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 in reply to the application filed on 7/23/2025 and the amendments and remarks filed 12/23/2025.
Claims 1 and 7 are currently amended
Claims 6 and 8 have been previously amended.
Claims 11-14 have been previously added.
No claims have been cancelled.
Claims 1-14 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 8/8/2023 has been received and considered.
Response to Arguments
Applicant’s arguments, see pages 1-5, filed 12/23/2025, with respect to the rejection(s) of independent claim(s) 1 and 7 under 35 USC 103 have been fully considered and are persuasive regarding the prior art not teaching the elements of the tracked navigation trails or a path scaling factor defining a width of the path. The prior 35 USC 103 rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made as necessitated by amendment in view of Wang (Wang, Jiankun et al, “EB-RRT: Optimal Motion Planning for Mobile Robots”, IEEE Transactions on Automation Science and Engineering, 2020), Anezaki (US 20100222925), and Nihei (US 20050222714).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 and 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (Wang, Jiankun et al, “EB-RRT: Optimal Motion Planning for Mobile Robots”, IEEE Transactions on Automation Science and Engineering, 2020, hereinafter referred to as Wang) in view of Nihei et al (US 20050222714, hereinafter “Nihei”), and Anezaki (US 20100222925, hereinafter “Anezaki”).
Regarding Claim 1, Wang teaches:
A system for controlling navigation of a robot in a dynamic environment based on heuristic learning, the system comprising: -a heuristic learning unit (Wang pg 4 Col 1 ¶ 3 line 2 - Col 2 ¶ 1 line 2 “The global planner focuses on perceiving the dynamic environment and planning a feasible heuristic trajectory,” teaching acceptance of surrounding data in a dynamic environment)
configured to determine at least a preferred path, (Wang pg 2 Col 1 ¶ 2 line 4-7 “As shown in Fig. l(b), for a specific task, the global planner generates the time-based RRT tree and a feasible initial trajectory according to the world model and the local obstacle.”)
a preferred position, and a preferred orientation for the robot (Wang pg 3 Col 1 § B(1) “The state (position and orientation) of the robot. For simplification, we also use x to denote the state,” teaching generation of position and orientation)
based on human robot interaction (HRI) during navigation of the robot […] (Wang pg 3 Col 1 § B(5) “The control input u” is a user control input)
[…] and -a navigation control unit operatively coupled to the heuristic learning unit and (Wang pg 4 Col 2 ¶ 1 line 2-3 “while the dynamic replanner is responsible for optimizing the heuristic trajectory”)
configured to generate at least one of: an optimal path, an optimal position, and an optimal orientation, (Wang pg 2 Col 1 ¶ 3 line 7-8 “Then, the dynamic replanner optimizes the generated trajectory, […]”)
for navigation of the robot in the dynamic environment in real-time, during navigation of the robot, (Wang pg 2 Col 1 ¶ 2 line 3-5 “we propose the EB-RRT algorithm to achieve real-time motion planning while optimizing the current trajectory.”)
based on at least one of: the preferred path, the preferred position, the preferred orientation […] (Wang pg 2 Col 1 ¶ 2 line 4-8 “As shown in Fig. l(b), for a specific task, the global planner generates the time-based RRT tree and a feasible initial trajectory according to the world model and the local obstacle. Then, the dynamic replanner optimizes the generated trajectory,”)
Wang does not teach:
[…] and a path scaling factor, […]
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot, wherein the heuristic learning unit is further configured to track past navigation trails performed based on the physical force-based interaction during navigation of the robot and determine the preferred path based on the tracked past navigation trails and the path scaling factor, the path scaling factor defining a width of the preferred path; […]
[…] or a previous navigation data associated with the robot.
Within the same field of endeavor as Wang, Anezaki teaches:
[…] and a path scaling factor, […] wherein the heuristic learning unit is further configured to track past navigation trails performed based on the […] interaction during navigation of the robot and determine the preferred path based on the tracked past navigation trails and the path scaling factor, the path scaling factor defining a width of the preferred path; […] (Anezaki ¶ 0146 “The second step S72 is a step of playback-type autonomous movement. In step S72, the robot 1 autonomously moves while avoiding the obstacle 103 by using a safety ensuring technique (for example, a technique for drive-controlling the drive unit 10 by the control unit 50 such that the robot 1 moves a path that the obstacle 103 and the robot 1 will not contact each other and which is spaced apart at a distance sufficient for the safety from the position where the obstacle 103 is detected, in order not to contact the obstacle 103 detected by the obstacle detection unit 36). Based on information of the basic path 104 stored on the basic path teaching data storage unit 34 which is taught by the human 102 to the robot 1 before the robot 1 autonomously moves, in the path information at the time of the robot 1 moving (for example, path change information that the robot 1 newly avoided the obstacle 103), each time additional path information is newly generated by the movable area calculation unit 35 since the robot 1 avoids the obstacle 103 or the like, the additional path information is added to the map information stored on the basic path teaching data storage unit 34. In this way, the robot 1 makes the map information of points and lines (basic path 104 composed of points and lines) to be grown as map information of a plane (path within movable area in which movable area (additional path information) 104a in the width direction (direction orthogonal to the robot moving direction) is added with respect to the basic path composed of points and lines), while moving the basic path 104, and then stores the grown planar map information on the basic path teaching data storage unit 34,” teaching tracking of past navigation trails (path information added to the map information stored on the basic path teaching data storage unit) based on interaction during navigation of the robot (basic path which is taught by the human) and determines the preferred path based on the tracked past navigation trails (basic path composed of points and lines) and a path scaling factor defining a width of the path (the path grown as a plane in the width direction))
[…] or a previous navigation data associated with the robot. (Anezaki ¶ 0146 as above)
Wang and Anezaki are both considered analogous because they both relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner that optimizes the feasible trajectory generated by the RRT tree of Wang with the simple addition of Anezaki’s storing basic path data taught by a human in a path data storage unit and used to generate path data grown in a width direction. This modification would be made with a reasonable expectation of success as motivated by ensuring safety by so that the robot will move in a path at a distance sufficient for safety (Anezaki ¶ 0146).
The combination of Wang and Anezaki does not teach:
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot; […] physical force-based […]
Within the same field of endeavor as Wang and Anezaki, Nihei teaches:
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot; […] physical force-based […] (Nihei ¶ 0008 lines 1-11 “The present invention introduces to movement of a robot by manual operation a robot control technique enabling an operator to easily obtain an intuitive grasp of the direction of movement of the robot by "movement by copying control", that is, by "moving the robot in accordance with external force applied to the robot", and thereby enables learning of the precise robot operation in a short time and, at the same time, limits the region in which the robot can be moved by such copying control to thereby make it possible to easily avoid interference with a nearby object or the operator,” and ¶ 0046 lines 1-5 “Then, when the operator 4 applies force (translational force and/or moment (the same for the following description)) to the robot 1 by the handle operation, the information of the external force is transferred to the robot control unit 2,” teaching a force-based HRI for teaching a robot.)
Wang, Anezaki, and Nihei are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner based that optimizes the feasible trajectory generated by the RRT tree of Wang and Anezaki’s storing basic path data taught by a human in a path data storage unit and used to generate path data grown in a width direction by the addition of Nihei’s operator force input to a robot to teach movement by copying control, with the simple addition of the movement teaching to the heuristic trajectory of Wang. This modification would be made with a reasonable expectation of success as motivated by enabling learning of precise robot operation in a short time and limiting the region in which the robot can be moved, making it possible to easily avoid interference with a nearby object or the operator (Nihei ¶ 0008).
Regarding Claim 4, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 1 as described above. Wang further teaches:
a plurality of sensors associated with the robot for sensing at least one of a path, a position, and an orientation of the robot during the navigation of the robot in the dynamic environment (Wang pg 4 Col 2 ¶ 2 line 1-2 “In Algorithm 1, the robot first perceives the current environment and obtains its state x,” and pg 3 Col 1 § B(1) “The state (position and orientation) of the robot. For simplification, we also use x to denote the state,”)
and transmitting the path, the position, and the orientation to the heuristic learning unit. (Wang pg 4 Col 1 ¶ 3 line 1 – Col 2 ¶ 1 line 2 “As shown in Algorithms l and 3, the EB-RRT algorithm consists of two planners. The global planner focuses on perce1vmg the dynamic environment and planning a feasible heuristic trajectory,” the global planner, algorithm 1, and the instant heuristic learning unit being equivalent.)
Regarding Claim 5, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 1 as described above. Wang further teaches:
[…] navigation data comprises at least a […] path, a […] position, and a […] orientation of the robot […] (Wang pg 3 Col 1 § B(1) “The state (position and orientation) of the robot. For simplification, we also use x to denote the state,”))
Wang does not teach:
wherein the previous [… navigation data comprises at least a previous path …] corresponding to a plurality of locations within the dynamic environment.
Within the same field of endeavor as Wang, Anezaki teaches:
wherein the previous [… navigation data comprises at least a previous path …] corresponding to a plurality of locations within the dynamic environment. (Anezaki ¶ 0146 line 1-6 “In step S72, the robot 1 autonomously moves while avoiding the obstacle 103 by using a safety ensuring technique (for example, a technique for drive-controlling the drive unit 10 by the control unit 50 such that the robot 1 moves a path that the obstacle 103 and the robot 1 will not contact each other and which is spaced apart at a distance sufficient for the safety from the position where the obstacle 103 is detected, in order not to contact the obstacle 103 detected by the obstacle detection unit 36). […] In this way, the robot 1 makes the map information of points and lines,” teaching tracking of past navigation trails (path information added to the map information stored on the basic path teaching data storage unit) across a plurality of locations)
Wang and Anezaki are both considered analogous because they both relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner that optimizes the feasible trajectory generated by the RRT tree of Wang with the simple addition of Anezaki’s storing basic path data taught by a human in a path data storage unit and used to generate path data grown in a width direction. This modification would be made with a reasonable expectation of success as motivated by ensuring safety by so that the robot will move in a path at a distance sufficient for safety (Anezaki ¶ 0146).
Regarding Claim 6, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 1 as described above. Wang further teaches:
- 1) placing a cell associated with the dynamic environment in an open list; (Wang pg 4 Col 2 ¶ 2 line 1-2 “In Algorithm 1, the robot first perceives the current environment and obtains its state x,” describing the algorithm perceiving the current environment)
- 2) calculating a potential and heuristic for the cell (Wang Pg 3 Eq (2), (3), and (4) describe the cost function being used to heuristically determine the path in the RRT tree)
using a greedy path finding algorithm; (Wang pg 4 Col 2 ¶ 2 line 1-2 “Therefore, the partial motion planning method [20] is used to implement the trajectory planning and robot execution in parallel. In each time step, the robot x, moves along the generated partial trajectory a for one step,” which is analogous to the greedy path finding algorithm described in the instant specification Pg 12 lines 21-23 as “any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage”)
- 3) determining if the cell is in a preferred cell list; (Wang pg 3 Col 2 ¶ 2 line 2-4 “However, it deletes the node that has a high probabilistic collision risk or has a smaller timestamp than that of the current tree root,” teaching a determination that a node will not be used further, i.e. not preferred)
- 4) multiply a preferred path factor and a preferred heuristic factor to a cost and a heuristic of the cell upon the cell being in the preferred cell list; (Wang Pg 3 Eq (2), (3), and (4) describe the cost function being used to heuristically determine the path in the RRT tree, and Pg 3 Col 1 ¶ 3 line 1 “Let L be the set of all feasible trajectories. The cost function c(a) maps each feasible trajectory a to a positive real number R”)
- 5) removing the cell from the open list and placing into a closed list and saving an index of the cell associated with the lowest cost, upon the cell not being in the preferred cell list; (Wang pg 3 Col 2 ¶ 2 line 3-8 “However, it deletes the node that has a high probabilistic collision risk or has a smaller timestamp than that of the current tree root. […] To optimize the generated trajectory, an intuitive idea is to reconnect the nodes on the trajectory with less cost.”)
- 6) determining if the cell is a goal cell (Wang pg 5 Col 1 ¶ 1 line 16-12 “The whole procedure is iterated until the robot reaches the goal region.”)
Regarding Claim 11, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 6 as described above. Wang further teaches:
wherein the navigation control unit is further configured to perform terminating the algorithm and using a pointer of indexes to determine at least an optimal path, an optimal position, and an optimal orientation for the robot, upon the cell being the goal cell. (Wang pg 5 Col 1 ¶ 1 line 16-12 “The whole procedure is iterated until the robot reaches the goal region,” and Pg 5 col 1 ¶ 2 lines 1-2 “In Algorithm 3, the dynamic replanner optimizes the heuristic trajectory σheu based on the EB method [6].”)
Regarding Claim 12, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 6 as described above. Wang further teaches:
wherein the navigation control unit is further configured to perform detecting a plurality of successors of the cell which do not exist in the closed list, upon the cell not existing in the goal cell. (Wang pg 4 Algorithm 1 steps 12 and 13, growing the time-based tree)
Claim(s) 2-3 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Anezaki and Nihei and further in view of Lee (US 20170057087, hereinafter referred to as Lee 087).
Regarding Claim 2, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 1 as described above. Wang further teaches:
[…] configured to navigate the robot along the optimal path in the optimal position and the optimal orientation. (Wang pg 2 Col 1 ¶ 2 lines 7-9 “Then, the dynamic replanner optimizes the generated trajectory, and control commands are sent to the robot.”)
Wang does not explicitly teach:
a drive unit operatively coupled to the navigation control unit, and […]
Within the same field of endeavor as Wang, Lee 087 teaches:
a drive unit operatively coupled to the navigation control unit, and […] (Lee 087 ¶ 0054 “Further, the path planning apparatus 100 of a mobile robot may further include a user interface unit 110, a robot control unit 130 and a driving unit 150 in addition to the recognition unit 140 and the path planning creation unit 120, so as to be provided in a form including the entire configuration for movement of the mobile robot to goal point.”)
Wang, Anezaki, and Lee 087 are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robot of Wang with the simple addition of Lee 087’s driving unit in a form including the entire configuration of movement of the mobile robot. This modification would be made with a reasonable expectation of success as motivated by combining prior art elements (a drive unit) to known methods to yield predictable results.
Regarding Claim 3, the combination of Wang, Anezaki, Nihei, and Lee 087 teaches the limitations of claim 2 as described above. Wang does not teach:
wherein the drive unit is further configured to navigate the robot based on the HRI, by recognizing a force feedback and actuating a drive in a direction of a force applied by the user to navigate the robot.
Within the same field of endeavor as Wang, Lee 087 teaches:
wherein the drive unit is further configured to navigate the robot based on the HRI, by recognizing a force feedback and actuating a drive in a direction of a force applied by the user to navigate the robot. (Lee 087 ¶ 0055 “The user interface unit 110 is provided to allow a user to input a work command for moving the position of the mobile robot through […] touch input”)
Wang, Anezaki, and Lee 087 are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control input of Wang with the simple addition of Lee 087’s work command touch input. This modification would be made with a reasonable expectation of success as motivated by combining prior art elements (a drive unit) to known methods to yield predictable results.
Regarding Claim 7, Wang teaches:
A method of controlling navigation of a robot in a dynamic environment based on heuristic learning, the method comprising: - determining, by a heuristic learning unit, (Wang pg 4 Col 1 ¶ 3 line 2 - Col 2 ¶ 1 line 2 “The global planner focuses on perceiving the dynamic environment and planning a feasible heuristic trajectory,” teaching acceptance of surrounding data in a dynamic environment)
at least a preferred path, (Wang pg 2 Col 1 ¶ 2 line 4-7 “As shown in Fig. l(b), for a specific task, the global planner generates the time-based RRT tree and a feasible initial trajectory according to the world model and the local obstacle.”)
a preferred position, and a preferred orientation for the robot (Wang pg 3 Col 1 § B(1) “The state (position and orientation) of the robot. For simplification, we also use x to denote the state,” teaching generation of position and orientation)
based on a human robot interaction (HRI) during navigation of the robot […] (Wang pg 3 Col 1 § B(5) “The control input u”)
[…]- generating, by a navigation control unit (Wang pg 4 Col 2 ¶ 1 line 2-3 “while the dynamic replanner is responsible for optimizing the heuristic trajectory”)
at least one of: an optimal path, an optimal position, and an optimal orientation, (Wang pg 2 Col 1 ¶ 3 line 7-8 “Then, the dynamic replanner optimizes the generated trajectory, […]”)
for navigation of the robot in the dynamic environment in real-time, during navigation of the robot, (Wang pg 2 Col 1 ¶ 2 line 3-5 “we propose the EB-RRT algorithm to achieve real-time motion planning while optimizing the current trajectory.”)
based on at least one of: the preferred path, the preferred position, the preferred orientation […] (Wang pg 2 Col 1 ¶ 2 line 4-8 “As shown in Fig. l(b), for a specific task, the global planner generates the time-based RRT tree and a feasible initial trajectory according to the world model and the local obstacle. Then, the dynamic replanner optimizes the generated trajectory,”)
[…]- navigating the robot along the optimal path in the optimal position and the optimal orientation in the dynamic environment […] (Wang pg 4 Col 2 ¶ 1 line 3-4 “Finally, the robot is directed to move along the optimized trajectory.”)
Wang does not teach:
[…]and a path scaling factor,
wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot;-
tracking, by the heuristic learning unit, past navigation trails performed based on the
physical force-based
interaction during navigation of the robot and determining the preferred path based on the tracked past navigation trails and the path scaling factor, the path scaling factor defining a width of the preferred path; […]
[…] or a previous navigation data associated with the robot; and […]
[…] by a drive unit.
Within the same field of endeavor as Wang, Anezaki teaches:
[…]and a path scaling factor, […] tracking, by the heuristic learning unit, past navigation trails performed based on the […] interaction during navigation of the robot and determining the preferred path based on the tracked past navigation trails and the path scaling factor, the path scaling factor defining a width of the preferred path; […] (Anezaki ¶ 0146 “The second step S72 is a step of playback-type autonomous movement. In step S72, the robot 1 autonomously moves while avoiding the obstacle 103 by using a safety ensuring technique (for example, a technique for drive-controlling the drive unit 10 by the control unit 50 such that the robot 1 moves a path that the obstacle 103 and the robot 1 will not contact each other and which is spaced apart at a distance sufficient for the safety from the position where the obstacle 103 is detected, in order not to contact the obstacle 103 detected by the obstacle detection unit 36). Based on information of the basic path 104 stored on the basic path teaching data storage unit 34 which is taught by the human 102 to the robot 1 before the robot 1 autonomously moves, in the path information at the time of the robot 1 moving (for example, path change information that the robot 1 newly avoided the obstacle 103), each time additional path information is newly generated by the movable area calculation unit 35 since the robot 1 avoids the obstacle 103 or the like, the additional path information is added to the map information stored on the basic path teaching data storage unit 34. In this way, the robot 1 makes the map information of points and lines (basic path 104 composed of points and lines) to be grown as map information of a plane (path within movable area in which movable area (additional path information) 104a in the width direction (direction orthogonal to the robot moving direction) is added with respect to the basic path composed of points and lines), while moving the basic path 104, and then stores the grown planar map information on the basic path teaching data storage unit 34,” teaching tracking of past navigation trails (path information added to the map information stored on the basic path teaching data storage unit) based on interaction during navigation of the robot (basic path which is taught by the human) and determines the preferred path based on the tracked past navigation trails (basic path composed of points and lines) and a path scaling factor defining a width of the path (the path grown as a plane in the width direction))
or a previous navigation data associated with the robot; and […] (Anezaki ¶ 0146 as above)
Wang and Anezaki are both considered analogous because they both relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner that optimizes the feasible trajectory generated by the RRT tree of Wang with the simple addition of Anezaki’s storing basic path data taught by a human in a path data storage unit and used to generate path data grown in a width direction. This modification would be made with a reasonable expectation of success as motivated by ensuring safety by so that the robot will move in a path at a distance sufficient for safety (Anezaki ¶ 0146).
The combination of Wang and Anezaki does not explicitly teach:
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot;- […] physical force-based [interaction…]
[…] by a drive unit.
Within the same field of endeavor as Wang and Anezaki, Lee 087 teaches:
[…] by a drive unit. (Lee 087 ¶ 0054 “Further, the path planning apparatus 100 of a mobile robot may further include a user interface unit 110, a robot control unit 130 and a driving unit 150 in addition to the recognition unit 140 and the path planning creation unit 120, so as to be provided in a form including the entire configuration for movement of the mobile robot to goal point.”)
Wang, Anezaki, and Lee 087 are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the robot of Wang with the simple addition of Lee 087’s driving unit in a form including the entire configuration of movement of the mobile robot. This modification would be made with a reasonable expectation of success as motivated by combining prior art elements (a drive unit) to known methods to yield predictable results.
The combination of Wang, Anezaki, and Lee 087 does not teach:
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot;- […] physical force-based [interaction…]
Within the same field of endeavor as Wang, Anezaki, and Lee 087, Nihei teaches:
[…] wherein the HRI is an interaction between a human and the robot, comprising at least receiving a physical force-based interaction from the human to navigate the robot;- […] physical force-based [interaction…] (Nihei ¶ 0008 lines 1-11 “The present invention introduces to movement of a robot by manual operation a robot control technique enabling an operator to easily obtain an intuitive grasp of the direction of movement of the robot by "movement by copying control", that is, by "moving the robot in accordance with external force applied to the robot", and thereby enables learning of the precise robot operation in a short time and, at the same time, limits the region in which the robot can be moved by such copying control to thereby make it possible to easily avoid interference with a nearby object or the operator,” and ¶ 0046 lines 1-5 “Then, when the operator 4 applies force (translational force and/or moment (the same for the following description)) to the robot 1 by the handle operation, the information of the external force is transferred to the robot control unit 2,” teaching a force-based HRI for teaching a robot, as well as keeping track of past trails (for example, navigation based on the HRI))
Wang, Anezaki, Lee 087, and Nihei are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner based that optimizes the feasible trajectory generated by the RRT tree of Wang and Anezaki’s storing basic path data taught by a human in a path data storage unit and used to generate path data grown in a width direction by the addition of Nihei’s operator force input to a robot to teach movement by copying control, with the simple addition of the movement teaching to the heuristic trajectory of Wang. This modification would be made with a reasonable expectation of success as motivated by enabling learning of precise robot operation in a short time and limiting the region in which the robot can be moved, making it possible to easily avoid interference with a nearby object or the operator (Nihei ¶ 0008).
Regarding Claim 8, the combination of Wang, Anezaki, Nihei, and Lee 087 teaches the limitations of claim 7 as described above. Wang further teaches:
wherein generating the optimal path, the optimal position, and the optimal orientation comprises: - 1) placing a cell associated with the dynamic environment in an open list; (Wang pg 4 Col 2 ¶ 2 line 1-2 “In Algorithm 1, the robot first perceives the current environment and obtains its state x,” describing the algorithm perceiving the current environment)
- 2) calculating a potential and heuristic for the cell (Wang Pg 3 Eq (2), (3), and (4) describe the cost function being used to heuristically determine the path in the RRT tree)
using a greedy path finding algorithm; (Wang pg 4 Col 2 ¶ 2 line 1-2 “Therefore, the partial motion planning method [20] is used to implement the trajectory planning and robot execution in parallel. In each time step, the robot x, moves along the generated partial trajectory a for one step,” which is analogous to the greedy path finding algorithm described in the instant specification Pg 12 lines 21-23 as “any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage”)
- 3) determining if the cell is in a preferred cell list; (Wang pg 3 Col 2 ¶ 2 line 2-4 “However, it deletes the node that has a high probabilistic collision risk or has a smaller timestamp than that of the current tree root,” teaching a determination that a node will not be used further, i.e. not preferred)
- 4) multiplying a preferred path factor and a preferred heuristic factor to a cost and a heuristic of the cell upon the cell being in the preferred cell list; (Wang Pg 3 Eq (2), (3), and (4) describe the cost function being used to heuristically determine the path in the RRT tree, and Pg 3 Col 1 ¶ 3 line 1 “Let L be the set of all feasible trajectories. The cost function c(a) maps each feasible trajectory a to a positive real number R”)
- 5) removing the cell from the open list and placing into a closed list and saving an index of the cell associated with the lowest cost, upon the cell not being in the preferred cell list; (Wang pg 3 Col 2 ¶ 2 line 3-8 “However, it deletes the node that has a high probabilistic collision risk or has a smaller timestamp than that of the current tree root. […] To optimize the generated trajectory, an intuitive idea is to reconnect the nodes on the trajectory with less cost.”)
- 6) determining if the cell is a goal cell (Wang pg 5 Col 1 ¶ 1 line 16-12 “The whole procedure is iterated until the robot reaches the goal region.”)
Regarding Claim 9, the combination of Wang, Anezaki, Nihei and Lee 087 teaches the limitations of claim 7 as described above. Wang does not teach:
wherein navigating the robot further comprises navigating the robot based on the HRI by recognizing a force feedback and actuating a drive in a direction of a force applied by the user to navigate the robot.
Within the same field of endeavor as Wang, Lee 087 teaches:
wherein navigating the robot further comprises navigating the robot based on the HRI by recognizing a force feedback and actuating a drive in a direction of a force applied by the user to navigate the robot. (Lee 087 ¶ 0055 “The user interface unit 110 is provided to allow a user to input a work command for moving the position of the mobile robot through […] touch input”)
Wang, Anezaki, and Lee 087 are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the control input of Wang with the simple addition of Lee 087’s work command touch input. This modification would be made with a reasonable expectation of success as motivated by combining prior art elements (a drive unit) to known methods to yield predictable results.
Regarding Claim 10, the combination of Wang, Anezaki, Nihei, and Lee 087 teaches the limitations of claim 7 as described above. Wang further teaches:
[…] navigation data comprises at least a […] path, a […] position, and a […] orientation of the robot […] (Wang pg 3 Col 1 § B(1) “The state (position and orientation) of the robot. For simplification, we also use x to denote the state,”))
Wang does not teach:
wherein the previous [… navigation data comprises at least a previous path …] corresponding to a plurality of locations within the dynamic environment.
Within the same field of endeavor as Wang, Anezaki teaches:
wherein the previous [… navigation data comprises at least a previous path …] corresponding to a plurality of locations within the dynamic environment. (Anezaki ¶ 0146 line 1-6 “In step S72, the robot 1 autonomously moves while avoiding the obstacle 103 by using a safety ensuring technique (for example, a technique for drive-controlling the drive unit 10 by the control unit 50 such that the robot 1 moves a path that the obstacle 103 and the robot 1 will not contact each other and which is spaced apart at a distance sufficient for the safety from the position where the obstacle 103 is detected, in order not to contact the obstacle 103 detected by the obstacle detection unit 36). […] In this way, the robot 1 makes the map information of points and lines,” teaching tracking of past navigation trails (path information added to the map information stored on the basic path teaching data storage unit) across a plurality of locations)
Wang, Anezaki, and Lee 087 are all considered analogous because they all relate to robot path planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the dynamic replanner based that optimizes the feasible trajectory generated by the RRT tree including a state representing position and orientation of Wang by applying the use of previous path data of Anezaki to use similar past state information. This modification would be made with a reasonable expectation of success as motivated by saving computation resources by beginning with known data.
Regarding Claim 13, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 8 as described above. Wang further teaches:
wherein generating the optimal path, the optimal position, and the optimal orientation further comprises terminating the algorithm and using a pointer of indexes to determine at least the optimal path, the optimal position, and the optimal orientation for the robot, upon the cell being the goal cell. (Wang pg 5 Col 1 ¶ 1 line 16-12 “The whole procedure is iterated until the robot reaches the goal region,” and Pg 5 col 1 ¶ 2 lines 1-2 “In Algorithm 3, the dynamic replanner optimizes the heuristic trajectory σheu based on the EB method [6].”)
Regarding Claim 14, the combination of Wang, Anezaki, and Nihei teaches the limitations of claim 8 as described above. Wang further teaches:
wherein generating the optimal path, the optimal position, and the optimal orientation further comprises detecting a plurality of successors of the cell which do not exist in the closed list, upon the cell not existing in the goal cell. (Wang pg 4 Algorithm 1 steps 12 and 13, growing the time-based tree)
Conclusion
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/ZACHARY E. F. GLADE/Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664