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 Amendment
This office action is responsive to the amendments filed on December 03, 2025. Claims 1-3 and 7-9 are being amended. Claims 4-6 are being canceled. Claims 1-3 and 7-9 are pending in this office action.
In view of the amendments filed on December 03, 2025, the 35 U.S.C 101 rejections are being withdrawn.
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.
Claims 1-3 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Teshima et al. (US PG Pub 20200380393) hereinafter Teshima in view of Youn et al. (US PG Pub 20190011970) hereinafter Youn further in view of (EP 3333799 A1) hereinafter EP799.
Regarding claim 1, Teshima teaches “An energy management system comprising: an energy consumption prediction device comprising” as (Teshima Paragraph 0035 “The energy demand prediction system 1 includes a factory F including a plurality of facilities E and an energy demand prediction device 10.”)
“a storage medium to store past energy consumption result data of a target facility in correlation with information for identifying a date and time at which the data has been acquired and incidental information which is information related to an operation situation of the target facility when the data has been acquired” as (Teshima Paragraph 0040 “The history acquisition unit 101 acquires an operation plan and a time series of power consumption from each facility E of the factory F. The history acquisition unit 101 may read the operation plan from the control device of the facility E or may acquire the operation plan by input from a user such as a manager of the factory F. The history acquisition unit 101 acquires power consumption from a sensor installed in the facility E. The operation plans of the plurality of facilities E, that is, the information on the operation time zones and operation days of the plurality of facilities E is an example of a plurality of explanatory variable candidates relating to the operation of the factory. Further, the power consumption of the plurality of facilities E is an example of an objective variable relating to the energy demand of the factory.” and Paragraph 0041 “The history storage unit 102 stores operation plans and a time series of power consumption of the plurality of facilities E acquired by the history acquisition unit 101.”)
“circuitry configured to: extract data in which the incidental information matches an extraction condition designated by a user from the energy consumption result data stored in the storage medium and to generate prediction data which is used to predict an amount of consumed energy” as (Teshima Paragraph 0042 “The selection unit 103 receives a selection of at least one explanatory variable among the operation plans of the plurality of facilities E from the user. For example, the user can select, as an explanatory variable, an operation plan of one or a plurality of facilities F that are considered to have a great influence on the increase or decrease in power consumption during the operation of the factory F. That is, the user does not need to input the operation plans of all the facilities F included in the factory F in order to predict the energy demand.” And Paragraph 0044 “Information stored in the history storage unit 102 is used as training data, the operation plan for the facility E selected by the user is input, and the time series of the total power consumption of the factory F is output. then the learning unit 104 trains the model stored in the model storage unit 105. That is, the learning unit 104 assigns parameters to the model stored in the model storage unit 105 based on the information stored in the history storage unit 102.”)
“generate energy consumption prediction data in accordance with an instruction from the user based on the prediction data” as (Teshima Paragraph 0046 “The energy demand identification unit 107 identifies the time series of the total power consumption of the factory F by inputting the operation plan input to the plan input unit 106 into the model stored in the model storage unit 105. That is, the energy demand identification unit 107 predicts the energy demand of the entire factory F based on the operation plan relating to some of the facilities F included in the factory F. Hereinafter, the time series of the total power consumption identified by the energy demand identification unit 107 is also referred to as a predicted energy demand.” Also see figure 3).
“an input interface and a display” as (Teshima figure 3, and 0052 “the selection unit 103 of the energy demand prediction device 10 displays the explanatory variable selection screen including a list of the facilities E that are explanatory variable candidates.”)
“the input interface that allows the user to designate details… which is used for the energy consumption prediction data in the prediction data in the circuitry” as (Teshima Figure 3, reference numerals S1-S2, S4-S5 and S7).
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“the display displays the data obtained by… on the prediction data as a candidate for the energy consumption prediction data, and the input interface acquires designation of data to be used for the energy consumption prediction data by the user out of the candidates displayed by the display” as (Teshima Paragraph 0016 “the prediction system according to any one of the first to seventh aspects, the output unit may output a display screen including the identified value of the objective variable, and a value of the objective variable or a comparison value of the objective variable included in the history data.” And Paragraph 0052 “the selection unit 103 of the energy demand prediction device 10 displays the explanatory variable selection screen including a list of the facilities E that are explanatory variable candidates (step S1). FIG. 4 is a diagram illustrating an example of explanatory variable selection screen. On the explanatory variable selection screen, a name, a rated output, and a check box for each facility E are displayed. The selection unit 103 receives a selection of the facility E used as an explanatory variable in the energy demand prediction process from the user (step S2).” Also see 0053-0055.)
Teshima teaches time series data but does not explicitly teach “identifying a date and time”, “a power generation facility configured to adjust an amount of energy supplied to the target facility based on the energy consumption prediction data”, “the circuitry prepares data obtained by performing statistical processing on the prediction data and generates the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing”, “the statistical processing” and “performing the statistical processing on data.”
However, Youn teaches “identifying a date and time” as (Youn Paragraph 0063 “the past data having a condition similar to that of the specific target date to be predicted is selected (S200). Since temperature-sensitive power consumption characteristics should be similar for similar weather conditions occurring around (e.g., plus or minus fifteen days) the same date of other years, the selected data may be data collected from a prior year for a day corresponding to a date near the target date.” And Paragraph 0066 “when data is stored at an interval of fifteen minutes as described above, a total of 96 data points on used power and temperature may be collected from 00:00 to 23:45 for a day.”
“the circuitry prepares data obtained by performing statistical processing on the prediction data and generates the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
“the statistical processing” and “performing the statistical processing on data” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
Teshima teaches prediction of energy demand of the factory F based on the operation plan of each facility E and each facility E operates and stops according to the operation plan and Youn teaches to control ESS charge/discharge based on the power demand prediction but do not explicitly teach “a power generation facility configured to adjust an amount of energy supplied to the target facility based on the energy consumption prediction data.”
However, EP799 teaches “a power generation facility configured to adjust an amount of energy supplied to the target facility based on the energy consumption prediction data” as (EP799 Abstract “A power consumption determining apparatus (100) is provided comprising an energy input interface (107) for receiving an energy consumption statistic (103) and a parameter input interface (106) for receiving at least one external consumption parameter (105). Furthermore, the energy consumption determination device (100) has a computing device (101) and an output interface (108) for providing a power consumption prediction (104). The computing device (101) is set up to determine the energy consumption prediction (104) from the energy consumption statistics (103) and the at least one external consumption parameter (105).”
EP799 Page 4 “In one example, the energy usage prediction may be used to control a customer's own power supply. A customer of, for example, his own wind turbine and / or solar system as a separate power supply device can see from the provided energy consumption forecast when it pays off for him to store self-generated electricity because, for example, the electricity purchased by an energy trader is more favorable. The energy consumption prediction and, in particular, an ordered list developed therefrom as a program can be used as a control program for the energy supply device.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of EP799 into the teaching of Teshima and Youn because all of the references are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima and Youn to enable efficient determination of power consumption by using the external consumption parameters provided as an ordered list that indirectly or directly impacts energy consumption such as stock market data of an external power exchange, a sunshine statistics and/or a wind statistics.
Regarding claim 2, Teshima does not explicitly teach “The energy management system according to claim 1, wherein the circuitry is further configured to: prepare data which is classified into a plurality of clusters by performing a clustering process on the data extracted; and use the data classified into the plurality of clusters as the prediction data.”
However, Youn teaches “The energy management system according to claim 1, wherein the circuitry is further configured to: prepare data which is classified into a plurality of clusters by performing a clustering process on the data extracted; and use the data classified into the plurality of clusters as the prediction data” as (Youn Paragraph 0147 “ the prediction algorithm of the artificial intelligence engine 7112 may be at least one of a supervised learning algorithm that predicts the power demand by analyzing how the collected data (power usage data, weather forecast pattern, and consumer characteristic pattern) is divergent from reference points having power consumption patterns consumed by actual consumers, a non-supervised learning algorithm that predicts the power demand by analyzing a degree of clustering of the data (power usage data, weather forecast pattern, and consumer characteristic pattern), and a reinforcement learning algorithm that predicts the power demand by analyzing a degree of interaction of data (such as power usage data, weather forecast pattern, and consumer characteristic pattern).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
Regarding claim 3, Teshima teaches “The energy management system according to claim 2, wherein the circuity generates the energy consumption prediction data… in accordance with an instruction from the user” as (Teshima Paragraph 0042 “The selection unit 103 receives a selection of at least one explanatory variable among the operation plans of the plurality of facilities E from the user. For example, the user can select, as an explanatory variable, an operation plan of one or a plurality of facilities F that are considered to have a great influence on the increase or decrease in power consumption during the operation of the factory F. That is, the user does not need to input the operation plans of all the facilities F included in the factory F in order to predict the energy demand.” And Paragraph 0044 “Information stored in the history storage unit 102 is used as training data, the operation plan for the facility E selected by the user is input, and the time series of the total power consumption of the factory F is output. then the learning unit 104 trains the model stored in the model storage unit 105. That is, the learning unit 104 assigns parameters to the model stored in the model storage unit 105 based on the information stored in the history storage unit 102.”)
Teshima does not explicitly teach “generates the energy consumption prediction data based on the data classified into one of the plurality of clusters.”
However, Youn teaches “generates the energy consumption prediction data based on the data classified into one of the plurality of clusters” as (Youn Paragraph 0147 “the prediction algorithm of the artificial intelligence engine 7112 may be at least one of a supervised learning algorithm that predicts the power demand by analyzing how the collected data (power usage data, weather forecast pattern, and consumer characteristic pattern) is divergent from reference points having power consumption patterns consumed by actual consumers, a non-supervised learning algorithm that predicts the power demand by analyzing a degree of clustering of the data (power usage data, weather forecast pattern, and consumer characteristic pattern), and a reinforcement learning algorithm that predicts the power demand by analyzing a degree of interaction of data (such as power usage data, weather forecast pattern, and consumer characteristic pattern).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
Regarding claim 7, Teshima teaches “The energy management system according to claim 1, wherein the display presents the incidental information corresponding to the past energy consumption result data to the user in correspondence with a time series” as (Teshima Paragraph 0016 “the prediction system according to any one of the first to seventh aspects, the output unit may output a display screen including the identified value of the objective variable, and a value of the objective variable or a comparison value of the objective variable included in the history data.” And Paragraph 0044 “Such that information stored in the history storage unit 102 is used as training data, the operation plan for the facility E selected by the user is input, and the time series of the total power consumption of the factory F is output.”)
Regarding claim 8, Teshima teaches “An energy management method comprising” as (Teshima Paragraph 0035 “The energy demand prediction system 1 includes a factory F including a plurality of facilities E and an energy demand prediction device 10.”)
“storing past energy consumption result data of a target facility in a storage medium in correlation with information for identifying a date and time at which the data has been acquired and incidental information which is information related to an operation situation of the target facility when the data has been acquired” as (Teshima Paragraph 0040 “The history acquisition unit 101 acquires an operation plan and a time series of power consumption from each facility E of the factory F. The history acquisition unit 101 may read the operation plan from the control device of the facility E or may acquire the operation plan by input from a user such as a manager of the factory F. The history acquisition unit 101 acquires power consumption from a sensor installed in the facility E. The operation plans of the plurality of facilities E, that is, the information on the operation time zones and operation days of the plurality of facilities E is an example of a plurality of explanatory variable candidates relating to the operation of the factory. Further, the power consumption of the plurality of facilities E is an example of an objective variable relating to the energy demand of the factory.” and Paragraph 0041 “The history storage unit 102 stores operation plans and a time series of power consumption of the plurality of facilities E acquired by the history acquisition unit 101.”)
“extracting data in which the incidental information matches an extraction condition designated by a user from the energy consumption result data stored in the storage medium and generating prediction data which is used to predict an amount of consumed energy” as (Teshima Paragraph 0042 “The selection unit 103 receives a selection of at least one explanatory variable among the operation plans of the plurality of facilities E from the user. For example, the user can select, as an explanatory variable, an operation plan of one or a plurality of facilities F that are considered to have a great influence on the increase or decrease in power consumption during the operation of the factory F. That is, the user does not need to input the operation plans of all the facilities F included in the factory F in order to predict the energy demand.” And Paragraph 0044 “Information stored in the history storage unit 102 is used as training data, the operation plan for the facility E selected by the user is input, and the time series of the total power consumption of the factory F is output. then the learning unit 104 trains the model stored in the model storage unit 105. That is, the learning unit 104 assigns parameters to the model stored in the model storage unit 105 based on the information stored in the history storage unit 102.”)
“allowing the user to designate details of…processing” as (Teshima Figure 3, reference numerals S1-S2, S4-S5 and S7).
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“displaying the data obtained by… on the prediction data as a candidate for the energy consumption prediction data, and acquiring designation of data to be used for the energy consumption prediction data by the user out of the candidates displayed” as (Teshima Paragraph 0016 “the prediction system according to any one of the first to seventh aspects, the output unit may output a display screen including the identified value of the objective variable, and a value of the objective variable or a comparison value of the objective variable included in the history data.” And Paragraph 0052 “the selection unit 103 of the energy demand prediction device 10 displays the explanatory variable selection screen including a list of the facilities E that are explanatory variable candidates (step S1). FIG. 4 is a diagram illustrating an example of explanatory variable selection screen. On the explanatory variable selection screen, a name, a rated output, and a check box for each facility E are displayed. The selection unit 103 receives a selection of the facility E used as an explanatory variable in the energy demand prediction process from the user (step S2).” Also see 0053-0055.)
Teshima teaches time series data but does not explicitly teach “identifying a date and time”, “preparing data obtained by performing statistical processing on the prediction data and generating the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing”, “statistical processing”, “performing the statistical processing on data” and “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data.”
However, Youn teaches “identifying a date and time” as (Youn Paragraph 0063 “the past data having a condition similar to that of the specific target date to be predicted is selected (S200). Since temperature-sensitive power consumption characteristics should be similar for similar weather conditions occurring around (e.g., plus or minus fifteen days) the same date of other years, the selected data may be data collected from a prior year for a day corresponding to a date near the target date.” And Paragraph 0066 “when data is stored at an interval of fifteen minutes as described above, a total of 96 data points on used power and temperature may be collected from 00:00 to 23:45 for a day.”
“preparing data obtained by performing statistical processing on the prediction data and generating the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
“statistical processing” and “performing the statistical processing on data” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
Teshima teaches prediction of energy demand of the factory F based on the operation plan of each facility E and each facility E operates and stops according to the operation plan and Youn teaches to control ESS charge/discharge based on the power demand prediction but do not explicitly teach “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data.”
However, EP799 teaches “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data” as (EP799 Abstract “A power consumption determining apparatus (100) is provided comprising an energy input interface (107) for receiving an energy consumption statistic (103) and a parameter input interface (106) for receiving at least one external consumption parameter (105). Furthermore, the energy consumption determination device (100) has a computing device (101) and an output interface (108) for providing a power consumption prediction (104). The computing device (101) is set up to determine the energy consumption prediction (104) from the energy consumption statistics (103) and the at least one external consumption parameter (105).”
EP799 Page 4 “In one example, the energy usage prediction may be used to control a customer's own power supply. A customer of, for example, his own wind turbine and / or solar system as a separate power supply device can see from the provided energy consumption forecast when it pays off for him to store self-generated electricity because, for example, the electricity purchased by an energy trader is more favorable. The energy consumption prediction and, in particular, an ordered list developed therefrom as a program can be used as a control program for the energy supply device.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of EP799 into the teaching of Teshima and Youn because all of the references are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima and Youn to enable efficient determination of power consumption by using the external consumption parameters provided as an ordered list that indirectly or directly impacts energy consumption such as stock market data of an external power exchange, a sunshine statistics and/or a wind statistics.
Regarding claim 9, Teshima teaches “A non-transitory computer-readable recording medium storing an energy management program causing a computer to perform” as (Teshima Paragraph 0035 “The energy demand prediction system 1 includes a factory F including a plurality of facilities E and an energy demand prediction device 10.” Also See 0078 “storage 93 is a non-transitory, tangible storage medium”).
“storing past energy consumption result data of a target facility in a storage medium in correlation with information for identifying a date and time at which the data has been acquired and incidental information which is information related to an operation situation of the target facility when the data has been acquired” as (Teshima Paragraph 0040 “The history acquisition unit 101 acquires an operation plan and a time series of power consumption from each facility E of the factory F. The history acquisition unit 101 may read the operation plan from the control device of the facility E or may acquire the operation plan by input from a user such as a manager of the factory F. The history acquisition unit 101 acquires power consumption from a sensor installed in the facility E. The operation plans of the plurality of facilities E, that is, the information on the operation time zones and operation days of the plurality of facilities E is an example of a plurality of explanatory variable candidates relating to the operation of the factory. Further, the power consumption of the plurality of facilities E is an example of an objective variable relating to the energy demand of the factory.” and Paragraph 0041 “The history storage unit 102 stores operation plans and a time series of power consumption of the plurality of facilities E acquired by the history acquisition unit 101.”)
“extracting data in which the incidental information matches an extraction condition designated by a user from the energy consumption result data stored in the storage medium and generating prediction data which is used to predict an amount of consumed energy” as (Teshima Paragraph 0042 “The selection unit 103 receives a selection of at least one explanatory variable among the operation plans of the plurality of facilities E from the user. For example, the user can select, as an explanatory variable, an operation plan of one or a plurality of facilities F that are considered to have a great influence on the increase or decrease in power consumption during the operation of the factory F. That is, the user does not need to input the operation plans of all the facilities F included in the factory F in order to predict the energy demand.” And Paragraph 0044 “Information stored in the history storage unit 102 is used as training data, the operation plan for the facility E selected by the user is input, and the time series of the total power consumption of the factory F is output. then the learning unit 104 trains the model stored in the model storage unit 105. That is, the learning unit 104 assigns parameters to the model stored in the model storage unit 105 based on the information stored in the history storage unit 102.”)
“allowing the user to designate details of…processing” as (Teshima Figure 3, reference numerals S1-S2, S4-S5 and S7).
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“displaying the data obtained by… on the prediction data as a candidate for the energy consumption prediction data, and acquiring designation of data to be used for the energy consumption prediction data by the user out of the candidates displayed” as (Teshima Paragraph 0016 “the prediction system according to any one of the first to seventh aspects, the output unit may output a display screen including the identified value of the objective variable, and a value of the objective variable or a comparison value of the objective variable included in the history data.” And Paragraph 0052 “the selection unit 103 of the energy demand prediction device 10 displays the explanatory variable selection screen including a list of the facilities E that are explanatory variable candidates (step S1). FIG. 4 is a diagram illustrating an example of explanatory variable selection screen. On the explanatory variable selection screen, a name, a rated output, and a check box for each facility E are displayed. The selection unit 103 receives a selection of the facility E used as an explanatory variable in the energy demand prediction process from the user (step S2).” Also see 0053-0055.)
Teshima teaches time series data but does not explicitly teach “identifying a date and time”, “preparing data obtained by performing statistical processing on the prediction data and generating the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing”, “statistical processing”, “performing the statistical processing on data” and “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data.”
However, Youn teaches “identifying a date and time” as (Youn Paragraph 0063 “the past data having a condition similar to that of the specific target date to be predicted is selected (S200). Since temperature-sensitive power consumption characteristics should be similar for similar weather conditions occurring around (e.g., plus or minus fifteen days) the same date of other years, the selected data may be data collected from a prior year for a day corresponding to a date near the target date.” And Paragraph 0066 “when data is stored at an interval of fifteen minutes as described above, a total of 96 data points on used power and temperature may be collected from 00:00 to 23:45 for a day.”
“preparing data obtained by performing statistical processing on the prediction data and generating the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
“statistical processing” and “performing the statistical processing on data” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
Teshima teaches prediction of energy demand of the factory F based on the operation plan of each facility E and each facility E operates and stops according to the operation plan and Youn teaches to control ESS charge/discharge based on the power demand prediction but do not explicitly teach “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data.”
However, EP799 teaches “adjusting an amount of energy supplied to the target facility based on the energy consumption prediction data” as (EP799 Abstract “A power consumption determining apparatus (100) is provided comprising an energy input interface (107) for receiving an energy consumption statistic (103) and a parameter input interface (106) for receiving at least one external consumption parameter (105). Furthermore, the energy consumption determination device (100) has a computing device (101) and an output interface (108) for providing a power consumption prediction (104). The computing device (101) is set up to determine the energy consumption prediction (104) from the energy consumption statistics (103) and the at least one external consumption parameter (105).”
EP799 Page 4 “In one example, the energy usage prediction may be used to control a customer's own power supply. A customer of, for example, his own wind turbine and / or solar system as a separate power supply device can see from the provided energy consumption forecast when it pays off for him to store self-generated electricity because, for example, the electricity purchased by an energy trader is more favorable. The energy consumption prediction and, in particular, an ordered list developed therefrom as a program can be used as a control program for the energy supply device.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of EP799 into the teaching of Teshima and Youn because all of the references are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima and Youn to enable efficient determination of power consumption by using the external consumption parameters provided as an ordered list that indirectly or directly impacts energy consumption such as stock market data of an external power exchange, a sunshine statistics and/or a wind statistics.
Response to Arguments
Applicant's arguments regarding 35 USC 101 filed December 03, 2025 have been fully considered and are persuasive in view of the amendments. Therefore the 35 USC 101 rejections are withdrawn.
Applicant's arguments regarding 35 USC 103 filed December 03, 2025 have been fully considered but they are not persuasive.
Applicant argues that Youn does not disclose providing an input interface that allows the user to specify the content of the statistical processing, nor an input interface that displays statistically processed data as candidates and accepts a user selection, which are limitations required by independent claims 1, 8, and 9 as amended.
In response to the preceding argument examiner respectfully submits that Teshima was used for “the input interface that allows the user to designate details… which is used for the energy consumption prediction data in the prediction data in the circuitry” and “the display displays the data obtained by… on the prediction data as a candidate for the energy consumption prediction data, and the input interface acquires designation of data to be used for the energy consumption prediction data by the user out of the candidates displayed by the display” as (Teshima Figures 2-3).
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Here it can be seen that the user is allowed to select the facility used as the explanatory variable by checking the check box in the column of the facility used as the explanatory variable.
Teshima Paragraph 0016 “the prediction system according to any one of the first to seventh aspects, the output unit may output a display screen including the identified value of the objective variable, and a value of the objective variable or a comparison value of the objective variable included in the history data.”
Teshima Paragraph 0052 “the selection unit 103 of the energy demand prediction device 10 displays the explanatory variable selection screen including a list of the facilities E that are explanatory variable candidates (step S1). FIG. 4 is a diagram illustrating an example of explanatory variable selection screen. On the explanatory variable selection screen, a name, a rated output, and a check box for each facility E are displayed. The selection unit 103 receives a selection of the facility E used as an explanatory variable in the energy demand prediction process from the user (step S2).”
Teshima Paragraph 0053 “When the selection unit 103 receives the selection of the facility E, by using information stored in the history storage unit 102 as training data, the learning unit 104 trains the model stored in the model storage unit 105 such that the operation plan relating to the facility E selected by the user is input and the time series of the total power consumption of the factory F is output (step S3).” Based on the selection of the facility E used as an explanatory variable, the history data is being processed by the learning unit.
Teshima Paragraph 0054 “Next, the plan input unit 106 displays an input screen of the operation plan relating to each selected facility F (step S4). That is, the plan input unit 106 displays the input screen for the operation time zone and the input screen for the operation days on the screen. FIG. 5 is a diagram illustrating an example of input screen of operation plan. On the input screen of the operation plan. a check box indicating operation or stop is displayed for a plurality of time zone or a plurality of days of the selected facility E. The plan input unit 106 receives an input of an operation plan relating to each selected facility E from the user (step S5). The user inputs the operation plan of each facility E by combining selection/deselection of the check boxes relating to the time zone or the date for each facility E.” After the processing of data by learning unit, the plan input unit 106 receives an input of an operation plan relating to each selected facility E from the user.
It can be seen from the above paragraphs that an input interface is allowing the user to specify the content for processing and selection of the plans based on the processed content by the learning unit. The energy demand identification unit predicts the energy demand of the entire factory F based on the operation plan relating to some of the facilities F included in the factory F.
Teshima did not explicitly teach “the circuitry prepares data obtained by performing statistical processing on the prediction data and generates the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing”, “the statistical processing” and “performing the statistical processing on data.”
However, Youn teaches “the circuitry prepares data obtained by performing statistical processing on the prediction data and generates the energy consumption prediction data in accordance with designation of the user from the data subjected to the statistical processing”, “the statistical processing” and “performing the statistical processing on data” as (Youn Paragraph 0022 “The power demand prediction unit may include an artificial intelligence engine; and a power demand prediction unit for using the artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern.” And Paragraph 0130 “The storage unit 716 may collect and store the past power usage data of the consumer from the power system 600 via communication. Here, the consumer 800 may be referred to as a residential group such as a building, a factory, and a house, as a consumer who purchases electricity. The past power usage data may be statistically generated in various forms based on power used in the past.” Also see paragraph 0167). Here Youn was brought in to show the processing being performed by the artificial intelligence engine can done via a statistical technique based on the consumer characteristics.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to incorporate the teachings of Youn into the teaching of Teshima because both of the reference are directed towards predicting consumer power demand. One of the ordinary skill in the art would have been motivated to modify Teshima to improve the accuracy of the power demand prediction to optimize energy consumption and ESS operation, thereby reducing energy consumption and energy costs (Youn Paragraph 0030).
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.
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/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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