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. Claims 1-3, 5-14, and 16-22 have been examined and rejected. This Office action is responsive to the amendment filed on June 1, 2026, which has been entered in the above identified application.
Claim Objections
3. Claim 1 is objected to because of the following informalities: On [line 3] of claim 1, Examiner suggests changing “deployed within in” to --deployed within--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
4. The corrections to claims 1 and 12 have been approved, and the rejections to claims 1-3, 5-14, and 16-22 are withdrawn.
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 processor configured to execute a plurality of programming software tools configured to: generate a model of 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]. 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 a programmable microgrid controller device physically deployed within in the microgrid power system;… generate a controller logic based on the model of the microgrid power system, wherein the control logic comprises executable control routines configured for autonomous execution by a programmable microgrid controller device, transmit the controller logic to the programmable microgrid controller device…; and… wherein the programmable microgrid controller device is configured to execute the controller logic during real-time operation of the microgrid power system based on operational measurement data received from microgrid assets, and communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid power system. 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] that is physically deployed within the microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] manages power generation and consumption within the microgrid by acquiring power properties through a power monitor [column 6, lines 11-23] 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 dispatch scenario is defined for multiple steps of a predetermined or specified time horizon, for example, hourly steps for day-ahead dispatch, and the steps may be executed on a configurable time schedule and/or input change [column 8, lines 19-25]. The scheduler implements the microgrid dispatch schedule to manage the microgrid and compensates for forecast errors in real-time, including controlling the charge/discharge energy storage [column 11, line 50 to column 12, line 39]. Actual system performance is monitored, and its performance is compared with the models for optimization and real-time control of the microgrid [column 12, line 66 to column 13, line 4]. 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].
6-12. 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 claims 1 and 12.
Regarding independent claim 1, Applicant alleges that 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) do not teach or suggest an executable controller logic and deploying it for real-time control of physical microgrid assets, as has been amended to the claim, because none of the cited references disclose generating executable control routines that are deployed to and executed by a controller device to directly control physical microgrid operations.
Contrary to Applicant’s arguments, 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 2310 is physically deployed within a microgrid 2000 [column 5, lines 45-46; column 6, lines 7-11; figure 2]. The asset management system manages power generation and consumption within the microgrid by acquiring power properties through a power monitor [column 6, lines 11-23] 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 dispatch scenario is defined for multiple steps of a predetermined or specified time horizon, for example, hourly steps for day-ahead dispatch, and the steps may be executed on a configurable time schedule and/or input change [column 8, lines 19-25]. The scheduler implements the microgrid dispatch schedule to manage the microgrid and compensates for forecast errors in real-time, including controlling the charge/discharge of an energy storage [column 11, line 50 to column 12, line 39]. Actual system performance is monitored, and its performance is compared with the models for optimization and real-time control of the microgrid [column 12, line 66 to column 13, line 4]. This would allow for more efficient control of the microgrid. Thus, Anichkov discloses an asset manager physically deployed within a microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [column 8, lines 3-18] for autonomous execution [column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [column 11, line 57 to column 12, line 39].
Applicant alleges that none of the references are directed to a controller development lifecycle in which controller logic is developed, tested, deployed to controller hardware, and executed to directly regulate physical operation of a microgrid.
Examiner notes that claim 1 only recites, “generate a controller logic based on the model of the microgrid power system, wherein the control logic comprises executable control routines configured for autonomous execution by the programmable microgrid controller device,… wherein the programmable microgrid controller is configured to execute the controller logic during real-time operation of the microgrid power system based on operational measurement data received from microgrid assets, and communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid power system. As stated above, Anichkov discloses an asset manager physically deployed within a microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [column 8, lines 3-18] for autonomous execution [column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [column 11, line 57 to column 12, line 39].
Applicant alleges that the cited references are directed to fundamentally different classes of systems than the present claim, which is directed to a controller engineering platform that enables development of controller logic within software tools, deployment of the controller logic to a programmable microgrid controller device physically deployed within a microgrid, and autonomous execution of the deployed controller logic to directly regulate physical microgrid assets during real-time operation.
Contrary to Applicant’s arguments, as stated above, Anichkov discloses an asset manager physically deployed within a microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [column 8, lines 3-18] for autonomous execution [column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [column 11, line 57 to column 12, line 39]. 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.
A. Applicant’s argument that The Cited References Do Not Teach or Suggest Generating and Deploying Controller Logic
Regarding independent claims 1 and 12, Applicant alleges that Anichkov does not teach generating controller logic based on a model of a microgrid power system, transmitting the controller logic to a programmable microgrid controller device, and executing the controller logic at the controller device to dynamically manage real-time operations of the microgrid, because the determined dispatch strategies of Anichkov remain optimization outputs and are not disclosed as executable controller logic generated within a controller engineering environment, deployed to programmable controller hardware, and executed for autonomous regulation of physical microgrid assets.
Contrary to Applicant’s arguments, as stated above, Anichkov discloses an asset manager physically deployed within a microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [column 8, lines 3-18] for autonomous execution [column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [column 11, line 57 to column 12, line 39].
B. Applicant’s argument that The Cited Combination Remains Directed to Analysis and Decision Support, Not Control Logic Synthesis and Deployment
Applicant alleges that none of the references teach or suggest generating, deploying, and executing controller logic that is deployed to and executed by a programmable microgrid controller device because the cited references only utilize analytical models to monitor systems, evaluate performance, forecast future conditions, optimize dispatch scenarios, or assess survivability metrics.
Contrary to Applicant’s arguments, as stated above, Anichkov discloses an asset manager physically deployed within a microgrid [column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [column 8, lines 3-18] for autonomous execution [column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [column 11, line 57 to column 12, line 39]. 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.
C. Applicant’s arguments that there is No Motivation to Combine the References to Achieve the Claimed Invention
Applicant alleges that the Office action does not provide an adequate rationale for modifying the analytical and optimization systems of Meagher and Anichkov with the survivability analysis of Avritzer to arrive at the claimed invention because the references address different problem domains.
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 [Anichkov, column 4, lines 31-39]. As stated above, Anichkov discloses an asset manager physically deployed within a microgrid [Anichkov, column 5, lines 45-46; column 6, lines 7-11; figure 2] that comprises an optimal microgrid dispatch module to determine a dispatch scenario corresponding to an objective function based on models of the microgrid, wherein the dispatch scenario is communicated to a microgrid scheduler [Anichkov, column 8, lines 3-18] for autonomous execution [Anichkov, column 8, lines 19-25; column 11, lines 57-58] during real-time operation of the microgrid [Anichkov, column 11, lines 50-53; column 12, line 66 to column 13, line 4] to communicate control actions to one or more microgrid assets to directly regulate physical operation of the microgrid, such as charging and discharging an energy storage at a certain rate [Anichkov, column 11, line 57 to column 12, line 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.
D. Applicant’s argument that The Addition of Avritzer Does Not Cure the Deficiencies of the Primary Combination
Applicant alleges that even with the addition of Avritzer, the cited combination still fails to teach or suggest the key limitations of generating, transmitting, and executing controller logic based on a microgrid model.
Contrary to Applicant’s arguments, as discussed above, the combination of Meagher in view of Anichkov are considered to teach generating, transmitting, and executing controller logic based on a microgrid model.
Conclusion
9. THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN H TAN whose telephone number is (571)272-8595. The examiner can normally be reached M-F 10AM-6PM.
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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