ensure the safety of people, assets, and the environment from the serving robot serving close to people. Accordingly, the present invention performs the detection and tracking of the obstacle using the RGB-D sensor in the obstacle recognition and avoidance method, so as to recognize the location of the 1st-risk cause by contact with a person or other moving component, and manage and detect the 2nd-risk cause of the autonomous driving error in advance.”; Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”,
Supplemental Note: based on the number of obstacles, the system is able to optimize a path for the vehicle to travel. The number of obstacles is interpreted as the congestion level as the system is able to route around them)
Regarding claim 5, Jeong, as modified, teaches recognizing an area of a passage corresponding to a section on which the obstacle is recognized;
calculating a variable area by applying a weight corresponding to the congestion level to the area of the passage; and (Jeong: Paragraphs 0147 – 0151: “Accordingly, the present invention performs the detection and tracking of the obstacle using the RGB-D sensor in the obstacle recognition and avoidance method, so as to recognize the location of the 1st-risk cause by contact with a person or other moving component, and manage and detect the 2nd-risk cause of the autonomous driving error in advance. In addition, by applying the movement trend calculation of the tracked obstacle movement trend calculation and the probability circle-based spatial search (PCSS) algorithm, the movement path of the obstacle after the current point can be predicted. At this time, the prediction path of the obstacle is used to predict the possibility of collision with the serving robot. Also, in the movement path of obstacles, it is possible to minimize the meaningless driving of the serving robot and the threat of pedestrians through the creation of a local path considering the mobility of the obstacle and a Kanayama control by calculating the caution cost function through a probabilistic modeling. Due to this, it is possible to detect and track obstacles quickly and accurately using only the RGB-D sensor information.”)
In sum, Jeong teaches recognizing an area of a passage corresponding to a section on which the obstacle is recognized; calculating a variable area by applying a weight corresponding to the congestion level to the area of the passage. Jeong however does not teach wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: controlling the serving robot to wait and then move to the target point through the specific optimal path when the variable area is less than a preset size and move to the target point through the avoidance path when the variable area is greater than or equal to the preset size whereas Knutson does.
Knutson teaches wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
controlling the serving robot to wait and then move to the target point through the specific optimal path when the variable area is less than a preset size and move to the target point through the avoidance path when the variable area is greater than or equal to the preset size. (Knutson: Paragraph 0082: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”,
Supplemental Note: the machine can determine if the area has the obstacle or not and make corresponding travel adjustments. The buffer zone is the preset size which is used to evaluate if on obstacle in the way of the robot)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. As stated for claim 1, one of ordinary skill in the art would find implementing the waiting method of Knutson with the server robot system of Jeong as combining prior art elements according to known methods to yield predictable results pertaining to server robots better handling obstacles along its optimal route. Jeong teaches a server robot able to autonomously travel to serve different tables with its own method of predicting an obstacle’s movement in path of the robot and able to course-correct its route. The ability of Knutson adds an additional step of waiting a predetermined time for the obstacle to move from its path and once the time is over, to travel around the obstacle. The ability to wait for the obstacle as taught by Knutson can correspond to a the serving robot of Jeong waiting for a customer or another waiter to move out of the way. If the obstacle has not cleared within a predetermined time, the serving robot will now be able to move around the obstacle to travel to their target location. Both methods of Jeong and Knutson identify an obstacle and have their own methods of evaluating the obstacles movement to determine how to adjust their own path, thus both methods yield predictable results.
Regarding claim 8, Jeong, as modified, teaches when the obstacle is not another serving robot, recognizing whether movement is detected in the obstacle and generating an avoidance path between a current position of the serving robot and a target point corresponding to the specific table; (Jeong: Paragraphs 0148 – 0149: “In addition, by applying the movement trend calculation of the tracked obstacle movement trend calculation and the probability circle-based spatial search (PCSS) algorithm, the movement path of the obstacle after the current point can be predicted. At this time, the prediction path of the obstacle is used to predict the possibility of collision with the serving robot.”)
predicting a second movement time for the serving robot to move from the current position to the specific table through the avoidance path; and (Jeong: Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”)
In sum, Jeong teaches when the obstacle is not another serving robot, recognizing whether movement is detected in the obstacle and generating an avoidance path between a current position of the serving robot and a target point corresponding to the specific table; predicting a second movement time for the serving robot to move from the current position to the specific table through the avoidance path. Jeong however does not teach wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: when movement is detected in the obstacle, predicting a first movement time for the serving robot to wait for a preset time at the current position and then move to the target point through the specific optimal path; determining a movement path for controlling the serving robot based on the first movement time and the second movement time and controlling the serving robot to move to the target point through the determined movement path whereas Knutson does.
Knutson teaches wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
when movement is detected in the obstacle, predicting a first movement time for the serving robot to wait for a preset time at the current position and then move to the target point through the specific optimal path; (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
determining a movement path for controlling the serving robot based on the first movement time and the second movement time and controlling the serving robot to move to the target point through the determined movement path. (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”,
Supplemental Note: the first movement path is interpreted as the robot first getting to the obstacle, then after the delay period, the robot is able to determine whether the obstacle has moved or created another movement path of traveling around the obstacle)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. Please refer to the rejection of claim 5 as both claim the same functional language and therefore rejected under the same pretenses.
Regarding claim 9, Jeong, as modified, teaches an apparatus comprising:
a memory configured to store one or more instructions; and
a processor configured to execute the one or more instructions stored in the memory,
wherein the processor performs the method of claim 1 by executing the one or more instructions. (Jeong: Paragraph 0047: “FIG. 1 illustrates a conceptual diagram of a multifunctional autonomous serving robot according to the present invention;”; Paragraph 0154: “In addition, the robot control means (130) further includes a robot driving control unit (136) that controls the driving of the serving robot by operating the power source control unit (133) based on the information loaded, generated, and set from the module deciphering unit (132), the driving space creation unit (134), and the path setting unit (135).”; Paragraphs 0199 – 0202: “For reference, a robot operating system (ROS) software platform may be applied to the robot control means (130) of the multifunctional autonomous serving robot (1) of the present invention. The ROS is a meta operating system that provides libraries, various development, and debugging tools necessary for the development environment, such as hardware abstraction, device control, sensing and recognition, map creation, motion planning, process message passing, and package management required for robot application programs. The ROS is also convenient to use for development on a PC, since it runs on an OS such as Ubuntu. A typical SBC (single board computer) such as Raspberry Pi, ODROID, Intel Edison, BeagleBone, TX2, etc. required for ROS operation in the actual robot is used.”)
Regarding claim 10, Jeong teaches a computer program stored in a computer-readable recording medium on which a program (Jeong: Paragraph 0047: “FIG. 1 illustrates a conceptual diagram of a multifunctional autonomous serving robot according to the present invention;”; Paragraph 0154: “In addition, the robot control means (130) further includes a robot driving control unit (136) that controls the driving of the serving robot by operating the power source control unit (133) based on the information loaded, generated, and set from the module deciphering unit (132), the driving space creation unit (134), and the path setting unit (135).”; Paragraphs 0199 – 0202: “For reference, a robot operating system (ROS) software platform may be applied to the robot control means (130) of the multifunctional autonomous serving robot (1) of the present invention. The ROS is a meta operating system that provides libraries, various development, and debugging tools necessary for the development environment, such as hardware abstraction, device control, sensing and recognition, map creation, motion planning, process message passing, and package management required for robot application programs. The ROS is also convenient to use for development on a PC, since it runs on an OS such as Ubuntu. A typical SBC (single board computer) such as Raspberry Pi, ODROID, Intel Edison, BeagleBone, TX2, etc. required for ROS operation in the actual robot is used.”)
for executing a method of obstacle avoidance control of a serving robot with a computing device is recorded, wherein the method comprises: (Jeong: Paragraph 0047: “FIG. 1 illustrates a conceptual diagram of a multifunctional autonomous serving robot according to the present invention;”; Paragraph 0154: “In addition, the robot control means (130) further includes a robot driving control unit (136) that controls the driving of the serving robot by operating the power source control unit (133) based on the information loaded, generated, and set from the module deciphering unit (132), the driving space creation unit (134), and the path setting unit (135).”)
generating optimal paths for each of a plurality of tables included in a space in which a serving robot performs serving (Jeong: Paragraphs 0138 – 0139: “The path setting unit (135) includes: a driving path execution module (135a) for performing path planning and path following of the shortest distance according to an input of a target coordinate, to which a probability circle-based spatial search (PCSS) algorithm is applied”: Paragraph 0008: “the present invention intends to provide a serving robot that accurately delivers to a customer table, and to provide a multifunctional indoor autonomous technology-applied serving robot that can be easily converted into a robot capable of performing other functions in addition to the serving function and can work 24 hours a day.”; Paragraph 0141: “a local/global path return setting module (135c) for setting a return of a local path and a global path by creating a local cost-map for obstacle recognition and avoidance by the surrounding environment information acquisition sensing means (140) while the serving robot is driving; and”)
based on map information on the space; (Jeong: Paragraph 0136: “a final driving space map creation module (134d) for creating a driving space map of the serving robot by integrating a result of RTAB-MAP and results of the SLAM execution module (134a), the creation map correction module (134b), and the creation map 2D conversion module (134c) into one 2D map, so that the driving space map of the serving robot can be created”)
when serving for a specific table is set, transmitting a specific optimal path for moving to a target point corresponding to the specific table to the serving robot and controlling the serving robot to move to the target point through the specific optimal path; and (Jeong: Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”)
when the serving robot recognizes an obstacle while moving to the target point and (Jeong: Paragraph 0147: “Accordingly, the present invention performs the detection and tracking of the obstacle using the RGB-D sensor in the obstacle recognition and avoidance method, so as to recognize the location of the 1st-risk cause by contact with a person or other moving component, and manage and detect the 2nd-risk cause of the autonomous driving error in advance.”)
In sum, Jeong teaches a computer program stored in a computer-readable recording medium on which a program for executing a method of obstacle avoidance control of a serving robot with a computing device is recorded, wherein the method comprises: generating optimal paths for each of a plurality of tables included in a space in which a serving robot performs serving based on map information on the space; when serving for a specific table is set, transmitting a specific optimal path for moving to a target point corresponding to the specific table to the serving robot and controlling the serving robot to move to the target point through the specific optimal path; and when the serving robot recognizes an obstacle while moving to the target point. Jeong however does not teach where the robot stops moving, controlling the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through an avoidance path whereas Knutson does.
Knutson teaches stops moving, controlling the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through an avoidance path. (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same functional language and therefore rejected under the same pretenses.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (US 20230244238 A1) and Knutson et al. (US 20250268443 A1) as applied to claim 1 above, and further in view of Shah et al. (US 20250121500 A1).
Regarding claim 3, Jeong, as modified, teaches wherein, when the serving for the specific table is set, the transmitting of the specific optimal path for moving to the target point corresponding to the specific table to the serving robot and the controlling of the serving robot to move to the target point through the specific optimal path includes: (Jeong: Paragraphs 0138 – 0139: “The path setting unit (135) includes: a driving path execution module (135a) for performing path planning and path following of the shortest distance according to an input of a target coordinate, to which a probability circle-based spatial search (PCSS) algorithm is applied”: Paragraph 0008: “the present invention intends to provide a serving robot that accurately delivers to a customer table, and to provide a multifunctional indoor autonomous technology-applied serving robot that can be easily converted into a robot capable of performing other functions in addition to the serving function and can work 24 hours a day.”; Paragraph 0141: “a local/global path return setting module (135c) for setting a return of a local path and a global path by creating a local cost-map for obstacle recognition and avoidance by the surrounding environment information acquisition sensing means (140) while the serving robot is driving; and”)
… transmitting the specific optimal path corresponding to the current congestion level among one or more optimal paths for the specific table to the serving robot. (Jeong: Paragraphs 0147 – 0148: “Accordingly, the present invention performs the detection and tracking of the obstacle using the RGB-D sensor in the obstacle recognition and avoidance method, so as to recognize the location of the 1st-risk cause by contact with a person or other moving component, and manage and detect the 2nd-risk cause of the autonomous driving error in advance. In addition, by applying the movement trend calculation of the tracked obstacle movement trend calculation and the probability circle-based spatial search (PCSS) algorithm, the movement path of the obstacle after the current point can be predicted.”)
In sum, Jeong teaches wherein, when the serving for the specific table is set, the transmitting of the specific optimal path for moving to the target point corresponding to the specific table to the serving robot and the controlling of the serving robot to move to the target point through the specific optimal path includes: transmitting the specific optimal path corresponding to the current congestion level among one or more optimal paths for the specific table to the serving robot. Jeong however does not teach recognizing a current congestion level based on information on the number of orders and the number of people received from at least one of the serving robot, a user terminal, and an external server, and the current time whereas Shah does.
Shah teaches recognizing a current congestion level based on information on the number of orders and the number of people received from at least one of the serving robot, a user terminal, and an external server, and the current time; and (Shah: Paragraph 0003: “Recently, there has been a significant increase in the use of robots to carry out different tasks in various settings such as manufacturing centers, warehouses, restaurants, delivery robots, etc. In such scenarios, robots may seek to safely navigate in a dynamic environment that includes actively working robots and humans to accomplish tasks. Such environments may change quickly and frequently.”; Paragraph 0044: “Notably c.sup.r(s) and c.sup.h(s) define robot and human congestion measurements. Notably, examples may seek to minimize the amount of robots that are on a same path, and/or position, at a same time. Doing so may reduce collisions and increase the speed that robots may travel since the robots may not encounter as many dynamic obstacles. Thus, Equation 1 penalizes paths that include robots and humans, and increases the penalties as the number of robots and humans increases. The hyperparameter β controls an influence of the human congestion measurement on the overall cost, and the hyperparameter γ controls an influence of the robot congestion measurement on the overall cost. Thus, depending on the situation, the hyperparameter β may be increased to increase the influence of the human congestion measurement on the overall cost. Similarly, the hyperparameter γ may be increased to increase an influence of the robot congestion measurement on the overall cost. In some examples, the robots may adjust to avoid regions even if the robots have just a few people and prefer regions with robots even if there are many robots there. That is, some examples may provide more weight to regions that have just a few people in and have less weight to regions with many robots. For example, robots may be able to more easily predict the path and motion of other robots, and therefore selectively avoid areas with people and enter areas with robots.”,
Supplemental Note: the number of tasks are interpreted as the number of orders as the robots are to complete them. Based on the number of people and robots in an area to perform a task, the system is able to determine the amount of congestion at a particular area)
Therefore, 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 invention disclosed by Jeong with the teachings of Shah with a reasonable expectation of success. One of ordinary skill in the art would find the ability of a robot to determine a congestion level as taught by Shah to be a use of known technique to improve similar devices in the same way pertaining to the server robot of Jeong also being able to detect congestion due to the number of obstacles (Jeong: Paragraphs 0147 – 0153). For example, both robots are able to acquire data about their environment related to obstacles and determine a route to take depending on the congestion result. Where Shah improves the technique is by also evaluating the position of others robots performing their respective tasks as a part of its congestion determination. Applying this technique to the server robot of Jeong would allow the robot to also detect other server robots, thus able to better navigate around the various robots and obstacles alike based on the congestion results of a particular area.
Claim(s) 4 and 6 – 7 are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (US 20230244238 A1) and Knutson et al. (US 20250268443 A1) as applied to claim 1 above, and further in view of Dupuis et al. (US 20200338733 A1).
Regarding claim 4, Jeong, as modified, teaches wherein, when the serving for the specific table is set, the transmitting of the specific optimal path for moving to the target point corresponding to the specific table to the serving robot and the controlling of the serving robot to move to the target point through the specific optimal path includes: (Jeong: Paragraphs 0138 – 0139: “The path setting unit (135) includes: a driving path execution module (135a) for performing path planning and path following of the shortest distance according to an input of a target coordinate, to which a probability circle-based spatial search (PCSS) algorithm is applied”: Paragraph 0008: “the present invention intends to provide a serving robot that accurately delivers to a customer table, and to provide a multifunctional indoor autonomous technology-applied serving robot that can be easily converted into a robot capable of performing other functions in addition to the serving function and can work 24 hours a day.”; Paragraph 0141: “a local/global path return setting module (135c) for setting a return of a local path and a global path by creating a local cost-map for obstacle recognition and avoidance by the surrounding environment information acquisition sensing means (140) while the serving robot is driving; and”)
In sum, Jeong teaches when the serving for the specific table is set, the transmitting of the specific optimal path for moving to the target point corresponding to the specific table to the serving robot and the controlling of the serving robot to move to the target point through the specific optimal path includes. Jeong however does not teach when there are two or more accessible target points on the specific table because at least two surfaces of the specific table are in contact with a passage recognizing movement paths of other serving robots; and controlling the serving robot to move to the target point through the specific optimal path that has a least overlapping path with the movement paths of the other serving robots among the optimal paths corresponding to each of the two or more target points whereas Dupuis does.
Dupuis teaches when there are two or more accessible target points on the specific table because at least two surfaces of the specific table are in contact with a passage, (Dupuis: Paragraph 0078: “During stage (F), the server system 104 transmits the path data 144 representing the results of the path planning process to the robot 110 over the network 106. The path data 144 can include the coordinates specifying points (e.g., GPS coordinates) along the path 138. For example, the coordinates may include locations such as, the starting location, the ending location, and intermediate points along the path. The coordinates may be represented as GPS coordinates or may be expressed in another coordinate frame, such as a coordinate frame understood by the robot 110. In other implementations, the server system 104 provides a pointer, address, or index to the robot 110 that enables the robot 110 to retrieve the path data 144 from the database 108.”,
Supplemental Note: an intermediate points along the path can represent two or accessible target points)
recognizing movement paths of other serving robots; and
controlling the serving robot to move to the target point through the specific optimal path that has a least overlapping path with the movement paths of the other serving robots among the optimal paths corresponding to each of the two or more target points. (Dupuis: Paragraph 0103: “In some implementations, the server system aggregates the swept regions and associates costs with each of those regions. For example, the illustration 405 visualizes a cost assigned to each of the swept regions corresponding to each of the other robots. A higher score is associated with regions of greater degrees of overlap among swept regions. Similarly, a lower score is associated with regions of lower degrees of overlap among swept regions. The swept regions may overlap at a particular level or overlap by some degree of overlay. In this manner, the regions and corresponding costs, shown in illustration 405, act in a “heat-map” manner. Typically, the regions associated with the highest costs are denoted as hot spots or hot regions on the heat-map. Likewise, regions associated with lower costs are noted as cold spots or cold regions on the heat-map…. The robot 408 or the server system associates costs with each of these regions in order to assist with determining a re-planned path for the robot 408.”; Paragraph 0102: “In view of the potential swept regions of the other robots, the path 409 of the robot 408 is subsequently re-planned to avoid the highly congested region of the other robots (e.g., the overlapping portions of stacked swept regions). The robot 408 and/or a server system that is in communication with the robot 408 can perform the re-planning of the robot 408's motion (e.g., server system 104).”)
Therefore, 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 invention disclosed by Jeong with the teachings of Dupuis with a reasonable expectation of success. Regarding Dupuis teaching the ability of its robot to travel to intermediate and target locations would be obvious to try to combine with the serving robot system of Jeong. For example, both Dupuis and Jeong teach the ability of a robot to travel to a target location whereas Dupuis also allows for intermediate points while traveling to the target location. Combining this ability with the server robot of Jeong, some of the intermediate points may represent points along the table the server robot is to attend, thus with the ability to already track obstacles real-time within the environment (Jeong: Paragraphs 0147 – 0153), the server robot will be able to determine an intermediate point to use to serve the table without any obstacles. Furthermore, Dupuis teaches the ability of the robot to recognize other robots and their overlapping paths to assist in re-planning the robot’s movement. One with knowledge in the art would find this as a use of known technique to improve similar devices in the same way when combined with Jeong. As cited above, Jeong already teaches the ability to detect obstacles, thus detecting other server robots is just applying the use of obstacle avoidance technique but with other server robots as well. The other server robots of Jeong can now also be treated as obstacles when combined with Dupuis, further leading to the server robot getting additional information about congestion in an area and able to determine a better optimal route.
Regarding claim 6, Jeong, as modified, does not teach wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: controlling the serving robot to wait so that the other serving robot is first movable and then move to the target point through the specific optimal path whereas Knutson does.
Knutson teaches wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same functional language and therefore rejected under the same pretenses. Jeong in view of Knutson however still do not teach when the obstacle is the other serving robot, recognizing a target point of the other serving robot; and when the target point of the other serving robot is a return point whereas Dupuis does.
Dupuis teaches when the obstacle is the other serving robot, recognizing a target point of the other serving robot; and (Dupuis: Paragraph 0102: “In view of the potential swept regions of the other robots, the path 409 of the robot 408 is subsequently re-planned to avoid the highly congested region of the other robots (e.g., the overlapping portions of stacked swept regions). The robot 408 and/or a server system that is in communication with the robot 408 can perform the re-planning of the robot 408's motion (e.g., server system 104).”)
when the target point of the other serving robot is a return point, (Dupuis: Paragraph 0050: “The obstacles can be stationary obstacles or dynamically movable obstacles. For example, some obstacles can move along a predetermined path, such as other robots moving along a planned path. Based on the other robots moving along a predetermined path, the robot 110 or the server system 104 can determine a swept volume of movement associated with the predetermined path. The swept volume for a robot, which will be further explained below, indicates a volume contains all points that the robot would pass through while travelling along its predetermined path. A swept area may alternatively be used, representing the sum of all areas that the robot would pass over along its path”,
Supplemental Note: the system is able to detect all of the points the other robots are traveling, thus it can be a starting location, intermediate location, or end location which can all correspond to a return point)
Therefore, 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 invention disclosed by Jeong with the teachings of Dupuis with a reasonable expectation of success. As stated for claim 4, Dupuis teaches the ability of the robot to recognize other robots and their overlapping paths to assist in re-planning the robot’s movement. One with knowledge in the art would find this as a use of known technique to improve similar devices in the same way when combined with Jeong. Jeong already teaches the ability to detect obstacles, thus detecting other server robots is just applying the use of obstacle avoidance technique but with other server robots as well. The other server robots of Jeong can now also be treated as obstacles when combined with Dupuis, further leading to the server robot getting additional information about congestion in an area and able to determine a better optimal route. Furthermore, Dupuis teaches the ability of all of the robots within the system to have data of all of the points of travel each robot is traveling to. One with knowledge in the art would find it obvious to try to implement this technique with the server robot system of Jeong as it improves the optimal routing for the server robots. For example, each server robot will be able to determine the routes of the other robots at any particular time, thus combined with obstacle detection, the server robots would be able to create an optimal route which avoids or minimizes any delays there would’ve been with any server robots overlapping routes. This increases the efficiency of the server robots as they are able to communicate each other’s travel paths which lead to better route optimization and better service for the users utilizing the service robots.
Regarding claim 7, Jeong, as modified, teaches generating an avoidance path between a current position of the serving robot and the specific table; (Jeong: Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”)
predicting a second movement time for the serving robot to move from the current position to the specific table through the avoidance path; and (Jeong: Paragraphs 0152 – 0153: “Instead of considering only the current location of dynamic obstacles, the probability circle-based spatial search (PCSS) algorithm is applied to allow the actual driving to be performed in a path planning method that considers the mobility of obstacles as well as the driving path. In other words, by comparing the caution cost function for obstacles, it is possible to create an efficient driving path to the destination with a small threat on pedestrians while driving the serving robot, enabling safe driving even in complex environments with dynamic obstacles.”)
In sum, Jeong teaches generating an avoidance path between a current position of the serving robot and the specific table predicting a second movement time for the serving robot to move from the current position to the specific table through the avoidance path. Jeong however does not teach wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: predicting a first movement time for the serving robot to wait for a time for the other serving robot to deviate from a section recognized as the obstacle from the current position and then move to the target point through the specific optimal path; determining a movement path for controlling the serving robot based on the first movement time and the second movement time and controlling the serving robot to move to the target point through the determined movement path whereas Knutson does.
Knutson teaches wherein, when the serving robot recognizes the obstacle while moving to the target point and stops moving, the controlling of the serving robot to wait and then move to the target point through the specific optimal path or move to the target point through the avoidance path includes: (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
… predicting a first movement time for the serving robot to wait for a time for the other serving robot to deviate from a section recognized as the obstacle from the current position and then move to the target point through the specific optimal path; (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”)
determining a movement path for controlling the serving robot based on the first movement time and the second movement time and controlling the serving robot to move to the target point through the determined movement path. (Knutson: Paragraph 0073: “If the identified moving object enters the machine buffer zone, control panel 18 can slow down movement of floor cleaning machine 10 and eventually stop floor cleaning machine 10 if the identified moving object continues to obstruct the cleaning path. Control panel 18 can be programmed to restart the cleaning operation, after a delay period, if the identified moving object is no longer detected. Alternatively, after the delay period, if the identified moving object remains in the cleaning path, control panel 18 can instruct floor cleaning machine 10 to move around the object and restart the cleaning operation along the route (e.g., cleaning path 94) of the cleaning path on the other side of the object.”,
Supplemental Note: the first movement path is interpreted as the robot first getting to the obstacle, then after the delay period, the robot is able to determine whether the obstacle has moved or created another movement path of traveling around the obstacle)
Therefore, 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 invention disclosed by Jeong with the teachings of Knutson with a reasonable expectation of success. As stated for claim 1, one of ordinary skill in the art would find implementing the waiting method of Knutson with the server robot system of Jeong as combining prior art elements according to known methods to yield predictable results pertaining to server robots better handling obstacles along its optimal route. Jeong teaches a server robot able to autonomously travel to serve different tables with its own method of predicting an obstacle’s movement in path of the robot and able to course-correct its route. The ability of Knutson adds an additional step of waiting a predetermined time for the obstacle to move from its path and once the time is over, to travel around the obstacle. The ability to wait for the obstacle as taught by Knutson can correspond to a the serving robot of Jeong waiting for a customer or another waiter to move out of the way. If the obstacle has not cleared within a predetermined time, the serving robot will now be able to move around the obstacle to travel to their target location. Both methods of Jeong and Knutson identify an obstacle and have their own methods of evaluating the obstacles movement to determine how to adjust their own path, thus both methods yield predictable results. Jeong in view of Knutson however still does not teach when the obstacle is the other serving robot, recognizing a table of the other serving robot; when the target point of the other serving robot is not a return point whereas Dupuis does.
Dupuis teaches when the obstacle is the other serving robot, recognizing a table of the other serving robot;
when the target point of the other serving robot is not a return point, (Dupuis: Paragraph 0050: “The obstacles can be stationary obstacles or dynamically movable obstacles. For example, some obstacles can move along a predetermined path, such as other robots moving along a planned path. Based on the other robots moving along a predetermined path, the robot 110 or the server system 104 can determine a swept volume of movement associated with the predetermined path. The swept volume for a robot, which will be further explained below, indicates a volume contains all points that the robot would pass through while travelling along its predetermined path. A swept area may alternatively be used, representing the sum of all areas that the robot would pass over along its path”,
Supplemental Note: the system is able to detect all of the points the other robots are traveling, thus it can be a starting location, intermediate location, or end location which can all correspond to a table or a return point)
Therefore, 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 invention disclosed by Jeong with the teachings of Dupuis with a reasonable expectation of success. As stated for claim 6, Dupuis teaches the ability of all of the robots within the system to have data of all of the points of travel each robot is traveling to. One with knowledge in the art would find it obvious to try to implement this technique with the server robot system of Jeong as it improves the optimal routing for the server robots. For example, each server robot will be able to determine the routes of the other robots at any particular time, thus combined with obstacle detection, the server robots would be able to create an optimal route which avoids or minimizes any delays there would’ve been with any server robots overlapping routes. This increases the efficiency of the server robots as they are able to communicate each other’s travel paths which lead to better route optimization and better service for the users utilizing the service robots.
Conclusion
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665