DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action has been issued in response to amendment filed 01/30/2026. Applicant's arguments have been carefully and fully considered; and they are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made. Accordingly, this action has been made FINAL.
Claim Status
Claims 1 and 11 have been amended. Claims 1-19 remain pending and are ready for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 7-11, 13-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US20240144052A1 -hereinafter Zhou) in view of McMullin (US20120130767A1 -hereinafter McMullin) in view of Chen et al. (CN114841627A -hereinafter Chen -Note: As the machine translation attached).
Regarding Claim 1, Zhou teaches an adaptive power grid management system, the system comprises:
a processor coupled to the asset database and the network of devices, the processor being configured to execute a portfolio manager module which causes the processor to: (see [0057]; Zhou: “an automated decision optimization process 300 executing on a computer (e.g., computer 101), a processor (e.g., a processor of processor set 110) and/or processing circuitry (e.g., processing circuitry of processor set 110) obtains (e.g., receives, is sent, is provided, retrieves, etc.) 310 output from an automated artificial intelligence process. The output includes, for instance, data obtained from one or more sources (e.g., sensors, monitors, etc.) that, optionally, has been preprocessed, a risk estimation score and/or a chosen predictive modeling technique. This output (or a selected portion of it) is input to automated decision optimization process 300 that performs 320 optimization modeling to generate a plurality of maintenance solution pipelines and to automatically select a particular maintenance solution pipeline to produce an output (e.g., a model).”)
determine operation objectives for the formation plan; (see FIG. 4B and [0063]; Zhou: “selecting 440 at least one decision objective: e.g., replacement (pro-active), maintenance (pro-active), repair (reactive), and/or other objectives,”)
determine operational index requirements for each of the plurality of assets based on the formation plan; (see FIG. 4B and [0063]; Zhou: “associating 450 the at least one selected decision objective with one or more assets (e.g., a plurality of assets) to identify the risk metrics, such as, e.g., expected end-of-life cycle for assets; expected next failure time; estimated performance deterioration stage, etc.”)
cause verified assets of the plurality of the network of devices to execute the formation plan. (see [0067]; Zhou: “Automated decision optimization process 490 performs optimization modeling 492 for one or more maintenance tasks, including, but not limited to, maintenance planning and/or maintenance scheduling, such as to repair, replace, reuse, inspect, etc. one or more (e.g., a plurality of) assets.”)
However, Zhou does not explicitly teach: an asset database storing asset attributes of assets on a network of devices geographically distributed in a power grid for power transmission and distribution; and identify a plurality of assets from a provisional logistics list listing assets assigned to one or more tasks of a formation plan having a plurality of states, wherein the formation plan comprises a plurality of tasks to adaptively reconfigure the network of devices on the power grid from a current state to a desired state according to states of the formation plan; verify whether the plurality of assets collectively have acceptable operation indexes to execute the formation plan based on the operational index requirements and asset data in the asset database; in response to verifying that the plurality of assets collectively has acceptable operation indexes to execute the formation plan, generate a logistics list comprising verified assets for the plurality of tasks for the formation plan;
McMullin from the same or similar field of endeavor teaches:
an asset database storing asset attributes of assets on a network of devices geographically distributed in a power grid for power transmission and distribution; and (see [0002]; McMullin: “Power system grids include a variety of system assets such as, for example, generators, transmission lines, transformers, and associated control systems. The management of the grids may be performed in part, by a distribution control center that manages maintenance of assets that includes identifying faulty or inoperable assets, and tasking technicians to repair the assets.” See [0015]: “The repair prediction application 118 also receives data from a geographic information system 126 that is used to locate identified grid assets geographically and output the asset properties. In some embodiments asset properties and geographic locations are stored in multiple databases and applications that may be synchronized and/or reconciled by another means of data management.”)
identify a plurality of assets from a provisional logistics list listing assets assigned to one or more tasks of a formation plan having a plurality of states (see [0016]; McMullin: “The DCC 116 includes a distribution management system (DMS) 128 that generates a switch plan 107 for the grid 101. The switch plan 107 is a series of steps that are performed in a sequence to restore service to the grid 101. For example, following a repair to the grid 101, the switch plan 107 identifies specific grid assets (e.g., generators, breakers, transformers, substations) that are aligned and energized in a particular sequence to properly restore grid service while maintaining balanced voltage and frequency levels, providing continuous electrical service to energized sections”), wherein the formation plan comprises a plurality of tasks to adaptively reconfigure the network of devices on the power grid from a current state to a desired state according to states of the formation plan; (see [0016]; McMullin: “In operation, the DMS 128 receives information from a distribution data model 130 that includes, for example, a model of the grid 101 that includes the grid assets, normal values of the grid assets, and the current status of the grid assets. The DMS 128 uses the distribution data model 130 to generate the switch plan 107. The switch plan 107 is output to the SCADA 103 that outputs commands to the substations and distribution automation equipment and the work crews.” See [0017]: “The DMS 128 uses the switch plan modification data 109 to reevaluate assets that can be energized when regenerating the switch plan 107.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhou to include McMullin’s features of an asset database storing asset attributes of assets on a network of devices geographically distributed in a power grid for power transmission and distribution; and identify a plurality of assets from a provisional logistics list listing assets assigned to one or more tasks of a formation plan having a plurality of states, wherein the formation plan comprises a plurality of tasks to adaptively reconfigure the network of devices on the power grid from a current state to a desired state according to states of the formation plan. Doing so would increase the efficiency and effectiveness of the distribution control center in managing the restoration of grid functions. (McMullin, [0003])
However, it does not explicitly teach: verify whether the plurality of assets collectively have acceptable operation indexes to execute the formation plan based on the operational index requirements and asset data in the asset database; in response to verifying that the plurality of assets collectively has acceptable operation indexes to execute the formation plan, generate a logistics list comprising verified assets for the plurality of tasks for the formation plan;
Chen from the same or similar field of endeavor teaches:
verify whether the plurality of assets collectively have acceptable operation indexes to execute the formation plan based on the operational index requirements and asset data in the asset database; (see page 2, first paragraph; Chen: “calculating a safety index value within an intersection time window of the current service plan and the associated service plan based on the operational outage status of equipment in the transshipment plan, the current service plan, and the associated service plan; and judging whether the checking passes according to whether the safety index value meets the preset requirement”)
in response to verifying that the plurality of assets collectively has acceptable operation indexes to execute the formation plan, generate a logistics list comprising verified assets for the plurality of tasks for the formation plan; (see page 12, paragraph 4; Chen: “And step S104, judging whether the checking passes according to whether the safety index value meets the preset requirement, and obtaining a safety checking result of the current maintenance plan.” See page 12, paragraph 5: “When the calculated safety index value meets the preset requirement, the checking is passed, and the current maintenance plan can be arranged.” See page 4, paragraph 3: “Specifically, the maintenance plans in the scheduling and auditing stage include all current major network maintenance plans and distribution network maintenance plans in the scheduling and auditing stage, which are loaded by a cloud intelligent power scheduling support management system (OMS), and key domains in the maintenance plans are extracted: the maintenance list number, the maintenance starting time, the maintenance ending time and the power failure equipment.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Zhou and McMullin to include Chen’s features of verifying whether the plurality of assets collectively have acceptable operation indexes to execute the formation plan based on the operational index requirements and asset data in the asset database; in response to verifying that the plurality of assets collectively has acceptable operation indexes to execute the formation plan, generating a logistics list comprising verified assets for the plurality of tasks for the formation plan. Doing so would reduce repeated power failure of equipment and reduce the possibility of risk superposition. (Chen, page 6, paragraph 5)
Regarding Claim 3, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 1 above, Zhou further teaches wherein the operational index requirements are determined based on vital attributes associated with the formation plan. (see [0094]; Zhou: “Embodiments of the present invention utilize varying techniques to select attributes (elements, patterns, features, constraints, etc.), including but not limited to, diffusion mapping, principal component analysis, recursive feature elimination (a brute force approach to selecting attributes), and/or a Random Forest, to select the attributes related to various events.”)
Regarding Claim 4, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 1 above, Zhou further teaches wherein the processor is further configured to execute a resource dependency analyzer module that causes the processor to:
determine operational interdependencies between assets based on performing a simulation, with a simulation engine, of the plurality of states of the formation plan; (see [0109]; Zhou: “condition-based maintenance for an asset fleet is facilitated, in which the asset fleet may have a large number of assets of varying ages, each with one or more health-related sensor signals, assets that are geographically distributed in a large area, affecting maintenance schedules; dependencies and interactions between assets that impact maintenance downtimes, schedules and network reliability, and a desire to minimize the unscheduled downtime due to asset failure. The use of automated artificial intelligence and automated decision optimization offers flexibility in terms of scope, time horizon, risk estimators, and/or operational constraints, etc.” See [0087]: “A graph structure (e.g., directed acyclic graph) based system is used to assist with the automated identification and development of decision variables, key performance indicators, constraints, objective function(s), risk estimation models and corresponding constraints as part of an optimization object based on abstract representation of problem scope, time horizon, asset types and features, and data.”)
wherein the operational index requirements are determined based at least in part on an operational interdependency level of each of the plurality of assets. (see [0025]; Zhou: “In one or more aspects, a data scientist, analyst, user, etc. (without deep optimization expertise) is able to automatically generate risk estimation and optimization pipelines to perform asset management, such as condition-based maintenance planning and/or scheduling for an asset fleet (i.e., a plurality of assets with certain similarities and/or some assets having interdependencies), based on, for instance, available input data, asset interdependencies (e.g., physical network based and/or resource constrained) and/or problem definition, over, e.g., a time horizon (e.g., one month plan or other time periods).”)
Regarding Claim 5, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 4 above, Zhou further teaches wherein the operational index requirements comprise cascading operational requirements determined based on the operational interdependencies. (see [0025]; Zhou: “In one or more aspects, a data scientist, analyst, user, etc. (without deep optimization expertise) is able to automatically generate risk estimation and optimization pipelines to perform asset management, such as condition-based maintenance planning and/or scheduling for an asset fleet (i.e., a plurality of assets with certain similarities and/or some assets having interdependencies), based on, for instance, available input data, asset interdependencies (e.g., physical network based and/or resource constrained) and/or problem definition, over, e.g., a time horizon (e.g., one month plan or other time periods).”)
Regarding Claim 7, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 4 above, Zhou further teaches the simulation engine utilizes a learning engine to execute an operation loop of the formation plan to calibrate asset interdependency, the learning engine being trained on historical logistic files and asset interdependency data. (see [0093]; Zhou: “Machine learning (ML) solves problems that are not solved with numerical means alone. In this ML-based example, program code extracts various attributes from ML training data 1310 (e.g., historical data collected from various data sources relevant to the event), which may be resident in one or more databases 1320 comprising event or task-related data and general data. Attributes 1315 are utilized to develop a predictor function, h(x), also referred to as a hypothesis, which the program code utilizes as a machine learning model 1330.”)
Regarding Claim 8, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 1 above, Zhou further teaches wherein operational indexes of each of the plurality of assets are determined based on an asset identifier, an asset function type, and associated asset type attribute. (see [0063]; Zhou: “In one example, automated artificial intelligence process 420 uses predictive modeling to provide a risk estimation (e.g., a score, value, etc.) of one or more conditions (e.g., health, failure, end-of-life cycle, etc.) of one or more assets of e.g., one or more components and/or machines/devices, etc”)
Regarding Claim 9, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 1 above, Zhou further teaches wherein the processor is further configured to:
determine a risk index and/or a health index for each of the plurality of assets in the provisional logistics list; and (see [0063]; Zhou: “In one example, automated artificial intelligence process 420 uses predictive modeling to provide a risk estimation (e.g., a score, value, etc.) of one or more conditions (e.g., health, failure, end-of-life cycle, etc.) of one or more assets of e.g., one or more components and/or machines/devices, etc”)
initiate an asset replacement process in the event that the risk index and/or the health index of an asset is below a predetermined threshold. (see [0058]; Zhou: “at periodic intervals or based on an update to selected data, such as, e.g., a change in risk scores above/below a threshold, etc., automated decision optimization process 300 obtains the output from the automated artificial intelligence process. Based on obtaining the output, the code and model rendering may be re-initiated 350 while still using the maintenance solution pipeline that was automatically selected. For instance, based on a change in risk scores (e.g., a change above/below a threshold, as an example), automated decision optimization process 300 re-initiates the code and model rendering to provide an output of decision support for one or more assets of a plurality of assets in which a maintenance action of repair, replace, reuse, inspect and/or maintain, etc. is performed.”)
Regarding Claim 10, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 9 above, Zhou further teaches wherein health indexes of the plurality of assets are determined based on performing asset health analysis based on asset status information stored in an asset health database and updated via network connections to the plurality assets on the network of devices. (see [0063]; Zhou: “In one example, automated artificial intelligence process 420 uses predictive modeling to provide a risk estimation (e.g., a score, value, etc.) of one or more conditions (e.g., health, failure, end-of-life cycle, etc.) of one or more assets of e.g., one or more components and/or machines/devices, etc”. See [0064]: “Further, in one example, automated risk assessment process 430 obtains 460 the latest (e.g., up-to-date) information to execute one or more risk estimation metrics. This latest information includes, for instance, latest sensor information, latest monitoring information, latest service work order and/or other information (e.g., utility, etc.), criticality information of each asset, etc.”)
Regarding Claim 11, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 1.
Regarding Claim 13, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 3.
Regarding Claim 14, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 4.
Regarding Claim 15, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 5.
Regarding Claim 17, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 8.
Regarding Claim 18, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 9.
Regarding Claim 19, the limitations in this claim is taught by the combination of Zhou, McMullin, and Chen as discussed connection with claim 10.
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of McMullin in view of Chen in view of Jia et al. (US20160072287A1 -hereinafter Jia).
Regarding Claim 2, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 1 above; however, it does not explicitly teach wherein operational indexes comprise observability, reachability, adaptability, controllability, security, sustainability, and stability indexes.
Jia from the same or similar field of endeavor teaches wherein operational indexes comprise observability (see [0268]; Jia: “According to another embodiment of the invention, throughout operation of an end user system connected to a MEDS system, a variety of data fusion techniques can be utilized by the server system to leverage observed data collected from building automation and industrial controls systems that are connected to a MEDS system via a network.”), reachability (see [0200]; Jia: “the probability of congestion occurring in a given region to which a participant has exposure or has indicated interest reaching a specific threshold”), adaptability (see Abstract; Jia: “A system for electric grid utilization and optimization comprising a communications interface executing on a network-connected server and adapted to receive information from a plurality of iNodes.”), controllability (see [0068]; Jia: “The continuous flow electrical distribution network can be thought of as a network of “pipes” or “channels” connecting a large number of eNodes; electricity flows through these channels (mostly these are wires of course) and is transformed, stored, controlled, and measured at various eNodes.”), security (see [0153]; Jia: “An EDS Index will have a number of series representing different realization times of securities and different tranches per series, using a weighting mechanism determined by statistics server 1030 based on actual volume of available securities to be indexed.”), sustainability (see [0236]; Jia: “Detailed information captured by MEDS systems can be leveraged by corporations, organizations, or individuals seeking to highlight responsible actions or improvements in sustainability.”), and stability indexes (see [0150]; Jia: ““Stability of results” refers to the variance of key output variables (revenues, profits, idle capacity levels, etc.) for a given parameter combination”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhou, McMullin, and Chen to include Jia’s features of operational indexes comprise observability, reachability, adaptability, controllability, security, sustainability, and stability indexes. Doing so would maintain grid stability during times of high stress. (Jia, [0006])
Regarding Claim 12, the limitations in this claim is taught by the combination of Zhou, McMullin, Chen, and Jia as discussed connection with claim 2.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of McMullin in view of Chen in view of He et al. (US 20240070352 A1 -hereinafter He).
Regarding Claim 6, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 4 above; however, it does not explicitly teach wherein the simulation is performed based on simulation scenarios stored in a historical scenarios database storing historical scenarios associated with a plurality of devices on the network of devices.
He from the same or similar field of endeavor teaches wherein the simulation is performed based on simulation scenarios stored in a historical scenarios database storing historical scenarios associated with a plurality of devices on the network of devices. (see [0038]; He: “The grid libraries 320 can include metadata about elements in the unified grid model and can include equipment libraries 325 and grid specifications 330. Application data 340 can include load curves 345 that can be based on historical observations, study scenarios 350 that can include data observed under studied conditions and simulation configurations 355. Simulation configurations 355 can include start/stop time, time step size, solver type and error tolerance, the maximum number of iterations, data recorder and sampling rate and unit, and so on.” See [0064]: “The grid libraries 320 can include metadata about elements in the unified grid model and can include equipment libraries 325 and grid specifications 330. Application data 340 can include load curves 345 that can be based on historical observations, study scenarios 350 that can include data observed under studied conditions and simulation configurations 355. Simulation configurations 355 can include start/stop time, time step size, solver type and error tolerance, the maximum number of iterations, data recorder and sampling rate and unit, and so on.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Zhou, McMullin, and Chen to include He’s features of performing the simulation based on simulation scenarios stored in a historical scenarios database storing historical scenarios associated with a plurality of devices on the network of devices. Doing so would reduce processing time and provide insights in data while maintaining data integrity and security. (He, [0004])
Regarding Claim 16, the combination of Zhou, McMullin, and Chen teaches all the limitations of claim 14 above, Zhou further teaches the simulation engine utilizes a learning engine to execute an operation loop of the formation plan to calibrate asset interdependency, the learning engine being trained on historical logistic files and asset interdependency data. (see [0093]; Zhou: “Machine learning (ML) solves problems that are not solved with numerical means alone. In this ML-based example, program code extracts various attributes from ML training data 1310 (e.g., historical data collected from various data sources relevant to the event), which may be resident in one or more databases 1320 comprising event or task-related data and general data. Attributes 1315 are utilized to develop a predictor function, h(x), also referred to as a hypothesis, which the program code utilizes as a machine learning model 1330.”)
However, it does not explicitly teach wherein the simulation is performed based on simulation scenarios stored in a historical scenarios database storing historical scenarios associated with a plurality of devices on the network of devices.
He from the same or similar field of endeavor teaches wherein the simulation is performed based on simulation scenarios stored in a historical scenarios database storing historical scenarios associated with a plurality of devices on the network of devices. (see [0038]; He: “The grid libraries 320 can include metadata about elements in the unified grid model and can include equipment libraries 325 and grid specifications 330. Application data 340 can include load curves 345 that can be based on historical observations, study scenarios 350 that can include data observed under studied conditions and simulation configurations 355. Simulation configurations 355 can include start/stop time, time step size, solver type and error tolerance, the maximum number of iterations, data recorder and sampling rate and unit, and so on.” See [0064]: “The grid libraries 320 can include metadata about elements in the unified grid model and can include equipment libraries 325 and grid specifications 330. Application data 340 can include load curves 345 that can be based on historical observations, study scenarios 350 that can include data observed under studied conditions and simulation configurations 355. Simulation configurations 355 can include start/stop time, time step size, solver type and error tolerance, the maximum number of iterations, data recorder and sampling rate and unit, and so on.”)
The same motivation to combine Zhou, McMullin, Chen, and He a set forth for Claim 6 equally applies to Claim 16.
Response to Arguments
Applicant’s arguments with respect to the claim rejection(s) of the independent claim(s) have been fully considered and are persuasive because of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goutard et al. (US20110282508A1) discloses analyzing the data, including performing predictive analysis, e.g., via simulation, root cause analysis, post mortem analysis, or complex event processing, when desired, to facilitate identifying a current or predicted future state of the PTDG, a cause or source of an abnormal condition, or a remedial action execution plan, new operation or maintenance guidance, etc.
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 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.
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/V.N.T./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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