FDETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Examiner’s Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the references as applied to the claims below for convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations with the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to the Applicant’s definition which is not specifically set forth in the claims.
Information Disclosure Statements
The Information Disclosure Statement(s) (IDS) filed on 03/10/2026 has/have been acknowledged.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Status of Application
The list of claims 1-20 is pending in this application. In the claim set filed 03/17/2026:
Claim(s) 1, 9 and 15 is/are the independent claim(s) observed in the application.
Claim(s) 1, 4, 9, 11, 15 and 17 has/have been amended.
Claim(s) 2, 3, 5-8, 10, 12-14, 16 and 18-20 has/have been indicated as originally presented.
Response to Arguments
With respect to Applicant’s remarks filed on 03/17/2026; the Applicant's “Amendments and Remarks” have been fully considered. The Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 101, the Applicant’s “Amendments and Remarks” have been fully considered and are found persuasive. Therefore the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 101 has/have been withdrawn.
With respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 103, the Applicant’s “Amendments and Remarks” have been fully considered and are found persuasive. Therefore the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 103 has/have been withdrawn.
Office Note: Due to applicant’s amendments, further claim rejections appear on the record as stated in the Final Office Action below.
Final Office Action
Claim Objections
Claim(s) 4, 11 and 17 is/are objected to because of the following informalities:
With respect to claims 4, 11 and 17, these claims recite the same minor antecedent basis issue. Claims, 4, 11 and 17 each recite: “provide instructions associated with the planned task to the EV.” However, claims 1, 9 and 15, which claims 4, 11 and 17, depend from, respectively, already recite “instructions associated with the planned task to the EV.” Therefore, claims 4, 11 and 17 should be amended to instead recite: “provide the instructions associated with the planned task to the EV,” accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claim(s) 1-3, 5, 7-10, 12, 14-16, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kessler (United States Patent Publication 2022/0051568 A1) in view of Ucar et al. (United States Patent Publication 2021/0319692 A1), referenced as Kessler and Ucar, respectively, moving forward.
With respect to claim 1, while Kessler discloses:
“A system, comprising: a processor; a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:”[Kessler; "The transportation system controller 102 may include a system state monitor 103 and a centralized dispatch server system 105 (referred to herein simply as a centralized dispatch system 105), among other possible modules, programs, or other systems that facilitate the operation of various aspects of the transportation system. The system state monitor 103 and the centralized dispatch system 105 may be implemented by any suitable combination of computer hardware (e.g., processors, memory, non-transitory computer readable storage media), computer software (e.g., computer programs, applications, firmware, etc.), sensors, communication systems, etc., that facilitate the operations performed by the system state monitor 103 and/or the centralized dispatch system 105;" Fig. 1; ¶: 0029];
“the group comprises an electric vehicle (EV)”[Kessler; "The transportation system controller 102 may receive information from, send information and/or commands to, control operations of, and/or otherwise communicate with the vehicles 108 of the transportation system 100 as well as transportation system infrastructure 106. The vehicles 108 may be a fleet of a dedicated type of vehicle that are configured to independently and at least semi-autonomously operate according to particular vehicle control schemes. An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel;" Fig. 1, 7A-9; ¶: 0030];
“schedule a time when the EV is to complete a planned task of the VMC in response to determining that a state of charge (SOC) of a battery of the EV satisfies an estimated energy requirement of the planned task”[Kessler; "In order to assign a vehicle to a trip request, the centralized dispatch system 105 may evaluate the statuses of numerous different vehicles to identify a suitable vehicle to assign to the trip. For example, the centralized dispatch system 105 may form a list of candidate vehicles by identifying a subset of vehicles in the transportation system such that each respective vehicle in the identified subset of vehicles has sufficient energy to travel from the origin location to the destination location (and optionally has sufficient energy to travel from the destination location to a charging station after termination of a trip associated with the trip request) and has a respective estimated time of arrival at the destination location corresponding to a time window in which the origin location is predicted to have available boarding capacity;" Fig. 1; ¶: 0039;
"Once the centralized dispatch system 105 has identified the subset of vehicles (which excludes those with insufficient energy or scheduled to arrive at the destination location when the destination is too busy), the centralized dispatch system 105 may select the vehicle, from the subset of vehicles, with the earliest estimated time of arrival at the origin location;" Fig. 1; ¶: 0041;]
“and provide instructions associated with the planned task to the EV to cause the EV to perform the planned task at the scheduled time”[Kessler; "and sending, to the vehicle, information about the target location, thereby causing the vehicle to begin travelling to the selected boarding zone;" ¶: 0008];
Kessler does not specifically state: “form a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks.”
Ucar, which is in the same field of invention of systems/methods for coordinating a plurality of robots/vehicles to complete a task, teaches: “form a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks” [Ucar; "After the vehicular micro cloud 540 is established, the formation module 360 can submit a task to the vehicular micro cloud 540 for collaborative execution;" ¶: 0055].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding performing tasks collaboratively using autonomous vehicles by forming micro clouds as taught by Ucar with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to enable “members in a micro cloud can share their resources with other members of the micro cloud to collaborate on operational tasks” such the collected data from the micro cloud can be aggregated and reported more comprehensively [Ucar; ¶: 0002, 0056].
With respect to claim 2, Kessler discloses: “wherein the machine-readable instruction that causes the processor to schedule the time when the EV is to complete the planned task comprises a machine-readable instruction that, when executed by the processor, causes the processor to instruct the EV to immediately complete the planned task based on at least one of: the SOC being greater than an SOC threshold amount; a change to the SOC being less than a change threshold amount; or the SOC allowing the EV to reach an intended destination”[Kessler; "In order to assign a vehicle to a trip request, the centralized dispatch system 105 may evaluate the statuses of numerous different vehicles to identify a suitable vehicle to assign to the trip. For example, the centralized dispatch system 105 may form a list of candidate vehicles by identifying a subset of vehicles in the transportation system such that each respective vehicle in the identified subset of vehicles has sufficient energy to travel from the origin location to the destination location (and optionally has sufficient energy to travel from the destination location to a charging station after termination of a trip associated with the trip request) and has a respective estimated time of arrival at the destination location corresponding to a time window in which the origin location is predicted to have available boarding capacity;" Fig. 1; ¶: 0039;
"Once the centralized dispatch system 105 has identified the subset of vehicles (which excludes those with insufficient energy or scheduled to arrive at the destination location when the destination is too busy), the centralized dispatch system 105 may select the vehicle, from the subset of vehicles, with the earliest estimated time of arrival at the origin location;" Fig. 1; ¶: 0041].
With respect to claim 3, Kessler discloses: “wherein: the machine-readable instruction that causes the processor to schedule the time when the EV is to complete the planned task comprises a machine-readable instruction that, when executed by the processor, causes the processor to, when the SOC is below a threshold amount, instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold amount; and the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to receive data associated with a subsequently completed task from the EV”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 5, Kessler discloses: “wherein: the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to estimate an energy discharge value for the planned task; the machine-readable instruction that causes the processor to schedule the time for the EV to complete the planned task comprises machine-readable instructions that cause the processor to: schedule the time based on the SOC and an estimated energy discharge value for the planned task; and when the estimated energy discharge value for the planned task reduces the SOC by a threshold discharge amount or reduces the SOC to below a threshold SOC value, instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 7, Kessler discloses: “wherein: the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a destination of the EV; the machine-readable instruction that causes the processor to schedule the time for the EV to complete the planned task comprises machine-readable instructions that cause the processor to: schedule the time based on the SOC, the estimated energy discharge value for the planned task, and the destination of the EV; and instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value when the estimated energy discharge value for the planned task: results in an estimated destination SOC from an original destination SOC greater than a threshold deviation amount; or renders the EV unable to reach the destination”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 8, Kessler discloses: “wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to provide an output of the VMC to the EV independent of the time when the EV completes the planned task”[Kessler; "Further, the centralized dispatch system 105 may continuously or cyclically monitor the state of the system, including updating predictions of vehicle demand at different boarding zones and which vehicles are located at which boarding zones, and reallocate the vehicles according to the updated system state. In this way, the centralized dispatch system 105 may respond quickly to the ever-changing vehicle demands in the transportation system to improve the efficiency and user experience by ensuring vehicles are staged near high-demand locations (wherever those locations may be);" ¶: 0049].
With respect to claim 9, while Kessler discloses:
“A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause the processor to:”[Kessler; "The transportation system controller 102 may include a system state monitor 103 and a centralized dispatch server system 105 (referred to herein simply as a centralized dispatch system 105), among other possible modules, programs, or other systems that facilitate the operation of various aspects of the transportation system. The system state monitor 103 and the centralized dispatch system 105 may be implemented by any suitable combination of computer hardware (e.g., processors, memory, non-transitory computer readable storage media), computer software (e.g., computer programs, applications, firmware, etc.), sensors, communication systems, etc., that facilitate the operations performed by the system state monitor 103 and/or the centralized dispatch system 105;" Fig. 1; ¶: 0029];
“the group comprises an electric vehicle (EV)”[Kessler; "The transportation system controller 102 may receive information from, send information and/or commands to, control operations of, and/or otherwise communicate with the vehicles 108 of the transportation system 100 as well as transportation system infrastructure 106. The vehicles 108 may be a fleet of a dedicated type of vehicle that are configured to independently and at least semi-autonomously operate according to particular vehicle control schemes. An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel;" Fig. 1, 7A-9; ¶: 0030];
“schedule a time when the EV is to complete a planned task of the VMC in response to determining that a state of charge (SOC) of a battery of the EV satisfies an estimated energy requirement of the planned task”[Kessler; "In order to assign a vehicle to a trip request, the centralized dispatch system 105 may evaluate the statuses of numerous different vehicles to identify a suitable vehicle to assign to the trip. For example, the centralized dispatch system 105 may form a list of candidate vehicles by identifying a subset of vehicles in the transportation system such that each respective vehicle in the identified subset of vehicles has sufficient energy to travel from the origin location to the destination location (and optionally has sufficient energy to travel from the destination location to a charging station after termination of a trip associated with the trip request) and has a respective estimated time of arrival at the destination location corresponding to a time window in which the origin location is predicted to have available boarding capacity;" Fig. 1; ¶: 0039;
"Once the centralized dispatch system 105 has identified the subset of vehicles (which excludes those with insufficient energy or scheduled to arrive at the destination location when the destination is too busy), the centralized dispatch system 105 may select the vehicle, from the subset of vehicles, with the earliest estimated time of arrival at the origin location;" Fig. 1; ¶: 0041;];
“and provide instructions associated with the planned task to the EV to cause the EV to perform the planned task at the scheduled time”[Kessler; "and sending, to the vehicle, information about the target location, thereby causing the vehicle to begin travelling to the selected boarding zone;" ¶: 0008];
Kessler does not specifically state: “form a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks.”
Ucar teaches: “form a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks” [Ucar; "After the vehicular micro cloud 540 is established, the formation module 360 can submit a task to the vehicular micro cloud 540 for collaborative execution;" ¶: 0055].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding performing tasks collaboratively using autonomous vehicles by forming micro clouds as taught by Ucar with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to enable “members in a micro cloud can share their resources with other members of the micro cloud to collaborate on operational tasks” such the collected data from the micro cloud can be aggregated and reported more comprehensively [Ucar; ¶: 0002, 0056].
With respect to claim 10, Kessler discloses: “wherein: the instruction that causes the processor to schedule the time when the EV is to complete the planned task comprises an instruction that, when executed by the processor, causes the processor to, when the SOC is below a threshold amount, instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold amount; and the instructions further comprise an instruction that, when executed by the processor, causes the processor to receive data associated with a subsequently completed task from the EV”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 12, Kessler discloses: “wherein: the instructions further comprise an instruction that, when executed by the processor, causes the processor to estimate an energy discharge value for the planned task; the instruction that causes the processor to schedule the time for the EV to complete the planned task comprises instructions that cause the processor to: schedule the time based on the SOC and an estimated energy discharge value for the planned task; and when the estimated energy discharge value for the planned task reduces the SOC by a threshold discharge amount or reduces the SOC to below a threshold SOC value, instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value.”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 14, Kessler discloses: “wherein: the instructions further comprise an instruction that, when executed by the processor, causes the processor to identify a destination of the EV; the instruction that causes the processor to schedule the time for the EV to complete the planned task comprises instructions that cause the processor to: schedule the time based on the SOC, the estimated energy discharge value for the planned task, and the destination of the EV; and instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value when the estimated energy discharge value for the planned task: results in an estimated destination SOC from an original destination SOC greater than a threshold deviation amount; or renders the EV unable to reach the destination”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 15, while Kessler discloses:
“the group comprises an electric vehicle (EV)”[Kessler; "The transportation system controller 102 may receive information from, send information and/or commands to, control operations of, and/or otherwise communicate with the vehicles 108 of the transportation system 100 as well as transportation system infrastructure 106. The vehicles 108 may be a fleet of a dedicated type of vehicle that are configured to independently and at least semi-autonomously operate according to particular vehicle control schemes. An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel;" Fig. 1, 7A-9; ¶: 0030];
“scheduling a time when the EV is to complete a planned task of the VMC in response to determining that a state of charge (SOC) of a battery of the EV satisfies an estimated requirement of the planned task”[Kessler; "In order to assign a vehicle to a trip request, the centralized dispatch system 105 may evaluate the statuses of numerous different vehicles to identify a suitable vehicle to assign to the trip. For example, the centralized dispatch system 105 may form a list of candidate vehicles by identifying a subset of vehicles in the transportation system such that each respective vehicle in the identified subset of vehicles has sufficient energy to travel from the origin location to the destination location (and optionally has sufficient energy to travel from the destination location to a charging station after termination of a trip associated with the trip request) and has a respective estimated time of arrival at the destination location corresponding to a time window in which the origin location is predicted to have available boarding capacity;" Fig. 1; ¶: 0039;
"Once the centralized dispatch system 105 has identified the subset of vehicles (which excludes those with insufficient energy or scheduled to arrive at the destination location when the destination is too busy), the centralized dispatch system 105 may select the vehicle, from the subset of vehicles, with the earliest estimated time of arrival at the origin location;" Fig. 1; ¶: 0041;];
“and providing instructions associated with the planned task to the EV to cause the EV to perform the planned task at the scheduled time”[Kessler; "and sending, to the vehicle, information about the target location, thereby causing the vehicle to begin travelling to the selected boarding zone;" ¶: 0008];
Kessler does not specifically state: “a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks.”
Ucar teaches: “forming a vehicular micro cloud (VMC) that comprises a group of interconnected vehicles that share resources to complete tasks,” [Ucar; "After the vehicular micro cloud 540 is established, the formation module 360 can submit a task to the vehicular micro cloud 540 for collaborative execution;" ¶: 0055].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding performing tasks collaboratively using autonomous vehicles by forming micro clouds as taught by Ucar with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to enable “members in a micro cloud can share their resources with other members of the micro cloud to collaborate on operational tasks” such the collected data from the micro cloud can be aggregated and reported more comprehensively [Ucar; ¶: 0002, 0056].
With respect to claim 16, Kessler discloses: “wherein: scheduling the time when the EV is to complete the planned task comprises, when the SOC is below a threshold amount, instructing the EV to complete the planned task at a future time when the SOC is greater than the threshold amount; and the method further comprises receiving data associated with a subsequently completed task from the EV”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 18, Kessler discloses: “wherein: the method further comprises estimating an energy discharge value for the planned task; scheduling the time for the EV to complete the planned task comprises: scheduling the time based on the SOC and an estimated energy discharge value for the planned task; and when the estimated energy discharge value for the planned task reduces the SOC by a threshold discharge amount or reduces the SOC to below a threshold SOC value, instruct the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
With respect to claim 20, Kessler discloses: “wherein: the method further comprises identifying a destination of the EV; scheduling the time for the EV to complete the planned task comprises: scheduling the time based on the SOC, the estimated energy discharge value for the planned task, and the destination of the EV; and instructing the EV to complete the planned task at a future time when the SOC is greater than the threshold SOC value when the estimated energy discharge value for the planned task: results in an estimated destination SOC from an original destination SOC greater than a threshold deviation amount; or renders the EV unable to reach the destination”[Kessler; In at least the paragraphs and figures cited, Kessler discloses assigning an autonomous electric vehicle from a fleet of autonomous electric vehicles, which may be charging at a charging station(¶: 0008 - "The vehicle may be parked at a charging station when it is identified") in order to arrive at a destination to complete a rideshare request at a specified arrival time. Kessler further discloses, if no vehicles may complete the trip by the required time, due to for example insufficient energy to complete the trip, the arrival time of the vehicle may be extended (¶: 0040 - "In some cases, if any of the criteria for identifying the subset of vehicles results in the list containing no viable candidate vehicles, that criteria may be eliminated or relaxed"). This may in turn allow the previously recited vehicle charging at a charging station to reach a sufficient charge level to complete the required trip within the extended deadline recited above; Fig. 3-6; ¶: 0030, 0031, 0044, 0045].
Claim(s) 4, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kessler in view of Ucar and Ebrahimi Afouzi et al. (United States Patent 11,656,082 B1), referenced as Ebrahimi moving forward.
With respect to claim 4, while Kessler discloses: “and the machine-readable instruction that causes the processor to provide data associated with the planned task to the EV comprises a machine-readable instruction that causes the processor to provide data associated with the remotely-completable planned task to the EV”[Kessler; "An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel. The vehicles 108 may send information to the transportation system controller 102 and receive information and commands from the transportation system controller 102. For example, the vehicles 108 may send to the transportation system controller 102 information such as their charge state, their current location, their passenger/payload status, their current target location, their future target location, vehicle status information (e.g., current speed, acceleration, turning direction, braking status, vehicle orientation), vehicle maintenance information (e.g., time of last charge, battery health, battery age, fluid levels, tire pressure levels), or the like. The transportation system controller 102 may send to the vehicles 108 information such as information about a trip request that has been assigned to the vehicle (e.g., an origin location, a destination location, a requested vehicle arrival time, etc.), vehicle control commands (e.g., to change vehicle control parameters such as maximum speed, minimum speed, vehicle following distances, etc.), destination and/or service commands (e.g., travel to a charging station to recharge, travel to a particular parking garage or boarding zone for staging, etc.). Other types of information may be sent and/or received between the transportation system controller 102 and the vehicles 108;" Fig. 7A-9; ¶: 0030; See also: ¶: 0031];
Kessler does not specifically state: “wherein: the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a remotely-completable planned task for the VMC that is completable at a later point in time.”
Ebrahimi, which is in the same field of invention of systems/methods for coordinating a plurality of robots/vehicles to complete a task, teaches: “wherein: the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, causes the processor to identify a remotely-completable planned task for the VMC that is completable at a later point in time” [Ebrahimi; "actuating, with the processor of the robot, the robot to return to a charging station during a cleaning task to recharge the battery of the robot when the battery charge is below a predetermined threshold and resume the cleaning task from where the robot left off after recharging the battery;" Col: 3, Lines: 37-41;
"In some cases, a scheduler may define a time bound system with well defined fixed time constraints. In some embodiments, the scheduler temporarily interrupts low priority tasks and schedules them for resumption at a later time when a high priority or privileged tasks require attention;" Col: 162, Lines: 26-31].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding controlling a plurality of robots to perform optimized task completion based on their state-of-charge as taught by Ebrahimi with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to devise intelligent path plans and task plans that enable efficient navigation and task completion [Ebrahimi; Col: 2, Lines: 5-10; Col: 117, Lines: 1-18].
With respect to claim 11, while Kessler discloses: “and the instruction that causes the processor to provide instructions associated with the planned task to the EV comprises an instruction that causes the processor to provide data associated with the remotely-completable planned task to the EV”[Kessler; "An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel. The vehicles 108 may send information to the transportation system controller 102 and receive information and commands from the transportation system controller 102. For example, the vehicles 108 may send to the transportation system controller 102 information such as their charge state, their current location, their passenger/payload status, their current target location, their future target location, vehicle status information (e.g., current speed, acceleration, turning direction, braking status, vehicle orientation), vehicle maintenance information (e.g., time of last charge, battery health, battery age, fluid levels, tire pressure levels), or the like. The transportation system controller 102 may send to the vehicles 108 information such as information about a trip request that has been assigned to the vehicle (e.g., an origin location, a destination location, a requested vehicle arrival time, etc.), vehicle control commands (e.g., to change vehicle control parameters such as maximum speed, minimum speed, vehicle following distances, etc.), destination and/or service commands (e.g., travel to a charging station to recharge, travel to a particular parking garage or boarding zone for staging, etc.). Other types of information may be sent and/or received between the transportation system controller 102 and the vehicles 108;" Fig. 7A-9; ¶: 0030; See also: ¶: 0031];
Kessler does not specifically state: “wherein: the instructions further comprise an instruction that, when executed by the processor, causes the processor to identify a remotely-completable planned task for the VMC that is completable at a later point in time.”
Ebrahimi teaches: “wherein: the instructions further comprise an instruction that, when executed by the processor, causes the processor to identify a remotely-completable planned task for the VMC that is completable at a later point in time” [Ebrahimi; "actuating, with the processor of the robot, the robot to return to a charging station during a cleaning task to recharge the battery of the robot when the battery charge is below a predetermined threshold and resume the cleaning task from where the robot left off after recharging the battery;" Col: 3, Lines: 37-41;
"In some cases, a scheduler may define a time bound system with well defined fixed time constraints. In some embodiments, the scheduler temporarily interrupts low priority tasks and schedules them for resumption at a later time when a high priority or privileged tasks require attention;" Col: 162, Lines: 26-31].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding controlling a plurality of robots to perform optimized task completion based on their state-of-charge as taught by Ebrahimi with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to devise intelligent path plans and task plans that enable efficient navigation and task completion [Ebrahimi; Col: 2, Lines: 5-10; Col: 117, Lines: 1-18].
With respect to claim 17, while Kessler discloses: “and providing instructions associated with the planned task to the EV comprises providing data associated with the remotely-completable planned task to the EV”[ Kessler; "An example vehicle 108 is described herein with respect to FIGS. 7A-9. In some cases, the vehicles 108 are electric vehicles, and are configured for bidirectional travel. The vehicles 108 may send information to the transportation system controller 102 and receive information and commands from the transportation system controller 102. For example, the vehicles 108 may send to the transportation system controller 102 information such as their charge state, their current location, their passenger/payload status, their current target location, their future target location, vehicle status information (e.g., current speed, acceleration, turning direction, braking status, vehicle orientation), vehicle maintenance information (e.g., time of last charge, battery health, battery age, fluid levels, tire pressure levels), or the like. The transportation system controller 102 may send to the vehicles 108 information such as information about a trip request that has been assigned to the vehicle (e.g., an origin location, a destination location, a requested vehicle arrival time, etc.), vehicle control commands (e.g., to change vehicle control parameters such as maximum speed, minimum speed, vehicle following distances, etc.), destination and/or service commands (e.g., travel to a charging station to recharge, travel to a particular parking garage or boarding zone for staging, etc.). Other types of information may be sent and/or received between the transportation system controller 102 and the vehicles 108;" Fig. 7A-9; ¶: 0030; See also: ¶: 0031];
Kessler does not specifically state: “The method further comprises identifying a remotely-completable planned task for the VMC that is completable at a later point in time.”
Ebrahimi teaches: “The method further comprises identifying a remotely-completable planned task for the VMC that is completable at a later point in time” [Ebrahimi; "actuating, with the processor of the robot, the robot to return to a charging station during a cleaning task to recharge the battery of the robot when the battery charge is below a predetermined threshold and resume the cleaning task from where the robot left off after recharging the battery;" Col: 3, Lines: 37-41;
"In some cases, a scheduler may define a time bound system with well defined fixed time constraints. In some embodiments, the scheduler temporarily interrupts low priority tasks and schedules them for resumption at a later time when a high priority or privileged tasks require attention;" Col: 162, Lines: 26-31].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding controlling a plurality of robots to perform optimized task completion based on their state-of-charge as taught by Ebrahimi with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to devise intelligent path plans and task plans that enable efficient navigation and task completion [Ebrahimi; Col: 2, Lines: 5-10; Col: 117, Lines: 1-18].
Claim(s) 6, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kessler in view of Ucar, and Popov (United States Patent Publication 2025/0246082 A1), referenced as Popov moving forward.
With respect to claim 6, Kessler does not specifically state: “wherein the machine-readable instruction that causes the processor to estimate the energy discharge value for the planned task comprises at least one of: a machine-readable instruction that, when executed by the processor, causes the processor to estimate the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or a machine-readable instruction that, when executed by the processor, causes the processor to execute a machine-learning estimate of the energy discharge value.”
Popov, which is in the same field of invention of systems/methods for coordinating a plurality of robots/vehicles to complete a task, teaches: “wherein the machine-readable instruction that causes the processor to estimate the energy discharge value for the planned task comprises at least one of: a machine-readable instruction that, when executed by the processor, causes the processor to estimate the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or a machine-readable instruction that, when executed by the processor, causes the processor to execute a machine-learning estimate of the energy discharge value” [Popov; "the mission planning unit implements a machine learning model for energy consumption prediction configured for determining the feasibility of completing the flight mission with available battery charge;" ¶: 0028].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding planning flight missions for a plurality of autonomous vehicles based on an expected power consumption to perform said missions as taught by Popov with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to optimize UAV flight paths based on real-time energy consumption patterns in order to achieve energy-efficient flight missions that can be further dynamically adapted “ensuring energy efficiency and safety in diverse conditions” [Popov; ¶: 0007-0010].
With respect to claim 13, Kessler does not specifically state: “wherein the instruction that causes the processor to estimate the energy discharge value for the planned task comprises at least one of: an instruction that, when executed by the processor, causes the processor to estimate the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or an instruction that, when executed by the processor, causes the processor to execute a machine-learning estimate of the energy discharge value.”
Popov teaches: “wherein the instruction that causes the processor to estimate the energy discharge value for the planned task comprises at least one of: an instruction that, when executed by the processor, causes the processor to estimate the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or an instruction that, when executed by the processor, causes the processor to execute a machine-learning estimate of the energy discharge value” [Popov; "the mission planning unit implements a machine learning model for energy consumption prediction configured for determining the feasibility of completing the flight mission with available battery charge;" ¶: 0028].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding planning flight missions for a plurality of autonomous vehicles based on an expected power consumption to perform said missions as taught by Popov with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to optimize UAV flight paths based on real-time energy consumption patterns in order to achieve energy-efficient flight missions that can be further dynamically adapted “ensuring energy efficiency and safety in diverse conditions” [Popov; ¶: 0007-0010].
With respect to claim 19, Kessler does not specifically state: “wherein estimating the energy discharge value for the planned task comprises at least one of: estimating the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or executing a machine-learning estimate of the energy discharge value.”
Popov teaches: “wherein estimating the energy discharge value for the planned task comprises at least one of: estimating the energy discharge value based on a lookup table that maps energy discharge values to VMC tasks; or executing a machine-learning estimate of the energy discharge value” [Popov; "the mission planning unit implements a machine learning model for energy consumption prediction configured for determining the feasibility of completing the flight mission with available battery charge;" ¶: 0028].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system/method for managing a plurality of autonomous electric vehicles to complete tasks as disclosed by Kessler to incorporate the teachings regarding planning flight missions for a plurality of autonomous vehicles based on an expected power consumption to perform said missions as taught by Popov with a reasonable expectation of success. By combining these inventions, the outcome is a system/method for managing a plurality of autonomous electric vehicles to complete tasks that is more robust in its ability to optimize UAV flight paths based on real-time energy consumption patterns in order to achieve energy-efficient flight missions that can be further dynamically adapted “ensuring energy efficiency and safety in diverse conditions” [Popov; ¶: 0007-0010].
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
SHARMA (United States Patent Publication 2022/0048186 A1) discloses: A system and a method to dynamically update plans and task allocation strategies on at least one or more of cloud and plurality of heterogeneous autonomous mobile devices (e.g. robot) has been described. The system or a platform continuously monitors various events internally and externally. The platform analyzes notification or a trigger on whether the existing plans and task allocation strategies need to be updated or replaced. The platform generates solutions depending on various factors and identifies relevant plans and task allocation strategies that may need to be updated. Based on the solutions that are generated, the existing plans and allocated task allocation strategies may be updated or replaced. Once the updating of plans and task allocation strategies are performed, the platform deploys the updated plans and tasks allocation strategies on at least one or more of the cloud and plurality of heterogeneous autonomous mobile devices.
CHEN et al. (United States Patent Publication 2022/0300011 A1) discloses: A system or a method includes defining missions based on factors associated with the missions or environmental data associated with the system, assigning the missions to the fleet of robots based on capabilities of the robots, generating a schedule of the missions and the robots, and managing the fleet of robots using feedback.
GOLUB et al. (United States Patent Publication 2023/0281046 A1) discloses: One or more autonomous vehicles are clustered into one or more microgrids and at least one computing task is scheduled on at least one microgrid. Activation signals are received from a client of one or more autonomous vehicles when the vehicles are plugged into charging stations. Utilization rates of the autonomous vehicles are determined based on a set of parameters, which includes at least one of location of the autonomous vehicle and time. The autonomous vehicles are clustered into one or more microgrids of autonomous vehicles based on the utilization rates. At least one request for performing at least one computing task is received from a user device. The at least one request includes an estimated runtime of the at least one computing task. The at least one computing task is scheduled on at least one microgrid of autonomous vehicles based on the estimated runtime.
Li (United States Patent Publication 2023/0306334 A1) discloses: A method and system for task assignment in autonomous mobile devices. The method may include associating a value to a task from a plurality of tasks assigned to an agent from a plurality of agents in an operating environment. The method may further include evaluating the associated value to the task based on information received from the one or more agents and dynamically updating the evaluated value in response to the received information from the one or more agents to generate a new value to be associated with the task. The method further includes generating a task assignment plan for the agent based on the new value to be associated with the task, the assigned task is terminated based on the completion of the generated task assignment plan.
Emerson (United States Patent Publication 2024/0094712 A1) discloses: In various implementations, a state of a mobile robot transitioning from a production mode to a staging mode may be determined. A state of a plurality of robot staging stations may also be determined. The plurality of robot staging stations may include at least one each of a charging station and a maintenance station. Based at least in part on the determined states of the mobile robot and plurality of robot staging stations, a robot staging station may be selected from the plurality of robot staging stations. The mobile robot may be assigned a staging mission, which may cause the mobile robot to travel to the selected robot staging station.
LEE et al. (United States Patent Publication 2024/0385620 A1) discloses: An autonomous mobile robot fleet includes a service transceiver module, a dispatch module, a robot fleet, a monitoring module, a storage module, and a certificate module. The service transceiver module is configured to receive and send mission information. The dispatch module couples to the service transceiver module, and is configured to generate dispatch information according to the mission information. The robot fleet wirelessly couples to the dispatch module. The robot fleet is used to execute the dispatch information. The monitoring module wirelessly couples to the robot fleet. The monitoring module generates monitoring information according to a status signal from the robot fleet. The storage module couples to the dispatch module and the monitoring module. The certificate module couples to the storage module, and wirelessly couples to the robot fleet. The certificate module is configured to generate certificate information according to the dispatch information and the monitoring information.
KIM et al. (United States Patent Publication A1) discloses: A method performed by a server configured to communicate with a plurality of robots, can include forming a queue of the plurality of robots, assigning a task to at least one of the plurality of robots within the queue, releasing a queue mode of the task-assigned robot when the task-assigned robot departs from a queue of the plurality of robots, and shifting at least one or more robots among the plurality of robots within the queue forward by checking an occupancy rate for a forward part of the queue, in response to the departure of the task-assigned robot from the queue.
SHAN et al. (United States Patent Publication 2025/0251743 A1) discloses: Systems and methods for estimating performance of an autonomous robot system that is configured to fulfil multiple line orders is described herein. The autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport one or more cases within an environment so as to fulfil the multiple line orders. In some variations, a method includes obtaining first input data, generating a simulation model based at least in part on the first input data, determining a travel duration for each of the plurality of autonomous guided vehicles based on an execution of the simulation model, generating an analytical model based at least in part on the travel duration, and estimating the performance of the autonomous robot system based on an execution of the analytical model. The first input data includes data associated with operation of each of the plurality of autonomous guided vehicles and data representing a layout of the environment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/R.N.B./Examiner, Art Unit 3666C
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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