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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter (NEW MATTER) which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites, “…wherein the UI includes an indication of anticipated charging-station conditions…” “…wherein the circuitry is operatively coupled to a charging -station controller or grid interface and, responsive to the estimated demand and determined selling price, automatically adjusts at least one physical operating parameter of the charging station, including: (ii) modulation of charging current or power delivery rate, or (iii) limiting simultaneous charging activity to maintain system stability.” Which is not disclosed in the original specification. This is NEW MATTER.
Claim 5 recites, “… for adjusting an operating parameter of a charging station…” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 6 recites, “…to reduce variation in overall grid load…” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 7 recites, “…charging current measurements…provided to the predictive model to refine subsequent demand estimation.” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 8, recites, “…limits simultaneous charging activity to maintain system stability.” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 11 recites, “…wherein the UI includes an indication of anticipated charging-station conditions…” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 14 recites, “…refining the predictive model based on feedback containing charging-load information received from charging stations.” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 15, recites, “…using a communication interface configured to adjust a charging-station operating” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 18, recites, “ storing operational data for later refinement of the predictive and pricing models.” This limitation is not disclosed in the original specification. This is NEW MATTER. Examiner note: the original specification does not disclose any determination or storing of “operational data.” Operational data which may be interpreted as various charging data, ie charging station rates or discharging rates. The data stored, as disclosed in the applicant’s specification [0035] “…records of the requests for the vehicle charging service received from the set of vehicles 108A . . . 108N, the estimated demand for the vehicle charging service, and the determined selling price for the vehicle charging service…” There is no indication that this data is “operational” in regards to the operation of the charging station or the vehicle receiving a charge.
Claim 19 recites, “…selecting charging stations located in regions of lower demand to redistribute energy usage geographically” This limitation is not disclosed in the original specification. This is NEW MATTER.
Claim 20 recites, “…to render indications of anticipated charging-station conditions.” “…adjust at least one operating parameter of a charging station in accordance with the predictive and pricing output.” These limitations are not disclosed in the original specification. This is NEW MATTER.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4 and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Ayoola (US 20230024900) in view of Dow (US 20230408273) in further view of Yaldo (US 20200324667).
Regarding claim 1, Ayoola teaches an electronic device (defined in paragraph [0153] wherein functions may be implemented on an electronic device, interpreted as a mobile computing device. Figures 18-21 show a computing device, items 10, 20, 30 and 40, respectively),
comprising: a memory (Figures 18-21 and paragraphs [0154]-[0155] show a memory, items 11,16, 25, 26, 34,38, 43 and 44) that stores a list of active charging stations (paragraph [0070] teaches wherein a list of charging stations, interpreted as a database of charging station information, is maintained. Paragraph [0070] teaches wherein the system item 100 via a controller item 130, communicates with a cloud-based application programming interface to maintain information about multiple charging stations. Paragraph [0074] teaches wherein the controller communicates information of the charging station such as active, charging or grid status); and
circuitry that: receives, from a service provider associated with a charging station, an input that includes a threshold selling price for a vehicle charging service (paragraph [0074] teaches wherein information such as power rates are communicated between stations and service providers. Paragraph [0085] teaches wherein optimization core item 900 ingests data provided from service providers or utility companies, power reserve stations and power plants. Paragraph [0093] teaches wherein pricing thresholds, including the range of prices of service are determined and communicated. Paragraph [0096] teaches wherein real-time pricing is output);
collects information that includes requests for the vehicle charging service from a set of electric vehicles (paragraph [0074] teaches wherein the controller item 130 requests various types of data from electric vehicles (EVs) including charging services such as fast charging and energy exchanging);
processes the collected information to generate data used for estimating demand, the data including at least vehicle state of charge, request time, vehicle type, and location (paragraphs [0012], [0074] and [0096] teaches wherein the collected information includes state of charge, including battery status, location and vehicle type);
estimates a demand for the vehicle charging service in a geographical region based on the collected information the processed information using a trained predictive model (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output),
wherein the circuitry is operatively coupled to a charging station controller or grid interface and, responsive to the estimated demand and determined selling price, automatically adjusts at least one physical operating parameter of the charging station, including: (i) activation or deactivation of charging ports, (ii) modulation of charging current or power delivery rate, or (iii) limiting simultaneous charging activity to maintain system stability (paragraph [0098] teaches wherein the grid is adjusted based on pricing predictions).
Ayoola does not explicitly teach wherein the controller controls, based on the updated list of active charging stations, rendering of a map-based User Interface (UI) for the vehicle charging service on one or more display devices wherein the UI includes an indication of anticipated charging-station conditions.
Dow teaches wherein the controller controls, based on the updated list of active charging stations, rendering of a map-based User Interface (UI) for the vehicle charging service on one or more display devices wherein the UI includes an indication of anticipated charging-station conditions (paragraphs [0139], [0272], and [0289] – [0293] teaches wherein the navigation system item 330 provides a map with updated charging station locations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Ayoola teaches determining or identifying a station as unavailable due to grid and charging constraints in paragraph [0081], but combine with the Dow references does not explicitly teach updating the list of active charging stations based on a comparison of the determined selling price with the threshold selling price wherein updating comprises removing the charging station from the list in response to determining that the determined selling price fails to meet the threshold selling price.
Yaldo teaches updates the list of active charging stations based on a comparison of the determined selling price with the threshold selling price wherein updating comprises removing the charging station from the list in response to determining that the determined selling price fails to meet the threshold selling price (defined in paragraph [0095] wherein when the price of a charging station does not meet the requirements, or a threshold, it is removed from the list or a network of available charging stations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Dow references with the charging system of the Yaldo reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Yaldo reference in paragraph [0095] wherein determining the most cost-effective charging is determined.
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Ayoola figure 1 shows a vehicle charging station system in which prices are communicated via a Cloud Based Service API
Regarding claim 2, Ayoola teaches the electronic device according to claim 1, wherein the collected information further includes at least one of a number of charging spots at each charging station of the plurality of charging stations, an average charging time for a full charge at each charging station of the plurality of charging stations, or an availability of one or more charging points at each charging station of the plurality of charging stations within a defined duration (defined in paragraph [0082] wherein data such as charging time for the station is collected).
Regarding claim 3, Ayoola teaches the electronic device according to claim 1, wherein the circuitry generates a list that maps the set of electric vehicles with compatible charging points at the plurality of charging stations, and the demand is estimated further based on the generated list (paragraph [0081] teaches wherein the demand of a plurality of vehicles is determined).
Ayoola does not explicitly teach mapping the set of vehicles with compatible charging points.
Dow teaches mapping the set of vehicles with compatible charging points (defined in paragraph [0197] wherein the data displayed on a map includes the number of vehicles capable of being charged in an area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Regarding claim 4, Ayoola teaches the electronic device according to claim 1, wherein the demand is estimated further based on a set of parameters that include at least one of: a count of the plurality of charging stations that operate in the geographical region, a count of the set of electric vehicles that require the vehicle charging service in the geographical region, a traffic density of electric vehicles in the geographical region, a count of the requests from the set of electric vehicles, a current State of Charge (SOC) of each electric vehicle of the set of electric vehicles, a time window for charging the set of electric vehicles, or a capacity of a battery pack of each vehicle of the set of electric vehicles (paragraphs [0086] and [0100] teaches wherein traffic data is considered in order to estimate or determine demand).
Regarding claim 5, Ayoola teaches the electronic device according to claim 1, but does not explicitly teach wherein the circuitry generates a control signal or adjusting an operating parameter of a charging station and transmits the control signal through a communication interface to the charging-station controller.
Dow teaches wherein the circuitry generates a control signal or adjusting an operating parameter of a charging station and transmits the control signal through a communication interface to the charging-station controller (paragraph [0246] discloses wherein the smart wired/wireless charging station 240 may perform the power control by adjusting the operating frequency based on the control error value contained in the power control request message (S1011). Thereafter, the smart wired/wireless charging station 240 may transmit a power-controlled reference power signal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Regarding claim 6, Ayoola in view of Dow teaches the electronic device according to claim 1, but does not explicitly teach wherein the determined selling price or demand estimate is used to schedule activation or deactivation of charging stations to reduce variation in overall grid load (paragraph [0077] teaches operating in grid balancing state).
Regarding claim 8, Ayoola teaches the electronic device according to claim 1, wherein the circuitry limits simultaneous charging activity to maintain system stability (paragraph [0073] teaches wherein charger actions are limited).
Regarding claim 9, Ayoola teaches the electronic device according to claim 1, wherein the UI presents an indication of anticipated availability or expected wait time for the charging stations based on the predictive model output (paragraph [0101] teaches wherein pricing and availability is based on wait times).
Regarding claim 10, Ayoola teaches the electronic device according to claim 1, further comprising a display device, wherein the circuitry stores historical control-action information for later refinement of the predictive and pricing model (paragraph [0074] teaches wherein parameters are updated and stored as history for later).
Regarding claim 7, Ayoola teaches the electronic device according to claim 1, wherein charging- current measurements and state-of-charge values are periodically provided to the predictive model to refine subsequent demand estimation (paragraphs [0080] – [0082] teaches monitoring charging current and state of charge values of the charging stations and vehicle batteries periodically in regards to demand).
Claims 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ayoola (US 20230024900) in view of Dow (US 20230408273).
Regarding claim 11, Ayoola teaches a method comprising: in an electronic device (defined in paragraph [0153] wherein functions may be implemented on an electronic device, interpreted as a mobile computing device. Figures 18-21 show a computing device, items 10, 20, 30 and 40, respectively) that includes a memory (Figures 18-21 and paragraphs [0154]-[0155] show a memory, items 11,16, 25, 26, 34,38, 43 and 44) for storing a list of active charging stations (paragraph [0070] teaches wherein a list of charging stations, interpreted as a database of charging station information, is maintained. Paragraph [0070] teaches wherein the system item 100 via a controller item 130, communicates with a cloud-based application programming interface to maintain information about multiple charging stations. Paragraph [0074] teaches wherein the controller communicates information of the charging station such as active, charging or grid status):
receiving, a threshold selling price (paragraph [0074] teaches wherein information such as power rates are communicated between stations and service providers. Paragraph [0085] teaches wherein optimization core item 900 ingests data provided from service providers or utility companies, power reserve stations and power plants. Paragraph [0093] teaches wherein pricing thresholds, including the range of prices of service are determined and communicated. Paragraph [0096] teaches wherein real-time pricing is output);
collecting charging-service request information (paragraph [0074] teaches wherein the controller item 130 requests various types of data from electric vehicles (EVs) including charging services such as fast charging and energy exchanging);
processing the information to generate data used for demand estimation (paragraphs [0012], [0074] and [0096] teaches wherein the collected information includes state of charge, including battery status, location and vehicle type);
estimating demand using a trained predictive model (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output. Paragraph [0110] discloses wherein a recurrent neural network is used);
determining a selling price using a trained model (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output. Paragraph [0110] discloses wherein a recurrent neural network is used);
updating the list based on the comparison of selling price and threshold (paragraphs [0093], [0096] and [0099] teaches wherein the pricing is updated and determined in a real-time system, thus pricing of service at a specific charging station and specific region is updated continuously).
Ayoola does not explicitly teach wherein the controller controlling User Interface (UI) to render indications of anticipated charging-station conditions.
Dow teaches wherein controller controlling User Interface (UI) to render indications of anticipated charging-station conditions (paragraphs [0139], [0272], and [0289] – [0293] teaches wherein the navigation system item 330 provides a map with updated charging station locations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Regarding claim 12, Ayoola teaches the method according to claim 11, wherein the processed information includes time-varying charging data (paragraph [0060] teaches time-varying charging data wherein historical pricing is based on daily, weekly, monthly and yearly transactions. A yearly transaction includes at least six months of a historical pricing).
Regarding claim 13, Ayoola teaches the method according to claim 11, wherein the pricing model is trained using historical charging-related information (paragraph [0060] teaches wherein historical pricing is based on daily, weekly, monthly and yearly transactions. A yearly transaction includes at least six months of a historical pricing).
Regarding claim 14, Ayoola teaches the method according to claim 11 further comprising refining the predictive model based on feedback containing charging-load information received from charging stations (paragraph [0074] teaches wherein information such as power rates are communicated between stations and service providers. Paragraph [0085] teaches wherein optimization core item 900 ingests data provided from service providers or utility companies, power reserve stations and power plants. Paragraph [0093] teaches wherein pricing thresholds, including the range of prices of service are determined and communicated. Paragraph [0096] teaches wherein real-time pricing is output).
Regarding claim 15, Ayoola teaches the method according to claim 11, but do not explicitly teach wherein transmitting a control command includes using a communication interface configured to adjust a charging-station operating parameter.
Dow teaches wherein transmitting a control command includes using a communication interface configured to adjust a charging-station operating parameter (paragraph [0246] discloses wherein the smart wired/wireless charging station 240 may perform the power control by adjusting the operating frequency based on the control error value contained in the power control request message (S1011). Thereafter, the smart wired/wireless charging station 240 may transmit a power-controlled reference power signal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Regarding claim 16, Ayoola teaches the method according to claim 11, wherein demand and pricing estimation are performed periodically (paragraph [0060] teaches wherein historical pricing is performed periodically based on daily, weekly, monthly and yearly transactions. A yearly transaction includes at least six months of a historical pricing).
Regarding claim 17, Ayoola teaches the method according to claim 11, herein the user interface renders visual indications of changes in expected congestion following issuance of the control commands (paragraphs [0086] and [0100] teaches wherein a predictive congestion indicator, is interpreted as traffic data, which is considered in order to estimate or determine demand).
Regarding claim 18, Ayoola teaches the method according to claim 11, further comprising storing operational data for later refinement of the predictive and pricing model (paragraph [0074] teaches wherein parameters are updated and stored as history for later).
Regarding claim 19, Ayoola teaches the method according to claim 11wherein transmitting includes selecting charging stations located in regions of lower demand to redistribute energy usage geographically (paragraphs [0052] and [0093] teaches prioritization of charging stations to redistribute energy use).
Regarding claim 20, Ayoola teaches a non-transitory computer-readable medium having stored thereon computer implemented instructions that, when executed by an electronic device, causes the electronic device to execute operations (defined in paragraph [0153] wherein functions may be implemented on an electronic device, interpreted as a mobile computing device. Figures 18-21 show a computing device, items 10, 20, 30 and 40, respectively),
the operations comprising: retrieving a list of active charging stations (paragraph [0070] teaches wherein a list of charging stations, interpreted as a database of charging station information, is maintained. Paragraph [0070] teaches wherein the system item 100 via a controller item 130, communicates with a cloud-based application programming interface to maintain information about multiple charging stations. Paragraph [0074] teaches wherein the controller communicates information of the charging station such as active, charging or grid status);
receiving a threshold selling price (paragraph [0074] teaches wherein information such as power rates are communicated between stations and service providers. Paragraph [0085] teaches wherein optimization core item 900 ingests data provided from service providers or utility companies, power reserve stations and power plants. Paragraph [0093] teaches wherein pricing thresholds, including the range of prices of service are determined and communicated. Paragraph [0096] teaches wherein real-time pricing is output);
collecting charging-service request information (paragraph [0074] teaches wherein the controller item 130 requests various types of data from electric vehicles (EVs) including charging services such as fast charging and energy exchanging);
processing the information to generate data for demand estimation (paragraphs [0012], [0074] and [0096] teaches wherein the collected information includes state of charge, including battery status, location and vehicle type);
estimating demand using a trained prediction model (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output. Paragraph [0079] teaches wherein predictions are generated by a machine learning model)
determining a selling price using a trained pricing model (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output. Paragraph [0079] teaches wherein predictions are generated by a machine learning model);
updating the based on the comparison of selling price threshold (paragraphs [0093], [0096] and [0099] teaches wherein the pricing is updated and determined in a real-time system, thus pricing of service at a specific charging station and specific region is updated continuously);
and issuing, a control signal to adjust at least one operating parameter of a charging station in accordance with the predictive and pricing output (paragraph [0051] teaches wherein a demand for charging in a region is estimated. Paragraph [0012] teaches wherein the vectors, interpreted as information such as state of charge, is input into a machine learning model or a neural network to generate an output. Paragraph [0079] teaches wherein predictions are generated by a machine learning model).
Ayoola does not explicitly teach controlling, based on the updated list of active charging stations, rendering of a map-based User Interface (UI) for the vehicle charging service on one or more display devices.
Dow teaches wherein the controller controls, based on the updated list of active charging stations, rendering of a map-based User Interface (UI) for the vehicle charging service on one or more display devices (paragraphs [0139], [0272], and [0289] – [0293] teaches wherein the navigation system item 330 provides a map with updated charging station locations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Dow reference so that user may easily determine which charging station provides the most cost-efficient charging service.
The suggestion/motivation for combination can be found in the Dow reference in paragraph [0002] wherein determining the most cost-effective charging is determined.
Response to Arguments
Applicant's arguments filed 11/05/2025 have been fully considered but they are not persuasive.
Rejections under 35 USC § 101:
The amended claims currently overcome the previous rejections over 101.
Regarding Rejections under 35 USC § 112(a) – Written Description & Enablement
The applicant asserts that the amended claims are supported by the originally filed specification. The examiner respectfully disagrees. As disclosed in detail above, the amended claims include NEW MATTER (emphasis added) which does not have sufficient support within the originally filed specification. There is no evidence in the specification that suggests the new subject matter, which was added to overcome the 101 rejections.
Regarding Rejections under 35 USC § 103 Prior Art
Regarding claim 1, The applicant argues that the Dow reference does not explicitly teach or suggest, “removing station based on profitability predictions generated by trained machine learning models.” Yaldo teaches wherein the list of active charging stations is updated by removing a record corresponding to the charging station, and the record is removed based on a determination that the determined selling price is below the threshold selling price defined in paragraph [0095] wherein when the price of a charging station does not meet the requirements, or a threshold, it is removed from the list or a network of available charging stations. Yaldo teaches updating the availability of charging stations displayed. The updating process is known in the art to inherently involve adding, removing, or modifying station entries in response to changing conditions. Yaldo teaches dynamically updating the list of available charging stations in response to system data establishes the same fundamental operation as claimed.
Regarding claim 1, the applicant argues that the Ayoola reference does not disclose “processing charging-service information” in the manner recited, nor does it teach predictive pricing to update station availability or modifying physical charging-station behavior based on predicted condition. The applicant’s original specification does not disclose, “modifying physical charging-station behavior based on predicted condition.” Ayoola teaches modifying a physical charging station behavior such as lowering the charging station power rates during certain market price and market price predictions. Ayoola teaches processing charging-service information (a request for charging as claimed) disclosed in paragraph [0070] wherein the charging station receives a request for charging via an application programming interface or mobile app. Paragraph [0081] discloses wherein a charging station is updated to show unavailable on the mobile application.
Regarding claims 11 and 20, the applicant argues that the Ayoola in view of Dow reference does not teach processing charging-service information for predictive demand estimation (Ayoola paragraphs [0012] and [0093] teaches predicting demand prices using modeling), determining real time pricing using modeling (this limitation is not required by the claims. Ayoola teaches real time pricing in paragraph [0093]), updating station lists (Ayoola teaches updating a station list, while Yaldo teaches removing the station from a list), adjusting hardware based on predicted demand and pricing (this is NEW MATTER. Ayoola paragraph [0074] and [0082] shows adjusting charging rates according to machine learning data).
Regarding dependent claims 2 – 10 and 12 – 19, please see arguments above. The dependent claims include NEW MATTER not found in the original specification pertaining to adjusting charging station parameters, adjusting physical charging parameters, reducing grid load variation, limiting simultaneous charging activity and redistributing load. The combination of Ayoola, Dow and Yaldo references teach these limitations as disclosed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS B PACHECO whose telephone number is (571)272-5979. The examiner can normally be reached M-F 9:00 - 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman can be reached at 571-272-2147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ALEXIS BOATENG PACHECO
Primary Examiner
Art Unit 2859
/ALEXIS B PACHECO/Primary Examiner, Art Unit 2859