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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/03/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/03/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 and 103 rejection of claims 1-20 are applied in light of Applicant's amendments.
The Applicant argues “Claims 1 and 19 have been amended to introduce the "at least one processor" or "computer." Thus, amended claims 1 and 19 and current claim 20 do not fall into mental processes performed in the human mind.” (Remarks 12/03/2025)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group, The mere nominal recitation of a generic computer does not take the claim limitation out of the mental processes grouping.
The claimed subject matter is merely claims a method for calculating and analyzing (forecasting) information regarding energy demand. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing (modeling and projecting) data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea. Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data can be performed by a human (mental process/pen and paper). The practice of calculating information and with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data , and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding demand data, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The Applicant argues “Applicant respectfully submits that the cited references fail to teach or suggest at least the claimed clarified features that "the time at which the moving body moves includes an arrival time to the carrying destination for the item and a departure time from the carrying destination for the item" as required by amended claim 1.” (Remarks 12/03/2025)
Applicant’s arguments with respect to the rejection to the 103 are not persuasive and the Applicant respectfully disagrees. Hishida explicitly teaches both arrival and departure times for a vehicle at a carrying destination (charge/discharge facility. See paragraph 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. 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).” and, paragraph 0070, “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.” The citations explicitly teach determining when the vehicle arrives at the facility (the charge/discharge facility being the “carrying destination”). Additionally see Hishida 0055: “the notification control unit 200 searches routes to the destination P2 via facilities where charge/discharge facilities 20 are provided, for a route formed by nodes between which a traffic jam will not occur in a time segment in which the vehicle 30 is predicted to pass therethrough assuming that the vehicle 30 leaves a facility where a charge/discharge facility 20 is provided after staying at the facility for a predetermined length of time. The notification control unit 200 causes information indicating the route found by the search to be sent to the user terminal 82.” And, see 0071 “ 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.” The citations above teach determining when the vehicle departs the facility (the connection end time corresponding to when the vehicle disconnects and departs). Thus, Hishida fully teaches the limitations in question by teaching the timing of when vehicles arrive at and depart from charge/discharge facilities.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 19), computer program product (claims 20), and system (claims 1-18) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of the mental processes grouping.
The limitations reciting the abstract idea(s) (Mental process), as set forth in exemplary claim 1, are: (I) a first demand …and including a demand amount and a demand time related to an energy demand of a carrying destination for an item of a moving body, and (II) a second demand …and including a demand amount, a demand time, and a demand position related to a demand of the moving body; …which 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 moving body moves or processing of determining whether it is possible to satisfy both demands of the first demand and the second demand; wherein the time at which the moving body moves includes an arrival time to the carrying destination for the item and a departure time from the carrying destination. Independent claims 19 and 20 recite the method and CRM for performing the system of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: the at least one processor acquires … sent from an energy control device which controls energy… sent from a moving body control device which controls a moving body… the at least one processor … (as recited in the claims). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: the at least one processor acquires … sent from an energy control device which controls energy… sent from a moving body control device which controls a moving body… the at least one processor … (as recited in the claims) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0102]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-18) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 of a moving body, and (II) a second demand sent from a moving body control device which controls a moving body and including a demand amount, a demand time, and a demand position related to a demand of the moving body; and the at least one processor which 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 moving body 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 the time at which the moving body moves includes an arrival time to the carrying destination and a departure time from 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 that the moving body is capable of further supplying over the demand amount included in the first demand and second supply information that the moving body is capable of further supplying over the demand amount indicated by the second demand, and re-decides the position and the time, at which the moving body moves, according to the first demand and the second demand newly acquired by the acquisition unit; 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 moving body 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 moving body 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 moving body 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 moving body 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 moving body 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 moving body moves, and determine, on a basis of the position and the time at which the moving body 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 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 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 moving body 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 moving body 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 moving body moves, and determine, on a basis of the position and the time at which the moving body 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 moving body 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 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 moving body 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 moving body 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 moving body 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 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 moving body 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 moving body 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beddo; Michael Ervin. SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR FORECASTING PRODUCT SALES, .U.S. PGPub 20140108094 The present invention relate to systems, methods, and computer program products for determining forecasting data relating to a product using a neural network and accessing that forecasting data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus. In some embodiments, the forecasting apparatus is configured to determine an initial sales forecast using at least a portion of the product information and the neural network, modify the initial sales forecast to generate a final sales forecast, and present the final sales forecast to the computing system via the web portal.
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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