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 .
This action is responsive to the RCE to U.S. Patent application 17/606,597 filed on 04 September 2025. Claims 1, 7, 11, 12, 19, 20, 26, 29, 31, 38, 39, 45, 48, 50, and 136-138 are pending in the case. Claims 1, 7, 11, 20, 26, 39, 45, and 136 have been amended. Claims 1, 20 and 39 are independent claims. Claims 4, 6, 23, 25, 42, and 44 have been cancelled.
This office action is Non-Final.
Response to Arguments/Remarks
35 U.S.C. 101
Applicant’s amendments have been fully considered and are persuasive. The rejections are withdrawn.
35 U.S.C. 103
Applicant’s prior art arguments have been considered and they are persuasive.
Applicant argues (pg. 11) that the cited references do not teach the comparison of forecasted values to the actual values for a future time period.
Examiner agrees. Accordingly, a new reference, Abe, has been added to the rejection, as further detailed below.
The foregoing applies to all independent claims and their dependent claims.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Doherty et al. (US Patent Application Publication US 20190165580 A1), referred to as Doherty herein.
Abe et al. (US 2004/0254899 A1) hereinafter known as Abe.
Nakayama et al. (US Patent Application Publication US 20190148945 A1), referred to as Nakayama herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7, 11, 12, 19, 20, 26, 29, 31, 38, 39, 45, 48, 50, and 136-138 are rejected under 35 U.S.C. 103 as being unpatentable over Doherty in view of Abe in view of Nakayama.
Regarding independent claim 1, Doherty discloses “A method of reserving a capacity of one or more energy storage devices (Doherty, at ¶ [0005], creating and executing an optimal dispatch schedule for controlling an operation of one or more energy storage systems.), comprising:
forecasting, based on past electricity demand of a site, future electricity demand of the site over a future time period (id. at ¶ [0071], a forecast engine use data collected from the Data Input Interface and Data Historian to generate a forecast, the forecast includes any possible parameter such as grid net load, renewable generation, non-renewable generation, market pricing, customer load, to name a few.);
determining a forecasting error between the forecasted future electricity demand and an actual electricity demand of the site over the future time period, ... (id. at ¶¶ [0085] and [0092], forecast engine uses various possible forecasting methods and algorithms to calculate forecast uncertainties, then forecast aggregation combine forecast uncertainties respectively associated with the aggregated forecasts.);
adjusting, based on the forecasting error, a target state of charge (SOC) of one or more energy storage devices (id. at ¶ [0098], based on the forecast accuracy, power requirements of ESS (energy storage system) can be predicated within a +/−20% window and appropriate reserves must be allocated, a chance-constraint algorithm to create stochastic system constraints (e.g., state of charge).);
reserving, based on the adjusted target SOC, a capacity of the one or more energy storage devices, comprising: (id. at ¶ [0098], a chance constraint algorithm can be used to rigorously calculate the statistically optimal amount of reserves.).” Although Doherty does not explicitly use the terminology “forecasting error” in the disclosure, Doherty further teaches calculating a future load equivalent to the future electricity demand (id. at ¶ [0088]), and aggregating the internally generated forecasts to combine forecast uncertainties respectively associated with the aggregated forecasts, e.g. when applied historically, the residual error of the linear regression with historical average residual forecast model can be a periodic function to which historical average forecasting may be applied to predict future residual error (id. at ¶¶ [0090] and [0092]).
...
...
Accordingly, it would have been obvious to one of ordinary skill in the art at the filing date of the invention to modify Doherty’s method of calculating residual error of the linear regression with historical average residual forecast model can be a periodic function to which historical average forecasting may be applied to predict future residual error as forecasting error because combining the predicting future residual error using known historical average residual forecast model to yield predictable results to one of ordinary skill in the art.
Doherty does not explicitly teach but Abe teaches:
... , by comparing, after the future time period, the forecasted electricity demand with the actual electricity demand observed during the future time period; (Abe: Fig. 2 and ¶[0073]-¶[0076]; Abe teaches calculating a prediction error by comparing the value of the demand prediction result with the value of the demand track record; wherein the data may be hourly data, day to day, etc...)
Doherty and Abe are in the same field of endeavor as the present invention, as the references are directed to prediction of energy demands. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine forecasting electricity demand to adjust a target state of charge as taught in Doherty with comparing forecasting data to actual data in a predetermined time period as taught in Abe. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Doherty to include teachings of Abe, because the combination would changing electricity usage based on demand prediction, as suggested by Abe: ¶[0017]-¶[0018].
Doherty in view of Abe does not explicitly teach but Nakayama teaches:
increasing a demand threshold below which an electricity demand of the site is met by one or more grid-based electricity sources, and above which the electricity demand is met by the one or more energy storage devices; (Nakayama: ¶[0032], ¶[0035], and ¶[0036]; Nakayama teaches that optimize a demand charge threshold (DCT) used by a behind the meter energy management system to determine at what point, and to what extent, the power demand by the customer should be supplemented with power from the batteries and PV cells (id. at ¶ [0032]), the inverter controller may control the inverter according to the DCT optimized by the multi-layer power demand management controller to discharge the batteries when power demand rises above the DCT (id. at ¶ [0035]), by preventing underestimation of the DCT, the BTM-EMS can prevent unnecessary charging and discharging of the batteries, and thereby reducing degradation of the batteries (id. at ¶ [0036]).
determining that the electricity demand of the site has dropped below the demand threshold; and in response thereto, recharging the one or more energy storage devices. (Nakayama: ¶[0098] and ¶[0107]; Nakayama teaches that having determined the demand threshold (DT) and the degree to which the battery can supplement energy to accommodate the DT according to a load reduction capability factor (LRC), the net demand Nd is compared to the DT. Depending on the relative magnitude of the net demand Nd and the DT, the controller can select a process for determining charge amounts.)
Nakayama is in the same field of systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine forecasting electricity demand to adjust a target state of charge as taught in Doherty with preventing unnecessary charging as well as supplementing energy as taught in Nakayama. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Doherty and Abe to include teachings of Nakayama, because it will allow increasing the lifespan of batteries and charging of the batteries could be maximized before greater power demand reached. (Nakayama, at ¶[0036] and ¶[0107]).
Independent claim 20 is directed towards a demand management system equivalent to a method found in claim 1, and is therefore similarly rejected.
Independent claim 39 is directed towards a computer-readable medium equivalent to a method found in claim 1, and is therefore similarly rejected.
Regarding claim 7, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 1. However, Doherty does not explicitly teach “further comprising: determining that the one or more energy storage devices are fully recharged; and in response thereto, decreasing the demand threshold.” (Nakayam teaches that the forecasted load and the battery state of charge (SOC) are used to calculate an adjustment value for each time-of-use period of the coming day. The adjustment value decreases the initial DCT to match decreased load forecasts to compensate for overestimation of the initial DCT (id. at ¶ [0043]).)
Regarding claim 11, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 1. Doherty further teaches “wherein the past electricity demand extends from a past point in time to a current point in time (Doherty, at ¶¶ [0079] and [0091], teaches when forecasting electricity demand, a human operator to monitor both the current and historical performance and state of some or all of the components of the system or the entire system, the User Visualization and Control Interface is implemented via a web-based tool, which is configured to provide easy access to well-curated data sets as well as more universal access to any data stored in the Data Historian, and the current and 15 previous time steps of electrical grid load, temperature, dew point is inputted to train a recursive neural network.).”
Regarding claim 12, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 1 and it dependent claim 11. Doherty further teaches “wherein forecasting the future electricity demand comprises inputting the past electricity demand data to a trained machine learning model comprised in a set of one or more trained machine learning models (Doherty, at ¶ [0091], teaches any neural network architecture may be utilized in this method as the forecast problem is a supervised regression problem of predicting a continuous output based on a set of training data that both inputs and outputs for a large historical dataset, one may construct and train a recursive neural network that has as input the current and 15 previous time steps of electrical grid load, temperature, dew point, as well as the 24-hour forecast for temperature and dew point, whereas exemplary neural network architectures used in this method may include feed-forward networks, recursive networks, recursive networks with external inputs, recurrent neural networks (RNNs), such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), Sequence-to-Sequence Learning, to name a few.).”
Claim 26 is directed towards a demand management system equivalent to a method found in claim 7, and is therefore similarly rejected.
Claim 31 is directed towards a demand management system equivalent to a method found in claim 12, and is therefore similarly rejected.
Claim 45 is directed towards a computer-readable medium equivalent to a method found in claim 7 respectively, and is therefore similarly rejected.
Claim 50 is directed towards a computer-readable medium equivalent to a method found in claim 12, and is therefore similarly rejected.
Regarding claim 19, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 1 and it dependent claim 11. Doherty further teaches “wherein the past electricity demand data further comprises data representing one or more of: weather; temperature; humidity; atmospheric pressure; months of a year; time of day; dates; days of a week; and the future time period (Doherty, at ¶ [0088], teaches the Historical Average Forecast technique calculates future values by calculating a weighted average of past values while keeping certain variable parameters constant, the variable parameters that are kept constant during the calculation of future values may include time ranges, day types, temperature ranges, weather condition ranges, time ranges since sunrise, and/or event conditions, to name a few.).”
Claim 38 is directed towards a demand management system equivalent to a method found in claim 19, and is therefore similarly rejected.
Regarding claim 29, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 20. Doherty further teaches “wherein forecasting the future electricity demand comprises: obtaining past electricity demand data representing past electricity demand of the site over a past time period; and forecasting, based on the past electricity demand data, the future electricity demand (Doherty, at ¶¶ [0077], [0084]-[0085], and [0088], teaches the Data Historian is configured to supply historical datasets to various components of the system, such as the Forecast Engine and User Visualization and Control Interface, the Forecast Engine is configured to receive up-to-date live data streams from the Data Input Interface and also to query the Data Historian for historical data sets, the Forecast Engine uses various possible forecasting methods and algorithms to generate a forecast based on input data, includes Historical Average Forecast technique and Linear Regression with Historical Average Residual Forecast technique, the Historical Average Forecast technique calculates future values by calculating a weighted average of past values while keeping certain variable parameters constant.).”
Claim 48 is directed towards a computer-readable medium equivalent to a method found in claim 29, and is therefore similarly rejected.
Regarding claim 137, Doherty in view of Abe in view of Nakayama teaches all the limitation of independent claim 20. Doherty further teaches “wherein the method further comprises: using the one or more grid-based electricity sources to meet electricity demand of the site that is below the demand threshold; and using the one or more energy storage devices to meet electricity demand of the site that is above the demand threshold (Doherty, at ¶ [0109], teaches one of rule-based modes called “Peak Shaving Mode” is a closed-loop mode where the ESS continuously monitors the electrical grid load and discharges only as needed to maintain that net load below a configurable parameter called the “peak shaving threshold.”).”
Claim 138 is directed towards a non-transitory computer-readable medium equivalent to a demand management system found in claim 137, and is therefore similarly rejected.
Regarding claim 136, Doherty in view of Nakayama teaches all the limitation of independent claim 1 and its dependent claim 4. Doherty further teaches “further comprising: using the one or more grid-based electricity sources to meet electricity demand of the site that is below the demand threshold; and using the one or more energy storage devices to meet electricity demand of the site that is above the demand threshold (Doherty, at ¶ [0109], teaches one of rule-based modes called “Peak Shaving Mode” is a closed-loop mode where the ESS continuously monitors the electrical grid load and discharges only as needed to maintain that net load below a configurable parameter called the “peak shaving threshold.”).”
Conclusion
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/ALEKSEY OLSHANNIKOV/Primary Examiner, Art Unit 2118