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 .
Priority
Applicant’s claim for the benefit of a provisional application 63/436,008 submitted on 12/29/2022 is acknowledged.
Claim Objections
Claims 19 and 20 objected to because of the following informalities:
Claims 19 and 20 appear to have been made dependent on claim 16, instead of 18.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Below is an analysis in accordance with the Subject Matter Eligibility Test for Products and Process found in MPEP 2106(III).
Claim 1: A method for achieving a net energy goal for building operations for a time period comprising a first subperiod before a current time and a second subperiod from the current time to an end of the time period, comprising:
generating first forecasted ranges for amounts of energy consumption for a plurality of time steps in the second subperiod;
generating second forecasted ranges for amounts of energy production for the plurality of time steps in the second subperiod;
generating third forecasted ranges for amounts of net energy for the plurality of time steps in the second subperiod, wherein the amounts of net energy are based on differences between the amounts of energy consumption and the amounts of energy production;
providing a strategy for the building operations based on the third forecasted ranges and the net energy goal.
Step 1
Claim 1 recites a method to perform a series of steps or acts. The claim is to a system, which is a statutory class of invention.
Step 2A, Prong One
In claim 1, the steps of “a time period comprising a first subperiod before a current time and a second subperiod from the current time to an end of the time period, comprising: generate first forecasted ranges…, generate second forecasted ranges…, generate third forecasted ranges…” is recited at a high level of generality such that it could be practically performed in the human mind. The limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for generic computer components. The “a time period comprising a first subperiod before a current time and a second subperiod from the current time to an end of the time period, comprising, generate first forecasted ranges…, generate second forecasted ranges…, generate third forecasted ranges…” limitations fall under the category of a mental process in that a person could observe the production and consumption data over set time frames as well as make a judgement on the differences of the two (energy data and one or more time steps). This interpretation appears in line with the specification which describes systems that output data that would be possible for a human to evaluate and plot (see paragraphs (93, 118 and 183).
That is, other than reciting steps for “providing a strategy for the building operations based on the third forecasted ranges and the net energy goal.” Nothing in the claim precludes the above steps from being practically performed in the mind.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites an additional element of steps for “providing a strategy for the building operations based on the third forecasted ranges and the net energy goal”.
The limitation of “providing a strategy for the building operations based on the third forecasted ranges and the net energy goal” amounts to extra-solution activity transmitting data (MPEP 2106.05(g): (“An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. ”) i.e. insignificant application for use in the claimed process. )
Step 2B
The limitations of providing a strategy amounts to no more than insignificant pre-activity of transmitting data. Further, the receiving and accessing steps simply append well-understood and conventional activity of receiving data over a network (see MPEP 2106.05(d)(II)(I): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
Thus, when taken alone, the individual elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claims 2-11, are directed to further apply the judicial exception to generic devices as recited in claim 1. Therefore, claims 2-11, are directed to a judicial exception that is not integrated into practical application. There are no additional limitations in the claim to apply, rely on, or use the judicial exception. The claims are not more than a drafting effort designed to monopolize the exception. The claims also do not include additional elements that integrate the judicial exception into a practical application that would be sufficient to amount to more than the judicial exception. Thus, claims 2-11, are not patent eligible.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,2,6-13,15-17,18, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ELBSAT US20190340709A1 published on 11/07/19.
Regarding claim 1, a method for achieving a net energy goal for building operations for a time period comprising a first subperiod before a current time and a second subperiod from the current time to an end of the time period, comprising (The optimizer/model are read as using the time frames depicted in the Figures 4 and 8. The time frames can be chosen from historical data, or before a current time, or it can be selected from a future time which would include all available times. These times are used with eLoad to create optimizations. ELBSAT discloses: FIG 4 and 8, Column 23, line 41 - line 46 “In some embodiments, load/rate predictor 622 uses a deterministic plus stochastic model trained from historical load data to predict loads τ ...” column 34, line 57 - line 62 “PF is the amount of battery power committed to frequency regulation at time step … is the effective power available and eLoad is the total electric demand at time step i.” column 30, line 56 - column 31, line 21 “Planning tool 702 may shift the optimization 802 forward in time, resulting in optimization period 804. The amount by which the prediction window is shifted may correspond to the duration of time steps”);
generating first forecasted ranges for amounts of energy consumption for a plurality of time steps in the second subperiod (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
generating second forecasted ranges for amounts of energy production for the plurality of time steps in the second subperiod (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 8 and column 31, line 34 -line 49, “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
generating third forecasted ranges for amounts of net energy for the plurality of time steps in the second subperiod, wherein the amounts of net energy are based on differences between the amounts of energy consumption and the amounts of energy production (See equation in col. 33 to see the difference between production and consumption in equation form where is shows production and consumption in a subtraction form which shows multiple differences. ELBSAT discloses Fig. 8 and column 32, line 66-67 and column 33, line 1 -line 3 “High level optimizer 632 can optimize the cost function J(x) subject to the following constraint, which guarantees the balance between resources purchased, produced, discharged, consumed, and requested over the optimization horizon”);
providing a strategy for the building operations based on the third forecasted ranges and the net energy goal. (Plant dispatch is read as the building operations and receiving the information from the optimizer as providing a strategy. ELBSAT discloses Fig. 8, item 830 plant dispatch and column 31, line 11 – line 18 “This process may be repeated for each subsequent optimization period to generate updated resource allocations and to select portions of each resource allocation to send to 15 plant dispatch 830. Plant dispatch 830 includes the first b time steps 818-824 from each of optimization periods...”);
Regarding claim 2, the limitations of claim 1 are discussed above. Comprising providing a graphical user interface comprising a net energy plot comprising a first line illustrating actual net energy over the first subperiod, a second line illustrating planned net energy over the second subperiod, and a region based on the third forecasted ranges for the second subperiod (Figure 7 and 8 show a GUI an NET energy graphs over differing time ranges. ELBSAT discloses Fig. 8 and column 30, line 35 - line 55. “In an exemplary embodiment, GUI engine 716 includes a graphical user interface component configured to provide graphical user interfaces to a user for selecting or defining plan information for the simulation (e.g., planned loads, utility rates, environmental conditions, etc.…”).
Regarding claim 6, the limitations of claim 1 have been discussed above. Wherein providing the strategy comprises:
generating, based on the first forecasted ranges and the second forecasted ranges, a net energy trajectory comprising net energy targets for the plurality of time steps, wherein each net energy target indicates a target difference between cumulative energy consumption and cumulative energy production or offset from a beginning of the time period to a corresponding time step of the plurality of time steps (ELBSAT discloses FIG 8 and the calculation of column 33 shows many forecasted ranges and the equation show summing the net energy taking into account a plurality of times and the production and consumption);
generating, for a given time step, a set of curtailment actions predicted to achieve the net consumption target for the given time step (Curtailment actions are able to be taken by the provider at the request, this is read as being able to set the actions. ELBSAT discloses column 16, line 66 - column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request”);
and implementing the set of curtailment actions (Curtailment actions are able to be taken by the provider at the request, this is read as being able to set the actions. ELBSAT discloses column 16, line 66 -column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request.”).
Regarding claim 7, the limitations of claim 1 and 6 have been discussed above. Wherein generating the net energy trajectory comprises performing an optimization constrained by the net energy goal. (ELBSAT discloses Fig. 8 and column 32, line 66-67 and column 33, line 1 -line 3 “High level optimizer 632 can optimize the cost function J(x) subject to the following constraint, which guarantees the balance between resources purchased, produced, discharged, consumed, and requested over the optimization horizon”).
Regarding claim 8, the limitations of claim 1 have been discussed above. Wherein providing the strategy comprises controlling building equipment serving a facility, the energy consumption at least partially corresponds to operation of the building equipment, and the energy production corresponds to green energy production at the facility (ELBSAT discloses Fig. 1 and column 6, line 37 - column 7, line 30, which discuss the connection of a campus and controlling its production and consumption systems either pulling resources or distributing unused. It further discloses use of solar panels or photovoltaic cells (green energy) which is analogous to building equipment and building operations);
Regarding claim 9, the limitations of claim 1 have been discussed above. further comprising disaggregating the first forecasted ranges for amounts of energy consumption into energy consumption categories, wherein the energy consumption categories comprise heating consumption, cooling consumption, and other consumption. (ELBSAT discloses column 6, line 60 - column 7, line 10. “In some embodiments, campus 102 includes a central plant 118. Central plant 118 may include one or more subplants that consume resources from utilities (e.g., water, natural gas, electricity, etc.) to satisfy the loads of buildings for example, central plant 118 may include a heater subplant, a heat recovery chiller subplant, a chiller subplant, a cooling tower subplant, a hot thermal energy storage (TES) subplant, and a cold thermal energy storage (TES) subplant”).
Regarding claim 10, the limitations of claim 1 have been discussed above. Wherein the strategy is configured to drive the amount of net energy for a final time step in the second subperiod to the net energy goal. (ELBSAT discloses column 27. line 18 - line 29. “Controller 552 may be configured to predict the thermal energy loads (e.g., heating loads, cooling loads, etc.) of the building for plurality of time steps in an optimization period (e.g., using weather forecasts from a weather service 604). Controller may generate control decisions that optimize the economic value of operating system 550 over the duration of the optimization period subject to constraints on the optimization process (e.g., energy balance constraints, load satisfaction constraints, etc.)”).
Regarding claim 11, the limitations of claim 1 have been discussed above. Wherein the amount of net energy for a given time step of the plurality of time steps is a cumulative difference between the amounts of energy consumption and the amounts of energy production during the time period up to the given time step. (FIG 8 and Calculation of column 33 which shows many forecasted ranges and the equation show summing the net energy taking into account a plurality of times and the production and consumption, ELBSAT discloses column 28, line 43 - line 58, “The portion of the simulation period over which high level optimizer 632 optimizes the resource allocation may be defined by a prediction window ending at a time horizon. With each iteration of the optimization, the prediction window is shifted forward and the portion of the dispatch schedule no longer in the prediction window is accepted…”);
Regarding claim 12, A system comprising:
an energy load operable to consume energy (ELBSAT Fig. 1 and column 6, line 37 - column 7, line 30 discloses the connection of a campus and controlling its production and consumption systems either pulling resources or distributing unused);
a green energy source configured to produce energy (ELBSAT discloses Fig. 1 and column 6, line 37 - column 7, line 30 discloses use of solar panels or photovoltaic cells (green energy) which is analogous to building equipment and building operations);
and processing circuitry programmed to: generate first forecasted ranges for amounts of energy consumption by the energy load for a plurality of time steps in the second subperiod (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45resource sold to energy purchasers”);
generate second forecasted ranges for amounts of energy production by the green energy source for a plurality of time steps in the second subperiod (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 1 and column 6, line 37 - column 7, “line 30 discloses use of solar panels or photovoltaic cells (green energy) which are part of the power output of the building”, Fig. 8 and column 31, line 34 -line 49 discloses “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
generate third forecasted ranges for amounts of net energy for the plurality of the plurality of time steps in the second subperiod, wherein the amounts of net energy are based on differences between the amounts of energy consumption and the amounts of energy production (ELBSAT discloses FIG 8 and Calculation of column 33 shows many forecasted ranges and the equation show summing the net energy taking into account a plurality of times and the production and consumption, column 28, line 43 - line 58, “The portion of the simulation period over which high level optimizer 632 optimizes the resource allocation may be defined by a prediction window ending at a time horizon. With each iteration of the optimization, the prediction window is shifted forward and the portion of the dispatch schedule no longer in the prediction window is accepted…”);
and control the energy load using a control strategy configured to drive one or more of the amounts of net energy to a target (Plant dispatch is read as the building operations and receiving the information from the optimizer as providing a strategy. ELBSAT discloses Fig. 8, item 830 plant dispatch and column 31, line 11 – line 18 “This process may be repeated for each subsequent optimization period to generate updated resource allocations and to select portions of each resource allocation to send to 15 plant dispatch 830. Plant dispatch 830 includes the first b time steps 818-824 from each of optimization periods...”).
Regarding claim 13, the limitations of claim 12 have been discussed above. Wherein the processing circuitry is further programmed to host a graphical user interface comprising a net energy plot comprising a first line illustrating actual net energy over the first subperiod, a second line illustrating planned net energy over the second subperiod, and a region based on the third forecasted ranges for the second subperiod (ELBSAT discloses Figure 7 and 8 which show a GUI an NET energy graphs over differing time ranges, Fig. 8 and column 30, line 35 - line 55, “In an exemplary embodiment, GUI engine 716 includes a graphical user interface component configured to provide graphical user interfaces to a user for selecting or defining plan information for the simulation (e.g., planned loads, utility rates, environmental conditions, etc.…”).
Regarding claim 15, the limitations of claim 12 have been discussed above. Wherein the processing circuitry is programmed to execute the control strategy by:
generating, based on the first forecasted ranges and the second forecasted ranges, a net consumption trajectory comprising net consumption targets for the plurality of time steps, wherein each net consumption target indicates a target difference from a beginning of the time period to a corresponding time step between total consumption and total production or offset (ELBSAT discloses FIG 8 and the calculation of column 33 shows many forecasted ranges and the equation show summing the net energy taking into account a plurality of times and the production and consumption);
generating, for a given time step of the plurality of time steps, a set of curtailment actions predicted to achieve the net consumption target for the subperiod (The provider is able to curtail the energy on request which is read as being able to set the actions. ELBSAT discloses column 16, line 66 - column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request”);
and implementing the set of curtailment actions by controlling the energy load (ELBSAT discloses column 16, line 66 - column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request”).
Regarding claim 16, the limitations of claim 12 and 15 have been discussed above. Wherein the processing circuitry is programmed to generate the net consumption trajectory by performing an optimization constrained by the target (ELBSAT discloses Fig. 8 and column 32, line 66-67 and column 33, line 1 -line 3 “High level optimizer 632 can optimize the cost function J(x) subject to the following constraint, which guarantees the balance between resources purchased, produced, discharged, consumed, and requested over the optimization horizon”);
Regarding claim 17, the limitations of claim 12 and 15 have been discussed above. Wherein the processing circuitry is programmed to generate the set of curtailment actions based on a disaggregation of types of energy usage of the energy load, the types of energy usage comprising heating and cooling (ELBSAT discloses column 16, line 66 - column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request”, column 6, line 60 - column 7, line 10. “In some embodiments, campus 102 includes a central plant 118. Central plant 118 may include one or more subplants that consume resources from utilities (e.g., water, natural gas, electricity, etc.) to satisfy the loads of buildings for example, central plant 118 may include a heater subplant, a heat recovery chiller subplant, a chiller subplant, a cooling tower subplant, a hot thermal energy storage (TES) subplant, and a cold thermal energy storage (TES) subplant”);
Regarding claim 18, One or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (ELBSAT discloses Column 3, line 25-28, “Another implementation of the present disclosure is one or more non-transitory computer-readable media containing
program instructions that, when executed by one or more processors, cause the one or more processors to perform operations.”);
generating first forecasted ranges for amounts of energy consumption or carbon
emissions for a plurality of time steps in a time period (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45resource sold to energy purchasers”);
generating second forecasted ranges for amounts of energy production or carbon capture for a plurality of time steps in a time period (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. ELBSAT discloses Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
providing a control strategy based on the first forecasted ranges for amounts of energy consumption or carbon emissions and the second forecasted for amounts of energy production or carbon capture, the control strategy configured to drive a cumulative difference between the energy production or carbon capture and the energy consumption or carbon emissions over the time period to a target. (Plant dispatch is read as the building operations and receiving the information from the optimizer as providing a strategy Calculation of column 33 shows many forecasted ranges and the equation show summing the net energy taking into account a plurality of times and the production and consumption. ELBSAT discloses Fig. 8, item 830 plant dispatch and column 31, line 11 – line 18, “This process may be repeated for each subsequent optimization period to generate updated resource allocations and to select portions of each resource allocation to send to 15 plant dispatch 830. Plant dispatch 830 includes the first b time steps 818-824 from each of optimization periods...”);
Regarding claim 20, the limitations of claim 18 have been discussed above. The one or more non-transitory computer-readable media of Claim 16, wherein providing the control strategy comprises implementing curtailment actions based on the target. (ELBSAT discloses column 16, line 66 - column 17, line 6. “In some embodiments, storage subplants 530 are used by energy storage system 500 to take advantage of incentive based demand response (IBDR) programs. IBDR programs provide incentives to customers who have the capability to store energy, generate energy, or curtail energy usage upon request.” Fig. 8, item 830 plant dispatch and column 31, line 11 – line 18 discloses “This process may be repeated for each subsequent optimization period to generate updated resource allocations and to select portions of each resource allocation to send to 15 plant dispatch 830. Plant dispatch 830 includes the first b time steps 818-824 from each of optimization periods...”);
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 3-5, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over ELBSAT (US20190340709A1) in view of WANG (CN 114372558 A).
Regarding claim 3, the limitations of claim 1 have been discussed above. ELBSAT discloses consumption and production modeling based on a mean and deviation (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
ELBSAT does not disclose expressly fitting a mean model to historical energy consumption data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period is performed using the Gaussian model.
WANG discloses further comprising: fitting a mean model to historical energy consumption data (¶[n0008] Based on the predicted electricity consumption and the actual electricity consumption, the corresponding relative error re<sub>j</sub>(t) of each prediction model is calculated, j＝1,2,...,n, where n is the total number of prediction models [n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly);
fitting a deviation model to error in outputs of the mean model (¶[n0008] Based on the predicted electricity consumption and the actual electricity consumption, the corresponding relative error re<sub>j</sub>(t) of each prediction model is calculated, j＝1,2,...,n, where n is the total number of prediction models [n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly);
and converting a combination of the mean model and the deviation model into a Gaussian model (¶ [n0012] “Furthermore, the prediction model includes multiple models such as the gray-scale prediction model, the Gaussian process regression model, and the long short-term memory network model [n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly ");
wherein generating the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period is performed using the Gaussian model wherein generating the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period is performed using the Gaussian model. ([n0012] “In the formula, true_val(t) is the actual electricity consumption at time t, and pre<sub>j</sub> (t) is the predicted electricity consumption at time t for the j-th prediction model”).
ELBSAT and WANG are analogous art because they are from the same field of endeavor: load forecasting and modeling.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify ELBSAT to fitting a mean model to historical energy consumption data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period is performed using the Gaussian model of WANG to produce the invention of claim 3.
The suggestion/motivation for doing so would have been increase the accuracy of the models. WANG ¶ n0004, “The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method, medium and equipment for predicting residential electricity load based on multi-model fusion that has a wide range of applications and high prediction accuracy.”
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine ELBSAT and WANG for the benefit of fitting a mean model to historical energy consumption data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period is performed using the Gaussian model to obtain the invention as specified in the claim 3.
Regarding claim 4, the limitations of claim 1 have been discussed above. ELBSAT discloses consumption and production data modeling based on a mean and deviation (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
ELBSAT does not disclose expressly further comprising: fitting a mean model to historical energy production data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; and wherein generating the second forecasted ranges for amounts of energy production for the plurality of time steps in the time period is performed using the Gaussian model.
WANG discloses further comprising: fitting a mean model to historical energy production data; (¶ [n0003] discloses that the data is historical residential data. ¶[n0008] Each prediction model is based on the residential electricity consumption data x<sub>i</sub>, i＝1, 2,...,t-1 to predict the predicted electricity consumption at time t, where the actual electricity consumption at time t is known. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.);
fitting a deviation model to error in outputs of the mean model; ("¶[n0019] Determine the relationship between each relative error re<sub>j</sub>(t) and 0. If all relative errors have re<sub>j</sub>(t) >= 0, then fusion(t+1) = max(pre<sub>j</sub>(t+1)), where max (·) represents taking the maximum value; if all relative errors have re<sub>j</sub>(t) < 0, then fusion(t+1) = min(pre<sub>j</sub>(t+1)), where min(·) represents taking the minimum value; otherwise, ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.");
and converting a combination of the mean model and the deviation model into a Gaussian model ("¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.");
wherein generating the second forecasted ranges for amounts of energy production for the plurality of time steps in the time period is performed using the Gaussian model (ELBSAT teaches the use of both production and consumption data, PHOTSITA would know that the data would be interchangeable to achieve predictable results "¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.");
ELBSAT and WANG are analogous art because they are from the same field of endeavor: load forecasting and modeling.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify ELBSAT with WANG for fitting a mean model to historical energy production data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; and wherein generating the second forecasted ranges for amounts of energy production for the plurality of time steps in the time period is performed using the Gaussian model.
The suggestion/motivation for doing so would have been increase the accuracy of the models. WANG ¶ n0004, “The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method, medium and equipment for predicting residential electricity load based on multi-model fusion that has a wide range of applications and high prediction accuracy.”
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine ELBSAT and WANG for the benefit of fitting a mean model to historical energy production data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; and wherein generating the second forecasted ranges for amounts of energy production for the plurality of time steps in the time period is performed using the Gaussian model to obtain the invention as specified in the claim 4.
Regarding claim 5, the limitations of claim 1 have been discussed above. ELBSAT discloses consumption and production data modeling based on a mean and deviation (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
ELBSAT does not disclose expressly wherein the first forecasted ranges and the second forecasted ranges are associated with confidence intervals of predictive models for predicting the energy consumption and the energy production.
WANG discloses expressly wherein the first forecasted ranges and the second forecasted ranges are associated with confidence intervals of predictive models for predicting the energy consumption and the energy production (¶[n0021] “the confidence interval for the predicted residential electricity consumption at time t+1 is calculated based on the Gaussian process regression model");
ELBSAT and WANG are analogous art because they are from the same field of endeavor: load forecasting and modeling.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify ELBSAT with WANG wherein the first forecasted ranges and the second forecasted ranges are associated with confidence intervals of predictive models for predicting the energy consumption and the energy production.
The suggestion/motivation for doing so would have been increase the accuracy of the models. WANG ¶ n0004, “The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method, medium and equipment for predicting residential electricity load based on multi-model fusion that has a wide range of applications and high prediction accuracy.”
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine ELBSAT with WANG for the benefit of wherein the first forecasted ranges and the second forecasted ranges are associated with confidence intervals of predictive models for predicting the energy consumption and the energy production to obtain the invention as specified in the claim 5.
Regarding claim 14, the limitations of claim 12 have been discussed above. ELBSAT discloses consumption and production data modeling based on a mean and deviation (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
ELBSAT does not disclose expressly wherein the processing circuitry is further programmed to fit a mean model to historical energy consumption data; fit a deviation model to error in outputs of the mean model; and convert a combination of the mean model and the deviation model into a Gaussian model; wherein the processing circuitry is programmed to generate the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period using the Gaussian model.
WANG discloses wherein the processing circuitry is further programmed to
fit a mean model to historical energy consumption data (¶[n0003] discloses that the data is historical residential data. [n0008] Each prediction model is based on the residential electricity consumption data x<sub>i</sub>, i＝1, 2,...,t-1 to predict the predicted electricity consumption at time t, where the actual electricity consumption at time t is known.);
fit a deviation model to error in outputs of the mean model; (" ¶[n0019] Determine the relationship between each relative error re<sub>j</sub>(t) and 0. If all relative errors have re<sub>j</sub>(t) >= 0, then fusion(t+1) = max(pre<sub>j</sub>(t+1)), where max (·) represents taking the maximum value; if all relative errors have re<sub>j</sub>(t) < 0, then fusion(t+1) = min(pre<sub>j</sub>(t+1)), where min(·) represents taking the minimum value; otherwise,¶ [n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly."); and
convert a combination of the mean model and the deviation model into a Gaussian model (("¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.));
wherein the processing circuitry is programmed to generate the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period using the Gaussian model (¶[n0008] Each prediction model is based on the residential electricity consumption data x <sub> I </sub>, i＝1, 2, ..., t-1 to predict the predicted electricity consumption at time t, where the actual electricity consumption at time t is known. ¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly).
ELBSAT and WANG are analogous art because they are from the same field of endeavor: load forecasting and modeling.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify ELBSAT with WANG for wherein the processing circuitry is further programmed to fit a mean model to historical energy consumption data; fit a deviation model to error in outputs of the mean model; and convert a combination of the mean model and the deviation model into a Gaussian model; wherein the processing circuitry is programmed to generate the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period using the Gaussian model.
The suggestion/motivation for doing so would have been increase the accuracy of the models. WANG ¶ n0004, “The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method, medium and equipment for predicting residential electricity load based on multi-model fusion that has a wide range of applications and high prediction accuracy.”
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine ELBSAT and WANG for the benefit of wherein the processing circuitry is further programmed to fit a mean model to historical energy consumption data; fit a deviation model to error in outputs of the mean model; and convert a combination of the mean model and the deviation model into a Gaussian model; wherein the processing circuitry is programmed to generate the first forecasted ranges for amounts of energy consumption for the plurality of time steps in the time period using the Gaussian model to obtain the invention as specified in the claim 14.
Regarding claim 19, the limitations of claim 18 have been discussed above. ELBSAT discloses consumption and production data modeling based on a mean and deviation (The high level optimizer is depicted as taking in both consumption data and production data to perform math operations. Fig. 8 and column 31, line 34 -line 49 “High level optimizer 632 may determine an optimal resource allocation across energy storage system 500 as a function of the load and rate predictions, the incentive predictions, and the subplant 40 curves. The optimal resource allocation may include an amount of each resource purchased from utilities 510, an amount of each input and output resource of generator subplants 520, an amount of each resource stored or withdrawn from storage subplants 530, and/or an amount of each 45 resource sold to energy purchasers”);
ELBSAT does not disclose expressly wherein the operations further comprise: fitting a mean model to historical energy consumption or carbon emissions data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for the plurality of time steps in the time period is performed using the Gaussian model.
WANG discloses fitting a mean model to historical energy consumption or carbon emissions data; ¶ [n0008] Each prediction model is based on the residential electricity consumption data x<sub>i</sub>, i＝1, 2,...,t-1 to predict the predicted electricity consumption at time t, where the actual electricity consumption at time t is known. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly."));
fitting a deviation model to error in outputs of the mean model; ("¶[n0019] Determine the relationship between each relative error re<sub>j</sub>(t) and 0. If all relative errors have re<sub>j</sub>(t) >= 0, then fusion(t+1) = max(pre<sub>j</sub>(t+1)), where max (·) represents taking the maximum value; if all relative errors have re<sub>j</sub>(t) < 0, then fusion(t+1) = min(pre<sub>j</sub>(t+1)), where min(·) represents taking the minimum value; otherwise,¶ [n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.")
and converting a combination of the mean model and the deviation model into a Gaussian model; (" ¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly.);
wherein generating the first forecasted ranges for the plurality of time steps in the time period is performed using the Gaussian model. (¶[n0008] Each prediction model is based on the residential electricity consumption data x <sub> I </sub>, i＝1, 2, ..., t-1 to predict the predicted electricity consumption at time t, where the actual electricity consumption at time t is known. ¶[n0021] the confidence interval for the predicted residential electricity consumption at time t+1 Is calculated based on the Gaussian process regression model. ¶[n0022] Furthermore, the reliability of the prediction method is determined by the absolute average value of the relative error, and then the weights of each prediction model are adjusted accordingly).
ELBSAT and WANG are analogous art because they are from the same field of endeavor: load forecasting and modeling.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify ELBSAT with WANG for wherein the operations further comprise: fitting a mean model to historical energy consumption or carbon emissions data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for the plurality of time steps in the time period is performed using the Gaussian model.
The suggestion/motivation for doing so would have been increase the accuracy of the models. WANG ¶ n0004, “The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method, medium and equipment for predicting residential electricity load based on multi-model fusion that has a wide range of applications and high prediction accuracy.”
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine ELBSAT for the benefit of wherein the operations further comprise: fitting a mean model to historical energy consumption or carbon emissions data; fitting a deviation model to error in outputs of the mean model; and converting a combination of the mean model and the deviation model into a Gaussian model; wherein generating the first forecasted ranges for the plurality of time steps in the time period is performed using the Gaussian model to obtain the invention as specified in the claim 19.
Conclusion
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/C.D.C./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
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