SYSTEMS AND METHODS FOR ROBOT FLEET MANAGEMENT

DETAILED ACTION Notice to Applicant The following is a FINAL Office action upon examination of application number 18/920,622 filed on 10/18/2024. Claims 1, 5, 8, 10-15, and 21-22 are pending in this application, and have been examined on the merits discussed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Priority Application 18/920,622 filed 10/18/2024 claims Priority from Provisional Application 63/544,800, filed 10/19/2023. Response to Amendment 4. In the response filed June 04, 2026, Applicant amended claims 1, 5, and 8, and cancelled claims 2-4, 6-7, 9, and 16-20. New claims 21 and 22 were presented for examination. 5. Applicant's amendments to claim 15 is hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn. 6. Applicant's amendments to claim 1 is hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been withdrawn. Response to Amendment 7. Applicant submits “Applicant does not acquiesce to the Examiner's rejection, but int the interest of expediting allowance of the instant application a terminal disclaimer is filed concurrently with this Response. Accordingly, Applicant respectfully requests that this double patenting rejection be withdrawn.” [Applicant’s Remarks, 06/04/2026, page 6] Applicant asserts that a Terminal Disclaimer was filed concurrently with Applicant’s Response. However, no Terminal Disclaimer has been filed or entered in the record of the instant application. Therefore, Applicant’s statement that a Terminal Disclaimer was filed is not supported by the record, and the Double Patenting rejection is maintained. If Applicant intends to overcome the Double Patenting rejection by way of a Terminal Disclaimer, Applicant should file a proper Terminal Disclaimer. Upon receipt and entry of an acceptable Terminal Disclaimer, the rejection will be reconsidered. 8. Applicant submits “By way of this Response, Applicant has amended independent claim 1 to recite additional elements that relate to direct physical application of the robots themselves; namely: causing, by the robot fleet management system, the first robot to perform the selected untethered task while untethered causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot Since amended independent claim 1 now recites elements that do produce physical effects in/by the robots. Applicant respectfully submits that amended independent claim 1 now clearly captures significantly more than an abstract idea and accordingly requests that the rejections under 35 USC 101 be withdrawn.” [Applicant’s Remarks, 06/04/2026, page 7] In response to Applicant’s remarks, it is noted that the claim amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C.101; accordingly, this rejection has been withdrawn. The §101 rejection has been withdrawn based on the determination that the claims recite additional elements that, when evaluated under Step 2A Prong Two of the eligibility inquiry, integrate the judicial exception into a practical application. In particular, the Examiner notes with respect to independent claim 1 that the additional elements including the at least one processor, at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, processor-executable instructions, the robot fleet management system, the first robot, the second robot, and a power source of the second robot, and performing the accessing, accessing, determining, allocating, causing, determining, allocating, causing, and initiating steps based thereon, as recited and arranged with the other limitations required by claim 1serve to integrate the abstract idea into a practical application by applying the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims are not “directed to” the abstract idea recited therein 9. Applicant submits “Applicant does not acquiesce to the Examiner's rejections, but in the interest of expediting allowance of the instant application Applicant has made significant amendments to the claims to further distinguish from the cited art.” [Applicant’s Remarks, 06/04/2026, page 7] In response to the Applicant’s argument that “in the interest of expediting allowance of the instant application Applicant has made significant amendments to the claims to further distinguish from the cited art,” it is noted that this argument is an allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicant does not rove any substantive argument explaining how the amendment overcome the rejection. Moreover, the Examiner notes the “significant amendments” being argued by Applicant as being newly amended to the claims in the response filed 06/04/2026, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates new references and new citations to address the amended limitations in claim 1 and supports a conclusion of obviousness of the amended claims. 10. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action. Double Patenting 11. The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. 12. Claims 1, 5, 8, 10-15, and 21-22 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of Patent No. US 12,400,158 B2 since the claims, if allowed, would improperly extend the "right to exclude" already granted in the patent. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 of the instant application is obvious in view of claims 1, 4-5, 7, and 9 in the listed patent. Claim 1 of the ‘158 patent recites “A method of operation of a robot fleet management system, the method comprising: accessing, by the robot fleet management system, a set of tasks available to be performed by a fleet of robots; accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks; accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots; and allocating, by the robot fleet management system, a first robot in the fleet of robots to a first task from the set of tasks, based at least in part on the power state of at least the first robot and the power consumption for at least the first task.” Claim 4 of the ‘158 patent recites “determining, by the robot fleet management system, the first robot has sufficient power to complete an untethered task selected from the at least one untethered task, and allocating, by the robot fleet management system, the first robot to the selected untethered task.” Claim 5 of the ‘158 patent recites allocating, by the robot fleet management system, the first robot in the fleet of robots to an untethered task selected from the at least one untethered task.” Claim 7 of the ‘158 patent recites “determining, by the robot fleet management system, the first robot has insufficient power to complete any untethered task selected from the at least one untethered task; and allocating, by the robot fleet management system, the first robot to a tethered task selected from the at least one tethered task.” Claim 9 of the ‘158 patent recites “initiating, by the robot fleet management system, a replenishment of a power source of the first robot during performance of a tethered task by the first robot.” Claims 1, 4-5, 7, and 9 of the ‘158 patent recites similar steps to those recited in claim 1 of the instant application. Claim 1 of the instant application recites: “A robot fleet management system comprising at least one processor and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, the at least one non-transitory processor-readable storage medium storing processor-executable instructions and/or data that, when executed by the at least one processor, cause the robot fleet management system to perform a method of operation of the robot fleet management system, wherein the method of operation includes: accessing, by the robot fleet management system, a set of tasks available to be performed by a fleet of robots, the set of tasks comprising at least one tethered task and at least one untethered task; accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks; accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots; determining, by the robot fleet management system, a first robot has sufficient power to complete an untethered task selected from the at least one untethered task; allocating, by the robot fleet management system, the first robot to the selected untethered task; causing, by the robot fleet management system, the first robot to perform the selected untethered task while untethered; determining, by the robot fleet management system, a second robot has insufficient power to complete any untethered task selected from the at least one untethered task allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task; causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot.” Although the claims are directed to inventions in different statutory classes (i.e., a system versus a method), the distinction in statutory class does not, by itself, render the claims patentably distinct. The subject matter of claim 1 of the instant application is not patentably distinct from the subject matter of claims 1, 4-5, 7, and 9 of the ‘158 patent because the claimed “system” in the instant application is merely a hardware embodiment configured to perform the same method steps as those recited in the ‘158 patent. Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to provide a system configured to perform the method steps of the ‘158 patent, since the method and system clams define the same overall invention in different claim formats, and therefore these claims are not patentably distinct from one another despite these slight differences. Claims of instant application (as filed on 10/18/2024) Claims of U.S. Pat. 12,400,158 B2 (issued on 08/26/2025) 1 1, 4-5, 7, 9 5 4 8 7, 8 10 10 11 11 12 12 13 13 14 14 15 15 21 6,9 22 4 The chart above maps claims of the instant application to corresponding claims of U.S. Patent 12,400,158 B2 that are patentably indistinct, though not identical. One of ordinary skill in the art would have recognized the slight differences between the claim language of the corresponding claims as being directed towards intention, slight variations in terminology, or obvious variants of claim elements and therefore these claims are not patentably distinct from one another despite these slight differences. Claim Rejections - 35 USC § 103 13. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 14. 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. 15. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 16. Claims 1, 5, 8, 10-15, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al., Pub. No.: US 2022/0100184 A1, [hereinafter Natarajan], in view of Chen et al., Pub. No.: US 2022/0269284 A1, [hereinafter Chen], in view of Singh et al., Pub. No.: US 2022/0201441 A1, [hereinafter Singh], in further view of Chrysanthakopoulos et al., Pub. No.: US 2023/0000003 A1, [hereinafter Chrysanthakopoulos]. As per claim 1, Natarajan teaches a robot fleet management system (paragraph 0001) comprising at least one processor and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor (paragraph 0145, discussing that the computing device 1200 may perform the various functionality as described with respect to…allocating AMRs (Autonomous Mobile Robots) 102 to tasks, performing enhanced path planning functions, calculating AMR health and failure probabilities with respect to the AMRs performing or completing tasks, communicating with the AMRs 102 to identify new tasks, etc. To do so, the computing device 1200 may include processing circuitry 1202, a transceiver 1204, communication interface 1206, and a memory 1208; paragraph 0146, discussing that the processing circuitry 1202 may be configured as any suitable number and/or type of computer processors, which may function to control the computing device 1200 and/or other components of the computing device 500…; paragraph 0150, discussing that the memory 1208 is configured to store data and/or instructions such that, when the instructions are executed by the processing circuitry 1202, cause the computing device 1200 to perform various functions as described, such as those described with reference to the MRTA system 200. The memory 1208 may be implemented as any well-known volatile and/or non-volatile memory, including, for example, read-only memory (ROM), random access memory (RAM), flash memory, a magnetic storage media, an optical disc, erasable programmable read only memory (EPROM), programmable read only memory (PROM), etc. The memory 1208 may be non-removable, removable, or a combination of both. The memory 1208 may be implemented as a non-transitory computer readable medium storing one or more executable instructions such as, for example, logic, algorithms, code, etc.), the at least one non-transitory processor-readable storage medium storing processor-executable instructions and/or data that, when executed by the at least one processor, cause the robot fleet management system to perform a method of operation of the robot fleet management system (paragraph 0147, discussing that the processing circuitry 1202 may be configured to carry out instructions to perform arithmetical, logical, and/or input/output (I/O) operations, and/or to control the operation of one or more components of computing device 1200 to perform various functions associated with the MRTA (multi-robot, multi-task allocation) system 200 as described. The processing circuitry 1202 may include one or more microprocessor cores, memory registers, buffers, clocks, etc., and may generate electronic control signals associated with the components of the computing device 1200 to control and/or modify the operation of these components. The processing circuitry 1202 may communicate with and/or control functions associated with the transceiver 1204, the communication interface 1206, and/or the memory 1208. The processing circuitry 1202 may additionally perform various operations to control the communications, communications scheduling, and/or operation of other network infrastructure components that are communicatively coupled to the computing device 1200; paragraph 0150, discussing that the memory 1208 is configured to store data and/or instructions such that, when the instructions are executed by the processing circuitry 1202, cause the computing device 1200 to perform various functions as described, such as those described with reference to the MRTA system 200. The memory 1208 may be implemented as any well-known volatile and/or non-volatile memory, including, for example, read-only memory (ROM), random access memory (RAM), flash memory, a magnetic storage media, an optical disc, erasable programmable read only memory (EPROM), programmable read only memory (PROM), etc. The memory 1208 may be non-removable, removable, or a combination of both. The memory 1208 may be implemented as a non-transitory computer readable medium storing one or more executable instructions such as, for example, logic, algorithms, code, etc.), wherein the method of operation (paragraphs 0150, 0217) includes: accessing, by the robot fleet management system, a set of tasks available to be performed by a fleet of robots (paragraph 0001, discussing task allocation for autonomous systems; paragraph 0203, discussing receive autonomous agent task data including (i) a set of tasks to be performed by a plurality of autonomous agents operating within an environment), the set of tasks comprising at least one untethered task (paragraph 0030, discussing allocating AMRs (Autonomous Mobile Robots) to tasks based on high level task inputs and AMR state and availability information…Once, the task begins execution, the techniques described further include monitoring the task progress and AMR actions. The continuous monitoring of tasks and AMR actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc. Moreover, once an AMR completes its task, it is re-assigned to a new task by a task allocation engine, and thus the AMRs are continuously and efficiently assigned to tasks autonomously. In addition, the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0033, discussing that the Autonomous Mobile Robots may operate autonomously and be configured as mobile robots that navigate within the environment to complete specific tasks; paragraph 0056, discussing that the task allocation engine block represents the execution of a MRTA (multi-robot, multi-task allocation) algorithm that functions to allocate AMRs to specific tasks within the set of tasks included as part of the autonomous agent task data…The task allocation engine may optionally include additional logic to support AMR allocation for urgent and/or unexpected scenarios such as AMR malfunctions, low battery, etc.,...; paragraph 0035). accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots (paragraph 0030, discussing that the continuous monitoring of tasks and AMR actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc. Moreover, once an AMR completes its task, it is re-assigned to a new task by a task allocation engine, and thus the AMRs are continuously and efficiently assigned to tasks autonomously. In addition, in the techniques discussed, the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs [i.e., robots] to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining [i.e., respective power state], the distance to charging stations, etc.). Then, each task may be allocated to the AMRs for each task having the highest reward metric value, which thus represents the “best” AMR to perform each task); determining, by the robot fleet management system, a first robot has sufficient power to complete an untethered task selected from the at least one untethered task (paragraph 0030, discussing that the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0060, discussing that in a multi-robot environment such as a warehouse or a factory, as a practical matter several parameters need to be considered to ensure a proper AMR task allocation, which may include the aforementioned set of task parameters, the priority and the urgency of a task, as well as cost parameters such as AMR distance to a specific task location, AMR battery life, distance and time required to navigate to a destination (such as a recharging station), etc.; paragraph 0114, discussing re-allocating AMRs [i.e., robots] that have a battery capacity less than a predetermined threshold value to charge at a charging station etc.; paragraphs 0067, 0111); allocating, by the robot fleet management system, the first robot to the selected untethered task (paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.). Then, each task may be allocated to the AMRs for each task having the highest reward metric value, which thus represents the “best” AMR to perform each task; paragraph 0034, discussing that the AMRs 102 may also receive commands (which may be in the form of transmitted control signals) with respect to allocated tasks to perform as well as the details associated with how and when to perform the allocated tasks, and/or planned path information from the computing device 108; paragraph 0042, discussing that the computing device may thus receive state-related information from each for the AMRs 102…, which may include…data related to the operating state of the AMRs 102, or any other suitable feedback related to the state of the AMRs… In any event, the computing device 108 may optionally use this state-related information, together with other information about the environment 100 that is already known, for task allocation; paragraph 0203, discussing generating autonomous agent task allocation data that allocates each one of the plurality of autonomous agents to a respective one of the set of tasks; and a communication means for transmitting commands to each one of the plurality of autonomous agents to perform the respectively allocated tasks); causing, by the robot fleet management system, the first robot to perform the selected untethered task while untethered (paragraph 0033, discussing that the AMRs [i.e., robots] may operate autonomously and be configured as mobile robots that navigate within the environment to complete specific tasks, which may be allocated to the AMRs; paragraph 0034, discussing that the AMRs may also receive commands (which may be in the form of transmitted control signals) with respect to allocated tasks to perform as well as the details associated with how and when to perform the allocated tasks, and/or planned path information from the computing device; paragraph 0042, discussing that the computing device may thus receive state-related information from each for the AMRs…, which may include…data related to the operating state of the AMRs, or any other suitable feedback related to the state of the AMRs…In any event, the computing device may optionally use this state-related information, together with other information about the environment that is already known, for task allocation; paragraph 0203, discussing generating autonomous agent task allocation data that allocates each one of the plurality of autonomous agents to a respective one of the set of tasks; and a communication means for transmitting commands to each one of the plurality of autonomous agents to perform the respectively allocated tasks; paragraphs 0030, 0044); determining, by the robot fleet management system, a second robot has insufficient power to complete any untethered task selected from the at least one untethered task (paragraph 0030, discussing that the continuous monitoring of tasks and AMR actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc. Moreover, once an AMR completes its task, it is re-assigned to a new task by a task allocation engine as described herein, and thus the AMRs are continuously and efficiently assigned to tasks autonomously. In addition, the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.). Then, each task may be allocated to the AMRs for each task having the highest reward metric value, which thus represents the “best” AMR to perform each task; paragraph 0034, discussing that the AMRs may also receive commands with respect to allocated tasks to perform as well as the details associated with how and when to perform the allocated tasks, and/or planned path information from the computing device; paragraph 0042, discussing that the computing device may thus receive state-related information from each for the AMRs 102…, which may include…data related to the operating state of the AMRs 102, or any other suitable feedback related to the state of the AMRs…In any event, the computing device may optionally use this state-related information, together with other information about the environment that is already known, for task allocation; paragraph 0049, discussing that a user may interact with the GUI to provide the appropriate specifications for the environment so that the task allocation engine may perform AMR [i.e., robot] task allocation functions…The GUI may also provide users with information of the environment, such as the status of AMRs, their current tasks, or other information obtained from the state-related information; paragraph 0051, discussing that the autonomous agent task data may define which tasks are potentially to be performed by the AMRs within the environment (i.e. the set of tasks), and details regarding the requirements with respect to how each of these tasks should be executed; paragraph 0058, discussing that the trained DL neural network architecture implemented via the DL neural network architecture may dynamically and intelligently allocate AMRs 102 to specific tasks as conditions in the environment change, as AMRs 102 complete other tasks, as AMRs are identified as malfunctioning, as new tasks are identified, etc. In other words, the task allocation engine implements the DL neural network architecture to output AMR task allocation data at block using the autonomous agent task data and target vectors received via the input layer(s)…; paragraphs 0094, 0203); and allocating, by the robot fleet management system, the second robot to a different task (paragraph 0030, discussing that the continuous monitoring of tasks and AMR actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc. Moreover, once an AMR completes its task, it is re-assigned to a new task by a task allocation engine as described herein, and thus the AMRs are continuously and efficiently assigned to tasks autonomously. In addition, the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0094, discussing that if any AMR has a problem or failure, the cluster-level logic block functions to make an appropriate decision to solve this problem. This may include switching the AMR to another role in the task cluster). While Natarajan describes cost factors that may include the required energy to perform a task (paragraph 0067) and allocating, by the robot fleet management system, the second robot to a different task (paragraph 0094), Natarajan does not explicitly teach the set of tasks comprising at least one tethered task; accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks; allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task; causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot. Chen in the analogous art of systems for robot fleet management teaches: accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks (paragraph 0052, discussing an amount of energy to be used in each mission of the mission profile; paragraph 0066, discussing a computer-implemented method of managing a fleet of robots comprises receiving a first task, determining at least one factor for the first task indicating information related to accomplishing the first task, receiving data indicating information related to an environment in which the first task is to be accomplished, determining at least one mission capability for accomplishing a mission that includes the first task, wherein the at least one mission capability is based on one or more of the at least one factor; or the data, and creating a mission profile based on the first task and one or more of the at least one factor, the data, or the at least one mission capability; paragraph 0170, discussing that fleet manager 628 may manage a fleet of robots with respect to, among other things, scheduling and dispatching missions and tasks to robots…; paragraph 0207, discussing that a mission file may contain one or more of the following: a mission identification, a mission priority level, and/or the identification of any other missions that are scheduled to be completed at the same time as the mission;…; fiducials associated with the robot; minimum battery/energy requirements of the robot to complete the mission, and any reserve built-in to account for any diversions by the robot during the mission…; paragraph 0211, discussing that a mission may be characterized as requiring a highest quality level of mission or task performance, performing the mission or the task in the shortest amount of time, performing the mission or the task using the least amount of energy; paragraph 0067). Natarajan is directed towards a system and method for task allocation for autonomous robots. Chen is directed to systems and methods for management of a robot fleet. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Natarajan with Chen because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying Natarajan to include Chen’s features for including accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks, in the manner claimed, would serve the motivation of allowing robots to be assigned to tasks in an efficient manner that may maximize the use of robots to perform the tasks within the facility (Chen at paragraph 0004); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The Natarajan-Chen combination does not explicitly teach the set of tasks comprising at least one tethered task; allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task; causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot. Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: the set of tasks comprising at least one tethered task (paragraph 0001, discussing autonomous mobile robot (AMR) scaling and resource-based task allocation for AMRs; paragraph 0045, discussing that the AMR may be configured to self-select a task profile from a plurality of task profiles. That is, the AMR may be configured with one or more predetermined task profiles, which may be locally stored and/or which may be remotely stored in a manner to which the AMR has access. The AMR may include one or more processors, which may be configured to determine one or more tasks, which the AMR is required to perform, and to select a task profile from the one or more task profiles based on the one or more required tasks; paragraph 0062, discussing that in some cases, the offloaded task may be purely or principally computational in nature. Such tasks may be performable by any nearly any entity with one or more processors. In this situation, data for the computation may be transmitted to the receiving device, which may then process the data in an appropriate fashion. For example, an AMR's sensor data representing a vicinity of the AMR may require one or more robust computational calculations to localize and/or navigate. Based on the task profile, the AMR may be unable to perform such a calculation. In this case, it may be power saving to simply transmit the data and await a response including localization information and/or navigation information. In this manner, such robust computations may be offloaded as a power saving matter. As a general matter, such calculations may be performed on-site or remotely. Under some circumstances, these tasks [i.e., tethered tasks]) may be performed by one or more devices that are not dependent on a battery (e.g., devices connected to a mains voltage). The Natarajan-Chen combination describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen combination with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen combination to include Singh’s feature for including the set of tasks comprising at least one tethered task, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The Natarajan-Chen combination does not explicitly teach allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task; causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot. However, Chrysanthakopoulos in the analogous art of robot management systems teaches these concepts. Chrysanthakopoulos teaches: allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task (paragraph 0002, discussing using the robots as cooperative or collaborative units whereby the robots can be used in unison to perform tasks that an individual robot cannot perform; or to assist other robots when needed, such as take over when another robot runs out of charge or fails to perform for whatever reason; paragraph 0069, discussing that the bots can perform a variety of tasks and will be extremely useful to the consumer, customer or user. It is envisioned that the bots be affordable, resilient, low maintenance, autonomous and environmentally friendly. Specifically, it is envisioned that the bots cost approximately $5,000 or less. That they have a duty cycle duration of approximately in 75% active and 25% low power mode, with the ability to charge while performing a task [i.e., tethered task]…The refueling for the bots will take place at the autonomous docking port whereby the bots automatically returns to the docking port when it is in need of refueling); causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered (paragraph 0070, discussing systems that provide: obstacle avoidance, autonomous docking, autonomous refueling that includes locating the docking port when fuel is low and connecting with the fuel source while docking (either through a plug in attachment or through inductive charging system, user guided topological learning tasks, that is, learning the logical graph of locations where the GURU will operate, virtual GPS markers restricting movement in a pre-defined area, marking obstacles or hazards to navigation, autonomous or semi-autonomous navigation using a learned topology map or global positioning coordinates, telemetry publish to stream ingestion compute nodes (in remote data centers, and to local peer robots), update of learned tasks from offline training, downloaded/synchronized from remote nodes, fleet management code and self-update of all code and configuration (part of common control company software platform), anomaly detection and peer monitoring software that enables robots to take over tasks for a robot that has failed performing its task, within predefined time and space parameters, and leader election software algorithm that enables one healthy robot, from a deployed group, to take over the task for a failed robot); and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot (paragraph 0002, discussing using the robots as cooperative or collaborative units whereby the robots can be used in unison to perform tasks that an individual robot cannot perform; or to assist other robots when needed, such as take over when another robot runs out of charge or fails to perform for whatever reason; p; paragraph 0003: “The invention relates first to the field of autonomous, modular, ground utility robot units, or GURU, to perform tasks.”; paragraph 0065, discussing that the GURU will automatically know when it needs to recharge based on programming that takes into account data including but not limited to its current charge level, its distance from the fueling port, and the amount of time and obstacles required to pass to return to the fueling port. Once this is calculated the GURU will self-navigate and return to the refueling fueling port to automatically refuel. Once at the fueling port the GURU will recharge either by docking into a fixed port via the fueling port connectors on the GURU or by utilizing an inductive charging port using a charging plate on a bottom of the GURU; paragraph 0069, discussing that the bots can perform a variety of tasks and will be extremely useful to the consumer, customer or user. It is envisioned that the bots be affordable, resilient, low maintenance, autonomous and environmentally friendly. Specifically, it is envisioned that the bots cost approximately $5,000 or less. That they have a duty cycle duration of approximately in 75% active and 25% low power mode, with the ability to charge while performing a task…The refueling for the bots will take place at the autonomous docking port whereby the bots automatically returns to the docking port when it is in need of refueling). The Natarajan-Chen-Singh combination describes features related to task allocation and robot fleet management. Chrysanthakopoulos relates to the field of autonomous, modular, ground utility robot units to perform tasks. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen-Singh combination with Chrysanthakopoulos because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen-Singh combination to include Chrysanthakopoulos’ feature for including allocating, by the robot fleet management system, the second robot to a tethered task selected from the at least one tethered task; causing, by the robot fleet management system, the second robot to perform the selected tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the second robot during performance of the selected tethered task by the second robot, in the manner claimed, would serve the motivation of improving overall performance between all deployed robots (Chrysanthakopoulos at paragraph 0144); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 5, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Natarajan further teaches wherein determining, by the robot fleet management system, a first robot has sufficient power to complete an untethered task selected from the at least one untethered task includes: determining, by the robot fleet management system, the power state of the first robot is above an upper threshold (paragraph 0030, discussing that the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0060, discussing that in a multi-robot environment such as a warehouse or a factory, as a practical matter several parameters need to be considered to ensure a proper AMR task allocation, which may include the aforementioned set of task parameters, the priority and the urgency of a task, as well as cost parameters such as AMR distance to a specific task location, AMR battery life, distance and time required to navigate to a destination (such as a recharging station), etc.; paragraph 0111, discussing that one or more of these metrics may be used as conditions that may form part of the autonomous agent task data, and thus part of the logic used by the AMR task allocation engine block 210 to perform AMR task allocation. In one scenario, an AMR health metric being below a certain threshold may result in the AMR being decommissioned and/or removed from the AMR availability pool. In another scenario, the allocation of the task may be conditioned upon the AMR 102 having an expected probability of task completion that is above a predetermined threshold value. In this way, the AMR task allocation engine block 210 may adapt the manner in which task allocation is performed based upon the data provided by the AMR health and failure prediction block; paragraph 0114, discussing re-allocating AMRs [i.e., robots] that have a battery capacity less than a predetermined threshold value to charge at a charging station etc.; paragraphs 0067). As per claim 8, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Natarajan further teaches wherein determining, by the robot fleet management system, a second robot has insufficient power to complete any untethered task selected from the at least one untethered task includes: determining, by the robot fleet management system, the power state of the second robot is below a lower threshold (paragraph 0030, discussing that the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0060, discussing that in a multi-robot environment such as a warehouse or a factory, as a practical matter several parameters need to be considered to ensure a proper AMR task allocation, which may include the aforementioned set of task parameters, the priority and the urgency of a task, as well as cost parameters such as AMR distance to a specific task location, AMR battery life, distance and time required to navigate to a destination (such as a recharging station), etc.; paragraph 0114, discussing re-allocating AMRs [i.e., robots] that have a battery capacity less than a predetermined threshold value to charge at a charging station etc.; paragraphs 0067, 0111). As per claim 10, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Although not explicitly taught by Natarajan, Chen in the analogous art of systems for robot fleet management teaches wherein accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption (paragraph 0052, discussing an amount of energy to be used in each mission of the mission profile; paragraph 0087, discussing that the information relating to performance of the task may include historical data, stored in a memory of the system configured to manage the fleet of robots, related to past missions in which the task was performed; paragraph 0101, discussing that the third mission profile may include historical data based on a past performance of the task; paragraph 0170, discussing that fleet manager 628 may manage a fleet of robots with respect to, among other things, scheduling and dispatching missions and tasks to robots…; paragraph 0207, discussing that a mission file may contain one or more of the following: a mission identification, a mission priority level, and/or the identification of any other missions that are scheduled to be completed at the same time as the mission;…; fiducials associated with the robot; minimum battery/energy requirements of the robot to complete the mission, and any reserve built-in to account for any diversions by the robot during the mission…; paragraph 0209, discussing that the one or more mission profiles may be received via a user input based on historical data as explained, and/or from another system, e.g., a third-party system. As described, each mission profile may include one or more tasks, factors necessary or useful for performance of the task, dynamic data based on the one or more factors, and/or mission capabilities of a robot for accomplishing the mission; paragraph 0211, discussing that a mission may be characterized as requiring a highest quality level of mission or task performance, performing the mission or the task in the shortest amount of time, performing the mission or the task using the least amount of energy; paragraph 0280, discussing that factors related to the task (such as those factors described in Step 1120) may be determined by accessing historical data associated with that task; paragraphs 0090, 0237, 0281, 0286). Natarajan is directed towards a system and method for task allocation for autonomous robots. Chen is directed to systems and methods for management of a robot fleet. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Natarajan with Chen because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying Natarajan to include Chen’s feature for including wherein accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption, in the manner claimed, would serve the motivation of allowing robots to be assigned to tasks in an efficient manner that may maximize the use of robots to perform the tasks within the facility (Chen at paragraph 0004); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The Natarajan-Chen combination does not explicitly teach based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks. However, Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks (paragraph 0001, discussing autonomous mobile robot (AMR) scaling and resource-based task allocation for AMRs; paragraph 0049, discussing that the AMR may include one or more artificial intelligence circuits, which may be configured to select and/or generate one or more task profiles. Such artificial intelligence circuits may be trained using any of historical task selection data, historical task profiles, historical battery life data, their corresponding historical module usage and functionality, or any combination thereof. Such artificial intelligence circuits may be trained to select a task profile from a plurality of task profiles in light of a given task, one or more given functionalities, one or more restrictions or limitations on modules and associated utilizations, and/or one or more power usage requirements; paragraph 0050, discussing that the one or more processors may be configured to select a task profile based on at least telemetry data. As described, the AMR may include a telemetry circuit…The sensor data may provide, or may be used by the one or more processors to determine, the utilization of the sensors/a power usage requirement associated with the sensors or the utilization. The telemetry circuit may be configured to determine the utilization of one or more processors. The telemetry circuit may be configured to determine battery data/charge data (e.g. a battery voltage, and amount of current drawn from the battery, a total charge (e.g. a total number of Colombes) received from the battery), and the telemetry circuit and/or the one or more processors may be configured to determine a remaining charge and/or a remaining time at the current or expected usage before a subsequent battery charging is required; paragraph 0035). The Natarajan-Chen combination describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen combination with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen combination to include Singh’s feature for including based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 11, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 10. Natarajan further teaches the battery onboard a battery-powered robot in the fleet of robots (Natarajan, paragraph 0030, discussing that the continuous monitoring of tasks and AMR [i.e., robot] actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc.; paragraph 0034, discussing that the AMRs 102 may transmit data to the computing device 108 indicative of current tasks being executed, new tasks to be executed, and/or other detected conditions within the environment 100, and may additionally or alternatively transmit any suitable type of state-related data such as an identified location, orientation, velocity, trajectory, heading, battery life, etc. within the environment 100; paragraph 0056). The Natarajan-Chen combination does not explicitly teach wherein accessing, by the robot fleet management system, a respective power consumption based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption based at least in part on an anticipated power to be drawn from a battery to complete each task from the set of tasks. However, Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: wherein accessing, by the robot fleet management system, a respective power consumption based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption based at least in part on an anticipated power to be drawn from a battery to complete each task from the set of tasks (paragraph 0001, discussing autonomous mobile robot (AMR) scaling and resource-based task allocation for AMRs; paragraph 0037, discussing that the following table illustrates, by way of example, sample robotic agent task profiles, associated modules, and utilization samples, and consequently resultant battery life expectations; paragraph 0049, discussing that the AMR may include one or more artificial intelligence circuits, which may be configured to select and/or generate one or more task profiles. Such artificial intelligence circuits may be trained using any of historical task selection data, historical task profiles, historical battery life data, their corresponding historical module usage and functionality, or any combination thereof. Such artificial intelligence circuits may be trained to select a task profile from a plurality of task profiles in light of a given task, one or more given functionalities, one or more restrictions or limitations on modules and associated utilizations, and/or one or more power usage requirements; paragraph 0050, discussing that the one or more processors may be configured to select a task profile based on at least telemetry data. As described, the AMR may include a telemetry circuit…The sensor data may provide, or may be used by the one or more processors to determine, the utilization of the sensors/a power usage requirement associated with the sensors or the utilization. The telemetry circuit may be configured to determine the utilization of one or more processors. The telemetry circuit may be configured to determine battery data/charge data (e.g. a battery voltage, and amount of current drawn from the battery, a total charge (e.g. a total number of Colombes) received from the battery), and the telemetry circuit and/or the one or more processors may be configured to determine a remaining charge and/or a remaining time at the current or expected usage before a subsequent battery charging is required; paragraphs 0035, 0054). Examiner notes that Singh, in addition to Natarajan as cited above, also teaches: the battery onboard a battery-powered robot in the fleet of robots (paragraph 0035, discussing that the task profile may be associated with one or more power usage requirements (e.g., power usage goals). Since AMRs [i.e., robots] are mobile, they are generally untethered from fixed power sources and are thus primarily battery-reliant. AMRs may be required to perform tasks for lengthy periods and thus require longevity of battery-charge and/or a predictable period of operation with a given battery charge. For example, it may be necessary that an AMR, once placed into duty, be operational for a period of hours (e.g., the AMR must operate for at least 5 hours, for at least 8 hours, for at least 10 hours, etc.). The Natarajan-Chen describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen to include Singh’s feature for including wherein accessing, by the robot fleet management system, a respective power consumption based at least in part on a power anticipated to be drawn by a robot in the fleet of robots from a power source to complete each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption based at least in part on an anticipated power to be drawn from a battery to complete each task from the set of tasks, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 12, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Although not explicitly taught by Natarajan, Chen in the analogous art of systems for robot fleet management teaches wherein accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption (paragraph 0052, discussing an amount of energy to be used in each mission of the mission profile; paragraph 0087, discussing that the information relating to performance of the task may include historical data, stored in a memory of the system configured to manage the fleet of robots, related to past missions in which the task was performed; paragraph 0101, discussing that the third mission profile may include historical data based on a past performance of the task; paragraph 0170, discussing that fleet manager 628 may manage a fleet of robots with respect to, among other things, scheduling and dispatching missions and tasks to robots…; paragraph 0207, discussing that a mission file may contain one or more of the following: a mission identification, a mission priority level, and/or the identification of any other missions that are scheduled to be completed at the same time as the mission;…; fiducials associated with the robot; minimum battery/energy requirements of the robot to complete the mission, and any reserve built-in to account for any diversions by the robot during the mission…; paragraph 0209, discussing that the one or more mission profiles may be received via a user input based on historical data as explained, and/or from another system, e.g., a third-party system. As described, each mission profile may include one or more tasks, factors necessary or useful for performance of the task, dynamic data based on the one or more factors, and/or mission capabilities of a robot for accomplishing the mission; paragraph 0211, discussing that a mission may be characterized as requiring a highest quality level of mission or task performance, performing the mission or the task in the shortest amount of time, performing the mission or the task using the least amount of energy; paragraph 0280, discussing that factors related to the task may be determined by accessing historical data associated with that task; paragraphs 0090, 0237, 0281, 0286). Natarajan is directed towards a system and method for task allocation for autonomous robots. Chen is directed to systems and methods for management of a robot fleet. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Natarajan with Chen because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying Natarajan to include Chen’s feature for including wherein accessing, by the robot fleet management system, a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, a respective power consumption, in the manner claimed, would serve the motivation of allowing robots to be assigned to tasks in an efficient manner that may maximize the use of robots to perform the tasks within the facility (Chen at paragraph 0004); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The Natarajan-Chen combination does not explicitly teach based at least in part on respective historical power consumption data for each task from the set of tasks. However, Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: based at least in part on respective historical power consumption data for each task from the set of tasks (paragraph 0001, discussing autonomous mobile robot (AMR) scaling and resource-based task allocation for AMRs; paragraph 0035, discussing that the task profile may be associated with one or more power usage requirements. Since AMRs are mobile, they are generally untethered from fixed power sources and are thus primarily battery-reliant. AMRs may be required to perform tasks for lengthy periods and thus require longevity of battery-charge and/or a predictable period of operation with a given battery charge. For example, it may be necessary that an AMR, once placed into duty, be operational for a period of hours (e.g., the AMR must operate for at least 5 hours, for at least 8 hours, for at least 10 hours, etc.); paragraph 0049, discussing that the AMR may include one or more artificial intelligence circuits, which may be configured to select and/or generate one or more task profiles. Such artificial intelligence circuits may be trained using any of historical task selection data, historical task profiles, historical battery life data, their corresponding historical module usage and functionality, or any combination thereof. Such artificial intelligence circuits may be trained to select a task profile from a plurality of task profiles in light of a given task, one or more given functionalities, one or more restrictions or limitations on modules and associated utilizations, and/or one or more power usage requirements; paragraph 0050, discussing that the one or more processors may be configured to select a task profile based on at least telemetry data. As described, the AMR may include a telemetry circuit…The sensor data may provide, or may be used by the one or more processors to determine, the utilization of the sensors/a power usage requirement associated with the sensors or the utilization. The telemetry circuit may be configured to determine the utilization of one or more processors. The telemetry circuit may be configured to determine battery data/charge data (e.g. a battery voltage, and amount of current drawn from the battery, a total charge (e.g. a total number of Colombes) received from the battery), and the telemetry circuit and/or the one or more processors may be configured to determine a remaining charge and/or a remaining time at the current or expected usage before a subsequent battery charging is required). The Natarajan-Chen combination describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen combination with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen combination to include Singh’s feature for including based at least in part on respective historical power consumption data for each task from the set of tasks, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 13, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. While the Natarajan-Chen combination teaches a category for each task from the set of tasks (Natarajan, paragraph 0034, discussing that upon being allocated to a particular task, each AMR 102 may execute the task in accordance with the task parameters identified with that particular type of task, paragraph 0092: “ type of task”; and Chen, paragraph 0299, discussing that missions may also be application dependent, e.g., missions may be assigned to a subset of robots based on the mission type (Robots A may be assigned to missions including button actuation, Robots B may be assigned to mission including moving a lever, etc.). Feedback and historical data may be used to optimize future missions, e.g., reassign future missions to different robots, such that future mission schedules may improve efficiency, reduce cost and energy usage, etc., based on robot usage), the Natarajan-Chen-Chrysanthakopoulos combination does not explicitly teach wherein accessing, by the robot fleet management system a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, at least one of a respective power consumption score and a power consumption category for each task from the set of tasks. However, Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: wherein accessing, by the robot fleet management system a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, at least one of a respective power consumption score and a power consumption category for each task from the set of tasks (paragraph 0027, discussing that resource utilization limit may be understood as a limit of power utilization (which may be referred to as a power usage requirement), such that an existing battery charge will support the AMR's activities for a predefined minimum period (e.g., for 8 hours, for 10 hours, etc.). According to another aspect of the disclosure, the resource utilization limit may be understood as a limit of activities that may be performed (e.g., a maximum threshold for performing an activity, which may be less than a theoretical maximum, for example engaging a navigation circuit at 70% capacity or even disabling the navigation circuit and thereby operating at 0% capacity). Such an understanding of the resource utilization limit is described in conjunction with the power usage requirement because a limit on use of a resource (e.g., limiting processing power, limiting motor operation, limiting sensor operation, etc.) may translate directly into a limitation of power consumption and therefore have a closely related effect on battery longevity, duration of operation from an existing charge, and the like; paragraph 0035, discussing that the task profile may be associated with one or more power usage requirements. Since AMRs are mobile, they are generally untethered from fixed power sources and are thus primarily battery-reliant. AMRs may be required to perform tasks for lengthy periods and thus require longevity of battery-charge and/or a predictable period of operation with a given battery charge. For example, it may be necessary that an AMR, once placed into duty, be operational for a period of hours; paragraph 0036, discussing that the various tasks may be associated with different power usage requirement (e.g. different demands upon the battery resources). For example, and merely for demonstrative purposes, operation of motors may be associated with considerable battery usage, which may be greater than the battery usage associated with operation of some sensors, which may be greater than the battery usage associated with the operation of some navigation computations, which may be greater than the battery usage associated with the operation of some basic processing demands. It may be the case that a power usage requirement and/or a minimum operational duration of a battery may be unattainable or unlikely to be attained without limitations on the various tasks, or the magnitude of processing resources (e.g., the percent of processing capacity overall, the percent of processing capacity useable for a certain task, etc.). As such, a task profile may be associated with one or more limitations in circuit usage, one or more limitations in activities/tasks, one or more limitations in processing activities, and/or one or more limitations in sensor operation, one or more limitations in motor operation; paragraph 0049, discussing that the AMR may include one or more artificial intelligence circuits, which may be configured to select and/or generate one or more task profiles. Such artificial intelligence circuits may be trained using any of historical task selection data, historical task profiles, historical battery life data, their corresponding historical module usage and functionality, or any combination thereof. Such artificial intelligence circuits may be trained to select a task profile from a plurality of task profiles in light of a given task, one or more given functionalities, one or more restrictions or limitations on modules and associated utilizations, and/or one or more power usage requirements; paragraphs 0064, 0066). The Natarajan-Chen combination describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen combination with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen combination to include Singh’s feature for including wherein accessing, by the robot fleet management system a respective power consumption for each task from the set of tasks includes accessing, by the robot fleet management system, at least one of a respective power consumption score and a power consumption category for each task from the set of tasks, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 14, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Natarajan further teaches wherein accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots includes accessing, by the robot fleet management system, a respective state of charge of a battery onboard each robot in the fleet of robots (paragraph 0030, discussing that the continuous monitoring of tasks and AMR [i.e., robot] actions facilitates a quick response to any AMR performance-related malfunctions, battery life issues, etc. Moreover, once an AMR completes its task, it is re-assigned to a new task by a task allocation engine, and thus the AMRs are continuously and efficiently assigned to tasks autonomously. In addition, in the techniques discussed, the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs [i.e., robots] to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0034, discussing that the AMRs 102 may transmit data to the computing device 108 indicative of current tasks being executed, new tasks to be executed, and/or other detected conditions within the environment 100, and may additionally or alternatively transmit any suitable type of state-related data such as an identified location, orientation, velocity, trajectory, heading, battery life, etc. within the environment 100; paragraph 0056). As per claim 15, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. While Natarajan describes a cumulative cost of each robot for a respective at least one task (paragraph 0066, discussing a weighted sum of a set of cost factors for each AMR 102 to perform the task) and identifying the overall health of the robots (paragraph 0109). Natarajan does not explicitly teach wherein accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots includes determining a respective power state of each robot based at least in part on a respective cumulative power consumption of each robot for a respective at least one completed task of the at least one untethered tasks. However, Chen in the analogous art of systems for robot fleet management teaches this concept. Chen teaches: wherein accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots includes determining a respective power state of each robot based at least in part on a respective cumulative power consumption of each robot for a respective at least one completed task of the at least one untethered tasks (paragraph 0157, discussing that coordinate-collect-control module 470 may provide overall management of robot-assigned activities, coordination or robot-assigned activities, and collection of data returned from robot-assigned activities; paragraph 0286, discussing that RSP (robotics services platform) 672 may record a success rate and/or mission completion times for one or more missions supported by multiple robot types. RSP 672 may adjust the assignment of missions based on this historical data by revising the assignment algorithm to assign the missions to robots to perform the missions at a higher success rate, to more quickly complete the missions, or the like. In some examples, a mission may be determined to be inconclusive or otherwise deemed a failure if, e.g., a photo is taken with a below-optimal resolution. In this instance, RSP 672 may revise the assignment algorithm to schedule similar missions to a robot having a better imaging package. RSP 672 may also select a power consumption of all robots by adjusting the assignment algorithm to assign the missions to robots that will have lower or higher power outputs based on the power demands of each robot. For example, if high quality images are desired but these robots require high power, assigning a mission to a robot to deliver high quality images may increase the overall power of the robots; paragraph 0226). Natarajan is directed towards a system and method for task allocation for autonomous robots. Chen is directed to systems and methods for management of a robot fleet. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Natarajan with Chen because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying Natarajan to include Chen’s feature for including wherein accessing, by the robot fleet management system, a respective power state of each robot in the fleet of robots includes determining a respective power state of each robot based at least in part on a respective cumulative power consumption of each robot for a respective at least one completed task of the at least one untethered tasks, in the manner claimed, would serve the motivation of allowing robots to be assigned to tasks in an efficient manner that may maximize the use of robots to perform the tasks within the facility (Chen at paragraph 0004); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 21, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Natarajan further teaches the method further comprising: determining, by the robot fleet management system, the first robot has insufficient power to continue the selected untethered task (paragraph 0030, discussing that the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0060, discussing that in a multi-robot environment such as a warehouse or a factory, as a practical matter several parameters need to be considered to ensure a proper AMR task allocation, which may include the aforementioned set of task parameters, the priority and the urgency of a task, as well as cost parameters such as AMR distance to a specific task location, AMR battery life, distance and time required to navigate to a destination (such as a recharging station), etc.; paragraph 0114, discussing re-allocating AMRs [i.e., robots] that have a battery capacity less than a predetermined threshold value to charge at a charging station etc.; paragraphs 0067, 0111). The Natarajan-Chen combination does not explicitly teach causing, by the robot fleet management system, the first robot to perform a tethered task selected from the at least one tethered task while tethered; and initiating, by the robot fleet management system, a replenishment of a power source of the first robot during performance of the tethered task by the first robot. Singh in the analogous art of resource-based task allocation for robots teaches this concept. Singh teaches: causing, by the robot fleet management system, the first robot to perform a tethered task selected from the at least one tethered task while tethered (paragraph 0001, discussing autonomous mobile robot (AMR) scaling and resource-based task allocation for AMRs; paragraph 0045, discussing that the AMR may be configured to self-select a task profile from a plurality of task profiles. That is, the AMR may be configured with one or more predetermined task profiles, which may be locally stored and/or which may be remotely stored in a manner to which the AMR has access. The AMR may include one or more processors, which may be configured to determine one or more tasks, which the AMR is required to perform, and to select a task profile from the one or more task profiles based on the one or more required tasks; paragraph 0062, discussing that in some cases, the offloaded task may be purely or principally computational in nature. Such tasks may be performable by any nearly any entity with one or more processors. In this situation, data for the computation may be transmitted to the receiving device, which may then process the data in an appropriate fashion. For example, an AMR's sensor data representing a vicinity of the AMR may require one or more robust computational calculations to localize and/or navigate. Based on the task profile, the AMR may be unable to perform such a calculation. In this case, it may be power saving to simply transmit the data and await a response including localization information and/or navigation information. In this manner, such robust computations may be offloaded as a power saving matter. As a general matter, such calculations may be performed on-site or remotely. Under some circumstances, these tasks may be performed by one or more devices that are not dependent on a battery (e.g., devices connected to a mains voltage) [i.e., tethered tasks]). The Natarajan-Chen combination describes features related to task allocation and robot fleet management. Singh is directed to systems and methods for task allocation for robots. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen combination with Singh because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen combination to include Singh’s feature for including causing, by the robot fleet management system, the first robot to perform a tethered task selected from the at least one tethered task while tethered, in the manner claimed, would serve the motivation of improving efficiency in robot resource utilization (Singh at paragraph 0026); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The Natarajan-Chen-Singh combination does not explicitly teach initiating, by the robot fleet management system, a replenishment of a power source of the first robot during performance of the tethered task by the first robot. However, Chrysanthakopoulos in the analogous art of robot management systems teaches this concept. Chrysanthakopoulos teaches: initiating, by the robot fleet management system, a replenishment of a power source of the first robot during performance of the tethered task by the first robot (paragraph 0003: “The invention relates first to the field of autonomous, modular, ground utility robot units, or GURU, to perform tasks.”; paragraph 0065, discussing that the GURU will automatically know when it needs to recharge based on programming that takes into account data including but not limited to its current charge level, its distance from the fueling port, and the amount of time and obstacles required to pass to return to the fueling port. Once this is calculated the GURU will self-navigate and return to the refueling fueling port to automatically refuel. Once at the fueling port the GURU will recharge either by docking into a fixed port via the fueling port connectors on the GURU or by utilizing an inductive charging port using a charging plate on a bottom of the GURU; paragraph 0069, discussing that the bots can perform a variety of tasks and will be extremely useful to the consumer, customer or user. It is envisioned that the bots be affordable, resilient, low maintenance, autonomous and environmentally friendly. Specifically, it is envisioned that the bots cost approximately $5,000 or less. That they have a duty cycle duration of approximately in 75% active and 25% low power mode, with the ability to charge while performing a task…The refueling for the bots will take place at the autonomous docking port whereby the bots automatically returns to the docking port when it is in need of refueling). The Natarajan-Chen-Singh combination describes features related to task allocation and robot fleet management. Chrysanthakopoulos relates to the field of autonomous, modular, ground utility robot units to perform tasks. Therefore, they are deemed to be analogous as they both are directed towards robot fleet management systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Natarajan-Chen-Singh combination with Chrysanthakopoulos because the references are analogous art because they are both directed to solutions for task allocation and management of robot services, which falls within applicant’s field of endeavor (systems and methods for robot fleet management), and because modifying the Natarajan-Chen-Singh combination to include Chrysanthakopoulos’ feature for including initiating, by the robot fleet management system, a replenishment of a power source of the first robot during performance of the tethered task by the first robot, in the manner claimed, would serve the motivation of improving overall performance between all deployed robots (Chrysanthakopoulos at paragraph 0144); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 22, the Natarajan-Chen-Singh-Chrysanthakopoulos combination teaches the robot fleet management system of claim 1. Natarajan further teaches the method further comprising: determining, by the robot fleet management system, the second robot has sufficient power to perform an untethered task from the at least one untethered task (paragraph 0030, discussing that the task allocation engine provides additional logic to support urgent unexpected scenarios such as robot malfunctions or low battery etc., which may require emergency AMR back-up; paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.); paragraph 0060, discussing that in a multi-robot environment such as a warehouse or a factory, as a practical matter several parameters need to be considered to ensure a proper AMR task allocation, which may include the aforementioned set of task parameters, the priority and the urgency of a task, as well as cost parameters such as AMR distance to a specific task location, AMR battery life, distance and time required to navigate to a destination (such as a recharging station), etc.; paragraph 0114, discussing re-allocating AMRs [i.e., robots] that have a battery capacity less than a predetermined threshold value to charge at a charging station etc.); and causing, by the robot fleet management system, the second robot to perform an untethered task selected from the at least one untethered task while untethered (paragraph 0031, discussing that the allocation of AMRs to tasks is determined by several factors such as task parameters and AMR state parameters and availability information (such as the number of assigned and unassigned AMRs, the AMR's current action sequence, idle times, the physical location of AMRs with respect to task location, battery life remaining, the distance to charging stations, etc.). Then, each task may be allocated to the AMRs for each task having the highest reward metric value, which thus represents the “best” AMR to perform each task; paragraph 0034, discussing that the AMRs 102 may also receive commands (which may be in the form of transmitted control signals) with respect to allocated tasks to perform as well as the details associated with how and when to perform the allocated tasks, and/or planned path information from the computing device 108; paragraph 0042, discussing that the computing device may thus receive state-related information from each for the AMRs 102…, which may include…data related to the operating state of the AMRs 102, or any other suitable feedback related to the state of the AMRs… In any event, the computing device 108 may optionally use this state-related information, together with other information about the environment 100 that is already known, for task allocation; paragraph 0203, discussing generating autonomous agent task allocation data that allocates each one of the plurality of autonomous agents to a respective one of the set of tasks; and a communication means for transmitting commands to each one of the plurality of autonomous agents to perform the respectively allocated tasks). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shannon, Pub. No.: US 2023/0202029 A1 – describes devices, and methods for powering robots, and particularly relate to replacing and/or replenishing depleted electrical power source for autonomous or semi-autonomous general purpose robots. Chen et al., Pub. No.: US 2022/0269286 A1 – describes systems and methods for management of a robot fleet. Vestal et al., Pub. No.: US 2014/0365258 A1 – describes a job management system for a fleet of autonomous mobile robots. Chatzisavvas, Antonios, et al. "A routing and task-allocation algorithm for robotic groups in warehouse environments." Information 13.6 (2022): 288 – describes an efficient model concerning the robotic fleet’s management. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. 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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. /Darlene Garcia-Guerra/ Primary Examiner, Art Unit 3625