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 .
Claims Status
Claims 1-20 are currently pending and claims 1, 7-12, 16-20 are currently amended.
Response to Arguments
Applicant’s amendments overcome the previous rejection under 35 U.S.C. 101, the rejection of claims 1-20 under 35 U.S.C. 101 are withdrawn.
Applicant's arguments filed 05/14/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art does not disclose the following limitations added to independent claims 1, 12 and 20 “…generates, based on the metric, a charging control schedule for a plurality of the vehicles associated with the EV-OEM, the charging control schedule specifying at least one of the charging power levels, charging start times, charging durations, or charging locations for the plurality of vehicles; transmits control signals, via a network interface, to one or more charging devices associated with the plurality of vehicles to cause the charging devices to implement the charging control schedule…”.
The examiner respectfully disagrees and explains that BRUSCHI discloses a demand response evaluation and implementation system and method wherein when a decision is made to accept a demand response event, the demand response controller decides how to satisfy the load shedding requirement by determining how to shed load or generate additional power. This is interpreted to mean when to charge and when and how much power to generate (i.e. charging time) in order to ensure that load is less than available power [Pars.5 and 22] and also how to reduce cost by considering load shedding cost and applicable fines.
The examiner further explains that UYEKI explicitly discloses generating a charging schedule to maximize profits and reducing cost by charging during low energy production cost timeframe (See Par.4).
The examiner further explains that BRUSCHI as modified by UYEKI disclose maximizing profits and reducing cost, the use of return on investment (ROI) and benefit cost ratio (BCR) are two obvious well known business ways to increase profit and it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by BRUSCHI and UYEKI by generating a charging schedule based on ROI or BCR for the benefit of maximizing profitability.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 10, 12-15, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over BRUSCHI et al. (US 2014/0062195 A1, hereinafter BRUSCHI) in view of UYEKI et al. (US 2020/0111175, hereinafter UYEKI).
Regarding claims 1, 12 and 20 (claim 1 is considered representative for limitations matching purposes), BRUSCHI discloses a system, comprising:
circuitry (See Fig.1, Item#16 and Par.39, disclose a demand response management system (DRMS)) that:
receives, from a server (See Fig.1, Item#12 and Par.35, discloses an aggregator comprising a server), a proposal for a consumer to participate in a utility program of an electric utility (See Fig.2, Step#S24 discloses the server generating a demand response event to each selected participant i.e. Participant site 1. Par.10: discloses the proposal is a request/Demand response (DR) to reduce electrical power usage);
determines statistical information that includes:
cost information associated with the utility program (See Pars.10, 24 and 45, disclose determining the cost of complying with the DR);
computes a metric based on contents of the proposal and the statistical information, wherein the metric indicates a viability of the utility program for consumer (See Para.24 and 54, disclose determining if the cost of the failure to comply with the demand response event and compare the cost to a threshold to determine whether the offer is viable and should be accepted, also comparing the DR request against the power needs of the participant and the costs of power curtailment, generation, storage); and
transmits, to the server, a message that includes an acceptance of the proposal or a rejection of the proposal based on whether the metric is above a threshold (See Par.24, discloses returning a commitment when the cost of compliance with the DR is within a threshold of acceptability).
However, BRUSCHI does not disclose the consumer is an Electric Vehicle Original Equipment Manufacturer (EV-OEM) and generates, based on the metric, a charging control schedule for a plurality of the vehicles associated with the EV-OEM, the charging control schedule specifying at least one of the charging power levels, charging start times, charging durations, or charging locations for the plurality of vehicles;
transmits control signals, via a network interface, to one or more charging devices associated with the plurality of vehicles to cause the charging devices to implement the charging control schedule
UYEKI discloses a system and method for providing OEM control to maximize profits comprising an EV-OEM in communication with a server to negotiate demand response plans to maximize profits (See Pars.31-32, and Fig.1, disclose an OEM central server which evaluates price points and submit the modified plan to the server to maximize profits);
generates, based on the metric, a charging control schedule for a plurality of the vehicles associated with the EV-OEM, the charging control schedule specifying charging start times (See Pars.4-5, disclose generating a charge schedule based on low energy production cost timeframe. This is interpreted to mean that charging is started when charging cost is low);
transmits control signals, via a network interface, to one or more charging devices associated with the plurality of vehicles to cause the charging devices to implement the charging control schedule (See Fig.7, Step#714 and Par.85, disclose communicating with the charging stations to implement the charging schedule that is based on cost).
BRUSCHI and UYEKI are analogous art since they both deal with demand response and smart grid systems and methods.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by BRUSCHI with the teachings of UYEKI by providing the demand response request to an EV-OEM and generate a schedule based on cost reduction for the benefit of maximizing profit of the OEM by allowing the OEM to negotiate a contract which complies with the demand response event constraints while reducing charging costs.
Regarding claim 2, BRUSCHI and UYEKI disclosed the system according to claim 1 as discussed above, wherein the proposal is one of an electronic document, an electronic contract, or an electronic message that includes the contents of the proposal in a structured format or a semi-structured format (See BRUSCHI, disclose the negotiations between the server/aggregator and the consumer involve exchanged messages).
Regarding claims 3 and 13 (claim 3 is considered representative for limitations matching purposes), BRUSCHI and UYEKI disclose the system according to claim 1 as discussed above, wherein the contents of the proposal include:
a duration of the utility program (See BRUSCHI, Par.40, discloses the DR event may entail a time frame in which the power is to be supplied).
Regarding claims 4 and 14 (claim 4 is considered representative for limitations matching purposes), BRUSCHI and UYEKI the system according to claim 3 as discussed above, wherein the operational statistics include:
a total battery capacity of the vehicles (See BRUSCHI, Par.48, discloses the energy storage devices used to store and release energy during a DR event are vehicle batteries. Par.39 further discloses the DRMS 16 at the participant site 1, determined the capacities of the site which include the capacities of the vehicle batteries).
Regarding claims 5 and 15 (claim 5 is considered representative for limitation matching purposes), BRUSCHI and UYEKI disclose the system according to claim 3 as discussed above, wherein the statistical information further includes historical charging data for each of the vehicles (See BRUSCHI, Par.54, discloses that the DR proposal is evaluated based on the power needs for the participants. BRUSCHI further discloses that forecasting power needs makes use of historical data [See Par.34]), and the historical charging data includes at least one of seasonal information (See BRUSCHI, Par.34, discloses energy demand forecast is based on external factors such as weather).
Regarding claims 10 and 18 (claim 10 is considered representative for limitation matching purposes), BRUSCHI and UYEKI discloses the system according to claim 1 as discussed above, wherein the server, based on the message, transmits information that indicates the acceptance of the proposal to a server associated with the electric utility wherein the information is used to coordinate implementation of the charging control schedule by the electric utility (See BRUSCHI, Fig.1, discloses bi-directional communication between the server 12 and the utility 11. It is implicit that the server communicates back to the utility the results of the participation in the demand response program in order for the utility to make the appropriate adjustments. Par.35 further indicates that the server 12 may be part of the utility and in this case the server receiving the response from the DRMS 16 means the utility also receives it. UYEKI, Fig.7 and Par.85, disclose communicating with the plurality of charging station to implement the generated charging schedule).
Claim(s) 6, 8-9 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over BRUSCHI in view of UYEKI and in further view of FIFE (US 11,695,298 B2, hereinafter FIFE).
Regarding claim 6, BRUSCHI and UYEKI disclose the system according to claim 3 as discussed above, However FIFE does not disclose wherein the statistical information further includes future sales data for the EV-OEM, and the future sales data includes: a third projection on a total return on investment (ROI) over the defined period due to the incentives.
FIFE discloses an electrical control system for receiving a demand response request, evaluating the request and determining if the offer is viable and should be accepted by considering the return on investment (See Col.8, lines 49-64 disclose calculating the cost function, also see Col.46, lines 42-55, disclose the cost function includes the return on investment ROI).
BRUSCHI, UYEKI and FIFE are analogous art since they all deal with demand response power management systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify of BRUSCHI and UYEKI with that of FIFE by calculating the return on investment for the benefit of ensuring that participation in a program is profitable before accepting to be a participant.
Regarding claims 8-9 and 16-17 (claim 8 is considered representative for limitation matching purposes), BRUSCHI and UYEKI disclose the system according to claim 1 as discussed above, UYEKI, discloses developing a charging schedule to reduce cost and maximizing profit (See Par.35).
However, BRUSCHI and UYEKI do not disclose wherein the metric is a return on investment (ROI) metric that is computed based on a net return associated with incentives offered in the proposal and a net cost that the EV-OEM is likely to incur for participating in the utility program and wherein the charging control schedule is generated based on the cost information to reduce an overall cost associated with controlling charging of the plurality of the vehicles.
FIFE discloses an electrical control system for receiving a demand response request, evaluating the request and determining if the offer is viable and should be accepted by considering the return on investment (See Col.8, lines 49-64 disclose calculating the cost function, also see Col.46, lines 42-55, disclose the cost function includes the return on investment ROI) that is computed based on a net return associated with incentives offered in the proposal and a net cost that the EV-OEM is likely to incur for participating in the utility program (the examiner explains that the basic formula for the ROI is ((Current value of investment-original cost)/Cost of investment ) X 100. Regarding claims 9 and 17, The examiner explains that using the ratio of incentives (profit) to expenses (cost) is another of calculating the return on investment).
BRUSCHI, UYEKI and FIFE are analogous art since they all deal with demand response power management systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify of BRUSCHI and UYEKI with that of FIFE by calculating the return on investment or benefit cost ratio and using the ROI or BCR to generate a charging schedule for the benefit maximizing profits.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over BRUSCHI in view of UYEKI and in further view of CRABTREE et al. (US 2010/0332373 A1, hereinafter CRABTREE).
Regarding claim 7, BRUSCHI and UYEKI disclose the system according to claim 1 as discussed above, and wherein the charging control schedule is generated based on the cost information to reduce an overall cost associated with controlling charging of the plurality of the vehicles (See UYEKI, Par.35).
However, BRUSCHI and UYEKI do not disclose wherein the cost information includes at least one of:
an Information Technology (IT) cost that the EV-OEM is likely to incur over the duration of the utility program.
CRABTREE discloses a demand response system comprising determining a return on investment by calculating the cost of infrastructure investment over the duration of the program (See Par.197, discloses Simulation and modeling server 2300 is used to calculate a likely return on investment (ROI) of the new infrastructure by comparing a cost of building the new infrastructure against expected savings in electricity costs over a specified time period).
BRUSCHI, UYEKI and CRABTREE are analogous art since they all deal with demand response power management systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify of BRUSCHI and UYEKI with that of CRABTREE by calculating the return on investment by calculating the cost of capital investment needed for infrastructure improvement for the benefit of ensuring that participation in a program is profitable before accepting to be a participant in the program.
Claim(s) 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over BRUSCHI in view of UYEKI and in further view of MARUYAM et al. (US 2023/0244197 A1, hereinafter MARUYAMA).
Regarding claims 11 and 19 (claim 11 is considered representative for limitation matching purposes), BRUSCHI and UYEKI disclose the system according to claim 1 as discussed above, However BRUSCHI and UYEKI do not disclose wherein the circuitry further: collects historical data that includes:
past proposals that are accepted or rejected by the EV-OEM, past statistical information corresponding to each of the past proposals, and past messages that include acceptances and rejections of the EV-OEM for the past proposals; generates a training dataset for a classifier based on the historical data; trains the classifier based on the training dataset; and prepares an input for the classifier based on the contents of the proposal; feeds the input to the trained classifier; and generates a recommendation that includes whether to accept or reject the proposal based on an output of the trained classifier for the input, wherein the message is transmitted based on the recommendation.
MARUYAMA discloses a demand response system which collects historical data (See Par.22) that includes:
past proposals that are accepted or rejected by the EV-OEM, past statistical information corresponding to each of the past proposals, and past messages that include acceptances and rejections of the EV-OEM for the past proposals (See Par.22, discloses collecting DR historical data including historical load-shedding participation data for historical DR events [acceptance/rejection]);
generates a training dataset for a classifier based on the historical data (See Par.22, discloses that training dataset includes data about a different historical DR event and data about weather during/around the historical DR and label indicating an amount of load capacity and type of DER that was made available during the DR event and whether the DER was applied);
prepares an input for the classifier based on the contents of the proposal; feeds the input to the trained classifier (See BRUSCHI, Pars.15 and 40, discloses the DR event may entail a time frame in which the power is to be supplied, the amount of energy needed to be stored or released; and
generates a recommendation that includes whether to accept or reject the proposal based on an output of the trained classifier for the input, wherein the message is transmitted based on the recommendation (BRUSCHI Par.21 discloses using the data to recommend an acceptance or negotiation. The BRUSCHI and UYEKI as modified by MARUYAMA would utilize machine learning using the previous historical DR event data such as acceptance/rejection of previous events in addition to their details and the DR event data disclosed by BRUSCHI in making a recommendation).
BRUSCHI, UYEKI and MURUYAMA are analogous art since they all deal with demand response power management systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed BRUSCHI and UYEKI with the teaching of MURUYAMA by using a machine learning model that makes use of previous DR event data in making recommendation for the benefit of improving the decision-making process by utilizing data from previous DR events.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 AHMED H OMAR whose telephone number is (571)270-7165. The examiner can normally be reached 10:00 am -7:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Drew Dunn can be reached at 571-272-2312. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AHMED H OMAR/ Primary Examiner, Art Unit 2859
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