DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 12/04/2025, Applicant, on 03/31/2026, amended claims 1-12; added claim 13. Claims 1-13 are pending in this application and have been rejected below.
Allowable Subject Matter
Claim 6 is allowable over the prior art, however stand rejected under a base claim. Claim 6 is would be allowed only if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 03/31/2026 have been fully considered, but they are not fully persuasive. The 35 USC § 101 has been overcome. However, the updated 35 USC § 103 rejection of claims 1-13 are applied in light of Applicant's amendments.
The Applicant argues “Neither Morozumi nor Sun describes determining, for an individual user, a usage pattern that jointly accounts for the user’s predicted self-consumption and predicted trade prices across a plurality of trade markets and then controls the user’s usage on that basis.”
In response the Examiner disagrees. Morozumi expressly determines, for the user, a single usage plan based on predicted consumption, predicted price, and a profit max rule, see Morozumi 0010 and 002. The recited “power use plan” reads on the claimed usage pattern. Further, Sun supplies the plurality of trade markets and the network mediated control device architecture that Morozumi does not recite. Sun claim 9 recites “wherein the market participant and power supplier participates in power production bidding for multiple energy and reserve markets, including day-ahead energy market, real-time energy market, operation reserve market, regulation up reserve market, and regulation down reserve market.” And Sun 0044-0047 recites “Step 156 of FIG. 1A, includes a controller 157 that can be used to control the delivery and operation of the mobile energy storages according to the determined delivery schedule and cleared bidding results from ISO… The VPP 130 comprises of mobile storages 131, renewable resources 132 and load demands 133. It communicates with ISO 140 which controls the operation of power grid 115. The control system 100 of a VPP 130 can include a computer 151 or like device, or multiple computers. It is contemplated the computer(s) can be located at different locations, and in communication with each other. Further, other components of the computer may be located at other locations, but are connected via a network, or some like arrangement.” Elements 131-133 from Sun read on the recited storage, generation, and load facilities, respectively. Applicants’ argument that Suns controller 157 serves a VPP rather than an individual user is not within scope of the claim. The claim recites a controls device associated with a user, not a control device dedicated toa a single user. Under BRI Suns 157 operating at a power supplier location to control that supplier’s mobile storage, renewable resource, and load demand is “associated with” that user/supplier with the BRI of the claim.
The Applicant argues “ the new claim specifies that the electric power storage facility includes at least one electric power storage device that is managed by a management entity other than the plurality of users. Support for this amendment can be found, for example, in paragraphs [0012] and [0062] of the originally filed specification. This combination of features is not shown or suggested by the applied references, nor would it have been obvious.”
In response the Examiner disagrees. Suns VPP is itself a management system distinct fome the users whose load, generation, and storage it aggregates; See Sun 0047 and 0053. The mobile storages 131 are operated by the VPP, not by any one user, and thus read on at least one electric power storage device that is managed by a management entity other than the plurality of users.
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 1-5, 8-9, and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over JP 2012053721 (hereinafter “Morozumi”) et al., in view of U.S. PGPub 20210304306 to (hereinafter “Sun”) et al.
As per claim 1, Morozumi teaches an information processing device comprising:
predict, as a predicted trade price, a price at which the user trades electric power in a future predetermined time period in each of the plurality of electric power trade markets; predict, as a predicted amount of self-consumption, an electric power amount to be consumed by the user in the future predetermined time period; determine a usage pattern of electric power by the user based on the current trade price, the predicted amount of self-consumption, the predicted trade price, and a predetermined rule aimed at maximizing profit for the user; and control usage of electric power by the user via the control device based on the usage pattern that has been determined, See Morozumi, Abstract and paragraphs 0010-0021: “an electric power retail supporting system has means for calculating a amount of electric power which can be generated within a fixed period, means for collecting elements necessary for the calculation, means for predicting an electric power amount demanded in the market, means for collecting information necessary for predicting the electric power amount demanded, means for storing the collected elements, means for calculating an electric power amount consumed in home, means for measuring and collecting the electric power amount consumed in home, means for collecting electric power sale prices in the market, and means for calculating the maximum profits, based on the electric power amount generated, the demand prediction, the amount consumed in home and the electric power sale price. …FIG. 3 shows details of the power generation amount calculation function 83. The power generation amount calculation function 83 has a function of obtaining a weather forecast for a predetermined period (N days) as initial input data. The weather forecast for N days is analyzed for each day, and it is determined whether the weather is "sunny", which allows power generation, or "other", which is unsuitable for power generation. This result is recorded in the score table of FIG. 7. Since the case where "other" is continuous indicates that other solar power generation retailers cannot generate power, it is assumed that there is a high possibility that the power price increases due to a decrease in the number of suppliers in this phase. In the score table, it is assumed that the final day of the aspect in which "others" are continuous has the highest selling price, and the candidates are arranged in the order of the selling day candidates and set as a result B. In addition, by counting "sunny" days in the score table, the amount of power generation in the period is calculated as a result A. FIG. 4 shows details of the power consumption calculation function. It is necessary to acquire the use state of each of the electric appliances 10 - 1 to 10 - 4 as initial input data of this function. The data are stored in a database or the like at the timing of each acquisition of the data, and the power consumption of each time zone, each day of the week, and each season of the household is patterned based on the data. Here, the power usage fee for N days is predicted and calculated as the result C.” Examiner note: Morozumi teaches calculating the sales volume of small-scale electricity suppliers such that the profits thereof are maximized, and describes having a means for gathering selling prices of electricity in the market. Electricity usage and electricity usage fees are forecasted as shown in paragraph 0020: Fig. 4 depicts details of an electricity consumption calculation function. As the initial input data of said function, it is necessary to acquire the usage status of each of electrical appliances 10-1 to 10-4. There is a function for accumulating the usage statuses in a database or the like at the timing at which each usage status was acquired, etc., and on the basis of said data, the electricity usage of the household per time period, day of the week, and season is patternized. The electricity usage fee for N days is forecast and calculated as a result C" Furthermore, the selling price of electricity is forecasted, as shown in paragraph 0019... it is assumed that there is a high possibility of the price of electricity increasing. The score table assumes that the final day in situations in which the 'other' types of weather continue corresponds to when the selling price is highest". The purchase price of electricity is forecasted, as shown in paragraph 0021 Fig. 5 depicts details of a market price calculation function 84. As already mentioned, the price of electricity supplied by electricity retailers constantly fluctuates, and therefore said function assumes fees for each day of the week and period of time on the basis of past performance.” Additionally , in order to maximize revenue, electricity consumption is monitored, and if the total electricity usage exceeds a defined value, the power supply is stopped or the like to prioritize electricity storage, as shown in paragraph 0017: As depicted in fig. 2, candidate dates calculated by a revenue maximization calculation function 8 are displayed such that retailers can confirm the sale date and target selling price, and when a confirmation is accordingly carried out, an electricity control function 9 monitors the electricity consumption of each of the electrical appliances 10-1 to 10-4 until a target amount of stored electricity is ensured, and if the total electricity usage exceeds a defined value, the power supply is stopped or the like to prioritize electricity storage. When so doing, it is necessary to register in advance, in the electricity control function 9, the order in which the electrical appliances 10-1 to 10-4 are stopped". In view of the above, Morozumi teaches calculating the sales volume of small-scale electricity suppliers such that the profits thereof are maximized, a means for gathering selling prices of electricity in the market, a means for forecasting/predicting electricity usage and electricity usage fees, a means for forecasting/predicting the selling price of electricity, a means for forecasting/predicting the purchase price of electricity, and a control means that monitors electricity consumption in order to maximize revenue, and if the total electricity usage exceeds a defined value, stops the power supply or the like to prioritize electricity storage.
Morozumi may not explicitly teach the following. However, Sun teaches:
a communication unit configured to communicate via a network with (i) a plurality of servers associated with a plurality of electric power trade markets and (ii) a control device associated with a user of a plurality of users, the control device being configured to control usage of electric power by the user; and a processor coupled to a storage unit and configured to execute control processing for trading of electric power, wherein the processor is configured to: Sun 0123-0125: “Still referring to FIG. 11B, 1100B includes a computing device 1152 (which is computing device 1152, which can be a controller such as a transactive controller or active controller), that includes a hardware processor 1154, in communication with a transceiver 1156… network 1149 can include, by non-limiting example, one or more local area networks (LANs) and/or wide area networks (WANs). Wherein the networking environments can be similar to enterprise-wide computer networks, intranets and the Internet…0044-0051: Step 156 of FIG. 1A, includes a controller 157 that can be used to control the delivery and operation of the mobile energy storages according to the determined delivery schedule and cleared bidding results from ISO… The control system 100 of a VPP 130 can include a computer 151 or like device, or multiple computers. It is contemplated the computer(s) can be located at different locations, and in communication with each other…Still referring to FIG. 1B, the control system 100 of the VPP 130 sends the determined bids for the upcoming time intervals to the ISO 140, and the controller 157 controls the delivery and operation of the mobile storages according to the determined delivery schedule and cleared bidding results from ISO.”
acquire, as a current trade price, a current price at which a user trades electric power in each of the plurality of electric power trade markets; Sun, claim 9: “wherein the market participant and power supplier participates in power production bidding for multiple energy and reserve markets, including day-ahead energy market, real-time energy market, operation reserve market, regulation up reserve market, and regulation down reserve market.”
wherein to control usage of electric power by the user, the processor is configured to cause the information processing device to transmit, via the communication unit and the control device, instructions for controlling at least one of: usage, generation, storage, and discharge of electric power, to at least one of an electric power generation facility, an electric power storage facility, and a load facility associated with the use;Sun 0044-0047: “Step 156 of FIG. 1A, includes a controller 157 that can be used to control the delivery and operation of the mobile energy storages according to the determined delivery schedule and cleared bidding results from ISO…The VPP 130 comprises of mobile storages 131, renewable resources 132 and load demands 133. It communicates with ISO 140 which controls the operation of power grid 115. The control system 100 of a VPP 130 can include a computer 151 or like device, or multiple computers. It is contemplated the computer(s) can be located at different locations, and in communication with each other. Further, other components of the computer may be located at other locations, but are connected via a network, or some like arrangement.”
Morozumi and Sun 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 Morozumi with the aforementioned teachings from Sun with a reasonable expectation of success, by adding steps that allow the software to utilize a plurality of markets with the motivation to more efficiently and accurately gather and analyze data [Sun 0108 ].
As per claim 2, Morozumi and Sun teach all the limitations of claim 1.
In addition, Sun teaches:
wherein the processor is configured to determine the usage pattern of electric power by the user by: accessing a transaction cost database (DB) in which information of a transaction cost regarding electric power by the user is stored as transaction cost information; and [[to]] determining the usage pattern further based on the transaction cost; Sun 0130-0141: “Another aspect of the present disclosure can include that the value hierarchy associated with energy and reserve bidding scenarios are based on expected profits gained from energy and reserve markets by the market participant which are determined as expected revenues minus associated MEES system costs, wherein the revenues are selected from a group including: a day-ahead market revenue, a real-time market revenue, an operating reserve market revenue, a regulation up reserve revenue, and a regulation down reserve revenue; wherein the MEES system costs associated with market revenues are selected from the group including: an energy storage operation cost, an energy storage delivery cost; and an energy storage installation cost.”
Morozumi and Sun 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 Morozumi with the aforementioned teachings from Sun with a reasonable expectation of success, by adding steps that allow the software to utilize a plurality of markets with the motivation to more efficiently and accurately gather and analyze data [Sun 0108 ].
As per claim 3, Morozumi and Sun teach all the limitations of claim 1.
In addition, Morozumi teaches:
includes a pattern for using an electric power storage facility received from one or more electric power provision sources including the user and the plurality of electric power trade markets, and to discharge stored electric power to one or more electric power provision destinations including the user and the plurality of electric power trade markets, wherein the electric power storage facility includes at least one storage device that is not owned by the user; Morozumi 0016-0021: “there is a possibility that the electric appliances 10-1 to 4 are operated using the inexpensive nighttime electric power sold by the system power supply or the electric power is stored. It is determined in advance by the profit maximization calculation function 8 which power source is used in which time zone. Regarding the switching of the input power source, the system power source, the power generation device, and the power storage device 3 are properly used appropriately by using the input power source switching function 7….The power consumption calculation function has a function of sequentially acquiring a power use state in a home and a function of analyzing a power use pattern for each time zone, each day of the week, and each season from accumulated data. On the basis of the results calculated by the above three functions, the profit maximization calculation function 8 formulates power sales date on which the profit is maximized… FIG. 4 shows details of the power consumption calculation function. It is necessary to acquire the use state of each of the electric appliances 10 - 1 to 10 - 4 as initial input data of this function. The data are stored in a database or the like at the timing of each acquisition of the data, and the power consumption of each time zone, each day of the week, and each season of the household is patterned based on the data. Here, the power usage fee for N days is predicted and calculated as the result C… For example, there is a possibility that the electric appliances 10 -1 to 10 - 4 are operated or electric power is stored using inexpensive nighttime electric power sold by a system power supply.”
As per claim 4, Morozumi and Sun teach all the limitations of claim 3.
In addition, Morozumi teaches:
includes a pattern for purchasing and storing[[,]] electric power in the electric power storage facility[[,]] in a predetermined electric power trade market is a first price , using electric power discharged from the electric power storage facility for self-consumption by the user or for selling in at least some of the plurality of electric power trade markets; Morozumi 0018: “When the profit maximization calculation function 8 is used to set the sales target in this way, in the present embodiment, power storage is given priority. However, as described in the section of the market price calculation function 84, the power price of the market fluctuates. For example, there is a possibility that the electric appliances 10 -1 to 10 - 4 are operated or electric power is stored using inexpensive nighttime electric power sold by a system power supply. Which power source is to be used in which time zone is determined in advance by the profit function 8. For the switching of the input power source, the system power source, the power generation device, and the power storage device 3 are properly used by using the input power source switching function 7. On the set power sales date, the power sales management function 6 sells the preset price and power amount in the market. For this function, the mechanism proposed in the above-mentioned Patent Document 2 is used. When the set sales amount is sold, profit calculation and calculation of the next sales target are performed.” The art describes storing inexpensive nighttime electricity for revenue maximization, [0018]: When sales targets have been set by using a revenue maximization calculation function 8 in said manner, electricity storage is prioritized in the present embodiment. However, the market price of electricity fluctuates, as mentioned in the portion pertaining to the market price calculation function 84. For example, it is also possible to use inexpensive nighttime electricity sold by a grid power source to the activate electrical appliances 10-1 to 10-4 and carry out electricity storage. The above is determined in advance by means of the revenue maximization calculation function 8, which determines the specific power source to be used at a specific time. As concerns switching between the input power sources, an input power source switching function 7 is utilized to properly use, as appropriate, a grid power source, an electricity generation device, and an electricity storage device 3".
As per claim 5, Morozumi and Sun teach all the limitations of claim 3.
In addition, Morozumi teaches:
wherein the processor is further configured to:acquire a result of prediction of an electric power generation amount at which electric power is to be generated in a future predetermined time period by an electric power generation facility owned by the user, and to calculate, based on the result of the prediction, a predicted electric power generation amount serving as an index to be used in predicting the predicted trade price and in determining the usage pattern; Morozumi 0019-0022: “FIG. 3 shows details of the power generation amount calculation function 83. The power generation amount calculation function 83 has a function of obtaining a weather forecast for a predetermined period (N days) as initial input data. The weather forecast for N days is analyzed for each day, and it is determined whether the weather is "sunny", which allows power generation, or "other", which is unsuitable for power generation. This result is recorded in the score table of FIG. 7. Since the case where "other" is continuous indicates that other solar power generation retailers cannot generate power, it is assumed that there is a high possibility that the power price increases due to a decrease in the number of suppliers in this phase. In the score table, it is assumed that the final day of the aspect in which "others" are continuous has the highest selling price, and the candidates are arranged in the order of the selling day candidates and set as a result B. In addition, by counting "sunny" days in the score table, the amount of power generation in the period is calculated as a result A… Using the power sale candidate date (result B) and the power market price (result D) calculated by the above-described function as inputs, the day with the highest sum among the two input values is calculated.For power consumption (result C), if it is known in advance in the household schedule (for example, long-term trip) that the power consumption will change in advance, the maximum revenue calculation will take this part into account. It is an input value to the conversion. Taking the above two input values and power generation amount (Result A) into consideration, determination of the power sale candidate date, and the power use plan and procurement plan based on the necessary power consumption and power generation amount.”Morozumi teaches that an electricity generation calculation function forecasts power generation from weather information so as to sell electricity, as shown in paragraph 0019: Fig. 3 depicts details of the electricity generation calculation function 83 mentioned above. The power generation calculation function 83 has a function of obtaining a weather forecast for a certain period (N days) as initial input data. The weather forecast for the N days is analyzed for each day to distinguish between 'clear' weather, which is suitable for electricity generation, and 'other' weather, which is unsuitable for electricity generation. The results are recorded in advance in the score table in fig. 7. A case in which 'other' weather continues indicate that other solar power retailers are also unable to generate electricity, and therefore, in such a situation, it is assumed that the decrease in suppliers would result in a high possibility of the price of electricity increasing. In the score table, it is assumed that the final day in situations in which 'other' weather continues corresponds to when the selling price is highest, and said day is arranged in the order of sale date candidates and set as result B. In addition, by adding up the days marked as 'clear' in the score table, the amount of electricity generated within said period is calculated as result A".
As per claim 8, Morozumi and Sun teach all the limitations of claim 1.
In addition, Morozumi teaches:
wherein, the processor is configured to predict the electric power amount to be consumed by the user based on; Morozumi 0019: “FIG. 3 shows details of the power generation amount calculation function 83 described above.The power generation amount calculation function 83 has a function of obtaining weather forecasts for a certain period (N days) as initial input data. The N day weather forecasts are analyzed one day at a time to determine whether the weather is “sunny” where power generation is possible or “other” weather unsuitable for power generation. This result is recorded in the score table of FIG. If “others” continues, it indicates that other solar power retailers have not been able to generate power. Therefore, in this phase, it is assumed that there is a high possibility that the electricity price will rise due to a decrease in the number of suppliers. In the score table, assuming that the selling price is the highest on the last day of the phase where “others” continue, the results are arranged in order of selling date candidates and set as the result B. Further, by summing up “sunny” days in this score table, the power generation amount within that period is calculated as a result A.”
As per claim 9, Morozumi and Sun teach all the limitations of claim 1.
In addition, Morozumi teaches:
adopting, as the predetermined rule, a rule for prioritizing, based on purchase prices, by the user, and durable years of an electric power generation facility and an electric power storage facility, acquisition of profit equivalent to the purchase prices; Morozumi 0010-0021: “Means for calculating the amount of power consumed in the home. Means for measuring and collecting power consumption in the home Means for collecting power selling price in the market Means for calculating maximum profit from power generation, demand forecast, home consumption and power selling price are provided… [Fig. 6]The profit maximization calculation flow in one embodiment of the present invention.”
As per claim 13, Morozumi and Sun teach all the limitations of claim 1.
In addition, Sun teaches:
wherein the electric power storage facility includes at least one electric power storage device that is managed by a management entity other than the plurality of users; Sun 0047-0053: “ renewable resources 132 and load demands 133. It communicates with ISO 140 which controls the operation of power grid 115. The control system 100 of a VPP 130 can include a computer 151 or like device, or multiple computers. It is contemplated the computer(s) can be located at different locations, and in communication with each other. Further, other components of the computer may be located at other locations, but are connected via a network, or some like arrangement… The VPP 130 can be regarded as a group of aggregated renewable generations, load demands and storages that connected with power grid at one or points, i.e. buses.”
Claims 11-12 are directed to the method and CRM for performing the system of claim 1 above. Since Morozumi and Sun teach the method and CRM, the same art and rationale apply.
Claims 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over JP 2012053721 (hereinafter “Morozumi”) et al., in view of U.S. PGPub 20210304306 to (hereinafter “Sun”) et al., in further view of JP 2020043634 (hereinafter “Kataoka”) et al.
As per claim 7, Morozumi and Sun teach all the limitations of claim 1.
Morozumi and Sun may not explicitly teach the following. However, Kataoka teaches:
wherein comprises either a first rule aimed at maximizing profit for the user in each of a plurality of sites or a second rule aimed at maximizing profit for a group including the plurality of sites to determine the usage pattern; Kataoka teaches calculating an electricity transaction volume that maximizes revenue from electricity transactions of an entire group which includes customers, etc. scattered throughout a prescribed region, as shown in the abstract:" an electricity transaction volume optimization device 1 that targets a group that includes electric vehicles and customers scattered throughout a prescribed region, wherein, on the basis of a constraint condition of meeting the electricity demands of electric vehicles in electricity transactions involving electric vehicles, the constraint condition of meeting the electricity demands of customers in electricity transactions involving customers, and a constraint condition on the possible quantity of electricity transactions that occur when electric vehicles and customers engage in electricity transactions with electricity transaction partners using the power transmission/distribution network of a prescribed region, an optimization calculation is carried out using, as an objective function, the maximization of revenue from electricity transactions of an entire group, and the electricity transaction volume transacted with electricity transaction partners by the electric vehicles and customers within the group is calculated…claim 1: perform an optimization calculation with the objective function to maximize profits from power trading for the entire group.”
Morozumi, Sun, and Kataoka 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 Morozumi and Sun with the aforementioned teachings from Kataoka with a reasonable expectation of success, by adding steps that allow the software to utilize rules with the motivation to more efficiently and accurately gather and analyze data [Kataoka 0100].
As per claim 10, Morozumi and Sun teach all the limitations of claim 1.
Morozumi and Sun may not explicitly teach the following. However, Kataoka teaches:
control the usage of electric power by the user by executing, as at least part of control for using electric power by the user, control for outputting a storage request to one or more of registered ones as electric power storage facilities capable of storing electric power, determine the usage pattern further based on an electric power storage situation in an electric power storage facility in accordance with the storage request, and predict the predicted amount of self-consumption based on the storage request, an operation plan of the user, and a result of prediction of future weather; Kataoka, claim 2 teaches the electricity transaction volume optimization device set forth in claim 1 being characterized in that electric vehicles and customers within a group that can carry out electricity transactions are selected, and the electricity transaction volume for the selected electric vehicles and customers is calculated on the basis of information regarding the scheduled parking times and parking locations of the electric vehicles and positional information of the customers, both of which are stored in a storage device ... [Claim 3]: The electricity transaction volume optimization device set forth in claim 1 or claim 2 being characterized in that the group includes solar power generation devices that distribute electricity to the customers and electric vehicles, and the optimization calculation is carried out on the basis of the three constraint conditions and a constraint condition pertaining to the amount of electricity generated by the solar power generation devices in electricity transactions involving the solar power generation devices". Furthermore, paragraph [0016] of document 3 indicates that PV expresses a solar power generation device. In addition, document 3 describes selling electricity to customers {dk} from PV {pvl} (solar power generation devices) at a time t, as shown in paragraph [0029]: "Paragraph [0029]: Fig. 3(c) is a drawing that depicts electricity transaction partners that can carry out electricity transactions centered on PV {pvl}. The electricity transaction partners of PV{pvl} depicted in fig. 3(c) include an electric power system {gm}, EV {vi}, and customers {dk}. In such a case, the amount of electricity sold to the electric power system {gm} from PV {pvl} at a time t: Xpvlgm(t)(1=1...r, m=1...o), the amount of electricity sold to EV {vi} from PV {pvl} at a time t: Xpvlvi(t)(1=1...r, i=1...q), and the amount of electricity sold to customers {dk} from PV {pvl} at a time t: Xpvldk(t)(l=1...r, k=1...p) serve as electricity transaction variables. However, as mentioned above, in step S12, the electricity transaction variable for Xpvlvi(t) is 0 except for times when a charging/discharging apparatus to which EV can connect was selected". In other words, document 3 describes selling electricity to any customer from any solar power generation device within aa group. In addition, as a matter of course, customers who purchased electricity could engage in charging to maximize revenue. For example, with reference to paragraph [0018], document 1 describes storing inexpensive nighttime electricity for revenue maximization.
Morozumi, Sun, and Kataoka 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 Morozumi and Sun with the aforementioned teachings from Kataoka with a reasonable expectation of success, by adding steps that allow the software to utilize rules with the motivation to more efficiently and accurately gather and analyze data [Kataoka 0100].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
WATANABE; Tohru. MANAGEMENT APPARATUS AND MANAGEMENT METHOD. US PGPub 20190130423The present invention generally relates to a management technology relating to at least one of energy demand and supply.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
Prosecution Insights