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 .
Response to Amendment
This action is in response to amendments and remarks filed on 08/18/2025. The examiner notes the following adjustments to the claims by the applicant: (i) Claims 1, 9, 17 and 21 are amended; and (ii) Claims 5, 13 and 20 are cancelled. Therefore, Claims 1-4, 6-12, 14-19 and 21 are pending examination, in which Claims 1, 9, 17 and 21 are independent claims.
In light of the instant amendments and arguments:
Regarding the rejection of Claims 1-21 under 35 U.S.C. § 101, the applicant’s arguments have been considered and found persuasive. The rejection is withdrawn.
Regarding the rejection of Claims 18-20 under 35 U.S.C. § 112(d), the rejection is maintained, as Claims 18-19 still depend from Claim 15, which is not of the same statutory category. [Note: Claim 20 has been cancelled]
Further examination resulted in a new rejection of Claims 1-4, 6-12, 14-19 and 21 under 35 U.S.C. § 103, as detailed below.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
To overcome the 35 U.S.C. § 103 rejection, the applicant has amended each independent claim to include the additional underlined limitations: "wherein determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.";
“Claims 1-3, 7-11, 15-18, and 21 stand rejected under 35 USC 103 as being unpatentable over US Publ. No. 2023/0085055 to Bhat (hereinafter “Bhat”) in view of US Pat. No. 11175664 to Boyraz (hereinafter “Boyraz”). To address this rejection, Applicant has amended claim 1 to incorporate features of cancelled claim 5, which now recites, among other things, “…at the processor, outputting a velocity pair for navigating movement of the robot along the set of next consecutive waypoints, based on the determined local planner; wherein determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.” Independent claims 9, 17 and 21 have been similarly amended, incorporating features of canceled claims 13 and 20, respectively.”;
“Claims 4-6, 12-14, and 19-20 stand rejected under 35 USC 103 as being unpatentable over Bhat and Boyraz and further in view of US Pat. No. 11527165 to Sprengart (hereinafter “Sprengart”). Applicant has canceled claims 5, 13, and 20, and incorporated their features into independent claims 1, 9, 17, and 21, respectively. Accordingly, the arguments against this rejection will be made with respect to amended claims 1, 9, 17, and 21. Applicant submits that none of the cited references, either alone or in combination, discloses or suggest all of the features now recited in amended claims 1, 9, 17, and 21.”.
Applicant's arguments A., B. and C. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
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.
Claims 1-4, 6-12, 14-19 and 21 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of BHAT (US 2023/0085055 A1) and Linh et al. (“All-in-one: A DRL-based control switch combining state-of-the-art navigation planners”), henceforth Linh.
Regarding Claim 1, BHAT discloses the limitations: a method for navigating a robot {“Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]; inclusive of the standard approach of combining a global planner algorithm and local planner algorithm to determine robot movements, Fig. 1}, the method comprising: at a mobile robot processor {controller 130, Fig. 1; “The controller 130 is configured to receive a local path and waypoints from the planning module 160 as well as the current pose from the odometry source 110.”, ¶[0021]}, generating a set of next consecutive waypoints for navigating movement of the robot {route determining system 100 in Fig.1 generates waypoint map in Fig. 2; “FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]}; at the processor, determining a local planner based on the set of next consecutive waypoints for navigating movement of the robot {with regard to the route determining system in Fig. 1, which includes local planning module 165 and local planner 167: “the processor is further configured to execute the instructions generating a path from the current pose to the pose at the first waypoint in response to selecting the first waypoint.”, ¶[0073]}; and at the processor,
outputting a velocity pair for navigating movement of the robot {in Fig. 1, controller 130 outputs “velocity commands”; “Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]} along the set of next consecutive waypoints, based on the determined local planner {“The controller, e.g., controller 130 (FIG. 1) uses the generated path and the set of waypoints to provide velocity commands to the vehicle either wirelessly or using a wired connection”, ¶[0038]; one skilled in the art knows that velocity is a vector quantity involving x, y and z components, which can be grouped into 3 different pairings}.
BHAT does not appear to explicitly recite the limitations: wherein determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
However, Linh explicitly recites the limitation: wherein determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear {legend for Fig. 1: “Our proposed all-in-one planner combines a variety of state-of-the-art planning approaches and trains an agent to decide which approach should be inferred in each specific situation. For instance, a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”; and “we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model based planners in long corridors or empty spaces.”, Abstract}.
BHAT and Linh are analogous art because they both deal with robotic navigation path planning using global and local planning algorithms.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of BHAT and Linh before them, to modify the teachings of BHAT to include the teachings of Linh to improve navigational performance in highly dynamic environments {Abstract}.
Regarding Claim 2, the combination of BHAT and Linh discloses all the limitations of Claim 1, as discussed supra. In addition, BHAT explicitly recites the limitation: generating a local cost map, and wherein the determining the local planner is further based on the local cost map {“The local planning module 165 further includes a local planner 167 configured to determine a path from the current pose to the pose at a selected waypoint based on the information from the local cost map 166”, ¶[0026]}.
Regarding Claim 3, the combination of BHAT and Linh discloses all the limitations of Claim 1, as discussed supra. In addition, BHAT explicitly recites the limitation: determining a plurality of path clearance statuses based on the set of next consecutive waypoints; filtering the plurality of path clearance statuses {“The waypoint cost block 430 is further configured to receive obstacle information 430. In some embodiments, the obstacle information 430 includes all known obstacles. In some embodiments, the obstacle information 430 only includes obstacles that pose a risk to the vehicle. For example, if an obstacle is located 5 meters above the ground and the vehicle is 2 meters tall, the obstacle is excluded from the obstacle information, in some embodiments, because the vehicle has no chance to collide with the obstacle 5 meters above the ground. In some embodiments, the obstacle information 430 is stored within a memory accessible by the waypoint cost block 440. In some embodiments, the obstacle information 430 is received from an external device, e.g., map server 140 (FIG. 1).”, ¶[0042]}.
BHAT does not appear to explicitly recite the limitation: wherein the determining the local planner is based on the filtered path clearance statuses.
However, Linh explicitly recites the limitation: wherein the determining the local planner is based on the filtered path clearance {legend for Fig. 1: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments.”}.
Regarding Claim 4, the combination of BHAT and Linh discloses all the limitations of Claim 1, as discussed supra. BHAT does not appear to explicitly recite the limitation: wherein the determining the local planner includes determining whether to use a traditional local planner or a reinforcement learning (RL) local planner.
However, Linh explicitly recites the limitation: wherein the determining the local planner includes determining whether to use a traditional local planner or a reinforcement learning (RL) local planner {legend for Fig. 1: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments.”}.
Regarding Claim 6, the combination of BHAT and Linh discloses all the limitations of Claim 1, as discussed supra. In addition, BHAT explicitly recites the limitation: generating an approximate path based on a global path and the set of next consecutive waypoints, based on a local cost map {“The local planning module 165 further includes a local planner 167 configured to determine a path from the current pose to the pose at a selected waypoint based on the information from the local cost map 166”, ¶[0026]}.
BHAT does not appear to explicitly recite the limitation: further comprising: determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map.
However, Linh explicitly recites the limitations: further comprising: determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map {deep-reinforced learning planner algorithm used when obstacle present and the less computationally intensive classic local planner algorithm used when a clear path ahead is present: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”, legend for Fig. 1; one skilled in the art will appreciate that a costmap is an inherent aspect of a deep-learning algorithm}.
Regarding Claim 7, the combination of BHAT and Linh discloses all the limitations of Claim 1, as discussed supra. In addition, BHAT explicitly recites the limitations: wherein the generating the set of next consecutive waypoints includes: discretizing a global path to generate discrete waypoints {“FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]}; and choosing, as the set of next consecutive waypoints {Fig. 2}, a number of next discrete waypoints from a current position of the robot on the global path {waypoint selection system 400, Fig. 4, receives obstacle information 430, ¶[0042], and in determining the waypoint total cost (formula in ¶[0059]) takes into account both obstacle proximity 470, Fig. 4 and local density: ” Rd is an obstacle proximity 470. In some embodiments, dopt is determined based on density of obstacles”}.
Regarding Claim 8, the combination of BHAT and Linh discloses all the limitations of Claim 7, as discussed supra. In addition, BHAT explicitly recites the limitations: wherein the choosing includes: determining, among the discrete waypoints, a first waypoint, after a closest waypoint to the current position of the robot {choosing the waypoint sequence is represented in Fig. 2 by waypoint 230, corresponding to the “goal for local planner”}; and wherein the choosing chooses the number of next discrete waypoints starting from the first waypoint {the waypoints following waypoint in 230 in Fig. 2, are the planned next set of waypoints to be navigated by a robot, ¶[0001], and are only deviated from if local planner 167, Fig. 7, deems the waypoint total cost, 495, Fig. 4 and ¶[0059], of each waypoint not to have the lowest cost, which includes evaluation for obstacles}.
Regarding Claim 9, BHAT discloses the limitations: a mobile robot {“Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]; inclusive of the standard approach of combining a global planner algorithm and local planner algorithm to determine robot movements, Fig. 1} comprising: at least one processor; and at least one memory storing instructions {controller 130, Fig. 1; “The controller 130 is configured to receive a local path and waypoints from the planning module 160 as well as the current pose from the odometry source 110.”, ¶[0021]} that, when executed by the at least one processor, cause the mobile robot to generate a set of next consecutive waypoints {route determining system 100 in Fig.1 generates waypoint map in Fig. 2; “FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]} for navigating movement of the mobile robot {“The controller, e.g., controller 130 (FIG. 1) uses the generated path and the set of waypoints to provide velocity commands to the vehicle either wirelessly or using a wired connection”, ¶[0038]; one skilled in the art knows that velocity is a vector quantity involving x, y and z components, which can be grouped into 3 different pairings}, and output a velocity pair {in Fig. 1, controller 130 outputs “velocity commands} for navigating movement of the mobile robot {“Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]} along the set of next consecutive waypoints, based on the determined local planner {with regard to the route determining system in Fig. 1, which includes local planning module 165 and local planner 167: “the processor is further configured to execute the instructions generating a path from the current pose to the pose at the first waypoint in response to selecting the first waypoint.”, ¶[0073]}.
BHAT does not appear to explicitly recite the limitations: determine to use a [traditional] local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determine to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
However, Linh explicitly recites the limitation: determine to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determine to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear {legend for Fig. 1: “Our proposed all-in-one planner combines a variety of state-of-the-art planning approaches and trains an agent to decide which approach should be inferred in each specific situation. For instance, a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”; and “we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model based planners in long corridors or empty spaces.”, Abstract}.
Regarding Claim 10, the combination of BHAT and Linh discloses all the limitations of Claim 9, as discussed supra. In addition, BHAT explicitly recites the limitation: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to: generate a local cost map; and determine the local planner further based on the local cost map {“The local planning module 165 further includes a local planner 167 configured to determine a path from the current pose to the pose at a selected waypoint based on the information from the local costmap 166”, ¶[0026]}.
Regarding Claim 11, the combination of BHAT and Linh discloses all the limitations of Claim 9, as discussed supra. In addition, BHAT explicitly recites the limitation: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to: determining a plurality of path clearance statuses based on the set of next consecutive waypoints; filtering the plurality of path clearance statuses {“The waypoint cost block 430 is further configured to receive obstacle information 430. In some embodiments, the obstacle information 430 includes all known obstacles. In some embodiments, the obstacle information 430 only includes obstacles that pose a risk to the vehicle. For example, if an obstacle is located 5 meters above the ground and the vehicle is 2 meters tall, the obstacle is excluded from the obstacle information, in some embodiments, because the vehicle has no chance to collide with the obstacle 5 meters above the ground. In some embodiments, the obstacle information 430 is stored within a memory accessible by the waypoint cost block 440. In some embodiments, the obstacle information 430 is received from an external device, e.g., map server 140 (FIG. 1).”, ¶[0042]}.
BHAT does not appear to explicitly recite the limitation: wherein the determining the local planner is based on the filtered path clearance statuses.
However, Linh explicitly recites the limitation: wherein the determining the local planner is based on the filtered path clearance {legend for Fig. 1: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments.”}.
Regarding Claim 12, the combination of BHAT and Linh discloses all the limitations of Claim 9, as discussed supra. BHAT does not appear to explicitly recite the limitation: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to determine whether to use a traditional local planner or a reinforcement learning (RL) local planner.
However, Linh explicitly recites the limitation: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to determine whether to use a traditional local planner or a reinforcement learning (RL) local planner {legend for Fig. 1: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments.”}.
Regarding Claim 14, the combination of BHAT and Linh discloses all the limitations of Claim 9, as discussed supra. In addition, BHAT explicitly recites the limitation: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to: generating an approximate path based on a global path and the set of next consecutive waypoints, based on a local cost map {“The local planning module 165 further includes a local planner 167 configured to determine a path from the current pose to the pose at a selected waypoint based on the information from the local cost map 166”, ¶[0026]}.
BHAT does not appear to explicitly recite the limitation: further comprising: determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map.
However, Linh explicitly recites the limitations: further comprising: determining whether all consecutive waypoints in the set of next consecutive waypoints are clear in response to no waypoint in the set of next consecutive waypoints being in a same position as an obstacle, based on a local cost map {deep-reinforced learning planner algorithm used when obstacle present and the less computationally intensive classic local planner algorithm used when a clear path ahead is present: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”, legend for Fig. 1; one skilled in the art will appreciate that a costmap is an inherent aspect of a deep-learning algorithm}.
Regarding Claim 15, the combination of BHAT and Linh discloses all the limitations of Claim 9, as discussed supra. In addition, BHAT explicitly recites the limitations: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the mobile robot to: discretize a global path to generate discrete waypoints {“FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]}; and choose, as the set of next consecutive waypoints {Fig. 2}, a number of next discrete waypoints from a current position of the robot on the global path {waypoint selection system 400, Fig. 4, receives obstacle information 430, ¶[0042], and in determining the waypoint total cost (formula in ¶[0059]) takes into account both obstacle proximity 470, Fig. 4 and local density: ” Rd is an obstacle proximity 470. In some embodiments, dopt is determined based on density of obstacles”}.
Regarding Claim 16, the combination of BHAT and Linh discloses all the limitations of Claim 15, as discussed supra. In addition, BHAT explicitly recites the limitations: wherein the at least one memory stores instructions that, when executed by the at least one processor, cause the apparatus to: determine, among the discrete waypoints, a first waypoint, after a closest waypoint to the current position of the robot {choosing the waypoint sequence is represented in Fig. 2 by waypoint 230, corresponding to the “goal for local planner”}; and wherein the choose chooses the number of next discrete waypoints starting from the first waypoint {the waypoints following waypoint in 230 in Fig. 2, are the planned next set of waypoints to be navigated by a robot, ¶[0001], and are only deviated from if local planner 167, Fig. 7, deems the waypoint total cost, 495, Fig. 4 and ¶[0059], of each waypoint not to have the lowest cost, which includes evaluation for obstacles}.
Regarding Claim 17, BHAT discloses the limitations: a non-transitory computer readable storage medium storing computer {controller 130, Fig. 1; “The controller 130 is configured to receive a local path and waypoints from the planning module 160 as well as the current pose from the odometry source 110.”, ¶[0021]} executable instructions {“A waypoint selection system includes a non-transitory computer readable medium configured to store instructions thereon”, Abstract} that, when executed at a mobile robot, cause the mobile robot to perform a method for navigating the mobile robot {“Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]; inclusive of the standard approach of combining a global planner algorithm and local planner algorithm to determine robot movements, Fig. 1}, the method comprising: generating a set of next consecutive waypoints for navigating movement of the mobile robot {route determining system 100 in Fig.1 generates waypoint map in Fig. 2; “FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]}; determining a local planner based on the set of next consecutive waypoints for navigating movement of the robot {with regard to the route determining system in Fig. 1, which includes local planning module 165 and local planner 167: “the processor is further configured to execute the instructions generating a path from the current pose to the pose at the first waypoint in response to selecting the first waypoint.”, ¶[0073]}; and at the processor, outputting a velocity pair for navigating movement of the robot {in Fig. 1, controller 130 outputs “velocity commands”} along the set of next consecutive waypoints, based on the determined local planner {“The controller, e.g., controller 130 (FIG. 1) uses the generated path and the set of waypoints to provide velocity commands to the vehicle either wirelessly or using a wired connection”, ¶[0038]; one skilled in the art knows that velocity is a vector quantity involving x, y and z components, which can be grouped into 3 different pairings}.
BHAT does not appear to explicitly recite the limitations: wherein the determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
However, Linh explicitly recites the limitation: wherein the determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear {legend for Fig. 1: “Our proposed all-in-one planner combines a variety of state-of-the-art planning approaches and trains an agent to decide which approach should be inferred in each specific situation. For instance, a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”; and “we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model based planners in long corridors or empty spaces.”, Abstract}.
Regarding Claim 18, the combination of BHAT and Linh discloses all the limitations of Claim supra. In addition, BHAT explicitly recites the limitation: further comprising: generating a local cost map, and wherein the determining the local planner is further based on the local cost map {“The local planning module 165 further includes a local planner 167 configured to determine a path from the current pose to the pose at a selected waypoint based on the information from the local costmap 166”, ¶[0026]}.
Regarding Claim 19, the combination of BHAT and Linh discloses all the limitations of Claim supra. BHAT does not appear to explicitly recite the limitation: wherein the determining the local planner includes determining whether to use a traditional local planner or a reinforcement learning (RL) local planner.
However, Linh explicitly recites the limitation: wherein the determining the local planner includes determining whether to use a traditional local planner or a reinforcement learning (RL) local planner {legend for Fig. 1: “a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments.”}.
Regarding Claim 21, BHAT discloses the limitations: a mobile robot {“Determining route for robots of vehicles to move from one location to another includes plotting waypoints between an initial location and a destination location”, ¶[0001]; inclusive of the standard approach of combining a global planner algorithm and local planner algorithm to determine robot movements, Fig. 1} comprising: at least one processor; and at least one memory storing instructions {controller 130, Fig. 1; “The controller 130 is configured to receive a local path and waypoints from the planning module 160 as well as the current pose from the odometry source 110.”, ¶[0021]} that, when executed by the at least one processor, cause the mobile robot to navigate based on a received velocity pair {in Fig. 1, controller 130 outputs “velocity commands”}, the received velocity pair based on a local planner for the mobile robot {with regard to the route determining system in Fig. 1, which includes local planning module 165 and local planner 167: “the processor is further configured to execute the instructions generating a path from the current pose to the pose at the first waypoint in response to selecting the first waypoint.”, ¶[0073]}, the local planner being determined based on a set of next consecutive waypoints {route determining system 100 in Fig.1 generates waypoint map in Fig. 2; “FIG. 2 is a schematic view of a route 200 including waypoints 205 in accordance with some embodiments. In some embodiments, the route 200 is a global path provided by a global planning module, e.g., the global planning module 162 (FIG. 1), to a local planning module, e.g., the local planning module 165 (FIG. 1).”, ¶[0027]} for the mobile robot {“The controller, e.g., controller 130 (FIG. 1) uses the generated path and the set of waypoints to provide velocity commands to the vehicle either wirelessly or using a wired connection”, ¶[0038]; one skilled in the art knows that velocity is a vector quantity involving x, y and z components, which can be grouped into 3 different pairings}.
BHAT does not appear to explicitly recite the limitations: wherein the determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear.
However, Linh explicitly recites the limitation: wherein the determining the local planner includes: determining to use a traditional local planner in response to all consecutive waypoints in the set of next consecutive waypoints being clear; and determining to use a reinforcement learning (RL) local planner in response to at least one consecutive waypoint in the set of next consecutive waypoints not being clear {legend for Fig. 1: “Our proposed all-in-one planner combines a variety of state-of-the-art planning approaches and trains an agent to decide which approach should be inferred in each specific situation. For instance, a DRL-based planner is inferred when there are multiple dynamic obstacles approaching, while a classic model-based planner is inferred in long corridors or maze-office environments. This way, the advantages of multiple planning approaches can be leveraged.”; and “we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model based planners in long corridors or empty spaces.”, Abstract}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 10,899,006 B2 - “A robot can dynamically switch between two-dimensional (2D) path planning and three-dimensional (3D) path planning based on conditions encountered. For example, the robot can use a 2D path planner most of the time for computational efficiency…The 2D planner further uses a 2D profile of the robot, e.g., a projection of the robot onto the plane of the floor, to determine whether there is a collision-free path for the robot around the objects in the space. If the robot identifies a collision-free path using the 2D occupancy grid, the robot can safely navigate using the 2D technique while avoiding the higher computational demands of 3D analysis of potential obstacles.”, ¶[0003].
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD EDWIN GEIST whose telephone number is (703)756-5854. The examiner can normally be reached Monday-Friday, 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665