DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. This Office action is responsive to the Request for Continued Examination (RCE) filed under 37 CFR §1.53(d) for the instant application on October 31, 2025. Applicants have properly set forth the RCE, which has been entered into the application, and an examination on the merits follows herewith.
Claims 1-3, 5-14, and 16-22 have been examined and rejected. This Office action is responsive to the amendment filed on October 31, 2025, which has been entered in the above identified application.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
4. Claims 1-3, 5-14, and 16-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
4-1. Claim 1 recites the limitation “a processor comprising a plurality of programming software tools” in [line 4] of the claim. A processor itself is hardware that executes instructions, and therefore it is unclear how a processor can comprise software tools. Examiner suggests changing the claim limitation to recite --a processor executing a plurality of programming software tools--.
4-2. Claim 12 recites similar limitations as claim 1 and is therefore rejected for similar reasons.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1-3, 5-14, 16-20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Meagher et al (Pub. No. US 2011/0082596), in view of Anichkov et al (U.S. Patent No. 11,262,718), and further in view of Avritzer et al (U.S. Patent No. 9,484,747).
6-1. Regarding claims 1 and 12, Meagher teaches a programmable microgrid control system for a microgrid power system comprising: a programmable microgrid controller device deployed in the microgrid power system, by disclosing a system 100 that utilizes real-time data for predictive analysis of the performance of a microgrid system, and that includes an analytics server 116 [paragraph 41; figure 1].
Meagher teaches a processor comprising a plurality of programming software tools configured to: generate a model of the microgrid power system, by disclosing that the analytics server 116 hosts an analytics engine 118, virtual system modeling engine 124, calibration engine 134, and databases 126, 130, and 132 used to provide a real-time model and virtual models of the microgrid system [paragraphs 36, 48-49, 55, 60; figure 1].
Meagher teaches analyze performance of the model of the microgrid power system, by disclosing that the analytics server 116 receives real-time data from sensors of the microgrid system [paragraph 49] and uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88] to determine health and performance levels of the processes and equipment in the microgrid system [paragraphs 53, 55], for calibration [paragraphs 57, 59], to indicate repair or maintenance on the microgrid system [paragraph 58], and to pinpoint the location, context, and cause of a failure [paragraph 64].
Meagher teaches… monitor the microgrid power system, by disclosing monitoring the microgrid system for health and performance, and alarm conditions that may be indicative of a need for a repair event or maintenance to be done on the microgrid system [paragraphs 50-53, 57-58].
Meagher teaches perform power system analyses including… short circuit analysis,… economic dispatch, and protection and coordination studies to enhance the performance and reliability of the microgrid power system, by disclosing that the analytic server 116 receives real-time data from sensors of the microgrid system [paragraph 49] comprising data relating to electrical power sensor measurements, e.g., voltage, current, etc. taken over a period of time [paragraph 43] and protective devices within an electrical distribution system [paragraph 100]. The analytic server 116 uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [paragraph 54]. These models include power flow models used to calculate expected kW, kVAR, power factor values, etc., short circuit models used to calculate maximum and minimum available fault currents, protection models used to determine proper protection schemes and ensure selective coordination of protective devices, power quality models used to determine voltage and current distortions at any point in the network, to name just a few [paragraph 55]. The models also include components for modeling reliability, modeling voltage stability, and modeling power flow [paragraphs 60, 65; paragraph 118, lines 1-8; paragraph 122, lines 1-5]. Based on predicted capacity and utilization, predictions regarding the cost of operation can also be generated using the cost of generating power at the microgrid and the cost of purchasing power from the macrogrid [paragraph 118, lines 16-22; paragraph 122, lines 5-8]. For example, if the predicted utilization exceeds the predicted capacity of the microgrid, electricity from the macrogrid may need to be purchased to meet the excess utilization, and alternatively, utilization might need to be curtailed to prevent utilization from exceeding the generation capacity of the microgrid [paragraph 122, lines 8-13].
Although Meagher discloses using the models to determine health status of the microgrid system such that the health status can be communicated to the processes and equipment of the microgrid system, e.g., via alarms and indicators [Meagher, paragraph 69], wherein a data acquisition hub 112 is configured to supply warnings and alarms signals as well as control signals to the microgrid system [Meagher, paragraphs 47, 49], Meagher does not expressly teach generate a controller logic based on the model of the microgrid power system, wherein the control logic comprises a set of programmable instructions or algorithms configured to dynamically manage and regulate real-time operations of the microgrid power system, including energy distribution, system stability, and integration of renewable energy sources, based on real-time data received from the microgrid’s physical components, transmit the controller logic to the programmable microgrid controller device… and wherein the programmable microgrid controller device is configured to execute the controller logic, and communicate with one or more microgrid assets. Anichkov discloses a system for managing microgrid assets, wherein the microgrid is connected to a power grid, the microgrid having an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [column 4, lines 31-39]. An asset management system [column 4, lines 40-53; figure 1] manages power generation and consumption within the microgrid by acquiring power properties through a power monitor [column 6, lines 7-23] and manages energy storage and an intermittent energy source by sending commands and acquiring information from energy storages [column 6, lines 23-33]. To send commands and acquire information, the system communicates with the microgrid devices and other devices over a computer network [column 6, lines 35-38]. The system utilizes a performance model for the microgrid to produce modeling results for a financial model for the microgrid, and an optimal microgrid dispatch module that determines a dispatch scenario corresponding to an objective function which is communicated to a microgrid scheduler [column 8, lines 3-18]. The scheduler implements the microgrid dispatch schedule to manage the microgrid, including controlling the charge/discharge energy storage [column 11, line 50 to column 12, line 39]. This would allow for more efficient control of the microgrid.
Since Meagher discloses that objectives of the microgrid operator include minimizing the annual cost of operation, minimizing the carbon footprint, minimizing the peak load, minimizing public utility consumption, or a combination thereof [Meagher, paragraph 38, lines 17-21], and that the system could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously [Meagher, paragraph 64], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the system of Meagher to dynamically manage and regulate the real-time operations of the microgrid, as taught by Anichkov. This would allow for more efficient control of the microgrid for optimization.
Meagher-Anichkov do not expressly teach perform power system analyses including time-domain load flow and transient stability studies. Avritzer discloses a metric for assessing the survivability of a smart grid distribution automation network after a failure, parameterizing a model for determining the metric, and using the metric for optimizing improvements to the distribution automation network [column 1, lines 23-29]. Power flow analysis using a time series of load values of each of a plurality of sections in a grid at each of a plurality of times a day is performed on a current circuit and on modifications to the current circuit to create parameterized phased-recovery survivability models [column 1, line 64 to column 2, line 10]. These phase-recovery survivability models are used to determine an average energy not supplied metric of the circuits, which is used along with cost to determine improvements to the grid [column 2, lines 10-25]. This would help improve the survivability of a distributed automation power grid at reasonable investment levels after a failure. Since Meagher-Anihkov disclose providing commodity market pricing for electricity and optimization of operation of a microgrid to meet the operational objectives of a microgrid operator [Meagher, paragraph 35], monitoring the health and performance levels of the microgrid system [Meagher, paragraph 53], and including components for modeling voltage stability [Meagher, paragraph 60], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform analyses including time-domain load flow and transient stability studies, as taught by Avritzer. This would help improve the survivability of a distributed automation power grid at reasonable investment levels after a failure.
6-2. Regarding claims 2 and 13, Meagher-Anichkov-Avritzer teach all the limitations of claims 1 and 12 respectively, wherein the plurality of programming software tools cond mprises at least one of analysis tools, communication tools and controller logic development tools, by disclosing that the plurality of programming software engines 118, 124, and 134 on the analytics server 116 includes at least one of analytics engine 118, drivers (communication tools) and virtual system modeling engine 124 (controller logic development tools) [Meagher, paragraphs 67, 81; figure 1].
6-3. Regarding claims 3 and 14, Meagher-Anichkov-Avritzer teach all the limitations of claims 1 and 12 respectively, wherein the one or more microgrid assets comprise at least one of Distributed Generations DGs, loads and Energy Storage Systems ESSs, by disclosing wherein the one or more microgrid/facility 102 includes at least one of Distributed Generations systems [Meagher, paragraph 70].
6-4. Regarding claims 4 and 15, Meagher-Anichkov-Avritzer teach all the limitations of claims 1 and 12 respectively, wherein the microgrid power system is at least one of AC, DC and hybrid microgrid, by disclosing wherein the microgrid power system is solar/wind (hybrid) microgrid [Meagher, paragraph 118].
6-5. Regarding claims 5 and 16, Meagher-Anichkov-Avritzer teach all the limitations of claims 1 and 12 respectively, wherein the plurality of programming software tools comprises at least one of graphical user interface and script-based development environment, by disclosing wherein the plurality of programming software tools includes at least a web browser having a website/webpage (graphical user interface and script-based development environment) [Meagher, paragraph 69].
6-6. Regarding claims 6 and 17, Meagher-Anichkov-Avritzer teach all the limitations of claims 5 and 16 respectively, wherein the script-based development environment is based on a software development framework for power system applications where an engineer accesses each power system element including at least one of settings, connectivity information, inputs, and outputs, by disclosing wherein the web browser having a website/webpage is based on a virtual simulation model database for electrical power system applications where an operator/user/client uses network connection 114 to access each power system A,B, C (power system elements) including at least one of voltage and current values [Meagher, paragraphs 42, 60, 78, 80].
6-7. Regarding claims 7 and 18, Meagher-Anichkov-Avritzer teach all the limitations of claims 1 and 12 respectively, wherein the plurality of programming software tools employs real-time data to tune the model of the microgrid power system to simulate real-time situation, by disclosing that the analytics server 116 receives real-time data from sensors of the microgrid system [paragraph 49] and uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88] for calibration [paragraphs 57, 59].
6-8. Regarding claims 8 and 19, Meagher-Anichkov-Avritzer teach all the limitations of claims 6 and 17 respectively, wherein the inputs and outputs are recorded in a file during the performance analysis for further testing and debugging, by disclosing wherein the voltage and current values are recorded in a copy file of the virtual simulation model database 130 during the performance analysis for further testing and upgrades [Meagher, paragraphs 37, 61, 81].
6-9. Regarding claims 9 and 20, Meagher-Anichkov-Avritzer teach all the limitations of claims 8 and 19 respectively, further comprises a tester program configured to play back the recorded inputs, by disclosing that test/"what if" simulations are capable to use the recorded voltage and current to allow a system designer to make hypothetical changes to the microgrid/facility 102 [Meagher, paragraphs 37, 61].
6-10. Regarding claim 10, Meagher-Anichkov-Avritzer teach all the limitations of claim 1, wherein the programmable microgrid controller device is further configured to request analyses from the programming software tools to make a control decision and to update the controller logic based on results of the requested analysis, by disclosing that analytics server 116 having analytics engine 118 to analyze performance of models [Meagher, paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [Meagher, paragraph 54]. The models include components for modeling reliability, modeling voltage stability, and modeling power flow [Meagher, paragraphs 60; paragraph 118, lines 1-8; paragraph 122, lines 1-5]. Based on predicted capacity and utilization, predictions regarding the cost of operation can also be generated using the cost of generating power at the microgrid and the cost of purchasing power from the macrogrid [Meagher, paragraph 118, lines 16-22; paragraph 122, lines 5-8]. For example, if the predicted utilization exceeds the predicted capacity of the microgrid, electricity from the macrogrid may need to be purchased to meet the excess utilization, and alternatively, utilization might need to be curtailed to prevent utilization from exceeding the generation capacity of the microgrid [Meagher, paragraph 122, lines 8-13]. As discussed above with respect to claim 1, the microgrid has an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [Anichkov, column 4, lines 31-39]. Thus, control logic to manage and regulate the real-time operations of the microgrid system will be updated based on the analyses performed on the current facility status.
6-11. Regarding claim 11, Meagher-Anichkov-Avritzer teach all the limitations of claim 1, wherein the controller logic is encrypted, by disclosing wherein the control logic is on a secure web server establishing a secure network connection 114 that encrypts the data [Meagher, paragraphs 78, 106].
4-12. Regarding claim 22, Meagher-Anichkov-Avritzer teach all the limitations of claim 1, wherein the programming software tools are configured to continuously tune the model of the microgrid power system using real-time operational data to dynamically simulate real-time conditions and improve system adaptability, by disclosing that the models are continuously and automatically synchronized with the actual facility status based on the real-time data provided by sensors of the monitored facility [Meagher, paragraph 54]. Various operating parameters or conditions of the models can be updated or adjusted to reflect the actual facility configuration [Meagher, paragraph 59].
7. Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Meagher et al (Pub. No. US 2011/0082596), in view of Anichkov et al (U.S. Patent No. 11,262,718), in view of Avritzer et al (U.S. Patent No. 9,484,747), in view of Zhang (U.S. Patent No. 8,914,262), and further in view of Gil et al (Pub. No. US 2017/0208151).
7-1. Regarding claim 21, Meagher-Anichkov-Avritzer teach all the limitations of claim 1. Meagher-Anichkov-Avritzer do not expressly teach wherein the programming software tools include both open-loop and closed-loop debugging tools that allow users to apply breakpoints, monitor variables in real time… during testing and debugging. Zhang discloses modeling, simulating, and analyzing dynamic systems by representing the systems as graphical models [column 2, lines 30-37], and allowing the user to more easily determine how a block diagram model should be modified by visually indicating dependencies within the model [column 2, lines 38-50]. A graph of the entire model may be represented by individual data dependency graphs representing block Jacobian patterns for the model blocks that are connected together, which represents an open loop Jacobian pattern [column 7, lines 46-53]. The open-loop Jacobian pattern of the model may be used to determine a closed loop Jacobian pattern of the model, which represent dependencies between variables in the model [column 8, lines 55-60]. Breakpoints for debugging may be set by interacting with the Jacobian pattern visualization [column 16, lines 19-35]. This would help the user more effectively modify the model as needed. Since Meagher-Anichkov discloses modifying the predicted data output from the simulation engine, adjusting the logic/processing parameters used by the model(s), adding/substracting functional elements from model(s), etc. [Meagher, paragraph 59], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use both open-loop and closed-loop debugging tools that allow users to apply breakpoints, monitor variables in real time, and replay recorded input data during testing and debugging, as taught by Zhang. This would help the user more effectively modify the model as needed.
Meagher-Anichkov-Avritzer-Zhang do not expressly teach replay recorded input data during testing and debugging. Gil discloses a platform for efficient data collection, monitoring, aggregation and analysis of facilities, resources and commercial equipment condition data [paragraph 8, lines 1-5]. By leveraging a plurality of internal and external sensor data streams to a device, blended with external wireless sensors, provides for case-based inspections and investigations, condition-based machine monitoring for: predictive maintenance; visual and acoustic inspection tools; alerts/instructions; and, real-time data analytics for an operator and/or management at their location with instant replay for correlating data and detecting anomaly using advanced pattern matching and unique code development and execution environment [paragraph 8, lines 5-16]. Sequences can be replayed individually or multiple sequence stream patterns may be compared side-by-side for anomaly detection or pattern recognition uses and displays [paragraph 51]. These features may be implemented in critical infrastructure including microgrids and electric grids [paragraph 67]. This would improve testing and debugging. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replay recorded input data during testing and debugging, as taught by Gil. This would improve testing and debugging.
Response to Arguments
8. The Examiner acknowledges the Applicant’s amendments to claim 10.
1. Applicant’s arguments that Meagher Is an Analytics/Planning Portal, Not a Real-Time Control System
Regarding independent claims 1 and 12, Applicant alleges that Meagher et al (Pub. No. US 2011/0082596) does not disclose or suggest programming software tools that perform static and dynamic power system studies (e.g., load flow, transient stability, short-circuit) on an electric network model of the microgrid.
Contrary to Applicant’s arguments, with regard to performing short circuit analysis, Meagher discloses that simulation engine 208 operates on models to produce predicted data based on the current facility status [paragraph 54]. These models include… short circuit models used to calculate maximum and minimum available fault currents [paragraph 55].
With regard to performing time-domain load flow and transient stability studies, Examiner has rejected these limitations under 35 U.S.C. 103 as being unpatenable over Meagher et al (Pub. No. US 2011/0082596), in view of Anichkov et al (U.S. Patent No. 11,262,718), and further in view of Avritzer et al (U.S. Patent No. 9,484,747). Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
Applicant alleges that Meagher does not disclose generating and deploying controller logic executable by a programmable microgrid controller device because the outputs in Meagher are reports and operator guidance, not executable instructions for automated real-time control.
Examiner notes that the combination of Meagher in view of Anichkov are considered to teach deploying controller logic comprising a set of programmable instructions as specified in claim 1, that is executable by a programmable microgrid controller device. Anichkov discloses a system for managing microgrid assets, wherein the microgrid is connected to a power grid, the microgrid having an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [column 4, lines 31-39]. An asset management system [column 4, lines 40-53; figure 1] manages power generation and consumption within the microgrid by acquiring power properties through a power monitor [column 6, lines 7-23] and manages energy storage and an intermittent energy source by sending commands and acquiring information from energy storages [column 6, lines 23-33]. To send commands and acquire information, the system communicates with the microgrid devices and other devices over a computer network [column 6, lines 35-38]. The system utilizes a performance model for the microgrid to produce modeling results for a financial model for the microgrid, and an optimal microgrid dispatch module that determines a dispatch scenario corresponding to an objective function which is communicated to a microgrid scheduler [column 8, lines 3-18]. The scheduler implements the microgrid dispatch schedule to manage the microgrid, including controlling the charge/discharge energy storage [column 11, line 50 to column 12, line 39]. This would allow for more efficient control of the microgrid. Since Meagher discloses that objectives of the microgrid operator include minimizing the annual cost of operation, minimizing the carbon footprint, minimizing the peak load, minimizing public utility consumption, or a combination thereof [Meagher, paragraph 38, lines 17-21], and that the system could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously [Meagher, paragraph 64], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the system of Meagher to dynamically manage and regulate the real-time operations of the microgrid, as taught by Anichkov. This would allow for more efficient control of the microgrid for optimization.
2. Applicant’s arguments that Anichkov Is an Asset Management System, Not Dynamic Control
Applicant alleges that the outcome in Anichkov is to “select optimal microgrid characterization” for financial/asset dispatch, not to produce control algorithms for real-time stability protection.
Examiner notes that the combination of Meagher in view of Anichkov are considered to teach the claim limitation. Meagher discloses an analytic server 116 that uses the analytics engine 118 to analyze performance of models [paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [paragraph 54]. The models include components for modeling reliability, modeling voltage stability, and modeling power flow [paragraphs 60; paragraph 118, lines 1-8; paragraph 122, lines 1-5]. Based on predicted capacity and utilization, predictions regarding the cost of operation can also be generated using the cost of generating power at the microgrid and the cost of purchasing power from the macrogrid [paragraph 118, lines 16-22; paragraph 122, lines 5-8]. For example, if the predicted utilization exceeds the predicted capacity of the microgrid, electricity from the macrogrid may need to be purchased to meet the excess utilization, and alternatively, utilization might need to be curtailed to prevent utilization from exceeding the generation capacity of the microgrid [paragraph 122, lines 8-13].
However, Meagher does not teach managing and regulating the real-time operations of the microgrid power system based on the analysis conducted by the models. Anichkov discloses a system for managing microgrid assets, wherein the microgrid is connected to a power grid, the microgrid having an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [column 4, lines 31-39]. Thus, when determining optimal dispatch of energy, a variety of factors are analyzed and considered including, not only a microgrid financial model, but also an environmental variable forecast, a microgrid performance model, and microgrid operating conditions [column 4, lines 31-39]. This would allow for more efficient control of the microgrid. Since Meagher discloses that objectives of the microgrid operator include minimizing the annual cost of operation, minimizing the carbon footprint, minimizing the peak load, minimizing public utility consumption, or a combination thereof [Meagher, paragraph 38, lines 17-21], and that the system could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously [Meagher, paragraph 64], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the system of Meagher to dynamically manage and regulate the real-time operations of the microgrid, as taught by Anichkov. This would allow for more efficient control of the microgrid for optimization.
3. Applicant’s argument of Different Type of “Model”
Applicant alleges that the models of Meagher and Anichkov are not configured for static and dynamic analyses (time-domain load flow, transient stability, protection coordination, etc.) and would not enable generation of executable control logic for real-time reliability.
With regard to protection coordination, Meagher discloses that the analytic server 116 receives real-time data from sensors of the microgrid system [paragraph 49] comprising data relating to electrical power sensor measurements, e.g., voltage, current, etc. taken over a period of time [paragraph 43] and protective devices within an electrical distribution system [paragraph 100]. The analytic server 116 uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [paragraph 54]. These models include… protection models used to determine proper protection schemes and ensure selective coordination of protective devices, power quality models used to determine voltage and current distortions at any point in the network, to name just a few [paragraph 55].
With regard to performing time-domain load flow and transient stability studies, Examiner has rejected these limitations under 35 U.S.C. 103 as being unpatenable over Meagher et al (Pub. No. US 2011/0082596), in view of Anichkov et al (U.S. Patent No. 11,262,718), and further in view of Avritzer et al (U.S. Patent No. 9,484,747). Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
4. Applicant’s argument of Different Time Horizons and Outputs
Applicant alleges that Applicants’ claim require generating controller logic based on real-time data from physical microgrid assets, with the logic deployed into a programmable microgrid controller to manage second-to-minute scale operations (faults, load changes, renewable fluctuations), and neither Meagher nor Anichkov teach or suggest this transformation from network study to executable control logic.
Contrary to Applicant’s arguments, the combination of Meagher in view of Anichkov are considered to teach the claim limitation. Meagher discloses an analytic server 116 that uses the analytics engine 118 to analyze performance of models [paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [paragraph 54]. The models include components for modeling reliability, modeling voltage stability, and modeling power flow [paragraphs 60; paragraph 118, lines 1-8; paragraph 122, lines 1-5]. Based on predicted capacity and utilization, predictions regarding the cost of operation can also be generated using the cost of generating power at the microgrid and the cost of purchasing power from the macrogrid [paragraph 118, lines 16-22; paragraph 122, lines 5-8]. For example, if the predicted utilization exceeds the predicted capacity of the microgrid, electricity from the macrogrid may need to be purchased to meet the excess utilization, and alternatively, utilization might need to be curtailed to prevent utilization from exceeding the generation capacity of the microgrid [paragraph 122, lines 8-13].
However, Meagher does not teach managing and regulating the real-time operations of the microgrid power system based on the analysis conducted by the models. Anichkov discloses a system for managing microgrid assets, wherein the microgrid is connected to a power grid, the microgrid having an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [column 4, lines 31-39]. Thus, when determining optimal dispatch of energy, a variety of factors are analyzed and considered including, not only a microgrid financial model, but also an environmental variable forecast, a microgrid performance model, and microgrid operating conditions [column 4, lines 31-39]. This would allow for more efficient control of the microgrid. Since Meagher discloses that objectives of the microgrid operator include minimizing the annual cost of operation, minimizing the carbon footprint, minimizing the peak load, minimizing public utility consumption, or a combination thereof [Meagher, paragraph 38, lines 17-21], and that the system could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously [Meagher, paragraph 64], it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the system of Meagher to dynamically manage and regulate the real-time operations of the microgrid, as taught by Anichkov. This would allow for more efficient control of the microgrid for optimization.
5. Applicant’s argument that there is No Motivation to Combine to Reach Claimed Invention
Applicant alleges that there is no disclosure or suggestion in either reference of converting planning/analytics outputs into executable controller logic for a microgrid controller because, at best, the combination would provide long-term planning recommendations and real-time analytic reports – not the dynamic, deployable control logic required by the claims.
Contrary to Applicant’s arguments, Meagher discloses that objectives of the microgrid operator include minimizing the annual cost of operation, minimizing the carbon footprint, minimizing the peak load, minimizing public utility consumption, or a combination thereof [Meagher, paragraph 38, lines 17-21]. The analytics server 116 receives real-time data from sensors of the microgrid system [paragraph 49] and uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88] to determine health and performance levels of the processes and equipment in the microgrid system [paragraphs 53, 55], to indicate repair or maintenance on the microgrid system [paragraph 58], and to pinpoint the location, context, and cause of a failure [paragraph 64]. The health and performance levels for the various processes and equipment of the microgrid system, when combined with the analytic capabilities of the analytics engine 118, allows an operator to minimize the risk of catastrophic equipment failure by predicting future failures and providing prompt, informative information concerning potential/predicted failures before they occur [paragraph 53]. Thus, such analytics of Meagher is used for immediate actions to be taken by an operator. The techniques in Meagher are ultimately used for optimization of a microgrid system [Abstract]. Meagher further states that the system could improve the alarm management process by either supporting the existing operator, or even managing the system autonomously [Meagher, paragraph 64].
Anichkov discloses a system for managing microgrid assets, wherein the microgrid is connected to a power grid, the microgrid having an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [column 4, lines 31-39]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the system of Meagher to dynamically manage and regulate the real-time operations of the microgrid, as taught by Anichkov. This would allow for more efficient control of the microgrid for optimization.
Applicant states that dependent claims 2-3, 5-11, 13-14, and 16-22 recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding independent claims 1 and 12. However, as discussed above, Meagher, in view of Anichkov, and further in view of Avritzer are considered to teach claims 1 and 12, and consequently, claims 2-3, 5-11, 13-14, and 16-22 are rejected.
Regarding dependent claim 2, Applicant alleges that neither Meagher nor Anichov disclose or suggest a structured tool chain because Meagher’s “analytics portal” provides data views and forecasting, not integrated controller-logic development tool.
Examiner notes that claim 2 recites “at least one of analysis tools, communication tools and controller logic development tools” and thus, only one of the listed tools need to be provided in the prior art to teach the limitation. Nonetheless, Meagher-Anichkov disclose that the plurality of programming software engines 118, 124, and 134 on the analytics server 116 includes at least one of analytics engine 118, drivers (communication tools) and virtual system modeling engine 124 (controller logic development tools) [Meagher, paragraphs 67, 81; figure 1].
Regarding dependent claim 3, Applicant alleges that while Meagher and Anichkov mention energy resources generally, neither provides disclosure of coordinated DG + load + ESS integration into a programmable controller framework.
Examiner notes that claim 3 recites “at least one of Distributed Generations (DGs), loads and Energy Storage Systems (ESSs)” and thus, only one of the listed assets need to be provided in the prior art to teach the limitation. Contrary to Applicant’s arguments, Meagher-Anichkov disclose wherein the one or more microgrid/facility 102 includes at least one of Distributed Generations systems [Meagher, paragraph 70].
Regarding dependent claim 5, Applicant alleges that Meagher’s analytics portal is not a controller programming environment, and Anichkov has no teaching of either GUI or script-based toolchains for power system controllers.
Examiner notes that claim 5 does not recite “a controller programming environment.” Claim 5 recites “wherein the plurality of programming software tools comprises at least one of graphical user interface and script-based development environment.” Meagher-Anichkov disclose wherein the plurality of programming software tools includes at least a web browser having a website/webpage (graphical user interface and script-based development environment) [Meagher, paragraph 69].
Regarding dependent claim 6, Applicant alleges that the cited prior art does not provide any disclosure of low-level access to power system elements for developing controller logic because Meagher calibrates forecasting models, and Anichkov optimizes dispatch strategies.
Contrary to Applicant’s arguments, as discussed above, Meagher-Anichkov disclose providing a plurality of programming software tools configured to generate a controller logic. See response to arguments in section 1. Meagher-Anichkov further disclose wherein the web browser having a website/webpage is based on a virtual simulation model database for electrical power system applications where an operator/user/client uses network connection 114 to access each power system A, B, C (power system elements) including at least one of voltage and current values [Meagher, paragraphs 42, 60, 78, 80].
Regarding dependent claim 7, Applicant alleges that neither reference teaches real-time model tuning because Meagher’s calibration uses historic/market data for forecasting and Anichkov relies on long-term statistical models.
Contrary to Applicant’s argument, Meagher-Anichkov disclose that the analytics server 116 receives real-time data from sensors of the microgrid system [paragraph 49] and uses the analytics engine 118 to analyze performance of the models [paragraphs 48, 50, 88] for calibration [Meagher, paragraphs 57, 59].
Regarding dependent claims 8 and 9, Applicant alleges that Meagher and Anichkov do not disclose any mechanism for open-loop testing, recording/replay of controller inputs, or use of tester programs because these are controller development tools, not features of analytics or asset-valuation platforms.
Examiner notes that nowhere in the claims recite that the recording of inputs and outputs and the test program are controller development tools. The claims only recite that the inputs and outputs are recorded for further testing and debugging (claim 8) and that the tester program is configured to play back the recorded inputs. Contrary to Applicant’s arguments, Meagher-Anichkov disclose that the voltage and current values are recorded in a copy file of the virtual simulation model database 130 during the performance analysis for further testing and upgrades [Meagher, paragraphs 37, 61, 81]. Test/"what if" simulations are capable to use the recorded voltage and current to allow a system designer to make hypothetical changes to the microgrid/facility 102 [Meagher, paragraphs 37, 61].
Regarding dependent claim 10, Applicant alleges that the cited prior art does not disclose a feedback mechanism in which a deployed controller dynamically updates its own logic based on the requested power system analyses because Meagher reports analytic results to an operator, and Anichkov selects characterizations for planning horizons, with neither suggesting updating executable controller logic.
Contrary to Applicant’s arguments, Meagher-Anichkov disclose analytics server 116 having analytics engine 118 to analyze performance of models [Meagher, paragraphs 48, 50, 88]. A simulation engine operates on the models to produce predicted data based on the current facility status [Meagher, paragraph 54]. The models include components for modeling reliability, modeling voltage stability, and modeling power flow [Meagher, paragraphs 60; paragraph 118, lines 1-8; paragraph 122, lines 1-5]. Based on predicted capacity and utilization, predictions regarding the cost of operation can also be generated using the cost of generating power at the microgrid and the cost of purchasing power from the macrogrid [Meagher, paragraph 118, lines 16-22; paragraph 122, lines 5-8]. For example, if the predicted utilization exceeds the predicted capacity of the microgrid, electricity from the macrogrid may need to be purchased to meet the excess utilization, and alternatively, utilization might need to be curtailed to prevent utilization from exceeding the generation capacity of the microgrid [Meagher, paragraph 122, lines 8-13]. As discussed above with respect to claim 1, the microgrid has an energy storage and intermittent energy source dependent on environmental variables, the energy storage optimally characterized and optimally dispatched based on one or more of an environmental variable forecast, a microgrid performance model, a microgrid financial model, and microgrid operating conditions [Anichkov, column 4, lines 31-39]. Thus, control logic to manage and regulate the real-time operations of the microgrid system will be updated based on the analyses performed on the current facility status.
Regarding dependent claim 11, Applicant alleges that the cited references do not address encryption of controller logic or any security considerations in deploying executable instructions.
Contrary to Applicant’s arguments, Meagher-Anichkov disclose wherein the control logic is on a secure web server establishing a secure network connection 114 that encrypts the data [Meagher, paragraphs 78, 106].
Similar argument have been presented for claims 13-14 and 16-20 and thus, Applicant’s arguments are not persuasive for the same reasons.
Regarding dependent claim 21, Applicant alleges that neither Meagher and Anichkov disclose any debugging or testing environment for controller logic. Examiner notes that dependent claim 21 has been rejected under 35 U.S.C. 103 as being unpatentable over Meagher, in view of Anichkov, in view of Avritzer, in view of Zhang (U.S. Patent No. 8,914,262), and further in view of Gil et al (Pub. No. US 2017/0208151).
Regarding dependent claim 22, Applicant alleges that Meagher’s portal calibration and Anichkov’s forecasting are not continuous real-time tuning for dynamic control.
Contrary to Applicant’s arguments, Meagher-Anichkov disclose that the models are continuously and automatically synchronized with the actual facility status based on the real-time data provided by sensors of the monitored facility [Meagher, paragraph 54]. Various operating parameters or conditions of the models can be updated or adjusted to reflect the actual facility configuration [Meagher, paragraph 59].
Conclusion
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/ALVIN H TAN/Primary Examiner, Art Unit 2118