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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 6, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
In response to Applicant’s amendments filed October 23, 2025, Examiner withdraws the prior art rejection under 35 U.S.C. 103 and puts out new grounds of rejection under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments, filed October 23, 2025, with respect to the rejections under 35 U.S.C. 103, regarding combination of Kattepur, Halder, and Blonder, with respect to assigning missions based on a “a failure rate for the given robot and the quality level of performance of the given mission” have been considered but is moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-28 are rejected under 35 U.S.C. 103 as being unpatentable by Kattepur (US 20190049975 A1), previously of record, herein after referred to simply as Kattepur, further in view of Halder (US 20200150687 A1), previously of record, Blonder (US 20220250658 A1), previously of record, and Dubey (US 20170359273 A1), newly of record, herein after referred to as Kattepur, Halder, Blonder, and Dubey respectively.
Regarding Claim 21,
Kattepur discloses the following limitations,
A computer-implemented method of managing a fleet of robots, (Abstract, “This disclosure relates generally to autonomous devices, and more particularly to method and system to optimally allocate warehouse procurement tasks to distributed autonomous devices.”)
the method comprising: receiving a plurality of missions … (Paragraph [0008], “The one or more hardware processors are further configured by the instructions to profile the global task to obtain a set of sub-tasks and identifying one or more constraints associated with the set of sub-tasks” where a set of sub-tasks is a mission, and there are a plurality of missions, e.g., Paragraph [0049], “For instance, rather than processing each order individually, the server agent may queue up a batch of tasks that need to be completed. These tasks may be sub-divided among multiple delivery and picking agents to optimize procurement of products across multiple warehouse racks. – where the overall task is subdivided into a plurality of missions, which are assigned to respective selected robots.)
for each mission among the plurality of missions, determining at least one mission priority for the mission indicating information related to accomplishing the mission (Paragraph [0052], “Further, the system 300 identifies one or more constraints associated with the set of sub-tasks. Herein, a constraint of the one or more constraints may include one of a utilization constraint and one or more pricing constraints. The utilization constraint may include maximization of resource utilization of the plurality of robotic agents. The one or more pricing constraints may include minimization of latency and minimization of energy usage.” where the utilization or pricing constraints are mission priorities for the plurality of missions)
creating a mission profile for each mission among the plurality of missions based on the mission and the respective at least one mission priority (Paragraph [0048], “Finally, the server agent forwards a set of sub-tasks associated with the set of item lists to the plurality of delivery agents. Each sub-task among the set of sub-tasks corresponds to procuring and/or dropping items from an item list from the set of item list.” – where the forwarded set of sub-tasks, to be executed by some number of agents, is a mission profile, and the forwarded lists depends on the contents of the mission itself and the assigned mission constraints)
determining a mission schedule for the plurality of missions, the determined mission schedule maximizing or minimizing a measure of the mission priority (Paragraph [0052], “The utilization constraint may include maximization of resource utilization of the plurality of robotic agents. The one or more pricing constraints may include minimization of latency and minimization of energy usage.” – where the missions are maximized or minimized based on a measure, such as utilization, latency, or energy usage, and, as there is an overall optimization, an overall mission schedule is produced, Paragraph [0035], “the coordinating agent provides an update of constraints to remaining robotic agents based on global task constraints. The remaining robotic agents perform optimization based on the updates received from the coordinating agent.”)
assigning each mission among the plurality of missions to a respective selected robot among the fleet according to the determined mission schedule (Paragraph [0049], “For instance, rather than processing each order individually, the server agent may queue up a batch of tasks that need to be completed. These tasks may be sub-divided among multiple delivery and picking agents to optimize procurement of products across multiple warehouse racks. – where the overall task is subdivided into a plurality of missions, which are assigned to respective selected robots.)
instructing each robot among the fleet of robots to perform the respective assigned mission (Paragraph [0047], “The procurement task may be a global task to be performed by the plurality of robotic agents in the warehouse environment.” – where performing the task involves instructing the robot to perform the assigned missions)
However, Kattepur does not teach the following limitation,
receiving a plurality of missions that each include a plurality of corresponding tasks
Kattepur, as shown, discloses an overall optimization of a global task, by dividing the task into subtasks, where the subtasks are carried out by agents (Paragraph [0009], “Further, the method includes profiling the global task to obtain a set of sub-tasks and identifying one or more constraints associated with the set of sub-tasks.”). These are interpreted as the mission schedule, and mission profile. However, Kattepur does not explicitly describe that a singular vehicle has a list of tasks that it accomplishes in order, such that each mission comprises a plurality of tasks. For example, it does not describe (1) driving to a location, (2) receiving an article, and then (3) moving to another location as 2 or 3 distinct tasks in the case of a delivery agent. However, this is taught by Halder, which teaches a discrete set of tasks assigned to a particular robot, which is capable of determining if the vehicle can perform tasks in a specified order, and rearrange tasks if needed (Paragraph [0071] “As part of 406, when needed, an AM can make small updates to the sequence of unit tasks performed by the AM. For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task. For example, when an AM encounters an obstacle or a hindrance during the performance of a unit task, the AM may reset it's time to complete the unit task, and the subtask, to a longer time, or can re-route or update the sequence of unit steps to complete the subtask.”). Note that in Halder, the robot activity is organized into tasks, subtasks of a task, and unit tasks of a task. (Paragraph [0052], “the task execution subsystem 220 is configured to determine a set of subtasks to be performed by the AM 218 related to the task. The task execution subsystem 220 may then cause these subtasks to be autonomously performed by the AM 218. In certain embodiments, a set of one or more unit tasks may be determined for a subtask.”). As read onto the claim language, the subtasks of Halder constitute missions and the unit tasks are tasks of a mission.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task allocation of Kattepur with the individual task sequence of Halder, as it describes features which may be implicit in Kattepur, and further, the method allows an individual vehicle to optimize its completion of its own unique and particular set of tasks, improving the flexibility and efficiency of the system (Paragraph [0071], “For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task.”). Further, the combination could be performed using known methods, yielding predictable results.
However, the combination of Kattepur and Halder does not teach the following limitation,
the mission priority being for the mission including the plurality of corresponding tasks for the mission
Kattepur, as shown, discloses an overall optimization of a global task (“Further, the method includes profiling the global task to obtain a set of sub-tasks and identifying one or more constraints associated with the set of sub-tasks. A constraint of the one or more constraints includes one of a utilization constraint and one or more pricing constraints. Furthermore, the method includes performing, distributed, decentralized optimal task allocation amongst the plurality of robotic agents based at least on the one or more constraints, to obtain optimal performance of the plurality of robotic agents in accomplishment of the global task.”). However, Kattepur appears to perform a global optimization, and the constraints do not straightforwardly constitute mission priorities for a mission composed of tasks (For example, as seen in Paragraph [0034], “In the ‘procurement latency’ mode, the autonomous delivery robots must coordinate amongst themselves to procure the set of items within a given latency constraint. In ‘energy limitation’ mode, the delivery robots accept tasks as long as overall battery degradation of each agent is minimized.”)
However, this is taught by Blonder, which teaches that missions have parameters (Paragraph [0167], “Optionally, the mission engine 230 splits the inspection mission to a plurality of sub-missions where each of the sub-missions is directed to acquire a respective one of a plurality of portions of the required inspection data. The mission engine 230 may compute mission parameters for each of the sub-missions and may further select a plurality of capable autonomous vehicles 202 which are each identified, based on analysis of their operational parameters with respect to the mission parameters, as capable to carry out a respective one of the plurality of sub-missions and acquire the respective portion of the required inspection data defined for acquiring during the respective sub-mission.” – the mission is divided into sub-missions, accordant with how a task is divided into subtask of Holder. The sub-missions have mission priorities, and mission parameters for each sub-mission are identified. The sub-mission accords with the subtask, which reads onto the claim language of a mission. The mission (identified within Blonder as the sub-mission) is known to constitute a plurality of unit tasks, as taught above by Holder. The mission priority of the plurality of sub-missions may constitute a measure of quality (Paragraph [0067], “Optionally, the mission engine may initiate one or more additional inspection missions to acquire additional inspection data in case the acquired inspection data is incompliant, for example, partial, incomplete, insufficient, insufficiently accurate, under quality and/or the like.” And Paragraph [0180], “determine the required inspection data to robustly inspect the asset 204, compute more accurate mission parameters for future inspection mission of the asset 204 which eventually may significantly improve accuracy, quality, completeness, reliability and/or the like of the inspection data relating to the asset 204.”) or a measure of time (Paragraph [0150], “The optimization function(s) may be directed to minimize one or more operational objectives of the inspection mission, for example, a shortest route of the selected capable autonomous vehicle(s) 202, a lowest operational cost of the selected capable autonomous vehicle(s) 202, a minimal number of autonomous vehicle(s) 202, a shortest mission time of the inspection mission, an earliest completion time of the inspection mission, a maximal utilization of the plurality of autonomous vehicles 202 and/or the like.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the mission planning of Kattepur, as previously modified by Halder, with the mission priorities assigned to missions composed of tasks as taught by Blonder, as this allows for a fleet of vehicles to complete a set of missions to a known degree of quality (Paragraph [0067], “Optionally, the mission engine may initiate one or more additional inspection missions to acquire additional inspection data in case the acquired inspection data is incompliant, for example, partial, incomplete, insufficient, insufficiently accurate, under quality and/or the like.” and Paragraph [0167], “The mission engine 230 may compute operation instructions accordingly for each of the plurality of selected capable autonomous vehicles 202 to operate the respective selected capable autonomous vehicle 202 to carry out its respective inspection mission and acquire its respective portion of the required inspection data.”). Further, the combination constitutes a simple substitution of elements yielding predictable results.
However, the combination of Kattepur, Halder, and Blonder does not teach the following limitation,
including assigning the given mission to a given robot based on a failure rate for the given robot and the quality level of performance of the given mission
However, this is taught by Dubey (Paragraph [0087], “The DRAM 112 may perform what-if analyses to help assess resources if, e.g., the pre-defined scores and metrics are not sufficient to make a selection decision. In one implementation, the DRAM 112 predicts the likelihood of task completion and the quality of the completed task if done by a particular resource. The DRAM 112 may execute the analysis on the historical data about tasks and resources, using a machine learning module which trains models for task completion and task quality. The trained model may predict task completion and the quality of the completed task.” – note that the measures are associated with both with the particular resource and the potential task, i.e., historical performance of the resource and known features or requirements of the task. Further, a resource may be a robot, Paragraph [0022], “A few examples of resource types include: software programs; trained and untrained machine learning models; artificial intelligence engines; robots;”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combination of Kattepur, Halder, and Blonder with a planning of failure rate alongside quality level, as this may assist the optimal selection and dispatching of a set of robots (Paragraph [0087], “The DRAM 112 may perform what-if analyses to help assess resources if, e.g., the pre-defined scores and metrics are not sufficient to make a selection decision.”). Further, the combination is a simple substitution of elements yielding results which are predictable to one of ordinary skill in the art.
Regarding Claim 22,
The combination of Kattepur, Halder, Blonder and Dubey, as shown, teaches all the limitations of Claim 21. Kattepur further discloses the following limitations,
wherein the corresponding tasks for a given mission among the plurality of missions include a first task, (Paragraph [0048], “The server agent creates an aggregated list of the set of items associated with each task. The server agent further categorizes the aggregated list into a set of item lists based on the item dimensions and/or weight and the location coordinates of each item. Finally, the server agent forwards a set of sub-tasks associated with the set of item lists to the plurality of delivery agents.” -– where a mission is a set of sub-tasks, and the sub-tasks are the plurality of tasks)
and wherein the given mission includes at least one factor, the at least one factor being an order in which the corresponding tasks for the given mission are to be performed (Paragraph [0047], “The remaining delivery agents/robots A.sub.D then approach the picker agent/robots A.sub.P to procure specific items in the provided list.” – where a delivery agent must first receive an article from a picker agent, or vis versa, to stock or unstock a shelf, and thus, subtasks are ordered)
Regarding Claim 23,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 22. Kattepur further discloses the following limitations,
wherein the corresponding tasks for the given mission further include a second task, wherein the order of the at least one factor instructs that the first task is to be completed before the second task (Paragraph [0027], “Due to the decomposition of tasks to agents, the typical AGV dependent warehouses can be upgraded with robotic Autonomous Mobile Robot (AMR) deployments. AMRs have more powerful on-board computational power that can understand dynamically changing environments, produce a real-time map of surroundings and efficiently plan paths to destinations. In warehousing environments where there are dynamic variations in demand and supply of inventory, assigning tasks to robotic AMR agents can be modeled as optimization problems. Typically, such optimizations are solved at a central entity such as Warehouse Management Systems, which are neither scalable nor can handle autonomous entities.” – where, because the missions fulfill dynamic goals, as the goals change, e.g. varying demand, or new information about an environment during navigation, tasks are redefined, and, as the system of Kattepur operates in real-time, a second task can be created or may already exist which is different from a first task, Finally, (Paragraph [0047], “The remaining delivery agents/robots A.sub.D then approach the picker agent/robots A.sub.P to procure specific items in the provided list.” – where subtasks are ordered. This applies even if there is a dynamic update to the system. A first task is to be completed before the second task.
Regarding Claim 24,
The combination of Kattepur, Halder, Blonder, and Dubey as shown, teaches all the limitations of Claim 23. Kattepur further discloses the following limitation,
receiving, by the processor of the system, a capability of each robot from the fleet of robots (Paragraph [0032], “Once a set of products are to be procured by the server agent A.sub.S, the products are subdivided based on dimensions (length, breadth, weight) and location coordinates (rack, row, column). These are then assigned to the delivery agents A.sub.D that have a limited capacity of battery lifetime, load bearing capacity and load bearing dimensions.” – where the vehicles have known battery status and load bearing abilities which is relevant to what subtasks are assigned to them, see also, Paragraph [0033], “While in the computational setting, resources may include CPU cycles, memory, network and battery capacities, in case of such cyber-physical interactions, allocating tasks with physical and environmental constraints is also important.”)
However, Kattepur does not disclose the following limitations,
determining, by the processor of the system, if the capability of each robot enables the robot to perform the task and the second task in the order indicated by the at least one factor of each of the first and the second task; and
if the robot is capable of performing the first task and the second task in the order, assigning, by the processor of the system, the first task and the second task to the robot, wherein assigning the first task and the second task to the robot comprises assigning the order of the first task and the second task
Kattepur, as shown, discloses an overall optimization of a global task, by dividing the task into sets of subtasks, where the subtasks or sets of subtasks are carried out by agents. These are interpreted as the mission schedule, a mission profile, and a plurality of tasks respectively. However, Kattepur does not explicitly describe that a singular vehicle has a list of tasks that it accomplishes in order. For example, it does not describe (1) driving to a location, (2) receiving an article, and then (3) moving to another location as 2 or 3 distinct tasks in the case of a delivery agent. The existence of a first and second task was shown because, in a collaboration between delivery and picker robots, the mission is associated with the list of items, and it includes at least a distinct sequence of delivering and then picking those items.. However, this does not disclose a first and second task which are both assigned to a particular robot. However, this is taught by Halder, which teaches a discrete set of tasks assigned to a particular robot, which is capable of determining if the vehicle can perform tasks in a specified order, and rearrange tasks if needed (Paragraph [0071] “As part of 406, when needed, an AM can make small updates to the sequence of unit tasks performed by the AM. For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task. For example, when an AM encounters an obstacle or a hindrance during the performance of a unit task, the AM may reset it's time to complete the unit task, and the subtask, to a longer time, or can re-route or update the sequence of unit steps to complete the subtask.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task allocation of Kattepur with the individual task sequence of Halder, as it describes features which may be implicit in Kattepur, and further, the method allows an individual vehicle to optimize its completion of its own unique and particular set of tasks, improving the flexibility and efficiency of the system (Paragraph [0071], “For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task.”). Further, the combination could be performed using known methods, yielding predictable results.
Regarding Claim 25,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 24. Halder further already teaches the following limitations,
wherein each capability is updated based on a passage of time between the creation of the mission profile and the determining of the mission schedule (Paragraph [0071] “As part of 406, when needed, an AM can make small updates to the sequence of unit tasks performed by the AM. For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task. For example, when an AM encounters an obstacle or a hindrance during the performance of a unit task, the AM may reset it's time to complete the unit task, and the subtask, to a longer time, or can re-route or update the sequence of unit steps to complete the subtask.” where ability to reach a destination is part of a vehicles capabilities, and such an ability may change)
Regarding Claim 26,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 21. Kattepur further discloses the following limitation,
receiving feedback, wherein the feedback includes information relating to an object along a route for accession a location of a step in the mission (Paragraph [0027], “AMRs have more powerful on-board computational power that can understand dynamically changing environments, produce a real-time map of surroundings and efficiently plan paths to destinations.” and Paragraph [0086], “The KUKA KMP 1500 robot is an autonomous platform that may be deployed on warehouse floors. By the use of safety laser scanners and wheel sensors, the robots are able to perform real time Simultaneous Localization and Mapping.” – where the vehicles receive feedback that allows for dynamic navigation based on objects in their environment)
However, Kattepur does not explicitly describe the following limitation,
wherein a position of the object prevents the selected robot from accessing the location via the route;
receiving map data to determine one or more routes for accessing the location of a step of the mission; and
altering the data in the mission profile, wherein altering the data in the mission profile causes the mission assigned to the selected robot to be rescheduled, such that the selected robot performs the mission in an order relative to all other missions assigned to the selected robot based on the altered data
However, this is taught by Halder, which teaches a discrete set of tasks assigned to a particular robot, which is capable of determining if the vehicle can perform tasks in a specified order, and rearrange tasks if needed (Paragraph [0071] “As part of 406, when needed, an AM can make small updates to the sequence of unit tasks performed by the AM. For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task. For example, when an AM encounters an obstacle or a hindrance during the performance of a unit task, the AM may reset it's time to complete the unit task, and the subtask, to a longer time, or can re-route or update the sequence of unit steps to complete the subtask.”) including the reallocation of tasks between vehicles (Paragraph [0106], “This may include monitoring the states of the resources performing the tasks, monitoring the actual times taken to perform the tasks or subtasks, or unit tasks and comparing them with expected completion times, monitoring for any unplanned or unforeseen incidences (e.g., failure of an AM, obstacles that prevent an AM from performing its task or cause a delay in the performance of the task, etc.) Certain states may cause the FMS 102 to go back to 808 and perform a reallocation of certain resources based upon the current state information monitored in 812.” and Paragraph [0146], “In certain embodiments, the resource allocation subsystem 206 can use the confidence measure associated with a task to determine if the execution of the task is progressing as expected and can take corrective actions (e.g., perform a reallocation of the available AMs) in response to the task or a subtask or unit task related to the task taking much longer to complete compared to its expected completion time.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task allocation of Kattepur with the task balancing of Halder as based on navigability, as it describes features which may be implicit in Kattepur, and further, the method allows for the optimization of resources even if a vehicle is blocked (Paragraph [0106], “This may include monitoring the states of the resources performing the tasks, monitoring the actual times taken to perform the tasks or subtasks, or unit tasks and comparing them with expected completion times, monitoring for any unplanned or unforeseen incidences (e.g., failure of an AM, obstacles that prevent an AM from performing its task or cause a delay in the performance of the task, etc.)”). Further, the combination could be performed using known methods yielding predictable results.
Regarding Claim 27,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 22. Kattepur further discloses the following limitations,
determining, by a processor of a system in which the fleet of robots is configured to operate, the fleet of robots (Paragraph [0007], “The method includes obtaining, at a coordinating agent of a plurality of robotic agents, a global task associated with the warehouse and an information associated with the plurality of robotic agents available for the global task,”)
receiving, by the processor of the system, for each robot in the fleet of robots, a robot capability defining a feature of the respective robot, wherein the robot capability is transmitted from the respective robot or a storage storing capabilities of the fleet (Paragraph [0032], “Once a set of products are to be procured by the server agent A.sub.S, the products are subdivided based on dimensions (length, breadth, weight) and location coordinates (rack, row, column). These are then assigned to the delivery agents A.sub.D that have a limited capacity of battery lifetime, load bearing capacity and load bearing dimensions. The delivery robots AD then approach the picker robots/agents AP to procure specific items in the provided list.” – where the vehicles have known battery status and load bearing abilities, which is relevant to what subtasks are assigned to them, and further, the vehicles are understood as having distinct toolsets that allow them to accomplish different kinds of tasks, see also, Paragraph [0033], “[0033] While in the computational setting, resources may include CPU cycles, memory, network and battery capacities, in case of such cyber-physical interactions, allocating tasks with physical and environmental constraints is also important.” And further, as a computer system, this information is at least stored in a storage,)
determining, by the processor of the system, if at least one mission capability of the mission matches the robot capability of a robot selected from the fleet of robots (Paragraph [0032], “Once a set of products are to be procured by the server agent A.sub.S, the products are subdivided based on dimensions (length, breadth, weight) and location coordinates (rack, row, column). These are then assigned to the delivery agents A.sub.D that have a limited capacity of battery lifetime, load bearing capacity and load bearing dimensions.”)
and assigning, by the processor of the system, the mission to the selected robot if the mission capability of the at least one mission matches the robot capability of the selected robot (Paragraph [0032], “These are then assigned to the delivery agents A.sub.D that have a limited capacity of battery lifetime, load bearing capacity and load bearing dimensions.”)
Regarding Claim 28,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 27. Kattepur further discloses the following limitations,
receiving, by a processor of a system in which the fleet of robots is configured to operate, a feedback (Paragraph [0027], “AMRs have more powerful on-board computational power that can understand dynamically changing environments, produce a real-time map of surroundings and efficiently plan paths to destinations.” and Paragraph [0086], “The KUKA KMP 1500 robot is an autonomous platform that may be deployed on warehouse floors. By the use of safety laser scanners and wheel sensors, the robots are able to perform real time Simultaneous Localization and Mapping.” – where the vehicles receive feedback that allows for dynamic navigation based on objects in their environment)
However, Kattepur does not disclose the following limitations,
updating, by the processor of the system, the mission profile based on the feedback, wherein the feedback indicates a change to one or more of the at least one factor, or the at least one mission capability
However, this is taught by Halder, which teaches a discrete set of tasks assigned to a particular robot, which is capable of determining if the vehicle can perform tasks in a specified order, and rearrange tasks if needed, where the factor is an order of the tasks, and a mission capability includes the ability of a robot to reach its destination (Paragraph [0071] “As part of 406, when needed, an AM can make small updates to the sequence of unit tasks performed by the AM. For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task. For example, when an AM encounters an obstacle or a hindrance during the performance of a unit task, the AM may reset it's time to complete the unit task, and the subtask, to a longer time, or can re-route or update the sequence of unit steps to complete the subtask.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the task allocation of Kattepur with the individual task sequence of Halder, as it describes features which may be implicit in Kattepur, and further, the method allows an individual vehicle to optimize its completion of its own unique and particular set of tasks, improving the flexibility and efficiency of the system (Paragraph [0071], “For example, these changes may be made to accommodate incidents such as problems encountered during the performance of a unit task.”). Further, the combination could be performed using known methods, yielding predictable results.
Claims 29 and 42-45 rejected under 35 U.S.C. 103 as being obvious over the combination of Kattepur, Halder, Blonder, and Dubey, further in view of Stevens (US 20210047121 A1), previously of record, herein after referred to simply as Stevens.
Regarding Claim 29,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 28. Kattepur further discloses the following limitations,
the at least one factor includes information relating to a first tool package that the selected robot from the fleet of robots is capable of using to achieve the mission, the first tool package being of a first type having a capability specific to the mission (Paragraph [0032], “Once a set of products are to be procured by the server agent AS, the products are subdivided based on dimensions (length, breadth, weight) and location coordinates (rack, row, column). These are then assigned to the delivery agents AD that have a limited capacity of battery lifetime, load bearing capacity and load bearing dimensions. The delivery robots AD then approach the picker robots/agents AP to procure specific items in the provided list.” where robots are identified as having a picker or delivery ability, and further, Table 1, where robot type is considered as a parameter, and further, Figure 1, where visually distinct robots are shown.)
However, Kattepur does not disclose the following limitation,
and wherein the feedback includes a change to the tool package
However, this is taught by Stevens, which describes a modular set of tools and robots which are interoperable and exchangeable (Paragraph [0084], “Each FAM of a group of FAMs has at least one function, capability or physical attribute which is missing in the vehicles and in the FAMs of a different group. In embodiments, the vehicles and the FAM(s) cooperate synergistically to perform various tasks according to the manner in which each vehicle is operated and the specific FAM(s) with which that vehicle is paired at a given time. By replacing one FAM or set of FAMs with one or more other FAMs, any of the vehicles can be readily configured to perform an alternate, or an additional, set of inventory management tasks.”), where, in performing tasks based on a particular pairing, the tool package provided by a particular FAM to a particular vehicle is tracked, as would be relevant to enabling the modular system to complete a set of tasks, e.g., Paragraph [0214], “The AGV and FAM selectors, 1467 and 1468, respectively, utilize available position, power and remaining subtask data to select an appropriate utilization of AGV and FAM resources to complete any subtasks which would otherwise remain unfinished by another AGV, and where possible, to ensure tasks are assigned to those AGVs and FAMs which have the resources to complete them.”), where the FAM provide functionality like lifting (Paragraph [0050] “To that end, each FAM 50 defines an interior column dimensioned and arranged to enable any of vehicles 12, while in the position shown occupied by vehicle 12b, to move vertically (up or down) within the FAM”) and storage (Paragraph [0090], “Each FAM 18 of the first group includes a base 20, a vertical support or stalk 22 extending in an upward direction from base 20, and a plurality of item storage cells 24 mounted on stalk 22.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have robotic management of Kattepur with the modular robots as taught by Stevens, as this improves the efficiency of a robotic system, particularly in the shared context of an inventory management system (Paragraph [0004], “In accordance with embodiments of the present disclosure, the disadvantages and problems associated with conventional warehouse automation approaches have been substantially reduced or eliminated by one or more vehicles configurable to perform a variety of tasks relevant to an inventory management operation.”). Further, the combination is a simple substitution of elements yielding predictable results.
Regarding Claim 42,
The combination of Kattepur, Halder, Blonder, Dubey, and Stevens, as shown, teaches all the limitations of Claim 29. Stevens further already discloses the following limitations,
wherein the selected robot is associated with a second tool package of a second type having a capability specific to a second mission different from the mission (Paragraph [0084], “Each FAM of a group of FAMs has at least one function, capability or physical attribute which is missing in the vehicles and in the FAMs of a different group.”)
wherein the second tool package is configured to be removed from the selected robot and replaced by the first tool package (Paragraph [0084], “By replacing one FAM or set of FAMs with one or more other FAMs, any of the vehicles can be readily configured to perform an alternate, or an additional, set of inventory management tasks.” – where the ability to reconfigure a robot having an alternate tool package so as to be configured for a next requested task is the advantage of the modular robotic system)
the method further comprising: receiving, by a processor of a system, a change in a capability of the selected robot, wherein the change in the capability includes one or more of … removal of the first tool package, replacement of first tool package (Paragraph [0214], “The AGV and FAM selectors, 1467 and 1468, respectively, utilize available position, power and remaining subtask data to select an appropriate utilization of AGV and FAM resources to complete any subtasks which would otherwise remain unfinished by another AGV, and where possible, to ensure tasks are assigned to those AGVs and FAMs which have the resources to complete them.” where, in performing tasks based on a particular pairing, the tool package provided by a particular FAM to a particular vehicle is tracked)
Regarding Claim 43,
The combination of Kattepur, Halder, Blonder, Dubey, and Stevens, as shown, teaches all the limitations of Claim 42. Stevens further teaches the following limitation,
receiving feedback, by one or more of a processor of a system or a processor of a robot from the fleet of robots, wherein the feedback includes a change to one or more tools of the first tool package (Paragraph [0214], “The AGV and FAM selectors, 1467 and 1468, respectively, utilize available position, power and remaining subtask data to select an appropriate utilization of AGV and FAM resources to complete any subtasks which would otherwise remain unfinished by another AGV, and where possible, to ensure tasks are assigned to those AGVs and FAMs which have the resources to complete them.” where, in performing tasks based on a particular pairing, the tool package provided by a particular FAM to a particular vehicle is tracked)
Regarding Claim 44,
The combination of Kattepur, Halder, Blonder, Dubey, and Stevens, as shown, teaches all the limitations of Claim 42. Stevens further already teaches the following limitation,
wherein the first tool package includes one or more of …. physical storage (Paragraph [0090], “Each FAM 18 of the first group includes a base 20, a vertical support or stalk 22 extending in an upward direction from base 20, and a plurality of item storage cells 24 mounted on stalk 22.”)
Regarding Claim 45,
The combination of Kattepur, Halder, Blonder, Dubey, and Stevens, as shown, teaches all the limitations of Claim 42. Kattepur further already discloses the following limitations,
receiving, by a processor of a system in which the fleet of robots is configured to operate, a feedback; and updating, by the processor of the system, the mission profile based on the feedback (Paragraph [0029], “Consensus on the global task may be provided by sharing knowledge among agents in a peer-to-peer or hierarchical fashion”)
Stevens further already teaches the following limitations,
wherein the feedback indicates a change to the first tool package (Paragraph [0214], “The AGV and FAM selectors, 1467 and 1468, respectively, utilize available position, power and remaining subtask data to select an appropriate utilization of AGV and FAM resources to complete any subtasks which would otherwise remain unfinished by another AGV, and where possible, to ensure tasks are assigned to those AGVs and FAMs which have the resources to complete them.” - where, in performing tasks based on a particular pairing, the tool package provided by a particular FAM to a particular vehicle is tracked)
Claims 51-54 are rejected under 35 U.S.C. 103 as being obvious over the combination of Kattepur, Halder, Bonder, and Dubey, further in view of Helsel (US 20210101748 A1), previously of record, herein after referred to simply as Helsel.
Regarding Claim 51,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 21. Blonder further already teaches the following limitation,
wherein each mission priority is a threshold measure of an outcome of a performance of the mission (Paragraph [0067], “Optionally, the mission engine may initiate one or more additional inspection missions to acquire additional inspection data in case the acquired inspection data is incompliant, for example, partial, incomplete, insufficient, insufficiently accurate, under quality and/or the like.”)
However, the combination does not teach the following limitation,
and wherein the determined mission schedule maximizes a number of missions among the plurality
However, this is taught by Helsel, which teaches that a robotic facility can maximize a work output as a component objective shared alongside other objectives (Paragraph [0059] “The AGV controller 111 can select and schedule workcells 127 and AGVs 125 to transport the workcells 127 based on various factors and various objectives. One example objective is to complete the largest number or highest percentage of tasks that need to be performed. In this example, the AGV controller 111 can select and schedule the workcells 127 and AGVs 125 based the number of workcells 127 that can perform the tasks, the number of AGVs 125 that can transport the workcells 127, and the battery level of each workcell 127 and AGV 125. For example, the AGV controller 111 can optimize the scheduling such that the most amount of work (e.g., the largest number of tasks) are completed per unit charge of the batteries of the workcells 127”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the robotic mission allocation of Kattepur with the mission maximization as taught by Helsel, as this allows the most possible work to be done, in consideration with other objectives, rather than simply stopping at a pre-defined level of work (Paragraph [0059], “One example objective is to complete the largest number or highest percentage of tasks that need to be performed.”) and further, this can minimize a necessary amount of user intervention, such as making it possible to set a larger number of tasks in a day than can actually be completed, such that simply a percentage completion is of interest (Paragraph [0052], “The task data 113 defines tasks that are to be performed by the workcells 127. For example, a user or an automated system can specify a set of tasks to be completed by the workcells 127, e.g., for a given time period such as a given day.”). Further, the combination could be performed using known methods, yielding predictable results.
Regarding Claim 52,
The combination of Kattepur, Halder, Blonder, Dubey, and Helsel, as shown, teaches all the limitations of Claim 51. Kattepur further discloses the following limitation,
wherein the threshold measure of the outcome of the performance of the mission includes an amount of energy to be used in the mission (Paragraph [0042], “an embodiment, the system 300 facilitates optimal allocation of warehouse procurement tasks to distributed robotic agents by utilizing optimization techniques such as primal optimization and dual optimization that may consider constraints such as utilization of robotic agent, procurement latency, energy depletion rates and fault tolerance capabilities.”)
Regarding Claim 53,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 21. Blonder further already teaches the following limitation,
wherein each mission priority is a time frame in which to complete the mission (Paragraph [0150], “The optimization function(s) may be directed to minimize one or more operational objectives of the inspection mission, for example, a shortest route of the selected capable autonomous vehicle(s) 202, a lowest operational cost of the selected capable autonomous vehicle(s) 202, a minimal number of autonomous vehicle(s) 202, a shortest mission time of the inspection mission, an earliest completion time of the inspection mission, a maximal utilization of the plurality of autonomous vehicles 202 and/or the like.”).
However, the combination does not teach the following limitation,
and wherein the determined mission schedule maximizes a number of missions among the plurality of missions that are completed within the time frame in which to complete the mission
However, this is taught by Helsel, which teaches that a robotic facility can maximize a work output as a component objective shared alongside other objectives (Paragraph [0059] “The AGV controller 111 can select and schedule workcells 127 and AGVs 125 to transport the workcells 127 based on various factors and various objectives. One example objective is to complete the largest number or highest percentage of tasks that need to be performed. In this example, the AGV controller 111 can select and schedule the workcells 127 and AGVs 125 based the number of workcells 127 that can perform the tasks, the number of AGVs 125 that can transport the workcells 127, and the battery level of each workcell 127 and AGV 125. For example, the AGV controller 111 can optimize the scheduling such that the most amount of work (e.g., the largest number of tasks) are completed per unit charge of the batteries of the workcells 127”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the robotic mission allocation of Kattepur with the mission maximization as taught by Helsel, as this allows the most possible work to be done, in consideration with other objectives, rather than simply stopping at a pre-defined level of work (Paragraph [0059], “One example objective is to complete the largest number or highest percentage of tasks that need to be performed.”) and further, this can minimize a necessary amount of user intervention, such as making it possible to set a larger number of tasks in a day than can actually be completed, such that simply a percentage completion is of interest (Paragraph [0052], “The task data 113 defines tasks that are to be performed by the workcells 127. For example, a user or an automated system can specify a set of tasks to be completed by the workcells 127, e.g., for a given time period such as a given day.”). Further, the combination could be performed using known methods, yielding predictable results.
Regarding Claim 54,
The combination of Kattepur, Halder, Blonder, Dubey, and Helsel, as shown, teaches all the limitations of Claim 53. Blonder further already discloses the following limitation,
wherein the time frame in which to complete the mission comprises one or more of: … a period of time in which the mission is performed (Paragraph [0150], “The optimization function(s) may be directed to minimize one or more operational objectives of the inspection mission, for example, … a shortest mission time of the inspection mission, an earliest completion time of the inspection mission,”).
Claim 55 is rejected under 35 U.S.C. 103 as being obvious over the combination of Kattepur, Halder, Blonder, and Dubey, further in view of Urano (US 20220081010 A1), previously of record, herein after referred to simply as Urano.
Regarding Claim 55,
The combination of Kattepur, Halder, Blonder, and Dubey, as shown, teaches all the limitations of Claim 21. The combination does not teach the following limitation,
wherein each mission priority is a predicted number of requests for further assistance or human intervention, and wherein the determined mission schedule minimizes a total predicted number of requests for further assistance or human intervention required by the plurality of missions
However, this is taught by Urano, which teaches to predict a particular number of needed requests (Paragraph [0011], “In the vehicle dispatch system according to an aspect of the present disclosure, the vehicle dispatch system may include a remote assistance request number calculation unit for calculating the remote assistance request number, which is the remote assistance request number that the dispatched candidate vehicle requests the remote operator for remote assistance, based on the route and a remote assistance request condition set in advance.”) so as to minimize a number of requests (Paragraph [0097], “The route search unit 45 may use the remote assistance request number calculated by the remote assistance request number calculating unit 47 described later to preferentially exclude the route has larger remote assistance request number.”) and to set an overall threshold for a mission planning system (Paragraph [0149], “The vehicle dispatch control unit 49 may narrow down dispatched candidate vehicles so that the total remote assistance request number in all the dispatched candidate vehicles dispatch routes to point of dispatch P is equal to or less than the total request threshold.”)
For the constraint of minimize procurement latency, as collecting products from picking stations and delivery of these products to delivery stations involves latency overheads, cases can be formulated where latency must be minimized.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Kattepur with the request prediction and minimization as taught by Urano, as this minimizes the burden on a robotic fleet operator for a given amount of work (Paragraph [0191], “since the plurality of the dispatched candidate vehicles are narrowed down so that the total remote assistance request number is equal to or less than the total request, the vehicle can be dispatched in consideration of the burden on the remote operator.”). Further, the combination could be performed using known methods, yielding predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mallik (US 20200174495 A1), previously of record, discloses an inventory management system which can consider navigation as a subtask within a larger task (Paragraph [0017], where, further, the order of subtasks can be adjusted (Paragraph [0028]). Buerger (US 11334069 B1). Previously of record, discloses a mission planning system performance should be optimized based on a simulated measure of required operator interventions (Column 19, Lines 14-22). Keravala (US 20210116889 A1), previously of record, discloses a modular robotic fleet including a robotic arm tool package (Paragraph [0042]). Billault (US 20210103295 A1), newly of record, discloses that a quality measure is a mission priority considered alongside other priorities (Paragraph [0053], “In one development, a mission constraint comprising one or more of the parameters comprising a mission type, a geographical region, a point of entry into and/or of exit from said geographical region, time management, fuel management and/or a quality of service as a target regarding one or more sensors on board the aircraft.”). Beth (US 20190235488 A1), newly of record, teaches a mission planning where vehicles have a schedule of missions, where the missions have divisible component segments (Paragraph [0044]). Ryan (US 20190324456 A1), previously of record, teaches an interchangeability success (Paragraph [0093], “The task allocation [maximizes] probability of success.” as compared with Paragraph [0092], “As an example, task details for a ground vehicle traveling to a specific location include total time duration for travel, total distance traveled, the specific path being traveled, total fuel/energy consumed, and probability of failure.”)
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/K.L.F./Examiner, Art Unit 3666
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665