DETAILED CORRESPONDENCE
This final office action is in response to the Amendments filed on 26 August 2026, regarding application number 19/125,605.
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 .
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 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.
Response to Amendment
Claims 1-2, 12, 17, 19, 22, 27 and 29-34 remain pending in the application, while claims 3-11, 13-16, 18, 20-21, 23-26 and 28 have been cancelled. Claims 30-34 are new.
Applicant’s amendments to the claims have overcome each and every 35 U.S.C. 101 rejections previously set forth in the non-final office action mailed 10 June 2026. Therefore, the rejections have been withdrawn.
Examiner further acknowledges that the claims will no longer be construed under 35 U.S.C. 112(f).
Response to Arguments
Applicant’s arguments, see Pages 7-9, filed 26 August 2026, with respect to the rejections of claims 1-2, 12, 17, 19, 22, 27 and 29 under 35 U.S.C. § 102 and 35 U.S.C. § 103, and with respect to new claims 30-34, have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made further in view of newly cited reference Lee et al. (US 20220276654 A1). See full details below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 31 and 32 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 31
Claim 31 recites "wherein the at least one processor is further configured to identify potentials for a plurality of respective postures of the robot on a candidate motion path of the plurality of candidate motion paths…", while independent claim 1 already recites a similar limitation "…based on potentials for positions in a plurality of postures of the robot on the candidate motion path…". It is unclear whether the "potentials" and "postures" in claim 31 are referring to the same or different "potentials" and "postures" recited in claim 1. As such, claim 31 is indefinite because the metes and bounds of the claims are unclear. For the purpose of compact prosecution, claim 31 will be interpreted as referencing the same features claimed in claim 1.
Regarding Claims 31-32
In claim 31, the limitation reciting "…each of the potentials is a potential at a position closest to a representative point of the robot in a corresponding posture of the plurality of postures in the physical workspace." is indefinite. The term "closest" renders the claim indefinite because it is unclear what criteria determines a closest position. For example, does closest mean that the position is closer to a representative point than all other positions in the potential field? As such, claim 31 is indefinite because the metes and bounds of the claims are unclear. See MPEP 2173.05(b). Claim 32 recites a similar limitation "each of the summations is a summation of potentials at a plurality of positions closest to a plurality of respective representative points of the robot in a corresponding posture of the plurality of postures in the physical workspace.", and therefore is also indefinite for at least the same reasons discussed above. For the purpose of examination, "closest" will be interpreted as "relative".
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 27, 29 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi et al. (US 5530791 A and Okabayashi hereinafter), in view of Lee et al. (US 20220276654 A1 and Lee hereinafter).
Regarding Claim 1
Okabayashi teaches a motion path setting device (see Col. 2, lines 1-38), comprising:
at least one processor (see Figs. 3A-3B, all; Col. 12, lines 32-39 "Respective functions shown in FIG. 3B are executed by software of a central processing unit (CPU), and in this connection operation of the system in FIG. 3B will be described with reference to flowcharts in FIGS. 37A and 37B.") configured to:
set a use priority of each of a plurality of candidate motion paths of a robot (see Fig. 3B, template limiting section 48 and priority order granting section 49; Fig. 37B, step 106; Col. 6, lines 52-58, "The template limiting section 48 limits the available trajectory templates T from among the trajectory templates T registered in the template storage 42, based on an attitude mode "m" of the manipulator M. The priority order granting section 49 grants a priority order based on the execution environment, to the trajectory template T which is determined as available by the template limiting section 48."; Col. 12, lines 64-67 "A number "n" of the priority order is assigned, by the priority order assigning section 49, for the trajectory template T the use of which is determined as available by the template limiting section 48 at step 106.");
select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see Fig. 3B, priority order granting section 49 and first interference inspecting section 50; Fig. 37B, steps 107-109; Col. 6, lines 55-64, "The priority order granting section 49 grants a priority order based on the execution environment, to the trajectory template T which is determined as availableby the template limiting section 48. The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49."; Col. 13, lines 1-4, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110.") and perform an interference determination process to determine whether the robot interferes with an obstacle for the selected at least one candidate motion path (see Fig. 3B, first/second interference inspecting sections 50/52; Fig. 37B, steps 110 and 112-113; Col. 6, line 59 - Col. 7, line 9, "The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49 ... The second interference inspecting section 52 executes a detailed interference-inspection between the manipulator M and the obstacle O1 by performing a simulation on the environment model on the basis of the trajectory data produced by the curve interpolation section."; Col. 13, lines 1-22, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110. In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated. Then a simulation is executed by the second interference inspecting section 52 on the basis of the trajectory data, and a detailed interference-inspection of the manipulator M and the obstacle O1 is executed at step 112 .. On the other hand, if it is determined that the interference is present at step 110 or step 113, then the process as described above is again executed based on the trajectory template T of the next priority order "n"at step 115.");
set a motion path of the robot based on a candidate motion path of the plurality of candidate motion paths on which the robot is determined not to interfere with the obstacle (see Fig. 3B, curve curve interpolation section 51; Fig. 37B, steps 111 and 113-114; Col. 6, lines 65 - Col. 7, line 4, "The curve interpolation section 51 produces thetrajectory data of the manipulator M when the first interference inspectingsection 50 determines that the interference is not generated, by interpolating the trajectory template T for which the above decision has been made based on the environment model on the basis of an operational characteristic of the manipulator M. "; Col. 13, lines 5-18 "In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated ... Through the interference inspection, if it is determined that the interference is not generated at step 113, the trajectory data described above is fed to an actual manipulator-driver (not shown) at step 114."); and
control the robot to move along the set motion path (see Fig. 37B, step 114; Col. 13, lines 15-18 "Through the interference inspection, if it is determined that the interference is not generated at step 113, the trajectory data described above is fed to an actual manipulator-driver (not shown) at step 114.").
Okabayashi is silent regarding set a specific potential field in a multidimensional space as a physical workspace of the robot, the specific potential field including, for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position; and
set the use priority of each of the plurality of candidate motion paths based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field.
Lee teaches a motion path setting device (see all Figs.; [0009]), comprising:
at least one processor (see [0039]) configured to:
set a use priority of each of a plurality of candidate motion paths of a robot; and select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see Figs. 4 and 10, all; [0009 "...an optimal path selection module that selects a path satisfying a preset cost function requirement among the “n” multiple paths planned, as an optimal path..."]-[0011], [0018]-[0019], [0066], [0084 "In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map. In addition, the path selector 320 may select a path with the minimum cost by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to an area in the proximity of each path."] and [0105 "In addition, the path selection process (S320) may include selecting an optimal path to be followed by the mobile robot according to one cost function factor selected by the cost function factor determination process or a combination of at least two or more cost function factors."]);
set a specific potential field in a multidimensional space as a physical workspace of the robot, the specific potential field including, for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position (see Figs. 2-4 and 10, all, especially Fig. 2; [0011]-[0015], [0044]-[0045], [0050 "In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. Accordingly, the determination as to which area of areas is to be included in a path and which area of areas is to be avoided may be achieved merely by comparing the costs allocated to the areas with each other."], [0051 "As an example of a cost map in which a cost is allocated to each area by the cost map generation module 100, as shown in FIG. 2, the highest cost of 10 is allocated to an area occupied by an obstacle, and a decreased cost is allocated as it is further away from the area occupied by the obstacle."], [0052]-[0055], [0060 "In addition, the multi-waypoint detector 210 may set, as an area where there is no collision with an obstacle, an area allocated a cost of no greater than 8, which is lower than a cost of 10 indicating that a cost allocated to each area represents an area occupied by an obstacle on the cost map and is lower than a cost of 9 indicating that the cost allocated to each area represents a nearest area to the obstacle on the cost map, to avoid an area with collision with the obstacle. In this case, the multi-waypoint detector 210 may change and set a cost for determining an area without collision with obstacles to a value lower than or higher than 8."]-[0061 "Accordingly, as shown in FIG. 3, the multi-waypoint detector 210 may detect, as multiple waypoints, areas located at the maximum distance, which are reached by passing through only areas with a cost of 8 or less on the cost map in eight directions within a certain radius from the mobile robot (indicated by a large circle around the mobile robot in FIG. 3) as shown in FIG. 3."], [0066] and [0084]);
set the use priority of each of the plurality of candidate motion paths based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field (see Figs. 4 and 10, all; [0009]-[0011], [0018]-[0019], [0050], [0066] and [0084 "In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map. In addition, the path selector 320 may select a path with the minimum cost by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to an area in the proximity of each path."]); and
control the robot to move along the set motion path (see [0011 "...a path control module that transmits a control signal for controlling the mobile robot to follow the selected optimal path to reach the destination to a robot driver of the mobile robot."], [0044] and [0086]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the motion path setting device of Okabayashi to set a specific potential field in a multidimensional space as a physical workspace of the robot, including for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position, and to set the use priority of each of the plurality of candidate motion paths based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field, as taught by Lee, in order to select an optimal path which satisfies various conditions including traveling a minimum distance while keeping a safe distance from obstacles.
Regarding Claim 27
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi further teaches wherein the at least one processor performs the interference determination process selectively for the at least one candidate motion path of the plurality of candidate motion paths selected based on the use priority (see Fig. 3B, first/second interference inspecting sections 50/52; Fig. 37B, steps 110 and 112-113; Col. 6, line 59 - Col. 7, line 9, "The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49 ... The second interference inspecting section 52 executes a detailed interference-inspection between the manipulator M and the obstacle O1 by performing a simulation on the environment model on the basis of the trajectory data produced by the curve interpolation section."; Col. 13, lines 1-22, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110. In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated. Then a simulation is executed by the second interference inspecting section 52 on the basis of the trajectory data, and a detailed interference-inspection of the manipulator M and the obstacle O1 is executed at step 112 .. On the other hand, if it is determined that the interference is present at step 110 or step 113, then the process as described above is again executed based on the trajectory template T of the next priority order "n"at step 115."), or
the at least one processor performs the interference determination process for one or more of the plurality of candidate motion paths selected in an order based on the use priority (see Fig. 37B, steps 110 and 112-113; Col. 6, line 59 - Col. 7, line 9, "The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49 ... The second interference inspecting section 52 executes a detailed interference-inspection between the manipulator M and the obstacle O1 by performing a simulation on the environment model on the basis of the trajectory data produced by the curve interpolation section."; Col. 13, lines 1-22, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110. In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated. Then a simulation is executed by the second interference inspecting section 52 on the basis of the trajectory data, and a detailed interference-inspection of the manipulator M and the obstacle O1 is executed at step 112 .. On the other hand, if it is determined that the interference is present at step 110 or step 113, then the process as described above is again executed based on the trajectory template T of the next priority order "n"at step 115.").
Regarding Claim 29
Okabayashi teaches a non-transitory computer-readable recording medium storing a program for causing a computer to perform operations (see Col. 2, lines 1-38) comprising:
setting a use priority of each of a plurality of candidate motion paths of a robot (see Fig. 3B, template limiting section 48 and priority order granting section 49; Fig. 37B, step 106; Col. 6, lines 52-58, "The template limiting section 48 limits the available trajectory templates T from among the trajectory templates T registered in the template storage 42, based on an attitude mode "m" of the manipulator M. The priority order granting section 49 grants a priority order based on the execution environment, to the trajectory template T which is determined as available by the template limiting section 48."; Col. 12, lines 64-67 "A number "n" of the priority order is assigned, by the priority order assigning section 49, for the trajectory template T the use of which is determined as available by the template limiting section 48 at step 106.");
selecting at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see Fig. 3B, priority order granting section 49 and first interference inspecting section 50; Fig. 37B, steps 107-109; Col. 6, lines 55-64, "The priority order granting section 49 grants a priority order based on the execution environment, to the trajectory template T which is determined as availableby the template limiting section 48. The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49."; Col. 13, lines 1-4, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110.") and performing an interference determination process to determine whether the robot interferes with an obstacle for the selected at least one candidate motion path (see Fig. 3B, first/second interference inspecting sections 50/52; Fig. 37B, steps 110 and 112-113; Col. 6, line 59 - Col. 7, line 9, "The first interference inspecting section 50 inspects an interference of the trajectory template T with the obstacle O1 on the environment model in a process in which the trajectory template T is read from the template storage 42 in the order of a higher priority order on the basis of the priority order assigned by the priorityorder assigning section 49 ... The second interference inspecting section 52 executes a detailed interference-inspection between the manipulator M and the obstacle O1 by performing a simulation on the environment model on the basis of the trajectory data produced by the curve interpolation section."; Col. 13, lines 1-22, "An interference of the trajectory template T having a highest priority order "n" (n=1) with the obstacle O1 is inspected by the first interference inspecting section 50 as shown in FIG. 37B at steps 107 to 110. In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated. Then a simulation is executed by the second interference inspecting section 52 on the basis of the trajectory data, and a detailed interference-inspection of the manipulator M and the obstacle O1 is executed at step 112 .. On the other hand, if it is determined that the interference is present at step 110 or step 113, then the process as described above is again executed based on the trajectory template T of the next priority order "n"at step 115.");
setting a motion path of the robot based on a candidate motion path of the plurality of candidate motion paths on which the robot is determined not to interfere with the obstacle (see Fig. 3B, curve interpolation section 51; Fig. 37B, steps 111 and 113-114; Col. 6, lines 65 - Col. 7, line 4, "The curve interpolation section 51 produces thetrajectory data of the manipulator M when the first interference inspectingsection 50 determines that the interference is not generated, by interpolating the trajectory template T for which the above decision has been made based on the environment model on the basis of an operational characteristic of the manipulator M. "; Col. 13, lines 5-18 "In this interference inspection, if it is determined that the interference is not generated at step 110, the control proceeds to step 111 and trajectory data are produced by interpolation through the curve interpolation section 51 with respect to the trajectory template T which is determined so that the interference is not generated ... Through the interference inspection, if it is determined that the interference is not generated at step 113, the trajectory data described above is fed to an actual manipulator-driver (not shown) at step 114."), and
controlling the robot to move along the set motion path (see Fig. 37B, step 114; Col. 13, lines 15-18 "Through the interference inspection, if it is determined that the interference is not generated at step 113, the trajectory data described above is fed to an actual manipulator-driver (not shown) at step 114.").
Okabayashi is silent regarding wherein, in setting the use priority,
a specific potential field is set in a multidimensional space as a physical workspace of the robot, the specific potential field including, for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position, and
the use priority of each of the plurality of candidate motion paths is set based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field.
Lee teaches a non-transitory computer-readable recording medium storing a program for causing a computer to perform operations (see all Figs.; [0009] and [0039]) comprising:
setting a use priority of each of a plurality of candidate motion paths of a robot; selecting at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see Figs. 4 and 10, all; [0009 "...an optimal path selection module that selects a path satisfying a preset cost function requirement among the “n” multiple paths planned, as an optimal path..."]-[0011], [0018]-[0019], [0066], [0084 "In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map. In addition, the path selector 320 may select a path with the minimum cost by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to an area in the proximity of each path."] and [0105 "In addition, the path selection process (S320) may include selecting an optimal path to be followed by the mobile robot according to one cost function factor selected by the cost function factor determination process or a combination of at least two or more cost function factors."]);
wherein, in setting the use priority,
a specific potential field is set in a multidimensional space as a physical workspace of the robot, the specific potential field including, for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position (see Figs. 2-4 and 10, all, especially Fig. 2; [0011]-[0015], [0044]-[0045], [0050 "In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. Accordingly, the determination as to which area of areas is to be included in a path and which area of areas is to be avoided may be achieved merely by comparing the costs allocated to the areas with each other."], [0051 "As an example of a cost map in which a cost is allocated to each area by the cost map generation module 100, as shown in FIG. 2, the highest cost of 10 is allocated to an area occupied by an obstacle, and a decreased cost is allocated as it is further away from the area occupied by the obstacle."], [0052]-[0055], [0060 "In addition, the multi-waypoint detector 210 may set, as an area where there is no collision with an obstacle, an area allocated a cost of no greater than 8, which is lower than a cost of 10 indicating that a cost allocated to each area represents an area occupied by an obstacle on the cost map and is lower than a cost of 9 indicating that the cost allocated to each area represents a nearest area to the obstacle on the cost map, to avoid an area with collision with the obstacle. In this case, the multi-waypoint detector 210 may change and set a cost for determining an area without collision with obstacles to a value lower than or higher than 8."]-[0061 "Accordingly, as shown in FIG. 3, the multi-waypoint detector 210 may detect, as multiple waypoints, areas located at the maximum distance, which are reached by passing through only areas with a cost of 8 or less on the cost map in eight directions within a certain radius from the mobile robot (indicated by a large circle around the mobile robot in FIG. 3) as shown in FIG. 3."], [0066] and [0084]), and
the use priority of each of the plurality of candidate motion paths is set based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field (see Figs. 4 and 10, all; [0009]-[0011], [0018]-[0019], [0050], [0066] and [0084 "In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map. In addition, the path selector 320 may select a path with the minimum cost by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to an area in the proximity of each path."]); and
controlling the robot to move along the set motion path (see [0011 "...a path control module that transmits a control signal for controlling the mobile robot to follow the selected optimal path to reach the destination to a robot driver of the mobile robot."], [0044] and [0086]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the non-transitory computer-readable recording medium of Okabayashi further include instructions to set a specific potential field in a multidimensional space as a physical workspace of the robot, including for each position in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through the position, and to set the use priority of each of the plurality of candidate motion paths based on potentials for positions in a plurality of postures of the robot on the candidate motion path in the specific potential field, as taught by Lee, in order to select an optimal path which satisfies various conditions including traveling a minimum distance while keeping a safe distance from obstacles.
Regarding Claim 31
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi is silent regarding wherein the at least one processor is further configured to identify potentials for a plurality of respective postures of the robot on a candidate motion path of the plurality of candidate motion paths, and set the use priority of the candidate motion path based on a summation of the potentials identified for the plurality of respective postures, and each of the potentials is a potential at a position closest to a representative point of the robot in a corresponding posture of the plurality of postures in the physical workspace.
Lee teaches wherein the at least one processor is further configured to identify potentials for a plurality of respective postures of the robot on a candidate motion path of the plurality of candidate motion paths, and set the use priority of the candidate motion path based on a summation of the potentials identified for the plurality of respective postures, and each of the potentials is a potential at a position closest to a representative point of the robot in a corresponding posture of the plurality of postures in the physical workspace (see Figs. 4 and 10, all; [0009]-[0011], [0018]-[0019], [0050], [0066] and [0084 "In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map. In addition, the path selector 320 may select a path with the minimum cost by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to an area in the proximity of each path."]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the motion path setting device of Okabayashi to identify potentials for a plurality of respective postures of the robot on a candidate motion path of the plurality of candidate motion paths and to set the use priority of the candidate motion path based on a summation of the potentials identified for the plurality of respective postures, as taught by Lee, in order to select an optimal path which satisfies various conditions including traveling a minimum distance while keeping a safe distance from obstacles.
Claims 2 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi (as modified by Lee) as applied to claim 1 above, and further in view of Hager et al. (US 20200276710 A1 and Hager hereinafter).
Regarding Claim 2
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi is silent regarding wherein the at least one processor sets the use priority based on obstacle information about the obstacle.
Hager teaches a motion path setting device (see all Figs.; [0011]-[0015]), comprising:
at least one processor (see [0014 "The traffic analysis unit further includes a processor running a flow module that processes (with one or more algorithms such as the Gunnar-Farneback optical flow algorithm) output from the traffic sensor to generate traffic analysis results."]) configured to:
set a use priority of each of a plurality of candidate motion paths of a robot; and select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see [0013 "An autonomous agent can use motion and density information to avoid crowded areas (if that is a navigation rule/goal) and make reasonable selections among potential routes when selecting a route or selecting a target or goal location (e.g., by rejecting routes through forcing the autonomous agent to cross a group's path or go upstream of flow, by rejecting target locations that are upstream of a zone/area that has a higher density (is heavily crowded with pedestrians), and the like). "]-[0015], [0037 "The rules 144 may call for routes/course 145 to be evaluated to choose one which allows the robot 130 to “go with the flow” by moving in the same direction as the traffic (e.g., move with or adjacent to group 122 in area/zone 112) or to “avoid dense flow” such as by moving through areas with single-to-few pedestrians or other obstacles (lower pedestrian density values) and no or few groups of obstacles 122, 126."], [0047]-[0049 "The processor also can process the frame 310 to provide density data (as part of the traffic data output to an autonomous agent such as a mobile robot or robotic assembly with parts that move in the monitored workspace), and, as shown, the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians). Such density information may be very useful as input to a navigation module (such as module 136 in FIG. 1) of a robot as it may be useful to select lower density areas/zones for a route/course through this space (or portion of a mall, in this example) when the route selection rules indicate a route with a shortest travel time be chosen or to select the higher density areas/zones for a route/course when the route selection rules differ (e.g., the purpose of the robot is to interact with and/or entertain the most possible people in the space)."], [0053] and [0058]-[0059]); and
set a specific potential field in a multidimensional space as a physical workspace of the robot, the specific potential field including, in the multidimensional space, a potential indicating a degree of acceptability for the robot to pass through (see Figs. 3-6, all; [0012]-[0015], [0037 "In some cases, the rules 144 may call for the navigation module 136 to avoid areas/zones 112 with a density over a particular preset value ... In some embodiments, the robot 130 may be operated to achieve other goals than mere efficient movement through a space 110 such as to change traffic flow by moving into an area of low density to attract people or other obstacles into this lower density and/or lower traffic flow area in the workspace 110"], [0047]-[0049 "The processor also can process the frame 310 to provide density data (as part of the traffic data output to an autonomous agent such as a mobile robot or robotic assembly with parts that move in the monitored workspace), and, as shown, the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians). Such density information may be very useful as input to a navigation module (such as module 136 in FIG. 1) of a robot as it may be useful to select lower density areas/zones for a route/course through this space (or portion of a mall, in this example) when the route selection rules indicate a route with a shortest travel time be chosen or to select the higher density areas/zones for a route/course when the route selection rules differ (e.g., the purpose of the robot is to interact with and/or entertain the most possible people in the space)."], [0049 "...the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians)"] and [0053 "Exemplary route selection rules for the agent presently may be: (1) avoid areas of a workspace that have a density greater than “X”..."]); and
control the robot to move along the set motion path (see [0035 "As shown, the robot 130 includes a controller (e.g., a processor and/or software) 132 that issues control signals to a drive system 134 to cause the robot 130 to move 135 through the space 110."]-[0036] and [0053]);
wherein the at least one processor sets the use priority based on obstacle information about the obstacle (see Figs. 3-6, all; [0012]-[0015], [0037 "The rules 144 may call for routes/course 145 to be evaluated to choose one which allows the robot 130 to “go with the flow” by moving in the same direction as the traffic (e.g., move with or adjacent to group 122 in area/zone 112) or to “avoid dense flow” such as by moving through areas with single-to-few pedestrians or other obstacles (lower pedestrian density values) and no or few groups of obstacles 122, 126."], [0047]-[0049 "The processor also can process the frame 310 to provide density data (as part of the traffic data output to an autonomous agent such as a mobile robot or robotic assembly with parts that move in the monitored workspace), and, as shown, the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians). Such density information may be very useful as input to a navigation module (such as module 136 in FIG. 1) of a robot as it may be useful to select lower density areas/zones for a route/course through this space (or portion of a mall, in this example) when the route selection rules indicate a route with a shortest travel time be chosen or to select the higher density areas/zones for a route/course when the route selection rules differ (e.g., the purpose of the robot is to interact with and/or entertain the most possible people in the space)."] and [0053]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set the use priority based on obstacle information about the obstacle, as taught by Hager, in order to select a candidate motion path traveling through an area of relatively low obstacle density to reduce overall travel time.
Regarding Claim 33
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi is silent regarding wherein the at least one processor is further configured to calculate an obstacle density, and set the potential based on the calculated obstacle density for each of a plurality of positions in the multidimensional space, and the obstacle density varies based on a degree to which the obstacle occupies an area around a corresponding position of the plurality of positions.
Lee teaches wherein the at least one processor is further configured to calculate an obstacle density, and set the potential based on the calculated obstacle density for each of a plurality of positions in the multidimensional space, and the obstacle density varies based on a degree to which the obstacle occupies an area around a corresponding position of the plurality of positions (see Fig. 2, all; [0011 "According to an aspect of the present disclosure, an apparatus for generating multiple paths for a mobile robot includes a cost map generation module that generates a cost map in which a cost is differentially allocated to areas according to a distance from an obstacle by using map information on a space divided into a plurality of areas and obstacle information on an obstacle in the space"], [0015], [0050 "In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. Accordingly, the determination as to which area of areas is to be included in a path and which area of areas is to be avoided may be achieved merely by comparing the costs allocated to the areas with each other."]-[0055] and [0060 "In addition, the multi-waypoint detector 210 may set, as an area where there is no collision with an obstacle, an area allocated a cost of no greater than 8, which is lower than a cost of 10 indicating that a cost allocated to each area represents an area occupied by an obstacle on the cost map and is lower than a cost of 9 indicating that the cost allocated to each area represents a nearest area to the obstacle on the cost map, to avoid an area with collision with the obstacle. In this case, the multi-waypoint detector 210 may change and set a cost for determining an area without collision with obstacles to a value lower than or higher than 8."]).
For the sake of compact prosecution and for the possible argument that "Lee is silent regarding calculate an obstacle density", Hager teaches the claim features.
That is, Hager teaches wherein the at least one processor is further configured to calculate an obstacle density, and set the potential based on the calculated obstacle density for each of a plurality of positions in the multidimensional space (see Figs. 3-6, all; [0012]-[0015], [0037 "The rules 144 may call for routes/course 145 to be evaluated to choose one which allows the robot 130 to “go with the flow” by moving in the same direction as the traffic (e.g., move with or adjacent to group 122 in area/zone 112) or to “avoid dense flow” such as by moving through areas with single-to-few pedestrians or other obstacles (lower pedestrian density values) and no or few groups of obstacles 122, 126."], [0047]-[0049 "The processor also can process the frame 310 to provide density data (as part of the traffic data output to an autonomous agent such as a mobile robot or robotic assembly with parts that move in the monitored workspace), and, as shown, the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians). Such density information may be very useful as input to a navigation module (such as module 136 in FIG. 1) of a robot as it may be useful to select lower density areas/zones for a route/course through this space (or portion of a mall, in this example) when the route selection rules indicate a route with a shortest travel time be chosen or to select the higher density areas/zones for a route/course when the route selection rules differ (e.g., the purpose of the robot is to interact with and/or entertain the most possible people in the space)."] and [0053]), and the obstacle density varies based on a degree to which the obstacle occupies an area around a corresponding position of the plurality of positions (see Figs. 3-5, all; [0012]-[0015], [0037], [0047]-[0049 "...the floor/mall space 311 has a higher density of obstacles/pedestrians on the left side than on the right side and includes very low density areas/zones in the center (e.g., between groups 316 and 324 there are no obstacles/pedestrians)"] and [0053 "Exemplary route selection rules for the agent presently may be: (1) avoid areas of a workspace that have a density greater than “X”..."]]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to calculate an obstacle density which varies based on a degree to which the obstacle occupies an area around a corresponding position of the plurality of positions and to set the potential based on the calculated obstacle density for each of a plurality of positions in the multidimensional space, as taught by Lee or Hager, in order to select an optimal path which satisfies various conditions including traveling a minimum distance while keeping a safe distance from obstacles.
Claims 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi (as modified by Lee) as applied to claim 1 above, and further in view of Yabushita et al. (JP 2007257274 A and Yabushita hereinafter).
Regarding Claim 12
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi is silent regarding wherein at least one processor sets the use priority based on a past motion path being the motion path previously set.
Yabushita teaches a motion path setting device (see all Figs.; [0009]; see the corresponding paragraphs in the attached reference JP_2007257274_A), comprising:
at least one processor (see Fig. 2, processing unit 31; [0026 "As shown in the figure, the route search system 3 includes a processing unit 31 and a storage unit 32. The processing unit 31 includes a candidate route extraction unit 311, an evaluation value calculation unit 312, and a route determination unit 313."]) configured to:
set a use priority of each of a plurality of candidate motion paths of a robot (see Fig. 4, all; [0009 "Evaluation value calculation means for calculating each evaluation value of a plurality of candidate routes extracted by the candidate route extraction means, and route determination means for determining a route to be adopted based on the evaluation value calculated by the evaluation value calculation means The evaluation value calculation means calculates the evaluation value based on the route length from the reference point to the target point and the route history information adopted in the past."], [0010 "Here, the evaluation value calculation means assigns a point value to a node on a route that has been adopted in the past, and is calculated according to the sum of the point values assigned to the node on the candidate route and the route length. By calculating the evaluation value of the candidate route based on the obtained value, it is possible to easily realize a process of adopting a route similar to a route adopted in the past."]-[0012], [0025]-[0027], [0029 "Further, since the point value having a negative value is given when passing through the same node as the previous time, the evaluation value becomes smaller as it passes through the same node as the previous time. Specifically, the computer reads information on the candidate route from the storage means, obtains a route length (corresponding to a value calculated according to the route length) from the coordinates of each node on the route, and similarly each node on the route. The sum of the point values having a negative value is obtained based on the point value. "]-[0032] and [0039]);
select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see [0030 "In this manner, a route to be adopted is determined based on the evaluation value calculated for each of the plurality of candidate routes (S103). In this example, the smallest evaluation value and the calculated candidate route are selected as adopted routes. Specifically, the computer reads the evaluation value of each candidate route from the storage unit, selects a candidate route having the smallest evaluation value by performing a comparison process, and stores information on the adopted route in the storage unit."]); and
control the robot to move along the set motion path (see [0033]-[0034]);
wherein at least one processor sets the use priority based on a past motion path being the motion path previously set (see Fig. 4, all; [0009 "Evaluation value calculation means for calculating each evaluation value of a plurality of candidate routes extracted by the candidate route extraction means, and route determination means for determining a route to be adopted based on the evaluation value calculated by the evaluation value calculation means The evaluation value calculation means calculates the evaluation value based on the route length from the reference point to the target point and the route history information adopted in the past."], [0010 "Here, the evaluation value calculation means assigns a point value to a node on a route that has been adopted in the past, and is calculated according to the sum of the point values assigned to the node on the candidate route and the route length. By calculating the evaluation value of the candidate route based on the obtained value, it is possible to easily realize a process of adopting a route similar to a route adopted in the past."]-[0012], [0025]-[0027], [0029 "Further, since the point value having a negative value is given when passing through the same node as the previous time, the evaluation value becomes smaller as it passes through the same node as the previous time. Specifically, the computer reads information on the candidate route from the storage means, obtains a route length (corresponding to a value calculated according to the route length) from the coordinates of each node on the route, and similarly each node on the route. The sum of the point values having a negative value is obtained based on the point value. "]-[0032] and [0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set the use priority based on a past motion path being the motion path previously set, as taught by Yabushita, in order to generate a stable motion path similar to a motion path in the past and therefore to avoid switching to a completely different motion path each time path generation is performed.
Regarding Claim 17
Modified Okabayashi teaches the motion path setting device according to claim 12,
Okabayashi is silent regarding wherein the at least one processor sets the use priority based on a result of motion of the robot along the past motion path.
Yabushita teaches wherein the at least one processor sets the use priority based on a result of motion of the robot along the past motion path (see Fig. 4, all; [0009 "Evaluation value calculation means for calculating each evaluation value of a plurality of candidate routes extracted by the candidate route extraction means, and route determination means for determining a route to be adopted based on the evaluation value calculated by the evaluation value calculation means The evaluation value calculation means calculates the evaluation value based on the route length from the reference point to the target point and the route history information adopted in the past."], [0010 "Here, the evaluation value calculation means assigns a point value to a node on a route that has been adopted in the past, and is calculated according to the sum of the point values assigned to the node on the candidate route and the route length. By calculating the evaluation value of the candidate route based on the obtained value, it is possible to easily realize a process of adopting a route similar to a route adopted in the past."]-[0012], [0025]-[0027], [0029 "Further, since the point value having a negative value is given when passing through the same node as the previous time, the evaluation value becomes smaller as it passes through the same node as the previous time. Specifically, the computer reads information on the candidate route from the storage means, obtains a route length (corresponding to a value calculated according to the route length) from the coordinates of each node on the route, and similarly each node on the route. The sum of the point values having a negative value is obtained based on the point value. "]-[0032] and [0039]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set the use priority based on a result of motion of the robot along the past motion path, as taught by Yabushita, in order to generate a stable motion path similar to a motion path in the past and therefore to avoid switching to a completely different motion path each time path generation is performed.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi (as modified by Lee) as applied to claim 1 above, and further in view of Takeda (US 20150239121 A1 and Takeda hereinafter).
Regarding Claim 19
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi further teaches wherein the at least one processor sets the use priority based on a specific area in the multidimensional space (see Figs. 4 and 6-8, all; Col. 6, lines 52-58, "The priority order granting section 49 grants a priority order based on the execution environment, to the trajectory template T which is determined as available by the template limiting section 48."; Col. 12, lines 64-67 "A number "n" of the priority order is assigned, by the priority order assigning section 49, for the trajectory template T the use of which is determined as available by the template limiting section 48 at step 106.").
Okabayashi is silent regarding the specific area is an area through which the robot is to avoid passing.
Takeda teaches a motion path setting device (see all Figs.; [0009]), comprising:
at least one processor (see [0017 "In a preferred embodiment, the robot simulation device is incorporated in a controller for controlling an actual robot."] and [0081]) configured to:
set a use priority of each of a plurality of candidate motion paths of a robot (see [0009 "...an evaluating part which evaluates each of the second motion paths based on at least one previously determined parameter; and a motion path selecting part which selects an optimum motion path of the robot from the second motion paths based on an evaluation result by the evaluating part."], [0028]-[0030], [0041]-[0054] and [0057]-[0060]);
select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see [0009 "...an evaluating part which evaluates each of the second motion paths based on at least one previously determined parameter; and a motion path selecting part which selects an optimum motion path of the robot from the second motion paths based on an evaluation result by the evaluating part."]-[0013] and [0057 "Then, by selecting the motion path having the highest evaluation value, a more practical motion path may be obtained."]-[0060]); and
control the robot to move along the set motion path (see [0017 "In a preferred embodiment, the robot simulation device is incorporated in a controller for controlling an actual robot."] and [0081]);
wherein the at least one processor sets the use priority based on a specific area in the multidimensional space (see [0009], [0028]-[0030], [0041]-[0054] and [0057]-[0060], especially [0042 "As the parameter (or evaluation item) for calculating the above evaluation value, following parameters may be used, for example. The following parameters may be calculated or estimated by executing the simulation along the second motion path."]-[0044 "...(b) a minimum distance between the robot and the peripheral object..."] and [0054 "Regarding above parameter (b), when the distance between the robot and the peripheral object is too short, the interference easily occurs between the actual robot and the actual peripheral object. On the other hand, when the distance therebetween is too long, the evaluation value regarding the other parameters tends to be decreased. Therefore, in relation to parameter (b), a proper distance between the robot and the peripheral object may be predetermined (for example, 5 cm to 10 cm), and the evaluation value may be increased as a difference between the proper distance and the minimum distance is decreased."]), and the specific area is an area through which the robot is to avoid passing (see [0009 "...generates different second motion paths by which the interference between the robot and the peripheral object does not occur…"], [0028]-[0030 "In the next step S3, at least one third teaching point is automatically added between start point Pm and end point Pn so as to generate a second motion path 34 by which the interference between robot 12 and peripheral object 14 does not occur..."], [0041]-[0054] and [0057]-[0060]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set the use priority based on a specific area which is an area through which the robot is to avoid passing, as taught by Takeda, in order to select a candidate motion path which is properly positioned at a distance not too far from and not too close to an obstacle.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi (as modified by Lee) as applied to claim 1 above, and further in view of Dupuis et al. (US 20220147058 A1 and Dupuis hereinafter).
Regarding Claim 22
Modified Okabayashi teaches the motion path setting device according to claim 1,
Okabayashi is silent regarding wherein the at least one processor sets the use priority based on a reference path being a shortest path connecting a starting point and an ending point of motion of the robot in the multidimensional space.
Dupuis teaches a motion path setting device (see all Figs.; [0004]), comprising:
at least one processor (see Fig. 2, processors 202; [0084]) configured to:
set a use priority of each of a plurality of candidate motion paths of a robot (see [0075 "In some implementations, the server system 104 generates one or more scores to characterize properties for each of the adjusted paths. The scores may be generated at various steps along the path planning process to evaluate the different versions of the adjusted paths, and can be used to determine whether each adjusted path sufficiently meets the criteria or constraints. A score can be based on a variety of factors, such as deviation distance from the initial path 112, a number of turns, a path distance, magnitude of the amplitudes of the turns, a length of time it takes for the robot to traverse the adjusted path, and so on."]-[0076] and [0091]-[0096]);
select at least one candidate motion path of the plurality of candidate motion paths based on the use priority (see [0063] and [0091 "The robot 200 can select and move along the path with the highest score (e.g., the planned path). Additionally, the scoring engine 214 may compare each of the generated scores to thresholds to determine whether the corresponding path meets the desired criteria."]); and
control the robot to move along the set motion path (see [0091 "The robot 200 can select and move along the path with the highest score (e.g., the planned path). Additionally, the scoring engine 214 may compare each of the generated scores to thresholds to determine whether the corresponding path meets the desired criteria."]);
wherein the at least one processor sets the use priority based on a reference path being a shortest path connecting a starting point and an ending point of motion of the robot in the multidimensional space (see Fig. 1, all, especially straight-line/initial path 112; Fig. 3, all; [0050]-[0052 "The robot 110 can receive the sensor data and analyze the sensor data to determine the obstacles along the straight-line path 112 from the robot 110's current location to the destination."], [0063]-[0066], [0074] and [0091 "For example, the scoring engine 214 generates the score for various paths based on their characteristics. The characteristics used for scoring can include the distance that a path deviates from the initial path 112 ... The smaller number of turns and the shorter the overall path distance of the path, the higher the score generated by the scoring engine 214, for example. Alternatively, if the deviation distance from the initial path is high or a magnitude of the amplitudes of the turns is high, the scoring engine 214 may generate a lower score, for example. The path evaluation module 212 can generate multiple paths (each different from one another) for a particular environment. The scoring engine 214 can generate scores for each path of the multiple paths."]-[0096]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set the use priority based on a reference path being a shortest path connecting a starting point and an ending point of motion of the robot, as taught by Dupuis, in order to select a candidate motion path which is closest in distance to the reference path.
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Okabayashi (as modified by Lee and Hager) as applied to claim 33 above, and further in view of Satou (US 20150127161 A1 and Satou hereinafter).
Regarding Claim 34
Modified Okabayashi teaches the motion path setting device according to claim 33,
Okabayashi is silent regarding wherein the at least one processor is further configured to set a plurality of representative points on the obstacle in the multidimensional space, and set the obstacle density at a position in the multidimensional space based on at least one of the plurality of representative points located within a predetermined range from the position.
Satou teaches a motion path setting device (see all Figs.; [0007]), comprising:
at least one processor (see Figs. 1 and 15, robot control device 13; [0007 "...a robot control unit controlling the robot so as to move the hand to the hand position posture calculated by the hand position posture calculation unit to pick up the article."]) configured to:
select at least one candidate motion path of the plurality of candidate motion paths (see [0007 "...a hand position posture calculation unit calculating a hand position posture based on the density local maximum position calculated by the maximum position calculation unit, the hand position posture being a position and a posture of the hand capable of picking up an article at the density local maximum position or in the vicinity of the density local maximum position..."]); and
control the robot to move along the set motion path (see [0007 "...a robot control unit controlling the robot so as to move the hand to the hand position posture calculated by the hand position posture calculation unit to pick up the article."] and [0060]-[0061]);
wherein the at least one processor is further configured to set a plurality of representative points on the obstacle in the multidimensional space (see Figs. 3-8, all; [0007], [0033 "Initially, step S1 measures surfaces of a plurality of articles 20 randomly piled in a three-dimensional space using the three-dimensional measurement instrument 11 and then acquires a three-dimensional point set 30 ... In the figure, the three-dimensional points 31 are illustrated with black circles and the three-dimensional point set 30 is illustrated as a region surrounded by a dotted line including all the black circles."]-[0035] and [0049]-[0052 "The projection point 34 corresponds to the three-dimensional point 31 and therefore, a density distribution of projection points 34 can be considered a density distribution of three-dimensional points 31."]), and set the obstacle density at a position in the multidimensional space based on at least one of the plurality of representative points located within a predetermined range from the position (see Figs. 9-10, all, especially circle 37; [0007], [0049]-[0052 "Step S5 sets measurement points on each plane 33 over the entire range of the respective planes 33 and determines the number of projection points 34 located within a predetermined distance from each measurement point. The number corresponds to a density of projection points 34 in each measurement point, and step S5 calculates a density distribution of projection points 34 on the plane 33. The projection point 34 corresponds to the three-dimensional point 31 and therefore, a density distribution of projection points 34 can be considered a density distribution of three-dimensional points 31."] and [0053]-[0055]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to further modify the motion path setting device of modified Okabayashi to set a plurality of representative points on the obstacle in the multidimensional space and to set the obstacle density at a position based on at least one of the plurality of representative points located within a predetermined range from the position, as taught by Satou, in order to calculate obstacle density data of obstacles positioned in various orientations and to calculate a density local maximum position based on the obstacle density data.
Allowable Subject Matter
Claim 30 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 32 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TANNER L CULLEN/Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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