DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-10 are pending
Claim 1 is independent
Specification (Abstract)
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3 and 4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Biryukov et al. US11605034B1 (hereinafter “Biryukov”).
As to claim 1 Biryukov teaches an energy management apparatus (Fig. 3 depicts an energy management apparatus) comprising:
A consumption amount forecast circuit configured to forecast an electric power consumption pattern of a user (Column 7, lines 41-49 “Inputting the demand training data to the machine learning model trains the machine learning model to forecast electricity usage at site 22 as a function of past electricity usage at site 22. In other words, the machine learning model is able to determine possible relationships between past electricity usage (including past weather conditions and date/time information) and future electricity usage, by analyzing the demand training data to determine trends within the demand training data.” Examiner interpreted determining trends based on past electricity usage to be a consumption pattern of the user.) ; and
a controller configured to (Column 6, lines 25-27 “The program code is configured, when executed by the one or more processors, to perform any of the methods described herein.” Examiner interpreted “processor” as controller.) provide energy cost saving information (Column 17, lines 19-23 “More particularly, using the predicted demand, expected savings resulting from the deploying the system 20 may be determined with the confidence score representing a confidence that those savings will be realized should the system 20 be deployed.” Examiner interpreted “expected savings” to be cost savings information.) that varies with a battery capacity, the energy cost saving information being based on a time span-based billing rate, the battery capacity, and the electric power consumption pattern (Column 18, lines 55-60 “The confidence score is based on demand-related data, which in FIG. 15B comprises three components: demand predictability (referred to as “load predictability” in FIG. 15B), load shape, and battery capacity, with each of demand predictability, load shape, and battery capacity scored analogously as the overall score.” And Column 1, lines 37-41 teaches that charge costs are determined based on a rate-tariff that varies with time “Each customer is assigned to a particular “rate tariff” which defines how demand charges are measured and assessed for that customer. While details of rate tariffs can vary from utility to utility, the demand charge is generally based on the maximum energy a site consumed during a time interval (for example 15 minutes or 1 hour) during the previous billing cycle.”)
As to claim 3, Biryukov teaches all limitations of the base claim, as outlined above.
Biryukov further teaches wherein the controller computes (Column 6, lines 25-27 “The program code is configured, when executed by the one or more processors, to perform any of the methods described herein.” Examiner interpreted “processor” as controller.) an average daily consumption electric power amount, based on the electric power consumption pattern (Column 7, lines 41-49 “Inputting the demand training data to the machine learning model trains the machine learning model to forecast electricity usage at site 22 as a function of past electricity usage at site 22. In other words, the machine learning model is able to determine possible relationships between past electricity usage (including past weather conditions and date/time information) and future electricity usage, by analyzing the demand training data to determine trends within the demand training data.” Examiner interpreted determining trends based on past electricity usage to be a consumption pattern of the user. And the reference discloses that the model, can predict the electricity usage on a day-to-day basis, Column 8, lines 64-68 and Column 9, lines 1-4 “The past demand data is represented using feature vectors as described below. Let d denote the current day and j−1 denote the current time. The first SVR model is used to forecast the electricity usage of day d at time j−1+1. The second SVR model is used to forecast the electricity usage of day d at time j−1+2. More generally, the mth SVR model is used to forecast the electricity usage of day d at time j−1+m.”) , and wherein the controller determines a number of batteries, which is to be recommended, based on the computed average daily consumption electric power amount and the battery capacity (Column 10, lines 42-60 “Turning to FIG. 7 , there is shown an example of a system 70 for reserving a capacity of batteries 25. System 70 includes a meter module 72, forecasting module 31 (which may be the same forecasting module 31 seen in FIG. 3 ), a battery system 74, and a proportional-integral-derivative (PID) control module 76 (which, according to some embodiments, may be control algorithm module 35 of FIG. 3 ). Meter module 72 obtains meter data (for example data relating to historical electricity demand) from meters 21 and provides the data to forecasting module 31. As described in more detail below, forecasting module 31 is configured to determine a forecasting error and provide the forecasting error to PID control module 76. Battery system 74 obtains battery data (for example data relating to a current state-of-charge (SOC)) from batteries 25 and provides the battery data to PID control module 76. PID control module 76 uses the forecasting error and the current SOC provided by battery system 74 to determine a capacity of batteries 25 that is to be reserved” Since the electrical power consumption forecast is based on a day to day model, the computed electric power amount used to determine the capacity of batteries, would necessarily include the average daily electrical consumption amount.)
As to claim 4, Biryukov teaches all limitations of the base claim, as outlined above.
Biryukov further teaches an electricity generation amount forecast circuit configured to forecast an amount electricity generated by a solar panel (Column 13, lines 40-42 “According to embodiments of the disclosure, the production of photovoltaic (PV) cells 24 may be forecasted using the above-described SVR model.”) , wherein the controller determines a number of batteries, which is to be recommended (Column 10, lines 42-43 “Turning to FIG. 7 , there is shown an example of a system 70 for reserving a capacity of batteries 25.”) , based on a time span-based electricity generation amount forecasted by the electricity generation amount forecast circuit (Column 14, lines 52-55 “In other words, the period of time corresponding to the past PV cell production extends from a past point in time to the point in time at which EMSP 36 is instructed to perform the forecasting… The past PV cell production data is inputted to the trained machine learning model, and at block 128 the trained machine learning model outputs projected PV cell production data representing projected PV cell production at site 22 for the future time period selected by the user.” Shows that the electricity generation amount forecasted is time-based. The SVR model is employed by the forecasting module, and Fig. 7 shows the forecasting module used to determine the reserve battery capacity.) , the computed electric power amount, and the battery capacity (Column 10, lines 42-60 “Turning to FIG. 7 , there is shown an example of a system 70 for reserving a capacity of batteries 25. System 70 includes a meter module 72, forecasting module 31 (which may be the same forecasting module 31 seen in FIG. 3 ), a battery system 74, and a proportional-integral-derivative (PID) control module 76 (which, according to some embodiments, may be control algorithm module 35 of FIG. 3 ). Meter module 72 obtains meter data (for example data relating to historical electricity demand) from meters 21 and provides the data to forecasting module 31. As described in more detail below, forecasting module 31 is configured to determine a forecasting error and provide the forecasting error to PID control module 76. Battery system 74 obtains battery data (for example data relating to a current state-of-charge (SOC)) from batteries 25 and provides the battery data to PID control module 76. PID control module 76 uses the forecasting error and the current SOC provided by battery system 74 to determine a capacity of batteries 25 that is to be reserved” Furthermore the storage capacity can be traditional batteries or photovoltaic cells “Non-grid electricity may be derived from various distributed energy/electricity resources, such as batteries 25 and/or photovoltaic cells 24”).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Biryukov et al. US11605034B1 (hereinafter “Biryukov”) in further view of Elma et al. “Investigation of Cost Reduction in Residential Electricity Bill using Electric Vehicle at Peak Times” (hereinafter “Elma”).
As to claim 2, Biryukov teaches all limitations of the base claim, as outlined above.
Biryukov further teaches wherein the controller computes (Column 6, lines 25-27 “The program code is configured, when executed by the one or more processors, to perform any of the methods described herein.” Examiner interpreted “processor” as controller.) an average daily electric power consumption amount, based on the electric power consumption pattern (Column 7, lines 41-49 “Inputting the demand training data to the machine learning model trains the machine learning model to forecast electricity usage at site 22 as a function of past electricity usage at site 22. In other words, the machine learning model is able to determine possible relationships between past electricity usage (including past weather conditions and date/time information) and future electricity usage, by analyzing the demand training data to determine trends within the demand training data.” Examiner interpreted determining trends based on past electricity usage to be a consumption pattern of the user. And the reference discloses that the model, can predict the electricity usage on a day to day basis, Column 8, lines 64-68 and Column 9, lines 1-4 “The past demand data is represented using feature vectors as described below. Let d denote the current day and j−1 denote the current time. The first SVR model is used to forecast the electricity usage of day d at time j−1+1. The second SVR model is used to forecast the electricity usage of day d at time j−1+2. More generally, the mth SVR model is used to forecast the electricity usage of day d at time j−1+m.”) , and
wherein, when storing the computed electric power amount in a battery (Column 9, lines 40-53 “Once EMSP 36 has performed the forecast, at block 46, EMSP 36 identifies one or more peaks in the projected electricity usage… At block 47, EMSP 36 transmits one or more instructions for securing non-grid electricity for managing the projected electricity demand. In particular, EMSP 36 transmits one or more instructions for securing non-grid electricity for use during the future periods corresponding to the identified peaks. Non-grid electricity may be derived from various distributed energy/electricity resources, such as batteries” Examiner interpreted “securing non-grid electricity” to be storing energy in a battery for later use. Since the electrical power consumption forecast is based on a day to day model, the computed electric power amount stored in the battery due to anticipation of demand peaks, would necessarily include the average daily electrical consumption amount.) , the controller computes (Column 6, lines 25-27 “The program code is configured, when executed by the one or more processors, to perform any of the methods described herein.” Examiner interpreted “processor” as controller.), as daily energy cost saving information (Column 17, lines 19-23 “More particularly, using the predicted demand, expected savings resulting from the deploying the system 20 may be determined with the confidence score representing a confidence that those savings will be realized should the system 20 be deployed.” Examiner interpreted “expected savings” to be cost savings information.).
But Biryukov does not explicitly teach a charging cost that results from subtracting a charging cost incurred during a time span having a lowest billing rate from a charging cost incurred during a time span having a highest billing rate.
However, Elma teaches a charging cost that results from subtracting a charging cost incurred during a time span having a lowest billing rate from a charging cost incurred during a time span having a highest billing rate (Introduction, page 125 “Savings can be found by finding the difference between residential electricity cost at peak times and EV charging cost at off-peak hours where the cost is lower.” Examiner interpreted cost at peak times to be charging cost incurred during a time span having a highest billing rate. Examiner interpreted charging cost at off-peak hours to be charging cost incurred during a time span having a lowest billing rate.) Biryukov and Elma are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov, and incorporate the cost savings strategy, as taught by Elma.
One of ordinary skill in the art would have been motivated to increase energy cost savings, by reducing residential demand at peak hours, as suggested by Elma (page 124).
Claims 5, 6, 7, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Biryukov et al. US11605034B1 (hereinafter “Biryukov”) in further view of Fang et al. US20230080737A1 (hereinafter “Fang”).
As to claim 5, Biryukov teaches all limitations of the base claim, as outlined above.
Biryukov further teaches the electricity generation amount forecast circuit (Column 13, lines 40-42 “According to embodiments of the disclosure, the production of photovoltaic (PV) cells 24 may be forecasted using the above-described SVR model.”) But Biryukov does not explicitly teach forecasts an electricity generation amount to be generated in an installation-scheduled region, based on an electricity generation amount generated in a solar panel of another energy management apparatus installed in the installation-scheduled region.
However, Fang teaches forecasts an electricity generation amount to be generated in an installation-scheduled region (Paragraph [0011] “The disclosure provides a federated learning-based regional photovoltaic power probabilistic forecasting method so as to at least solve the above problems.” Examiner interpreted photovoltaic power forecasting to be forecasting electricity generation amount. Examiner interpreted “regional” to be the installation-scheduled region.), based on an electricity generation amount generated in a solar panel of another energy management apparatus installed in the installation-scheduled region (Paragraph [0013] “pinpointing all photovoltaic power stations within a region which participate in a federated learning framework for probabilistic forecasting, collecting weather information and corresponding photovoltaic power variables within a time step, and grouping the variables according to time order into a sample dataset;” Examiner interpreted the “power variables” of each photovoltaic station to reasonably include the electricity generated by said photovoltaic station.).
Biryukov and Fang are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov, and incorporate the regional photovoltaic power forecasting, as taught by Fang.
One of ordinary skill in the art would have been motivated to enhance forecasting accuracy of short-term regional photovoltaic power, as suggested by Fang (Paragraph [0011]).
As to claim 6, the combination of Biryukov and Fang teaches all limitations of the base claims, as outlined above.
Biryukov further teaches the electricity generation amount forecast circuit (Column 13, lines 40-42 “According to embodiments of the disclosure, the production of photovoltaic (PV) cells 24 may be forecasted using the above-described SVR model.”).
But Biryukov does not explicitly teach generates an electricity generation amount forecast model on a per-region basis, and forecasts the electricity generation amount to be generated in the installation-scheduled region, based on electricity generation amount information of a solar panel installed in the installation-scheduled region.
However, Fang teaches generates an electricity generation amount forecast model on a per-region basis (Paragraph [0011] “The disclosure provides a federated learning-based regional photovoltaic power probabilistic forecasting method” Examiner interpreted “regional” to be the installation-scheduled region. Furthermore, the regional forecasting model, is built using selected photovoltaic power station information within the region, paragraph [0013] “pinpointing all photovoltaic power stations within a region which participate in a federated learning framework for probabilistic forecasting, collecting weather information and corresponding photovoltaic power variables within a time step, and grouping the variables according to time order into a sample dataset” The system then builds a global forecasting model using the local forecasting models, paragraph [0023] “receiving, by the central server, the local forecasting models in step 9 which pass testing, and updating the global forecasting model”), and forecasts the electricity generation amount to be generated in the installation-scheduled region (Paragraph [0097] “the global forecast neural network model is subjected to T=10 times of forward propagation processes; in this way, the photovoltaic power and uncertainty at a future time may be forecasted”), based on electricity generation amount information of a solar panel installed in the installation-scheduled region (Paragraph [0013] “pinpointing all photovoltaic power stations within a region which participate in a federated learning framework for probabilistic forecasting, collecting weather information and corresponding photovoltaic power variables within a time step, and grouping the variables according to time order into a sample dataset;” Examiner interpreted the “power variables” of each photovoltaic station to reasonably include the electricity generated by said photovoltaic station. And Fig. 1 shows the central server receiving training information from other photovoltaic stations in the region.).
Biryukov and Fang are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov and incorporate the regional photovoltaic power forecasting, as taught by Fang.
One of ordinary skill in the art would have been motivated to enhance forecasting accuracy of short-term regional photovoltaic power, as suggested by Fang (Paragraph [0011]).
As to claim 7, the combination of Biryukov Fang teaches all limitations of the base claims, as outlined above.
Biryukov further teaches the electricity generation amount forecast circuit (Column 13, lines 40-42 “According to embodiments of the disclosure, the production of photovoltaic (PV) cells 24 may be forecasted using the above-described SVR model.”).
But Biryukov does not explicitly teach generates a community electricity generation amount forecast model through communication with at least one electricity generation amount forecast model included in an energy management apparatus installed in the installation-scheduled region, and wherein the electricity generation amount forecast circuit forecasts the electricity generation amount to be generated in the installation-scheduled region using the community electricity generation amount forecast model.
However, Fang teaches generates a community electricity generation amount forecast model (Paragraph [0018] “building, by a central server based on a forecast requirement, a global forecasting model” Which forecasts the amount of photovoltaic power, Paragraph [0011] “The disclosure provides a federated learning-based regional photovoltaic power probabilistic forecasting method” Examiner interpreted “photovoltaic power” to be electricity generation amount. And examiner interpreted “global forecasting model” to be community electricity generation amount forecast model. Furthermore, the global forecast model can be used as a community forecast model, since the system is able to determine which photovoltaic power stations are used to train the model. Proving that the scale of the forecasting region is not fixed.) through communication with at least one electricity generation amount forecast model (Paragraph [0023] “receiving, by the central server, the local forecasting models in step 9 which pass testing, and updating the global forecasting model”) included in an energy management apparatus installed in the installation-scheduled region (Paragraph [0033] “pinpointing all photovoltaic power stations within a region which participate in a federated learning framework for probabilistic forecasting” Examiner interpreted “photovoltaic power stations” to be energy management apparatus, since each power station is designed to aid in the optimization of renewable energy by forecasting power. Examiner interpreted “region” to be installation-scheduled region.), and
wherein the electricity generation amount forecast circuit forecasts the electricity generation amount to be generated (Paragraph [0097] “the global forecast neural network model is subjected to T=10 times of forward propagation processes; in this way, the photovoltaic power and uncertainty at a future time may be forecasted”) in the installation-scheduled region (Paragraph [0011] “The disclosure provides a federated learning-based regional photovoltaic power probabilistic forecasting method” Examiner interpreted “regional” to be the installation-scheduled region.) using the community electricity generation amount forecast model (Paragraph [0097] “the global forecast neural network model is subjected to T=10 times of forward propagation processes; in this way, the photovoltaic power and uncertainty at a future time may be forecasted” Examiner interpreted “global forecast model” to be community electricity generation amount forecast model.)
Biryukov and Fang are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov and incorporate the regional photovoltaic power forecasting, as taught by Fang.
One of ordinary skill in the art would have been motivated to improve efficiency and automation in energy generation and storage systems, as suggested by Fang (Paragraph [0011]).
As to claim 8, the combination of Biryukov and Fang teaches all limitations of the base claims, as outlined above.
Biryukov further teaches wherein the controller determines (Column 6, lines 25-27 “The program code is configured, when executed by the one or more processors, to perform any of the methods described herein.” Examiner interpreted “processor” as controller.) the electricity generation amount of the solar panel (Column 13, lines 40-42 “According to embodiments of the disclosure, the production of photovoltaic (PV) cells 24 may be forecasted using the above-described SVR model.”) and the electric power consumption pattern (Column 7, lines 41-49 “Inputting the demand training data to the machine learning model trains the machine learning model to forecast electricity usage at site 22 as a function of past electricity usage at site 22. In other words, the machine learning model is able to determine possible relationships between past electricity usage (including past weather conditions and date/time information) and future electricity usage, by analyzing the demand training data to determine trends within the demand training data.” Examiner interpreted determining trends based on past electricity usage to be a consumption pattern of the user.).
But Biryukov does not explicitly teach the forecasted electricity generation amount.
However, Fang teaches the forecasted electricity amount (Paragraph [0011] “The disclosure provides a federated learning-based regional photovoltaic power probabilistic forecasting method so as to at least solve the above problems.” Examiner interpreted “regional” to be the installation-scheduled region. And Paragraph [0097] “the global forecast neural network model is subjected to T=10 times of forward propagation processes; in this way, the photovoltaic power and uncertainty at a future time may be forecasted”).
Biryukov and Fang are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They all relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov and incorporate the regional photovoltaic power forecasting, as taught by Fang.
One of ordinary skill in the art would have been motivated to enhance forecasting accuracy of short-term regional photovoltaic power, as suggested by Fang (Paragraph [0011]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Biryukov et al. US11605034B1 (hereinafter “Biryukov”) in further view of Fang et al. US20230080737A1 (hereinafter “Fang”) in further view of PALLAM et al. WO2021257968A1 (hereinafter “PALLAM”).
As to claim 9, the combination of Biryukov and Fang teaches all limitations of the base claims as outlined above.
Biryukov further teaches the number of batteries that is determined (Column 10, lines 42-60 “Turning to FIG. 7 , there is shown an example of a system 70 for reserving a capacity of batteries 25. System 70 includes a meter module 72, forecasting module 31 (which may be the same forecasting module 31 seen in FIG. 3 ), a battery system 74, and a proportional-integral-derivative (PID) control module 76 (which, according to some embodiments, may be control algorithm module 35 of FIG. 3 ). Meter module 72 obtains meter data (for example data relating to historical electricity demand) from meters 21 and provides the data to forecasting module 31. As described in more detail below, forecasting module 31 is configured to determine a forecasting error and provide the forecasting error to PID control module 76. Battery system 74 obtains battery data (for example data relating to a current state-of-charge (SOC)) from batteries 25 and provides the battery data to PID control module 76. PID control module 76 uses the forecasting error and the current SOC provided by battery system 74 to determine a capacity of batteries 25 that is to be reserved”) based on the energy cost saving information (Column 17, lines 19-23 “More particularly, using the predicted demand, expected savings resulting from the deploying the system 20 may be determined with the confidence score representing a confidence that those savings will be realized should the system 20 be deployed.” Examiner interpreted “expected savings” to be cost savings information. Furthermore, the battery capacity effects the energy cost savings information, Column 18, lines 55-60 “The confidence score is based on demand-related data, which in FIG. 15B comprises three components: demand predictability (referred to as “load predictability” in FIG. 15B), load shape, and battery capacity, with each of demand predictability, load shape, and battery capacity scored analogously as the overall score.”)
But Biryukov does not explicitly teach wherein the controller determines the electricity generation capacity of the solar panel.
However, PALLAM teaches wherein the controller (Paragraph [0017] “The user device 208 comprises at least one processor 210, support circuits 212 and memory 214.” Examiner interpreted “processor” as controller) determines (Paragraph [0016] “user device 208 accesses a web page from the server 204 and displays the web page for user interaction.” and “Specifically, the server operates as a capacity estimator 202” Showing that the controller, which operates in conjunction with the server, is what performs the capacity determination.) the electricity generation capacity of a solar panel (Paragraph [0013] “Using the available roof area and the amount of sunlight, the estimator determines a model of an energy generation and storage system including, for example, a solar array size (i.e., an estimate of a number of panels and amount of energy generation available for that particular facility)” Examiner interpreted the amount of generation available based, on the size of the solar panel, to be the capacity of the solar panel.).
Biryukov, Fang and PALLAM are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They all relate to energy management systems and methods.
Therefore, at the time of the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov, and incorporate the regional photovoltaic power forecasting, as taught by Fang, and incorporate the user generated power consumption profile, and storage capacity estimation, as taught by PALLAM,
One of ordinary skill in the art would have been motivated to enhance forecasting accuracy of short-term regional photovoltaic power, as suggested by Fang (Paragraph [0011]), and to improve efficiency and automation in energy generation and storage systems, as suggested by PALLAM (Paragraph [0006]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Biryukov et al. US11605034B1 (hereinafter “Biryukov”) in further view of Lane et al. US7062361B1 (hereinafter “Lane”).
As to claim 10, Biryukov teaches all limitations of the base claim as outlined above.
Biryukov further teaches the electric power consumption pattern (Column 7, lines 41-49 “Inputting the demand training data to the machine learning model trains the machine learning model to forecast electricity usage at site 22 as a function of past electricity usage at site 22. In other words, the machine learning model is able to determine possible relationships between past electricity usage (including past weather conditions and date/time information) and future electricity usage, by analyzing the demand training data to determine trends within the demand training data.” Examiner interpreted determining trends based on past electricity usage to be a consumption pattern of the user.)
But Biryukov does not explicitly teach wherein the time span-based billing rate varies among electric power supply companies, and wherein the controller determines any one of different time span-based rates.
However, Lane teaches wherein the time span-based billing rate (Column 4, lines 35-39 “The moderator control computer selects the provider's offering the lowest rate at each time block and provides that rate to each end user” Examiner interpreted “rate at each time block” to be the rate changing with respect to the time of day.) varies among electric power supply companies (Column 4, lines 35-39 “The moderator control computer selects the provider's offering the lowest rate at each time block and provides that rate to each end user” Examiner interpreted “providers” to be electric power companies.) , and wherein the controller determines (Column 8, lines 6-7 “the controller will continuously monitor the spot market price being set by the commodity market”) any one of different time span-based rates (Column 4, lines 24-26 “Once a provider has been selected, the moderator of the power exchange can monitor the actual electricity consumed by the user by collecting meter readings.”)
Biryukov and Lane are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They both relate to energy management systems and methods.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the energy management apparatus, as taught by Biryukov, and incorporate the electric power supply company determination, taught by Lane.
One of ordinary skill in the art would have been motivated to improve determination of a favorable k Wh rate from a particular utility company, as suggested by Lane (Column 5, lines 5-8).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KIM et al. US20130162037A1 teaches a method for charging and discharging control in a solar generation system, power consumption determination, and solar power generation estimation (Paragraph [0008], Paragraph [0014], [0016]).
ASGHARI et al. US20180090935 teaches minimization of demand charges in an energy management system, minimization of consumption charges, and system battery charge/ discharge scheduling (Paragraph [0006], Paragraph [0019], Paragraph [0027]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW DILLON ROBERTS whose telephone number is (571)270-1582. The examiner can normally be reached M-F, 7:30am to 5:00pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.D.R./Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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