DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 04/17/2026, Applicant, on 07/16/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 07/16/2026 have been fully considered, but they are not fully persuasive. The35 USC § 101 rejection has been overcome. However, the updated 35 USC § 103 rejections of claims 1-20 are applied in light of Applicant's amendments.
The Applicant argues that the amended limitations are not taught by Hakim in view of Hishida. (Remarks 07/16/2026)
In response the Examiner respectfully disagrees. Hishida’s scheduled period is bound by the estimated time of arrival at commercial facility 150 and the connection end time at which the cable is disconnected, see (0060 and 0070-0072). The claimed arrival and departure times at the carrying destination, and 0066 clearly provides that “the scheduled period is set so as to cover time during which power transfer is performed between the vehicle 30 and the power network 10”, which is the claimed period defined by those times during which power is transmitted to or received from the vehicle. Hishida’s charge/discharge facility 20 transfers power in both directions, teaching power “transmitted to or received from” the delivery vehicle. That an item is delivered during that same period recites the purpose of the vehicle’s stop, not any structure or processing of the claimed control device and does not distinguish over the combination. Thus, for the reasons stated above, the Examiner does not find the arguments/amendments persuasive to overcome the prior art and maintains the 103 rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7, 10, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20080281663 (hereinafter “Hakim”) et al., in view of U.S. PGPub 20200234575 to (hereinafter “Hishida”) et al.
As per claim 1 Hakim teaches an integrated control device comprising at least one processor, wherein:
the at least one processor acquires (I) a first demand sent from an energy control device which controls energy and including a demand amount and a demand time related to an energy demand of a carrying destination to which a delivery vehicle carries an item, and (II) a second demand sent from a moving body control device which controls the delivery vehicle and including a demand amount, a demand time, and a demand position related to a demand of thedelivery vehicle; and the at least one processor performs at least one of processing of deciding, on a basis of the first demand and the second demand acquired, a position and time at which the delivery vehicle moves or processing of determining whether it is possible to satisfy both demands of the first demand and the second demand; Hakim 0005: “One distributed energy resource is plug-in electric vehicles (“PEVs”). A PEV is any vehicle such as a car, truck, bus, motorcycle, etc that draws electricity from a power distribution network (“grid”), stores the electricity through some means, and uses electricity to power the vehicle…0042: In order to match electricity supply and demand, a utility control system operator may desire to curtail load or dispatch energy from distributed energy resources. One method of meeting a request to dispatch or curtail energy is to command individual distributed energy resources differently, based on the state of each energy resource at the time the command is executed, while at the same time attempting to ensure that the sum of all the individual actions meets the requirements of the overall request…0100-0116: to determine the optimum charging routine for the vehicle based upon least cost algorithms across the fleet of PEVs within the service territory of the utility…a utility may offer an incentive, such as discounted electricity, or favorable billing rates, to a municipality to make its vehicle fleet of mobile energy resources available at a particular location or at a particular time. The utility make provide levels of incentives, for example, in accordance with the greatest need for electricity at on a particular day, or at a particular time. The utility may thus use incentives to align the needs of a private or public entity with the needs of the utility to match energy supply to energy demand…claim 1: receiving a dispatch request comprising an amount of power required during a dispatch event and a duration of the event; determining accomplishability of the dispatch request; determining individual resource participation in the dispatch event utilizing rules that set the amount of energy to be discharged from each participating resource so as to keep the level of energy stored in each individual resource equal relative to the energy level of other participating resources; and, scheduling individual resource dispatches.”
Hakim may not explicitly teach the following. However, Hishida teaches:
wherein when the at least one processor decides the position and the time at which the delivery vehicle moves, the time includes an arrival time at the carrying destination to deliver the item and a departure time from the carrying destination after delivery of the item; Hishida 0066: “As the scheduled period, information indicating a scheduled period over which the vehicle 30 of the user 80 is kept connected to the charge/discharge facility 20 is stored. As the scheduled period, a period that starts at a timing which is a predetermined length of time before an estimated time at which the vehicle 30 is estimated to arrive at a commercial facility 150 if the vehicle 30 travels along the route, and ends at a timing which is a predetermined length of time after the estimated time is stored…0070-0072: As the connection start time, information indicating a time at which the vehicle 30 became available for power transfer with the power network 10 is stored. The connection start time may be identified based on power transferability information sent periodically from the charge/discharge ECU of the vehicle 30 to the managing server 40. As the connection start time, a time at which it became possible for the power transfer control unit 280 to control charge/discharge of the battery 32 after the charge/discharge cable 22 is attached to the vehicle 30, and charge/discharge facility 20 may be stored… As the connection end time, information indicating a time at which it became impossible to perform power transfer between the vehicle 30 and the power network 10 is stored. The connection end time may be identified based on power transferability information sent periodically from the charge/discharge ECU of the vehicle 30 to the managing server 40. As the connection end time, information indicating an end time of a period over which the vehicle 30 was kept connected to the charge/discharge facility 20 through the charge/discharge cable 22 may be stored. As the connection end time, information indicating a time at which a power cable was disconnected from at least one of the vehicle 30 and the charge/discharge facility 20 may be stored. As the connection end time, information indicating a time at which it became impossible for the power transfer control unit 280 to control charge/discharge of the battery 32 may be stored.”
and wherein the arrival time and the departure time define a period during which the item is delivered at the carrying destination and power is transmitted to or received from the delivery vehicle at the carrying destination to satisfy the energy demand of the carrying destination;Hishida 0066-0072: “As the scheduled period, information indicating a scheduled period over which the vehicle 30 of the user 80 is kept connected to the charge/discharge facility 20 is stored. As the scheduled period, a period that starts at a timing which is a predetermined length of time before an estimated time at which the vehicle 30 is estimated to arrive at a commercial facility 150 if the vehicle 30 travels along the route, and ends at a timing which is a predetermined length of time after the estimated time is stored. The scheduled period is set so as to cover time during which power transfer is performed between the vehicle 30 and the power network 10 (e.g., “two hours” which is a recommended length of a stay)… As the connection end time, information indicating a time at which it became impossible to perform power transfer between the vehicle 30 and the power network 10 is stored. The connection end time may be identified based on power transferability information sent periodically from the charge/discharge ECU of the vehicle 30 to the managing server 40. As the connection end time, information indicating an end time of a period over which the vehicle 30 was kept connected to the charge/discharge facility 20 through the charge/discharge cable 22 may be stored. As the connection end time, information indicating a time at which a power cable was disconnected from at least one of the vehicle 30 and the charge/discharge facility 20 may be stored. As the connection end time, information indicating a time at which it became impossible for the power transfer control unit 280 to control charge/discharge of the battery 32 may be stored.”
Hakim and Hishida are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim with the aforementioned teachings from Hishida with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Hishida 0066].
As per claim 2, Hakim and Hishida teach all the limitations of claim 1.
In addition, Hakim teaches:
the at least one processor manages first supply information information that the Hakim 0015: “the invention provides a computerized method for dispatching energy from distributed resources that defers evaluation of event parameters. A dispatch request is received, and a determination is made of the accomplishability of the dispatch request. Individual resource participation in a planned dispatch event is then determined. Individual resource dispatches are scheduled at a future time. Accomplishability of the dispatch request is redetermined prior to said future time, and individual resources are commanded to dispatch energy based upon such re-determination of accomplishability.”
As per claim 3, Hakim and Hishida teach all the limitations of claim 1.
In addition, Hakim teaches:
when determining that it is not possible to satisfy the both demands of the first demand and the second demand, the at least one processor decides the position and the time, at which the delivery vehicle moves, by giving priority to one of the first demand and the second demand over another; Hakim 0070: “Participation information for each available resource may thus be determined by prioritizing resources based on each unit's potential discharge duration, such that the longer a resource may discharge its stored energy, the greater its level of participation... claims 17-18: receiving a dispatch request; determining accomplishability of the dispatch request, determining individual resource participation in a planned dispatch event; scheduling individual resource dispatches at a future time; re-determining accomplishability of the dispatch request prior to said future time; and, commanding said individual resources to dispatch energy based upon said re-determination of accomplishability; wherein said receiving, determining, scheduling, re-determining and commanding steps are performed by one or more computing devices… wherein said re-determining step comprises repeatedly re-determining accomplishability of the dispatch request prior up until said future time.”
As per claim 4, Hakim and Hishida teach all the limitations of claim 1.
In addition, Hakim teaches:
wherein the first demand further includes a demand position related to an energy demand, sent from the energy control device; Hakim 0109-0116: “A utility may also enter into arrangements with other commercial entities, or with municipalities or other governmental organizations, and provide incentives to such larger entities. For example, a utility may offer an incentive, such as discounted electricity, or favorable billing rates, to a municipality to make its vehicle fleet of mobile energy resources available at a particular location or at a particular time. The utility make provide levels of incentives, for example, in accordance with the greatest need for electricity at on a particular day, or at a particular time. The utility may thus use incentives to align the needs of a private or public entity with the needs of the utility to match energy supply to energy demand.”
As per claim 7, Hakim and Hishida teach all the limitations of claim 1.
In addition, Hakim teaches:
wherein after the moving body starts moving on a basis of the position and the time at which the delivery vehicle moves and which are decided on a basis of the first demand and the second demand, the at least one processor re-decides the position and the time, at which the delivery vehicle moves, each time a predetermined condition is satisfied; Hakim 0073: “It is therefore desirable to re-evaluate the accomplishability of a utility-commanded dispatch event repeatedly between the time the dispatch request is initially made and the start of the dispatch event. Such re-evaluation provides the utility control system operator lead time to act on a notification that a previously accomplishable event is now no longer accomplishable because of a change in circumstances. Conversely, repeated evaluation of accomplishability may also show that an event that was unaccomplishable when scheduled has become accomplishable without any further interaction by the operator. For example, distributed resources may have been charged, or additional mobile energy storage may have become available for dispatch.”
As per claim 10, Hakim and Hishida teach all the limitations of claim 2.
In addition, Hakim teaches:
when determining that it is not possible to satisfy the both demands of the first demand and the second demand, the at least one processor decides the position and the time, at which the delivery vehicle moves, by giving priority to one of the first demand and the second demand over another; Hakim 0070: “Participation information for each available resource may thus be determined by prioritizing resources based on each unit's potential discharge duration, such that the longer a resource may discharge its stored energy, the greater its level of participation... claims 17-18: receiving a dispatch request; determining accomplishability of the dispatch request, determining individual resource participation in a planned dispatch event; scheduling individual resource dispatches at a future time; re-determining accomplishability of the dispatch request prior to said future time; and, commanding said individual resources to dispatch energy based upon said re-determination of accomplishability; wherein said receiving, determining, scheduling, re-determining and commanding steps are performed by one or more computing devices… wherein said re-determining step comprises repeatedly re-determining accomplishability of the dispatch request prior up until said future time.”
Claims 19-20 are directed to the method and CRM for performing the system of claim 1 above. Since Hakim and Hishida teach the method and CRM, the same art and rationale apply.
Claims 5-6, 8-9, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20080281663 (hereinafter “Hakim”) et al., in view of U.S. PGPub 20200234575 to (hereinafter “Hishida”) et al., in view of U.S. PGPub 20160247106 to (hereinafter “Dalloro”) et al.
As per claim 5, Hakim and Hishida teach all the limitations of claim 4.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein when determining that it is not possible to satisfy the both demands of the first demand and the second demand, the processing unit decides the position and the time, at which the delivery vehicle moves, to satisfy at least the second demand and satisfy at least a demand amount and a demand time at some predetermined demand positions among a plurality of demand positions including the demand position that are included in the first demand; Dalloro 0011: “According to other embodiments, a system for managing a fleet of electric vehicles comprises electric vehicles (each having a battery) and a fleet management computing system. The fleet management computing system is configured to select discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest, and receive user requests for transportation to locations within the geographic area. The fleet management computing system is further configured to generate a routing instruction for each respective electric vehicle that satisfies one of the user requests and facilitates discharging of the battery associated with the respective electric vehicle at one of the discharging parking lot locations, and provide each of the electric vehicles with its respective routing instruction…0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 6, Hakim and Hishida teach all the limitations of claim 4.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein the at least one processor decides, on a basis of the first demand, the position and the time at which the delivery vehicle moves, and determine, on a basis of the position and the time at which the delivery vehicle moves and which are decided on the basis of the first demand, whether the second demand is satisfied; Dalloro 0011: “According to other embodiments, a system for managing a fleet of electric vehicles comprises electric vehicles (each having a battery) and a fleet management computing system. The fleet management computing system is configured to select discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest, and receive user requests for transportation to locations within the geographic area. The fleet management computing system is further configured to generate a routing instruction for each respective electric vehicle that satisfies one of the user requests and facilitates discharging of the battery associated with the respective electric vehicle at one of the discharging parking lot locations, and provide each of the electric vehicles with its respective routing instruction…0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 8, Hakim and Hishida teach all the limitations of claim 7.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein the at least one processor re-decides the position and the time, at which the moving body moves, each time the delivery vehicle arrives at the demand position; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 9, Hakim and Hishida teach all the limitations of claim 7.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein the at least one processor re-decides the position and the time, at which the delivery vehicle moves, each time the moving body departs from the demand position; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 11, Hakim and Hishida teach all the limitations of claim 10.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
when determining that it is not possible to satisfy the both demands of the first demand and the second demand, the at least one processor decides the position and the time, at which the delivery vehicle moves, to satisfy at least the second demand and satisfy at least a demand amount and a demand time at some predetermined demand positions among a plurality of demand positions including the demand position that are included in the first demand; Dalloro 0011: “According to other embodiments, a system for managing a fleet of electric vehicles comprises electric vehicles (each having a battery) and a fleet management computing system. The fleet management computing system is configured to select discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest, and receive user requests for transportation to locations within the geographic area. The fleet management computing system is further configured to generate a routing instruction for each respective electric vehicle that satisfies one of the user requests and facilitates discharging of the battery associated with the respective electric vehicle at one of the discharging parking lot locations, and provide each of the electric vehicles with its respective routing instruction…0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 12, Hakim and Hishida teach all the limitations of claim 2.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein the at least one processor decides, on a basis of the first demand, the position and the time at which the delivery vehicle moves, and determine, on a basis of the position and the time at which the delivery vehicle moves and which are decided on the basis of the first demand, whether the second demand is satisfied; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 13, Hakim and Hishida teach all the limitations of claim 2.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein after the delivery vehicle starts moving on a basis of the position and the time at which the delivery vehicle moves and which are decided on a basis of the first demand and the second demand, the processing unit re-decides the position and the time, at which the moving body moves, each time a predetermined condition is satisfied; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 14, Hakim and Hishida teach all the limitations of claim 3.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein after the delivery vehicle starts moving on a basis of the position and the time at which the moving body moves and which are decided on a basis of the first demand and the second demand, the processing unit re-decides the position and the time, at which the moving body moves, each time a predetermined condition is satisfied; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 15, Hakim and Hishida teach all the limitations of claim 4.
Hakim and Hishida may not explicitly teach the following. However, Dalloro teaches:
wherein after the delivery vehicle starts moving on a basis of the position and the time at which the moving body moves and which are decided on a basis of the first demand and the second demand, the processing unit re-decides the position and the time, at which the moving body moves, each time a predetermined condition is satisfied; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 16, Hakim, Hishida, and Dalloro teach all the limitations of claim 5.
Hakim and Hishida teach may not explicitly teach the following. However, Dalloro teaches:
wherein after the delivery vehicle starts moving on a basis of the position and the time at which the delivery vehicle moves and which are decided on a basis of the first demand and the second demand, the processing unit re-decides the position and the time, at which the moving body moves, each time a predetermined condition is satisfied; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 17, Hakim, Hishida, and Dalloro teach all the limitations of claim 13.
Hakim and Hishida teach may not explicitly teach the following. However, Dalloro teaches:
wherein the processing unit re-decides the position and the time, at which the delivery vehicle moves, each time the moving body arrives at the demand position; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
As per claim 18, Hakim, Hishida, and Dalloro teach all the limitations of claim 13.
Hakim and Hishida teach may not explicitly teach the following. However, Dalloro teaches:
wherein the processing unit re-decides the position and the time, at which the delivery vehicle moves, each time the moving body arrives at the demand position; Dalloro claims 1-4: “using, by the fleet management computing system, (i) the optimal vehicle fleet size, (ii) the plurality of discharging parking lot locations, and (iii) the transportation demand data to select routing information for each of a plurality of electric vehicles; and routing, by the fleet management computing system, each respective autonomous vehicle according to its respective routing information… wherein the transportation demand data is continuously updated based on new transportation requests received from the plurality of users via the applications or from one or more additional users… updating routing information for one or more of the plurality of electric vehicles based on updated transportation demand data.”0031-0034: at step 220, real-time transportation demand (e.g., in terms of origin, destination, and/or time frame) of individuals using the autonomous vehicle fleet… the demand information collected at step 220 and information output by step 210 are used to optimize the path of each autonomous vehicle such that (i) the demand of each user is met; and (ii) the autonomous vehicles reach the area (parking stations) where a need for power injection is expected, to provide ancillary services such as frequency regulation, loss compensation, load following, etc. This optimization may be performed throughout the day at discrete intervals…claim 13: selecting a plurality of discharging parking lot locations based on (i) historical electrical energy consumption for a geographic area, and (ii) historical traffic flow though the geographic area during one or more time periods of interest; receiving a plurality of user requests for transportation to locations within the geographic area; selecting a plurality of electric vehicles in the fleet of electric vehicles, each respective electric vehicle comprising a battery; generating routing information for each respective electric vehicle that satisfies one of the plurality of user requests for transportation and facilitates discharging of the battery associated with the respective electric vehicle at one of the plurality of discharging parking lot locations; and providing each of the plurality of electric vehicles an instruction dataset corresponding to its respective routing information.”
Hakim, Hishida, and Dalloro are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hakim and Hishida with the aforementioned teachings from Dalloro with a reasonable expectation of success, by adding steps that allow the software to utilize demand data with the motivation to more efficiently and accurately organize and analyze information [Dalloro 0031].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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