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 .
Response to Remarks/Arguments
Applicant’s arguments regarding claims 1 ,11, and 12 have been fully considered and are found to be persuasive. The Applicant’s arguments are directed towards newly added claim language which changed the scope of the claims and necessitated new grounds of rejection as set forth in the 35 USC 103 section below. Therefore, the Applicant arguments are now moot in view of new grounds of rejection.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 10, 11, 12, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 20210281077 A1.), and in view of Tsujii et al. (US 20220123739 A1.), and further in view of Taniguchi et al. (US 20170140481 A1.).
As per claim 1, Cai et al. teach
A distribution apparatus comprising a controller (Fig.1, para 9) configured to:
acquire, for each power source in a plurality of types of power sources used in providing adjustability during a plurality of time periods, information on either a cost by time period or carbon dioxide emissions by time period :(para 9; for first and second time period cost is acquired for plurality of power supply)
set, for each time period in the plurality of time periods, with reference to the acquired information, a ratio of use of each power source in the plurality of types of power sources in providing the adjustability : (para 9, para 29, “The power control system 104 uses the power control information to cause the ECMs of the plurality of power resources 102 to control respective amounts of power that are contributed from the plurality of power resources 102 to a load”; para 30; “output of energy by the plurality of power resources 102 based on a load profile. The load profile indicates a power demand and/or variations in a load over a specified time period”. )
Cai et al. do not teach
use of distribution ratio and output a setting value of the distribution ratio by time period.
However, given that Cai et al. teach the controller is performing “control respective amounts of power that are contributed from the plurality of power resources 102 to a load” (see para 29), it is understood that related setting would be needed.
In the same field of endeavor Tsujii et al. teach
use of distribution ratio; and output a setting value of the distribution ratio by time period (para 84, distribution ratio for different power source/generators. For different time interval {para 72}. They are also based on cost para 82-83; Also see Fig.1, Fig.2 and Fig.3).
It would have been obvious to a person of ordinary skill in art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and to include the distribution power source ratio by time period taught by Tsujii et al. into the power distribution system taught by Cai et al. This would have been obvious because both Cai et al. and Tsujii et al. teach of power distribution system. By adding the power distribution ratio, the system can balance the power storage and adjust the distribution process to save cost or reduce carbon emission (Tsujii et al. – para 11).
The combination of Cai et al. and Tsuji et al. do not teach
wherein the adjustability includes a positive adjustability for procuring lacking power due to forecast errors about power demand or power generation during the plurality of time periods, and a negative adjustability for absorbing surplus power resulting from forecast errors about power demand or power generation during the plurality of time periods
However, Cai et al. teach a power conservation process with positive adjustability for procuring lacking power in para 9, by determining power deficit based on weather forecast. In para 111, Cai et al. provided weather forecasting information to the ECM of the power control system 104 to build a prediction model. Cai et al. also teach negative adjustability for absorbing surplus in para 35 by stating when PV energy resource 106 is greater than the power demand, the power control system 104 determines the surplus portion of the available energy.
However, the combination of Cai et al. and Tsuji et al. do not mention that the positive adjustability and the negative adjustability is due to a forecast error.
In the same field of endeavor, Taniguchi et al. teach
wherein the adjustability includes a positive adjustability for procuring lacking power due to forecast errors about power demand or power generation during the plurality of time periods, and a negative adjustability for absorbing surplus power resulting from forecast errors about power demand or power generation during the plurality of time periods (Taniguchi et al. paras 5-6, a method for creating forecast error distribution based on past forecast performance, a model of demand variation is generated by using covariance matrix based on load curve data, and demand scenario. For more clarification, see Fig. 1, Fig, 2 and Fig. 4, current input data include forecast data. Also see, para 50, Fig. 1, a procurement plan is configured based on forecast errors in the solar power generation on real time data or current day input data. A demand and supply control unit 21 controls the respective supply sources. Also see para 66, in response to a forecast error, if there is a deficiency in the necessity of power, the storage battery supplies power to the consumer 26. Also, if there is a need for power that exceeds the contracted power from the utility company 22, the storage battery 25 discharges the power corresponding to the excessive power demand. Also see Fig. 7, Step 133 and 134. If usable power storage amount is greater than the contracted power exceeding amount, that means there is a surplus power in step 133, then step 134 generates power storage amount allocation).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and to include the distribution power source ratio by time period taught by Tsujii et al. and electric power procurement adjustment program through forecasting error taught Taniguchi et al. This would have been obvious because the combination of Cai et al., Tsujii et al. and Taniguchi et al. teach of power distribution system. By adding the power distribution ratio taught by Tsuji et al. and forecasting error determination for energy adjustment taught by Taniguchi et al. the system can balance the power storage and adjust the distribution process to save cost or reduce carbon emission. (Tsujii et al. – para 11, and Taniguchi, para 5,50,66, Fig.1, Fig. 7).
As per claim 10, the combination of Cai et al., Tsujii et al., and Taniguchi et al. teach
A power control system comprising: the distribution apparatus according to claim 1 (Cai et al., Fig.1, para 9); and a power control apparatus configured to control the plurality of types of power sources (Cai et al., para 22, Fig. 1, para 29, “the plurality of power resources 102 to a load”) according to the setting value of the distribution ratio by time period as outputted by the distribution apparatus (Tsujii et al., para 84, distribution ratio for different power source/generators. For different time interval {para 72}. They are also based on cost para 82-83; Also see Fig.1, Fig.2 and Fig.3, Taniguchi et al., Fig. 7, Step135,, ratio of usable power storage amount and total contracted power exceeding amount each time period).
As per claim 11, Cai et al. teach
A distribution method comprising: acquiring, by a distribution apparatus, for each power source in a plurality of types of power sources used in providing adjustability during a plurality of time periods, information on either a cost by time period or carbon dioxide emissions by time period (Fig. 1 , para 9, also please see para 22); setting, by the distribution apparatus, for each time period in the plurality of time periods, with reference to the acquired information, a ratio of use of each power source in the plurality of types of power sources in providing the adjustability as a distribution ratio (para 9; para 29 “The power control system 104 uses the power control information to cause the ECMs of the plurality of power resources 102 to control respective amounts of power that are contributed from the plurality of power resources 102 to a load”; para 30; “output of energy by the plurality of power resources 102 based on a load profile. The load profile indicates a power demand and/or variations in a load over a specified time period”.); and
However, Cai et al. do not teach outputting, from the distribution apparatus, a setting value of the distribution ratio by time period.
In the same field of endeavor, Tsujii et al. teach
outputting, from the distribution apparatus, a setting value of the distribution ratio by time period (Tsujii et al., para 84, distribution ratio for different power source/generators. For different time interval {para 72}. They are also based on cost, para 82-83; Also, please see Fig.1, Fig.2 and Fig.3).
wherein the adjustability includes a positive adjustability for procuring lacking power due to forecast errors about power demand or power generation during the plurality of time periods, and a negative adjustability for absorbing surplus power resulting from forecast errors about power demand or power generation during the plurality of time periods (please refer to the analysis of claim 1 above).
As per claim 12, the combination of Cai et al. and Tsujii et al. teach
A non-transitory computer readable medium storing a distribution program configured to cause a computer to execute operations, the operations comprising (Cai et al. para 43, Fig. 2, machine learning techniques used in a computing device):
acquiring, for each power source in a plurality of types of power sources used in providing adjustability during a plurality of time periods, information on either a cost by time period or carbon dioxide emissions by time period (please refer to the analysis of claim 1 above);
setting, for each time period in the plurality of time periods, with reference to the acquired information, a ratio of use of each power source in the plurality of types of power sources in providing the adjustability as a distribution ratio (please refer to the analysis of claim 1 above); and outputting a setting value of the distribution ratio by time period (please refer to the analysis of claim 1 above)
Please see analysis of claim 1 above for further clarification.
As per claim 14, the combination of Cai et al., Tsuji et al., and Taniguchi et al. teach
The distribution apparatus according to claim 1, wherein the plurality of time periods spans at least 24 hours, each time period in the plurality of time periods corresponds to a frame, and a length of each frame is at least 30 minutes (Cai et al. Fig. 1, 24-hour weather forecast to load PV prediction model. Tsuji et al. para 72, output distribution ratio section 12A can be updated every 20 minutes and also can be updated in every 5 second, Taniguchi et al., para 5-6, based on time-series the target value of output per unit is calculated in one hour, also see, Fig. 29. Procurement adjustments scenario aggregated data is updated every 30 minutes. Also see Fig. 31 statistic amount per time period data).
As per claim 15, the combination of Cai et al., Tsuji et al., and Taniguchi et al. teach
The distribution method according to claim 11, wherein the plurality of types of power sources includes a power generator and a storage battery, and the setting value of the distribution ratio by time period comprises upper and lower limits of a state of charge of the storage battery, upper and lower limits of an output of the power generator, or both (Cai et al. ,para 22, Fig. 1, plurality of power sources 102, PV energy resource 106, Engine 108 and Battery 110, Tsuji et al., para 26, Fig. 1, Storage batteries 26A, 26B,26C,and 26D, power generator, 23A, 23B, 23C,and 23D. Also see, Tsuji et al. para 84, a distribution ratio for power generator A to E. The generator A has the lowest ratio and generator E has the highest ratio. Also see paras 82 and 83 for more clarification, also see, Fig. 4, ratio output distribution with low and upper limit, Fig. 6A, Fig. 6B, upper limit for power generator A, B, C, and D, Fig. 10, ratio output distribution).
As per claim 16, the combination of Cai et al., Tsuji et al., and Taniguchi et al. teach
The distribution method according to claim 11, wherein the plurality of time periods spans at least 24 hours, each time period in the plurality of time periods corresponds to a frame, and a length of each frame is at least 30 minutes (Cai et al., Fig. 1, 24-hour weather forecast to load PV prediction model. Tsuji et al. para 72, output distribution ratio section 12A can be updated every 20 minutes and can be updated in every 5 second, Taniguchi et al., para 5-6, based on time-series the target value of output per unit is calculated in one hour, also see, Fig. 29. Procurement adjustments scenario aggregated data is updated every 30 minutes. Also see Fig. 31 statistic amount per time period data).
As per claim 17, the combination of Cai et al., Tsuji et al., and Taniguchi et al. teach
The non-transitory computer readable medium according to claim 12 (Cai et al. para 43, Fig. 2 ,machine learning techniques used in a computing device), wherein the plurality of types of power sources includes a power generator and a storage battery, and the setting value of the distribution ratio by time period comprises upper and lower limits of a state of charge of the storage battery, upper and lower limits of an output of the power generator, or both (Cai et al., para 22, Fig. 1, plurality of power sources 102, PV energy resource 106, Engine 108 and Battery 110, Tsuji et al., para 26, Fig. 1, Storage batteries 26A, 26B,26C, and 26D, power generator, 23A, 23B, 23C, and 23D. Also see, Tsuji et al. para 84, a distribution ratio for power generator A to E. The generator A has the lowest ratio and generator E has the highest ratio. Also see paras 82 and 83 for more clarification, also see, Fig. 4, ratio output distribution with lower and upper limit, Fig. 6A, Fig. 6B, upper limit for power generator A, B, C, and D, Fig. 10, ratio output distribution weight).
As per claim 18, the combination of Cai et al., Tsuji et al., and Taniguchi et al. teach
The non-transitory computer readable medium according to claim 12 (Cai et al. para 43, Fig. 2 ,machine learning techniques used in a computing device ), wherein the plurality of time periods spans at least 24 hours, each time period in the plurality of time periods corresponds to a frame, and a length of each frame is at least 30 minutes (Cai et al., Fig. 1, 24 hour weather forecast to load PV prediction model. Tsuji et al., para 72, output distribution ratio section 12A can be updated every 20 minutes and also can be updated in every 5 second, Taniguchi et al., para 5-6, based on time-series the target value of output per unit is calculated in one hour, also see, Fig. 29. Procurement adjustments scenario aggregated data is updated every 30 minutes. Also see Fig. 31 statistic amount per time period data).
Claims 2-9, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US 20210281077 A1.), and in view of Tsujii et al. (US 20220123739 A1), further in view of Taniguchi et al. (US 20170140481 A1.).and further in view of Yerli (US 20230350363 A1.).
As per claim 2, Cai et al. teach
The distribution apparatus according to claim 1, wherein
the plurality of types of power sources includes a power generator and a storage battery (Fig. 1, plurality of power sources 102 includes Battery 110, Engine 108(configured to provide electric power to the load, Engine 108 teaches power generator),
the information on the cost by time period (para 9, para 42, determining cost over the time period) includes information on a fuel price by time period (para 51, “The power control system 104 determines an amount of the type of fuel required for the engine 108 to produce a unit of energy (e.g., a kilowatt-hour), based on the type of the fuel. The unit of energy is being calculated by hour, this teaches the time period., para 52, “The power control system 104 multiplies the total amount of fuel by the cost of the type of fuel per unit to determine an initial engine cost.” Overall, it teaches the fuel cost by time period.),
the controller is configured to set, for each time period, with reference to information on a corresponding fuel price (para 154, Fig. 9 a master controller, power control system 104 may receive load profile indicating a power demand and based on the power demand the controller is supplying the energy. The amount of energy is multiplying by the per unit cost to determine the total engine cost, see para 51-52).
at least a ratio (para 80, “ The diversity factor is a ratio of a sum of individual, non-coincident maximum loads of a sub-division of the power generation system 100 to a maximum demand of the power generation system 100.The demand factor is a fractional amount of energy being provided by a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110.” , “The utilization is a ratio of a time that a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110”), of use of each of the power generator and the storage battery in providing the positive adjustability for the lacking power as the distribution ratio (para 25, fig 1, 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client, also please see para 80 for power distribution, the diversity factor is a ratio of sum of a ratio. Also please see para 30-31, Fig. 1, The power control system 104 or the optimizer module determines sufficient (not lacking) energy to meet the power demand.).
However, the combination of Cai et al. and Tsujii et al. and Taniguchi et al. do not teach
information on a corresponding power purchase price by time period.
In the same field of endeavor Yerli teaches,
information on a corresponding power purchase price by time period (para 9, Fig.10 #1008 “optimal revenues”, #1018 “shortfall”; para 78),
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and to include the information on power purchase price taught by Yerli into the power distribution system taught by Cai et al. This would have been obvious because the combination of Cai et al. and Tsujii et al., Taniguchi et al., and Yerli teach of power distribution system. By adding the information on power purchase price, the system can balance the power storage and adjust the distribution process to save cost.
As per claim 3, The combination of Cai et al., Tsujii et al.. Taniguchi et al., and Yerli teach
The distribution apparatus according to claim 2, wherein the controller is configured to set the ratio of use of each of the power generator and the storage battery in providing the positive adjustability for the lacking power by comparing, for each time period (Cai et al., para 30-31, The power control system 104 or the optimizer module determines sufficient (not lacking) energy to meet the power demand ,para 58, the control system104 compares the current voltage to the maximum operating voltage to determine the amount of energy stored in the battery, para 25, fig 1, 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client), ( also please see Tsujii et al., ratio calculation method in Fig, 2, Fig, 3 and Fig, 4 , also see Taniguchi et al., para 66, in respond to a forecast error, if there is a deficiency in the necessary of power, the storage battery supplies power to the consumer 26), a cost in a case of procuring the lacking power by supplying fuel to the power generator and causing the power generator to generate power with a cost in a case of procuring the lacking power by supplying power obtained by purchasing power to the storage battery and discharging the storage battery( Tsujii et al., para 27, determining a ratio of output distribution according to economic efficiency. Balancing the load frequency based on ascending and descending value of cost, “The cost may include fuel cost for the regulated power source and procurement cost.” Also please see, Para 26, fig. 1 “The regulated power source can be selected from at least one of a power generator, a storage battery, and a demand response, Taniguchi et al., para 6, an optimal generation schedule is calculated and from the schedule, the lowest power generation cost is selected as the optimal power generation schedule. Also see Taniguchi et al., Fig. 7, Step 133 and 134. If usable power storage amount is greater than the contracted power exceeding amount, that means there is a surplus power in step 133, then step 134 generates power storage amount allocation).
As per claim 4, Cai et al. teach
The distribution apparatus according to claim 1, wherein the plurality of types of power sources includes a power generator and a storage battery (Fig. 1, para 9, para 25, plurality of power sources 102 includes Battery 110, Engine 108 (configured to provide electric power to the load, Engine 108 teaches power generator)
However, the combination of Cai et al. and Tsuji et al. do not teach,
the information on the cost by time period includes information on a power sale price by time period and information on a power purchase price by time period, and
the controller is configured to set, for each time period, with reference to information on a corresponding power sale price and information on a corresponding power purchase price, at least a ratio of use of each of the power generator and the storage battery in providing adjustability for surplus power as the distribution ratio.
In the same field of endeavor, Yerli teaches
the information on the cost by time period includes information on a power sale price by time period and information on a power purchase price by time period (Yerli, para 9, Fig.10 #1008 “optimal revenues”, #1018 “shortfall”; para 78), and
the controller is configured to set, for each time period, with reference to information on a corresponding power sale price and information on a corresponding power purchase price, at least a ratio of use of each of the power generator and the storage battery in providing negative adjustability for the surplus power as the distribution ratio (Cai teaches a ratio of use as seen in claim 2 above; Yerli, para 9, Fig.10, #1018, para 78 teaches “surplus” also Taniguchi teaches a ratio for surplus power absorption, Fig 7, para 85,Step 133, determining if usable power storage amount greater than total contracted power exceeding amount, then step 134, allocates power storage allocation scenario by contracted power exceeding amount of respective time period).
It would have been obvious to a person of ordinary skill in art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and to include the information on power purchase price taught by Yerli into the power distribution system taught by Cai et al. This would have been obvious because the combination of Cai et al. and Yerli teach of power distribution system. By adding the information on power purchase price information by time period, the system can balance the power storage and adjust the distribution process for optimal power usage and respond to future peaks (Yerli, para 78).
As per claim 5, The combination of Cai et al., Tsujii et al., Yerli and Taniguchi et al. teach
The distribution apparatus according to claim 4, wherein the controller is configured to set the ratio of use of each of the power generator and the storage battery in providing the negative adjustability for the surplus power by comparing , for each time period, a cost in a case of absorbing the surplus power (Yerli, para 9, Fig.10, #1018 “surplus”; para 78, Taniguchi et al. ,Fig 7, para 85,Step 133, determining if usable power storage amount greater than total contracted power exceeding amount, then step 134, allocates power storage allocation scenario by contracted power exceeding amount of respective time period),
by reducing power obtained by causing the power generator to generate power for sale with a cost in a case of absorbing the surplus power by reducing power obtained by purchasing power to charge the storage battery (Tsuji et al. para 84, distribution ratio for different power source/generators is calculated based on cost, also please see para 82-83, Yerli, para 78, using an AI agent to optimize the power generations of a smart home and identify the peak demand of electricity. Using the collected energy from the solar panel and storing the power into the home own’s battery. Therefore, when there is a peak demand in the neighborhood, the demanded power can be supplied from the stored battery).
As per claim 6, Cai et al. teach
The distribution apparatus according to claim 1, wherein the plurality of types of power sources includes a power generator and a storage battery (Cai et al., Fig. 1, plurality of power sources 102 includes Battery 110, Engine 108 (configured to provide electric power to the load, Engine 108 teaches power generator)), and
The combination of Cai et el. and Tsujii et al. teach
at least a ratio of use of each of the power generator and the storage battery in providing the positive adjustability for lacking power as the distribution ratio (Cai et al. para 30-31, The power control system 104 or the optimizer module determines the sufficient energy to meet the power demand ,para 58, the control system104 compares the current voltage to the maximum operating voltage to determine the amount of energy stored in the battery, para 80, “ The diversity factor is a ratio of a sum of individual, non-coincident maximum loads of a sub-division of the power generation system 100 to a maximum demand of the power generation system 100.The demand factor is a fractional amount of energy being provided by a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110.” , “The utilization is a ratio of a time that a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110” ,para 25, fig 1, 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client, also please see para 80 for power distribution. also please see, Tsujii et al. para 84, distribution ratio for different power source/generators is calculated based on cost, also please see para 82-83).
However, the combination of Cai et al. and Tsujii et al. do not teach
the controller is configured to set, for each time period, with reference to information on corresponding carbon dioxide emissions.
In the same field oof endeavor Yerli teaches,
the controller is configured to set, for each time period, with reference to information on corresponding carbon dioxide emissions (Yerli, para 9, “carbon emissions”, Fig.10 #1010 “Calculate carbon Footprint”; para 78).
It would have been obvious to a person of ordinary skill in art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and Tsujii et al. to include the carbon emissions reduction process taught by Yerli. This would have been obvious because the combination of Cai et al., Tsujii et al. and Yerli teach of power distribution system. By reducing carbon emissions, the system can optimize energy generation by reducing cost and carbon emissions (Yerli, para 9, para 78).
As per claim 7, The combination of Cai et al., Tsujii et al., Yerli, and Taniguchi et al. teach
The distribution apparatus according to claim 6, wherein the controller is configured to set the ratio of use of each of the power generator and the storage battery in providing the positive adjustability for the lacking power by comparing, for each time period (Cai et al. ,para 30-31, The power control system 104 or the optimizer module determines the sufficient (not lacking) energy to meet the power demand, para 25, fig 1, power control system 104, Engine 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client. Also see Tsujii et al., para 82-83, and ratio calculation method in Fig, 2, Fig, 3 and Fig, 4),
carbon dioxide emissions in a case of procuring the lacking power by supplying fuel to the power generator and causing the power generator to generate power with carbon dioxide emissions in a case of procuring the lacking power by supplying power obtained by purchasing power to the storage battery and discharging the storage battery (Yerli, para 9, “carbon emissions”, Fig.10 #1010 “Calculate carbon Footprint”; para 78, using an AI agent to optimize the power generations of a smart home and identify the peak demand of electricity. Using the collected energy from the solar panel and storing the power into the home own’s battery. Therefore, when there is a peak demand in the neighborhood, the demanded power can be supplied from the stored battery, also please see para 87,” the carbon footprint data 518 may be calculated by the DER SCADA from the early stage of energy fuel production”).
As per claim 8, Cai et al. teach
The distribution apparatus according to claim 1, wherein the plurality of types of power sources includes a power generator and a storage battery (Fig. 1, plurality of power sources 102 includes Battery 110, Engine 108(configured to provide electric power to the load, Engine 108 teaches power generator), and the controller is configured to set, for each time period (para 25, fig 1, power control system 104, Engine 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client, also please see para 34, “The power control system 104 (e.g., the supervisor module) transmits power control information to the PV energy resource 106 to cause the PV energy resource 106 to supply energy to the load during the specified time period”),
The combination of Cai et al. and Tsujii et al. teach
at least a ratio of use of each of the power generator and the storage battery in providing the negative adjustability for surplus power as the distribution ratio (Cai et al., para 80, “ The diversity factor is a ratio of a sum of individual, non-coincident maximum loads of a sub-division of the power generation system 100 to a maximum demand of the power generation system 100.The demand factor is a fractional amount of energy being provided by a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110.”) , “The utilization is a ratio of a time that a power resource (e.g., PV energy resource 106, engine 108, and/or battery 110, ,also see Tsujii et al., ratio calculation method in Fig, 2, Fig, 3 and Fig, 4, also see, Taniguchi et al. , para 85, Fig. 7, Step 133 and 134. If usable power storage amount is greater than the contracted power exceeding amount, that means there is a surplus power in step 133, then step 134 generate power storage amount allocation, if not at step 135 generates a ratio).
However, the combination of Cai et al. and Tsujii et al. do not teach
with reference to information on corresponding carbon dioxide emissions, for surplus power as the distribution ratio.
In the same field of endeavor, Yerli teaches
with reference to information on corresponding carbon dioxide emissions, for surplus power as the distribution ratio (Yerli, para 9, “carbon emissions”, Fig.10 #1010 “Calculate carbon Footprint”; para 87, carbon emission cost, also please see Fig. 10, #1018, “surplus”, para 78, using an AI agent to optimize the power generations of a smart home and identify the peak demand of electricity. Using the collected energy from the solar panel and storing the power into the home own’s battery. Therefore, when there is a peak demand in the neighborhood, the demanded power can be supplied from the stored battery. The power supply is determined based on the energy demand ratio. When there is more demand in the neighborhood for a game, the power from the stored battery supply will be increased, and the alternation power supply will be decreased. also please see para 87,” the carbon footprint data 518 may be calculated by the DER SCADA from the early stage of energy fuel production”.).
It would have been obvious to a person of ordinary skill in art, before the effective filing date of the claimed invention, to modify the teaching of Cai et al. and to include the carbon emissions reduction process taught by Yerli and ratio calculation method taught by Tsujii et al. into the energy storage system. This would have been obvious because the combination of Cai et al., Tsujii et al., and Yerli teach of power distribution system. By reducing carbon emissions, the system can optimize energy generation by reducing cost and carbon emissions (Yerli, para 9, para 78).
As per claim 9, The combination of Cai et al., Tsujii et al., and Yerli, and Taniguchi et al. teach
The distribution apparatus according to claim 8, wherein the controller is configured to set the ratio of use of each of the power generator and the storage battery in providing the negative adjustability for the surplus power by comparing, for each time period (Cai et al., para 25, fig 1, power control system 104, Engine 108 is configured to adjust power load, the secondary power resource is configured to accept power input from a primary power source and provides the power output to a client. the secondary power resource is a dispatchable energy resource that can be used on demand (e.g., can be turned on or off) and can adjust the power output supplied to the client, also please see para 34, “The power control system 104 (e.g., the supervisor module) transmits power control information to the PV energy resource 106 to cause the PV energy resource 106 to supply energy to the load during the specified time period.” Also please see, Tsujii et al. para 84, distribution ratio for different power source/generators is calculated based on cost, also please see para 82-83, also see Taniguchi et al. para 85, Fig. 7, Step 133 and 134. If usable power storage amount is greater than the contracted power exceeding amount, that means there is a surplus power in step 133, then step 134 generate power storage amount allocation, if not at step 135 generates a ratio),
carbon dioxide emissions in a case of absorbing the surplus power by reducing power obtained by causing the power generator to generate power for sale with carbon dioxide emissions in a case of absorbing the surplus power by reducing power obtained by purchasing power to charge the storage battery (Yerli, para 9, “carbon emissions”, Fig.10 #1010 “Calculate carbon Footprint”; para 78, using an AI agent to optimize the power generations of a smart home and identify the peak demand of electricity. Using the collected energy from the solar panel and storing the energy into the home own’s battery. Therefore, when there is a peak demand in the neighborhood, the demanded power can be supplied from the stored battery. The power supply is determined based on the energy demand ratio. When there is more demand in the neighborhood for a game, the power from the stored battery supply will be increased, and the alternation power supply will be decreased. Also please see para 22 “If there is no shortfall or surplus energy, the coordinated dispatch sends the coordinated forecasted energy to an energy trading command. However, if there is any shortfall or surplus energy, the coordinated dispatch sends a shortfall/surplus request to a peer-to-peer parallel auctioning process.”, also please see Fig. 1, #130, para 43 is an energy storage device that includes batteries. Batteries may be, for example, lithium-ion batteries, lead acid batteries, sodium sulphur batteries, nickel cadmium batteries, vanadium redox flow batteries, zinc bromine flow batteries, polysulphide bromine flow batteries, and residential battery systems.).
As per claim 13, the combination of Cai et al., Tsuji et al., Taniguchi et al., and Yerli teach
The distribution apparatus according to claim 2, wherein the setting value of the distribution ratio by time period comprises upper and lower limits of a state of charge of the storage battery, upper and lower limits of an output of the power generator, or both (Tsuji et al. para 84, a distribution ratio for power generator A to E. The generator A has the lowest ratio and generator E has the highest ratio. Also see paras 82 and 83 for more clarification, also see, Tsuji et al., Fig. 4, ratio output distribution with lower and upper limit, Fig. 6A, Fig. 6B, upper limit for power generator A, B, and D, Fig. 10, ratio output distribution weight).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to the form 892.
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).
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/ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
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