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 .
DETAILED ACTION
2. The action is responsive to the communications filed on 7/31/2026. Claims 1-23 are pending in the case. Claims 1, 2, 11, 13, 14, 15, 18, 19 are amended. Claims 22-23 are newly added. Claims 1, 15, 18 are independent claims. Claims 1-23 are rejected.
Summary of claims
3. Claims 1-23 are pending,
Claims 1, 2, 11, 13, 14, 15, 18, 19 are amended,
Claims 22-23 are newly added,
Claims 1, 15, 18 are independent claims,
Claims 1-23 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 7/31/2026, with respect to the rejection(s) of claim(s) 1-23 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
Applicant argued on pages 9-10 that the cited references including Cruickshank did not teach the newly amended features in claim 1, such as, “quantifying net uncertainty for a predetermined time period based on an aggregation of the risk inputs; and dynamically setting reserve requirements for an energy grid based on the quantified net uncertainty.” Applicant argued Cruickshank only teaches observing uncertainty. Examiner respectfully submits that Cruickshank discloses dynamically optimizing production and consumption of energy (Abstract) using artificial intelligence or machine learning ([0027]), uncertainty is observed based on different risk parameters ([0089]), further, Cruickshank discloses quantifying load shaping opportunities and uncertainty across a varying number of homes ([0138]), for example, an uncertainty is observed by looking at electric energy usage for hearting DHW over a time interval ([0169]), that is, in Cruickshank, risk parameters may be used as input to determine the observed uncertainty in order to generate optimized energy production and consumption. Cruickshank does not clearly teach quantifying net uncertainty, an analogous art of predicting energy generation and consumption using machine learning, Tennant (US Publication 20230139514) is cited to disclose a formulaic method using weather parameters such as wind, rain, etc., to produce the prediction of energy availability over a duration of time, the formula may provide degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions (Tennant: [0117]); please note “degrees of uncertainty” determined in the formula is the quantified uncertainty.
With respect to claim 2, Examiner respectfully submits that Tennant discloses providing the predicted energy profiles over the course of a day (Tennant: [0117]) using machine learning model (Tennant: [0023]).
With respect to claim 11, Examiner respectfully submits that Cruickshank discloses observing an uncertainty for electric energy usage data over the interval 00:00 to 00:15 on Apr. 1, 2012 ([0169]), and wavelet analysis for analyzing, visualizing, and simulating localized variations of power within a time series ([0189]), that is, in Cruickshank, forecast data is generated at a time series interval.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 1-12, 15-16, 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Robert Cruickshank (US Publication 20210296897 A1, hereinafter Cruickshank), and in view of Kevin Meagher et al (US Publication 20170046458 A1, hereinafter Meagher), and Robert Tennant (US Publication 20230139514 A1, hereinafter Tennant).
As for independent claim 1, Cruickshank discloses: A computer implemented method for dynamically setting reserve requirements for an energy grid based on net uncertainty (Abstract, A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy; [0089], Schedules were chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which tracked errors in the forecast of power requirements as they were revealed, and which could be time-coupled) comprising: receiving risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters) including at least three of the following: (a) one or more load forecasts ([0027], the forecast load is expected to be X), (b) one or more wind forecasts ([0080], a review of forecast wind), (c) one or more solar forecasts ([0356], utility wind, utility solar), (d) one or more generator availability risk forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), (e) one or more generator fail-to-start or fail-to-run predictions, (f) one or more net scheduled interchange forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), or (g) one or more transmission congestion forecasts ([0022], Power quality sensors detect, predict, and then avoid hours of congestion by applying a local OLS designed to mitigate congestion by keeping load under congestion limits); [quantifying] net uncertainty for a predetermined time period based on [an aggregation of] the risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters); and dynamically setting reserve requirements for an energy grid based on the quantified net uncertainty (Abstract, A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy; [0089], Schedules were chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which tracked errors in the forecast of power requirements as they were revealed, and which could be time-coupled; [0138], quantifying load shaping opportunities and uncertainty across a varying number of homes).
Cruickshank does not expressly disclose equipment failure prediction, in an analogous art of predicting energy data using machine learning, Meagher discloses: (e) one or more generator fail-to-start or fail-to-run predictions (Meagher: [0074], informative information concerning potential/predicted failures before they occur),
Cruickshank and Meagher are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Meagher to include taking in account generator failure predictions. It would provide Cruickshank’s method with enhanced capabilities of performing energy analytics with more risk concerns so the operator may minimize the risk of catastrophic equipment failure.
Further, Cruickshank discloses observed uncertainty but does not clearly disclose quantifying uncertainty, in an analogous art of predicting energy generation and consumption using machine learning, Tennant discloses: quantifying net uncertainty for a predetermined time period based on an aggregation of the risk inputs (Tennant: [0117], a formulaic method using weather parameters such as wind, rain, etc., to produce the prediction of energy availability over a duration of time, the formula may provide degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions; please note “degrees of uncertainty” determined in the formula is the quantified uncertainty, and the formula is an aggregation of input parameters);
Cruickshank and Tennant are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Tennant to include providing the degree of uncertainty based on input parameters. It would provide Cruickshank’s method with enhanced capabilities of adjusting the energy conservation strategy using the tool in order to optimize the use of grid power as suggested by Tennant ([0070], [0122]).
As for claim 2, Cruickshank-Meagher-Tennant discloses: further comprising using a machine learning model (Cruickshank: [0027], use of artificial intelligence or machine learning) to provide a predicted daily risk profile based on the quantified net uncertainty (Tennant: [0023], The renewable energy prediction model may be a neural network or other machine learning model; [0117], This can give smoother predicted energy profiles over the course of a day…Buffer parameters, such as a wind buffer or a solar buffer, may also be used to parameterise the formula by providing degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions).
As for claim 3, Cruickshank-Meagher-Tennant discloses: wherein the machine learning model predicts high, medium or low uncertainty levels based on which normal or high reserve requirements will be set for the day-ahead and real-time markets (Cruickshank: [0348], In three scenarios of RES penetration (A) low, (B) medium, and (C) high).
As for claim 4, Cruickshank-Meagher-Tennant discloses: wherein the machine learning model comprises one or more of: a tree-based machine learning model; or a deep neural network model (Meagher: [0346], the self-executing algorithm is in a force directed layout format. In another embodiment, the self-executing algorithm is in a tree layout format).
As for claim 5, Cruickshank-Meagher-Tennant discloses: wherein the machine learning model utilizes a feature engineering gradient model that ties multiple approaches to build a set of features to forecast net uncertainty (Cruickshank: [0165], A simple auto-regressive process was applied to determine if energy usage could be successfully regressed on its own lagged (i.e., prior) values; [0241], any air-conditioning setpoint adjustment has time-lagged effects because the aggregate building responses are slower than the 5-minute price changes).
As for claim 6, Cruickshank-Meagher-Tennant discloses: wherein the set of features comprise: lagged features (Cruickshank: [0165], A simple auto-regressive process was applied to determine if energy usage could be successfully regressed on its own lagged (i.e., prior) values; [0241], any air-conditioning setpoint adjustment has time-lagged effects because the aggregate building responses are slower than the 5-minute price changes); seasonality features, including indicators for daily, weekly and seasonal patterns (Cruickshank: [0061], a function of time-of-day, day-of-week, season, desired comfort levels, and load management strategies such as the scheduling and control of appliances); non-linear and interaction terms (Cruickshank: [0137], residential loads often exhibit non-stationary energy usage behavior based on the unknown needs of occupants that may vary by hour of the day, day of the week, seasons, holidays, shopping schedules, home cleaning schedules, and vacations); and external regressors.
As for claim 7, Cruickshank-Meagher-Tennant discloses: wherein external regressors include one or more of wind forecast, solar forecast, load forecast and weather forecast (Cruickshank: [0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers).
As for claim 8, Cruickshank-Meagher-Tennant discloses: wherein the machine learning model utilizes a feature engineering in deep neural network model that incorporates regressors, autoregressive inputs and time-based features (Cruickshank: [0165], a recent development in wavelet-based simulation methods, was applied and compared to the auto-regressive and autoregressive integrated moving average (ARIMA) simulations of the wavelet decomposed energy usage of a DHW heater over the same hour of a day for an entire year).
As for claim 9, Cruickshank-Meagher-Tennant discloses: wherein the regressors represent factors including one or more of weather conditions, load forecast and weather forecast (Cruickshank: [0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers).
As for claim 10, Cruickshank-Meagher-Tennant discloses: wherein the time-based features include weather-based seasonality components (Cruickshank: [0469], The simulation framework could be expanded to scale the aggregate city-to-weather zone load based on Winter, Spring, Summer, and Fall seasons. For example, in summer, the load simulation model could better fit the data by using scaling that reflects the high use of air-conditioning).
As for claim 11, Cruickshank-Meagher-Tennant discloses: wherein the machine learning model generates a time series forecast of uncertainty in megawatts at a predetermined time-based granularity (Cruickshank: [0169], observing an uncertainty for electric energy usage data over the interval 00:00 to 00:15 on Apr. 1, 2012; [0189] wavelet analysis for analyzing, visualizing, and simulating localized variations of power within a time series; please note in Cruickshank, forecast data is generated at a time series interval).
As for claim 12, Cruickshank-Meagher-Tennant discloses: wherein the time series forecast of uncertainty is translated into low, medium or high levels of uncertainty (Cruickshank: [0348], In three scenarios of RES penetration (A) low, (B) medium, and (C) high).
As for independent claim 15, Cruickshank discloses: A computer implemented method for operating an energy grid controller (Abstract, A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy; [0089], Schedules were chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which tracked errors in the forecast of power requirements as they were revealed, and which could be time-coupled) comprising: receiving risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters) including one or more of the following: (a) one or more load forecasts ([0027], the forecast load is expected to be X), (b) one or more wind forecasts ([0080], a review of forecast wind), (c) one or more solar forecasts ([0356], utility wind, utility solar), (d) one or more generator availability risk forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), (e) one or more generator fail-to-start or fail-to-run predictions, (f) one or more net scheduled interchange forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), or (g) one or more transmission congestion forecasts ([0022], Power quality sensors detect, predict, and then avoid hours of congestion by applying a local OLS designed to mitigate congestion by keeping load under congestion limits); [quantifying] a net uncertainty for a predetermined time period based on [an aggregation of] the risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters); and providing a graphical display comprising a geographic map overlayed with information pertaining to the quantified one or more of net uncertainty ([0066], a graphical user interface (GUI), cloud-based energy controller, cloud appliance, data center, dashboard; [0183], Heat maps can be helpful in viewing energy usage over multiple intervals. An energy use heat map displays time on both axes, e.g., individual days on the horizontal axis and individual 15-minute intervals on the vertical axis. As depicted near the center of FIG. 6, Day 4 had several periods of high energy use; [0138], quantifying load shaping opportunities and uncertainty across a varying number of homes).
Cruickshank does not expressly disclose equipment failure prediction, in an analogous art of predicting energy data using machine learning, Meagher discloses: (e) one or more generator fail-to-start or fail-to-run predictions (Meagher: [0074], informative information concerning potential/predicted failures before they occur),
Cruickshank and Meagher are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Meagher to include taking in account generator failure predictions. It would provide Cruickshank’s method with enhanced capabilities of performing energy analytics with more risk concerns so the operator may minimize the risk of catastrophic equipment failure.
Further, Meagher discloses: and providing a graphical display comprising a geographic map overlayed with information (Meagher: [0009], graphical displays (e.g., two-dimensional and three-dimensional views) of the operational aspects of an electrical system greatly enhances the ability of a system operator, owner and/or executive to understand the health and predicted performance of the electrical system);
Cruickshank and Meagher are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Meagher to include graphical displays comprising energy information. It would provide Cruickshank’s method with enhanced capabilities of visualization of the performance of the energy system.
Further, Cruickshank discloses observed uncertainty but does not clearly disclose quantifying uncertainty, in an analogous art of predicting energy generation and consumption using machine learning, Tennant discloses: quantifying net uncertainty for a predetermined time period based on an aggregation of the risk inputs (Tennant: [0117], a formulaic method using weather parameters such as wind, rain, etc., to produce the prediction of energy availability over a duration of time, the formula may provide degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions; please note “degrees of uncertainty” determined in the formula is the quantified uncertainty, and the formula is an aggregation of input parameters);
Cruickshank and Tennant are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Tennant to include providing the degree of uncertainty based on input parameters. It would provide Cruickshank’s method with enhanced capabilities of adjusting the energy conservation strategy using the tool in order to optimize the use of grid power as suggested by Tennant ([0070], [0122]).
As for claim 16, Cruickshank-Meagher-Tennant discloses: wherein the graphical display comprises a fail-to-start dashboard that displays a geographic map (Cruickshank: Fig. 15) overlayed with visual fail-to-start risk information (Meagher: Fig. 25).
As for independent claim 18, Cruickshank discloses: A computer implemented methods for providing an uncertainty platform for an energy grid controller (Abstract, A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy; [0089], Schedules were chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which tracked errors in the forecast of power requirements as they were revealed, and which could be time-coupled) comprising: receiving risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters) including (a) one or more load forecasts ([0027], the forecast load is expected to be X), (b) one or more wind forecasts ([0080], a review of forecast wind), (c) one or more solar forecasts ([0356], utility wind, utility solar), (d) one or more generator availability risk forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), (e) one or more generator fail-to-start or fail-to-run predictions, (f) one or more net scheduled interchange forecasts ([0058], using, for example, historical load, weather, building stock attributes, operating schedules of electrical devices, distribution feeder models, and generator constraints to quantify the value of residential load shaping for decision and policymakers), and (g) one or more transmission congestion forecasts ([0022], Power quality sensors detect, predict, and then avoid hours of congestion by applying a local OLS designed to mitigate congestion by keeping load under congestion limits); [quantifying] net uncertainty for a predetermined time period based on an aggregation of the risk inputs ([0089], The model indicated a relative benefit of time-coupled response to uncertainty observed under different approximate treatments of the chance constraint, for different risk parameters); and providing dynamic risk outputs based on the quantified net uncertainty, including one or more of (a) a forecast scenario, (b) reserve requirements or (c) reserve margin thresholds (Abstract, A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy; [0089], Schedules were chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which tracked errors in the forecast of power requirements as they were revealed, and which could be time-coupled).
Cruickshank does not expressly disclose equipment failure prediction, in an analogous art of predicting energy data using machine learning, Meagher discloses: (e) one or more generator fail-to-start or fail-to-run predictions (Meagher: [0074], informative information concerning potential/predicted failures before they occur),
Cruickshank and Meagher are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Meagher to include taking in account generator failure predictions. It would provide Cruickshank’s method with enhanced capabilities of performing energy analytics with more risk concerns so the operator may minimize the risk of catastrophic equipment failure.
Further, Cruickshank discloses observed uncertainty but does not clearly disclose quantifying uncertainty, in an analogous art of predicting energy generation and consumption using machine learning, Tennant discloses: quantifying net uncertainty for a predetermined time period based on an aggregation of the risk inputs (Tennant: [0117], a formulaic method using weather parameters such as wind, rain, etc., to produce the prediction of energy availability over a duration of time, the formula may provide degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions; please note “degrees of uncertainty” determined in the formula is the quantified uncertainty, and the formula is an aggregation of input parameters);
Cruickshank and Tennant are analogous arts because they are in the same field of endeavor, predicting energy data using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Tennant to include providing the degree of uncertainty based on input parameters. It would provide Cruickshank’s method with enhanced capabilities of adjusting the energy conservation strategy using the tool in order to optimize the use of grid power as suggested by Tennant ([0070], [0122]).
As for claim 19, Cruickshank-Meagher-Tennant discloses: wherein the step of determining net uncertainty includes generating a time series forecast of uncertainty in MW at hourly granularity (Cruickshank: [0197], Maximum, minimum, and sum comparing the performance of reconstruction methods in simulating 5 AM hourly energy usage by the electric DHW heater in RBSA home 13088 were obtained, from Q2 Year 1-Q1 Year 2 inclusive).
As for claim 20, Cruickshank-Meagher-Tennant discloses: wherein the predetermined time period includes the next-day (Cruickshank: [0356], location-specific weather were used to forecast the day-ahead 5-minute time-series load per house, distribution feeder, city, and the ERGOT serving area).
As for claim 21, Cruickshank-Meagher-Tennant discloses: wherein the predetermined time period includes the next-day and one or more subsequent days (Cruickshank: [0184], In a heat map, the number of time intervals on the horizontal axis may be increased in order to view more days, for example electricity use over 30 days; [0356], location-specific weather were used to forecast the day-ahead 5-minute time-series load per house, distribution feeder, city, and the ERGOT serving area).
6. Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Cruickshank and Meagher and Tennant as applied on claim 1, and further in view of HongXing Ye et al (US Publication 20200258168 A1, hereinafter Ye).
As for claim 13, Cruickshank-Meagher-Tennant does not disclose forward reliability assessment commitment process, in an analogous art of power data analysis, Ye discloses: wherein the step of quantifying net uncertainty quantifies net uncertainty as a difference between real-time and day-ahead forecast in forward reliability assessment commitment (FRAC) process (Ye: [0064], Transmission security planning (TSP) may be responsible for: assessing the reliability of controller 10's transmission system based on latest system conditions; and providing controller 10's day-ahead (DA) and forward reliability assessment and commitment (FRAC) necessary inputs. Conditions may include load forecasting, interchanges, generators availability, and/or another condition. These inputs may include reserve requirements, load pocket study, watchlist constraints, etc.).
Cruickshank and Meagher and Tennant and Ye are analogous arts because they are in the same field of endeavor, analyzing and predicting energy data. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Ye to include using FRAC process. It would provide Cruickshank’s method with enhanced capabilities of performing energy analytics and providing more reliable data.
As for claim 14, Cruickshank-Meagher-Tennant-Ye discloses: wherein the step of quantifying net uncertainty calculates quantified net uncertainty as follows: Net Uncertainty = ∆Generation −∆𝐿𝑜𝑎𝑑 + ∆Wind + ∆Solar + ∆NSI −𝑆𝑡𝑟𝑎𝑛𝑑𝑒𝑑𝑀𝑊, where ∆= Actual – Forecast at FRAC (Eq. 1) wherein, ∆Generation tracks the change in “available non-intermittent generation capacity” or the availability of conventional thermal generation, ∆𝐿𝑜𝑎𝑑, ∆Wind and ∆Solar quantify the uncertainty from forecast error of load, wind and solar generation ∆NSI quantifies the uncertainty of the grid operator’s Net Scheduled Interchange (NSI) with neighboring grid operators, and 𝑆𝑡𝑟𝑎𝑛𝑑𝑒𝑑𝑀𝑊 is the generation MW unavailable for meeting load due to transmission constraint. By including this term, the model is forecasting transmission congestion in the net uncertainty forecast process (Cruickshank: [0425], the net generation is calculated as shown in the table to subtract the forecast renewables of 6.0 GWh from the forecast load of 36.1 GWh to yield 30.1; Please note the calculation of certain value may use equations; Meagher: [0270], predictions about harmonic distortions in an electrical system may be accurately calculated in real-time; [0352], Such a system can be configured to make predictions regard ing the expected energy efficiency, energy costs, cost of inherent system losses and cost due to running the electrical system at poor power factors along with calculating and comparing the availability and reliability of the electrical system in real-time. These predictions and calculations can then be used to arrive at actionable, reliability centered maintenance and energy management strategies for mission critical or business critical operations which may lead to the re-alignment of the electrical system for optimized performance, maintenance or security; Tennant: [0117], a formulaic method using weather parameters such as wind, rain, etc., to produce the prediction of energy availability over a duration of time, the formula may provide degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions; Ye: [0065], A TSP study may aim to examine whether the planned operations for the next day within controller 10's and neighboring systems may exceed any system operating limits (SOL) or interconnection reliability operating limits (IROL), with the consideration of normal condition and contingency events. The TSP engineer may coordinate and work with transmission owners (TOs), transmission operators (TOPs), controller 10's real-time/forward market processes, controller 10's outage coordination, and/or, when necessary, neighboring reliability coordinators (RCs) to develop and help implement re-dispatch/binding, operating processes, procedures, or plans to prevent or mitigate an instance of exceeding an SOL or IROL. TSP engineers may collect OC analysis results, validate them, and/or incorporate them into a DAMS database. The TSP process and result postings may be performed to meet or exceed the applicable compliance requirements of NERC reliability standards IRO-008-2 and IRO-009-2).
7. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cruickshank and Meagher and Tennant as applied on claim 15, and further in view of Brian Brooks et al (US Publication 20170207624 A1, hereinafter Brooks).
As for claim 17, Cruickshank-Meagher-Tennant does not clearly disclose a gas pipeline dashboard, in another analogous art of power data analysis, Brooks discloses: wherein the graphical display comprises a gas pipeline dashboard that provides a visualization of a gas pipeline network that covers a geographic region of the energy grid operator with visual generation-at-risk information associated with the gas pipeline network (Brooks: Fig. 2 and [0038], The map 200 illustrates an area with many lines and other grid elements).
Cruickshank and Meagher and Tennant and Brooks are analogous arts because they are in the same field of endeavor, analyzing and predicting energy data. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Brooks to include displaying a map illustrating an area with many lines and other grid elements. It would provide Cruickshank’s method with enhanced capabilities of visualizing the analysis data in map so user experience is improved.
8. Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Cruickshank and Meagher and Tennant as applied on claims 1 and 18, and further in view of Prabir Sen et al (US Publication 20140281645 A1, hereinafter Sen).
As for claim 22, Cruickshank-Meagher-Tennant does not clearly disclose utilizing a Gradient Boosted uncertainty prediction machine learning model, in another analogous art of power data analysis, Sen discloses: utilizing a Gradient Boosted uncertainty prediction machine learning model to predict uncertainty levels for a subsequent time period based on the quantified net uncertainty (Sen: [0068], The estimation engine 115 may use a number of algorithms or functions, alone or in combination, including for example: Markov decision functions (e.g., Markov Chain Monte Carlo (MCMC)) executed on event data, "partial memory-based" Markov functions executed on historical data, sheaf-stack descent (SSD) functions to estimate aggregated functions and stochastic gradient descent (SGD) algorithms to estimate optimization of objective functions. The algorithms or functions may further include stochastic gradient boosting (SGB) functions to estimate decision trees and ranking, optimal control estimates (OCE) to estimate demand, capacity, reserve, pricing and metrics, and optimal power flow control (OPFC) to estimate to load and reserve balancing).
Cruickshank and Meagher and Tennant and Sen are analogous arts because they are in the same field of endeavor, analyzing and predicting energy data. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Cruickshank using the teachings of Sen to include using gradient boosting functions. It would provide Cruickshank’s method with enhanced capabilities of optimizing the estimation engine.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
Prosecution Insights