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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Examiner’s Note
This Office Action is in response to application filed on 6/21/2024, where claims 1-20 are currently pending.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13 and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Matan et al., (US 2021/0328456 A1) (hereinafter Matan).
Referring to claim 1, Matan teaches a power grid system comprising:
an asset management controller associated with one or more of a plurality of zones in a power grid (¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230...RTO (regional transmission operator) 226 refers to regional transmission controllers such as transformers and substations”. ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.”), the asset management controller configured to:
receive primary asset data for at least one primary asset associated with at least one zone of a plurality of zones (¶ [0039], “iGOS includes an automatic self-sufficient system that manages realtime data from sensing equipment, realtime data feeds, and measurements of generating resources (renewable, storage, generators, and more), with algorithmic computational devices to collect and analyze the realtime data.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.”);
receive secondary asset data for at least one secondary asset (¶ [0087], gateway) associated with the at least one zone of the plurality of zones (¶ [0087], fig. 6, “system 600 includes gateway that can be and/or be part of a control node…router 632 enables gateway 630 to communicate with data center 680…Thus, data center 680 represents a source of grid-based information, such as control, dispatch information, or other data about grid operation, as well as other aggregation information…router 632 represents a stack or protocol engine within gateway 630 to generate and process communication in addition to the hardware connectors that provide an interface or connection to the grid.” ¶ [0093], fig. 7, “a gateway aggregator system. System 700…of a gateway device, and can be or be included in a control node or DER node…system 700 can be considered part of iGOS. Based on local and network information, aggregator 710 can determine how to manage energy within a DER node.” ¶ [0094], “Aggregation logic 720 represents logic that enables aggregator 710 to gather multiple elements of data related to electrical grid conditions.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.”);
determine, via at least one machine learning model, at least one zonal analytics parameter (¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0073], “the distributed control in system 300 can provide dynamic control over power demand and power generation as seen at a PCC…the control node includes a power converter to control real and reactive power demand and real and reactive power generation.” ¶ [0147], “iGOS 1240 also factors in grid interface 1244…Grid information 1220 represents what is happening at the grid from an operational standpoint (e.g., what is the power factor at the point of connection)”. Examiner recognizes the zonal analytics parameter as the power factor.);
identify at least one asset management control action for a control device associated with the at least one zone based on the at least one zonal analytics parameter (¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0104], “execution logic 750 receives one or more conditions, one or more actions, or one or more predictions, respectively, from aggregation logic 720, forecast logic 730, and forward prediction logic 740. Execution logic 750 can analyze the input data and compute or calculate one or more operations to perform based on the received data.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0073], “the distributed control in system 300 can provide dynamic control over power demand and power generation as seen at a PCC…the control node includes a power converter to control real and reactive power demand and real and reactive power generation.” ¶ [0147], “iGOS 1240 also factors in grid interface 1244…Grid information 1220 represents what is happening at the grid from an operational standpoint (e.g., what is the power factor at the point of connection)…Based on the grid and the energy markets, iGOS 1240 can cause output hardware 1230 to adjust operation to present a different interface to the grid.” ¶ [0178], “Controller 1640 controls at least one electrical parameter of the interfaces of converter 1622 to control its operation.” ¶ [0179], “converter 1620 includes tables 1650, which provides a table-based method for controlling power factor, to adjust the operation of converter 1620 to generate reactive power as desired.”); and
communicate a command to a distribution zonal controller associated with the at least one zone based on the at least one asset management control action (¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0093], fig. 7, “System 700…of a gateway device, and can be or be included in a control node or DER node…system 700 can be considered part of iGOS. Based on local and network information, aggregator 710 can determine how to manage energy within a DER node.” ¶ [0092], “converter 650 adjusts operation in response to one or more commands from data center 680 to adjust a combination of real and reactive power provided by the DER at PCC 622.” ¶ [0041], “The iGOS platform enables intelligent control individually at each node of a network of DERs, as well as aggregation for overall network stability.” ¶ [0051], “Control center 210 can receive information from DERs and provide commands to them through network interface 212.” ¶ [0178], “Controller 1640 controls at least one electrical parameter of the interfaces of converter 1622 to control its operation.” ¶ [0179], “converter 1620 includes tables 1650, which provides a table-based method for controlling power factor, to adjust the operation of converter 1620 to generate reactive power as desired.” ¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230”. Examiner recognizes the asset management control action as the adjusted operation.)
Referring to claim 2, Matan further teaches the power grid system of claim 1, wherein the at least one primary asset is a piece of electrical distribution equipment (¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230...RTO (regional transmission operator) 226 refers to regional transmission controllers such as transformers and substations”).
Referring to claim 3, Matan further teaches the power grid system of claim 1, wherein the at least one primary asset includes at least one of a transformer, a breaker, or a generator (¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230...RTO (regional transmission operator) 226 refers to regional transmission controllers such as transformers and substations”).
Referring to claim 4, Matan further teaches the power grid system of claim 1, wherein the at least one secondary asset is an intelligent electronic device (IED) that controls the at least one primary asset (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0041], “The iGOS platform enables intelligent control individually at each node of a network of DERs, as well as aggregation for overall network stability.” ¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230...RTO (regional transmission operator) 226 refers to regional transmission controllers such as transformers and substations”. ¶ [0087], “system 600 includes gateway that can be and/or be part of a control node”.)
Referring to claim 5, Matan further teaches the power grid system of claim 1, wherein the at least one secondary asset is a relay, a gate control unit, or a gateway (¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260”. ¶ [0087], “system 600 includes gateway that can be and/or be part of a control node”.)
Referring to claim 6, Matan further teaches the power grid system of claim 1, wherein the at least one zonal analytics parameter includes at least one of a process analytics parameter, a health analytics parameter, a performance analytics parameter, or a security analytics parameter (¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0073], “the distributed control in system 300 can provide dynamic control over power demand and power generation as seen at a PCC…the control node includes a power converter to control real and reactive power demand and real and reactive power generation.” ¶ [0147], “iGOS 1240 also factors in grid interface 1244…Grid information 1220 represents what is happening at the grid from an operational standpoint (e.g., what is the power factor at the point of connection)…Based on the grid and the energy markets, iGOS 1240 can cause output hardware 1230 to adjust operation to present a different interface to the grid.”)
Referring to claim 7, Matan further teaches the power grid system of claim 6, wherein the process analytics parameter includes information indicative of at least one of hardware performance, health, or a life cycle of the at least one secondary asset (¶ [0041], “The iGOS platform enables intelligent control individually at each node of a network of DERs, as well as aggregation for overall network stability.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0087], “system 600 includes gateway that can be and/or be part of a control node”. ¶ [0134], “The power consumed by node 900 can be referred to as tare loss, which indicates how much power the controlling devices consume when the node is not generating power.”)
Referring to claim 8, Matan further teaches the power grid system of claim 6, wherein the process analytics parameter includes at least one of a device computation time, a device performance during an event, hardware diagnostics, firmware logs, device related watchdog events, hardware BIOS logs, device time accuracy (¶ [0041], “The iGOS platform enables intelligent control individually at each node of a network of DERs, as well as aggregation for overall network stability.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0134], “The power consumed by node 900 can be referred to as tare loss, which indicates how much power the controlling devices consume when the node is not generating power.”)
Referring to claim 9, Matan further teaches the power grid system of claim 6, wherein the at least one machine learning model includes a process machine learning model that is trained via at least one of historical primary asset data, historical secondary asset data, or historical process analytics parameters (¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0134], “The power consumed by node 900 can be referred to as tare loss, which indicates how much power the controlling devices consume when the node is not generating power.” ¶ [0103], “historical data can identify particular states of operation and subsequent states of operation and how long elapsed between them. Thus, for example, forward prediction can determine whether or not to perform a determined action based on historical information indicating whether such a condition or state is likely to persist for long enough for economic benefit…the historical data can be referred to as operating history or operational data, referring to operations within the monitored/controlled grid node.” ¶ [0179], “converter 1620 includes tables 1650, which provides a table-based method for controlling power factor, to adjust the operation of converter 1620 to generate reactive power as desired…By matching output performance to an idealized representation of the input power, better system performance is possible than simply attempting to filter and adjust the output in traditional ways.”)
Referring to claim 10, Matan further teaches the power grid system of claim 6, wherein the health analytics parameter is indicative of at least one of a health risk, a performance, a condition state, a criticality, or a reliability of the at least one primary asset (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “Examples of sensor data can include, but are not limited to, load information, local temperature, light conditions, and/or other information…load information is gathered or monitored by a meter that determines what loads are drawing power, such as by energy signatures that indicate complex current vectors for the load…load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.”); and wherein the health analytics parameter is determined based on data measured from one or more sensors associated with the at least one primary asset (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “Examples of sensor data can include, but are not limited to, load information, local temperature, light conditions, and/or other information…load information is gathered or monitored by a meter that determines what loads are drawing power, such as by energy signatures that indicate complex current vectors for the load…load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.”)
Referring to claim 11, Matan further teaches the power grid system of claim 6, wherein the at least one machine learning model includes a health machine learning model that is trained via at least one of historical primary asset data, historical secondary asset data, or historical health analytics parameters (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0103], “historical data can identify particular states of operation and subsequent states of operation and how long elapsed between them. Thus, for example, forward prediction can determine whether or not to perform a determined action based on historical information indicating whether such a condition or state is likely to persist for long enough for economic benefit…the historical data can be referred to as operating history or operational data, referring to operations within the monitored/controlled grid node.”)
Referring to claim 12, Matan further teaches the power grid system of claim 6, wherein the performance analytics parameter is indicative of at least one of design constraints or a loading of the at least one primary asset based on thermal characteristics of the at least one primary asset (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.”); and wherein the performance analytics parameter is determined based on at least one of historical loading condition, historical loading patters, current loading conditions, or current loading patterns for the at least one primary asset (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0103], “historical data can identify particular states of operation and subsequent states of operation and how long elapsed between them. Thus, for example, forward prediction can determine whether or not to perform a determined action based on historical information indicating whether such a condition or state is likely to persist for long enough for economic benefit…the historical data can be referred to as operating history or operational data, referring to operations within the monitored/controlled grid node.”)
Referring to claim 13, Matan further teaches the power grid system of claim 6, wherein the at least one machine learning model includes a performance machine learning model that is trained via at least one of historical primary asset data, historical secondary asset data, or historical performance analytics parameters (¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0095], “load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0103], “historical data can identify particular states of operation and subsequent states of operation and how long elapsed between them. Thus, for example, forward prediction can determine whether or not to perform a determined action based on historical information indicating whether such a condition or state is likely to persist for long enough for economic benefit…the historical data can be referred to as operating history or operational data, referring to operations within the monitored/controlled grid node.”)
Referring to claim 17, Matan further teaches the power grid system of claim 6, wherein the at least one asset management control action is based on the health analytics parameter and the performance analytics parameter, and wherein the at least one asset management control action is at least one of a loading guideline for the at least one primary asset (¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0041], “The iGOS platform enables intelligent control individually at each node of a network of DERs, as well as aggregation for overall network stability.” ¶ [0051], “Control center 210 can receive information from DERs and provide commands to them through network interface 212.” ¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230”. ¶ [0178], “Controller 1640 controls at least one electrical parameter of the interfaces of converter 1622 to control its operation.” ¶ [0136], “iGOS 1010 executes software platform 1020, which can include features and services such as market analytics, behavior monitoring and learning, and prediction.” ¶ [0050], “the iGOS system or intelligent platform as operational in DERs 130 or control center 110 or both, can provide analytical aggregation of information.” ¶ [0073], “the distributed control in system 300 can provide dynamic control over power demand and power generation as seen at a PCC…the control node includes a power converter to control real and reactive power demand and real and reactive power generation.” ¶ [0147], “iGOS 1240 also factors in grid interface 1244…Grid information 1220 represents what is happening at the grid from an operational standpoint (e.g., what is the power factor at the point of connection)… Based on the grid and the energy markets, iGOS 1240 can cause output hardware 1230 to adjust operation to present a different interface to the grid.” ¶ [0095], “Examples of sensor data can include, but are not limited to, load information, local temperature, light conditions, and/or other information…load information is gathered or monitored by a meter that determines what loads are drawing power, such as by energy signatures that indicate complex current vectors for the load…load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.” ¶ [0104], “execution logic 750 receives one or more conditions, one or more actions, or one or more predictions, respectively, from aggregation logic 720, forecast logic 730, and forward prediction logic 740…execution logic 750 selectively generates an operation based on computed conditions, actions, and predictions.”)
Referring to claim 18, Matan further teaches the power grid system of claim 17, wherein the asset management controller is further configured to:
communicate the command to a zonal controller associated with the at least one zone (¶ [0043], fig. 1, “FIG. 1 is a block diagram of a system to manage distributed energy resources of a power grid.” ¶ [0054], fig. 2, “Control center 210 can provide energy distribution or other distributed energy resource management for DERs 260 based on information from, or to provide service to, one or more of IPP 222, ISO 224, RTO 226, IOU 228, or rate payers 230...RTO (regional transmission operator) 226 refers to regional transmission controllers such as transformers and substations”. ¶ [0100], “a determined condition as calculated by aggregation logic 720 can identify a specific state or zone of operation for an interface managed by aggregator 710.” ¶ [0060], “control center 210 may send commands to selected ones of DERs 260 for services to provide services for an energy market…In response to control signals or commands, the DERs can change operation to provide the services on the grid…the DERs provide energy for local customers.”)
Referring to claim 19, Matan further teaches the power grid system of claim 17, wherein the loading guideline (¶ [0095], maximum load capacities allowed) controls a loading level on the at least one primary asset to reduce a risk or to re-dispatch sources in another one of the plurality of zones (¶ [0072], “Grid operators (e.g., utilities) typically set limits on how much local power generation can be coupled to the grid, to reduce the risk of a scenario where significant amounts of energy get pushed back up the grid to the power plant.” ¶ [0095], “Examples of sensor data can include, but are not limited to, load information, local temperature, light conditions, and/or other information…load information is gathered or monitored by a meter that determines what loads are drawing power, such as by energy signatures that indicate complex current vectors for the load…load information can be configured into aggregator 710, which can be maximum load capacities allowed for specific load connections (e.g., breakers, outlets, or other connection)…the operation of a local energy source can be affected by temperature, or the temperature can be an indication of expected efficiency or demand for certain loads and/or energy sources.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Matan as applied to claim 6 above, and in view of Koval et al., (US 20190260204 A1) (hereinafter Koval).
Referring to claim 14, Matan teaches the power grid system of claim 6. However, Matan does not explicitly teach the security analytics parameter is indicative of a communication and cyber security (CCS) risk associated with the at least one secondary asset; and wherein the CCS risk is determined from a security log, a communication log, a sequence of events, or a traffic analysis associated with a device.
Koval teaches the security analytics parameter is indicative of a communication and cyber security (CCS) risk associated with the at least one secondary asset (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0284], “Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).”); and wherein the CCS risk is determined from a security log, a communication log, a sequence of events, or a traffic analysis associated with a device (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0284], “Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).”)
Matan and Koval are analogous art to the claimed invention because they are concerning with interface with power distribution system (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Matan and Koval before them to modify the intelligent grid operating system of Matan to incorporate the function of security analytics parameter of Koval. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Koval (¶ [0278]-[0295]), because the function of security analytics parameter does not depend on the intelligent grid operating system. That is the function of security analytics parameter performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to allowed desired change to perform based on predictions as suggested by Koval (¶ [0294]).
Referring to claim 15, Matan teaches the limitations above. However, Matan does not explicitly teach the CCS risk relates a level of bandwidth, a level of latency, a cyber security issue, a security patch upgrade pending, a communication-related misconfiguration, a security related misconfiguration, or a needed update.
Koval further teaches the CCS risk relates a level of bandwidth, a level of latency, a cyber security issue, a security patch upgrade pending, a communication-related misconfiguration, a security related misconfiguration, or a needed update (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0284], “Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).”)
Referring to claim 16, Matan teaches the power grid system of claim 6. However, Matan does not explicitly teach the at least one machine learning model includes a cyber security machine learning model that is trained via at least one of historical primary asset data, historical secondary asset data, or historical security analytics parameters.
Koval teaches the at least one machine learning model includes a cyber security machine learning model that is trained via at least one of historical primary asset data, historical secondary asset data, or historical security analytics parameters (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).” ¶ [0278], “the machine learning algorithm and automated data analysis may be implemented in a system 1100 shown in FIG. 14, also known as an artificial intelligence (AI) system. System 1100 includes a data library 1102 for storing data samples, machine learning module 1104 for executing one or more machine learning algorithms on data samples received from library 1102 and outputting a prediction and/or recommendation based on the received samples, and an action module 1106 for receiving the output (e.g., predictions and/or recommendations) from machine learning module 1104 and performing an action based on the output.” ¶ [0019], “the plurality of data samples include a first trained set based on historical readings by the one or more IEDs”.)
Matan and Koval are analogous art to the claimed invention because they are concerning with interface with power distribution system (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Matan and Koval before them to modify the intelligent grid operating system of Matan to incorporate the function of machine learning of Koval. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Koval (¶ [0277]-[0295]), because the function of machine learning does not depend on the intelligent grid operating system. That is the function of machine learning performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to allowed automated analysis as suggested by Koval (¶ [0277]-[0278]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Matan in view of Koval as applied to claim 14 above, and further in view of Greenberg et al., (US 2010/0080144 A1) (hereinafter Greenberg).
Referring to claim 20, Matan teaches the limitations above. However, Matan does not explicitly teach the asset management controller is further configured to:
determine a life cycle risk for the at least one secondary asset based on the process analytics parameter;
determine a communications and cyber security (CCS) risk for the at least one secondary asset based on the security analytics parameter; and
determine a ranking for the at least one secondary asset based on the life cycle risk and the CCS risk.
Koval further teaches the asset management controller is further configured to:
determine a life cycle risk for the at least one secondary asset based on the process analytics parameter (¶ [0319], “system 1100 is used to determine and predict component wear percentage. One example may be where module 1104 computes the estimated remaining life of a related hardware component, such as, but not limited to, connected wiring, CT's, PT's, or relays based on samples provided via module 1102. When the estimated remaining life of any of the hardware components is below a predetermined threshold value, action module 1106 may send an alert to one or more one or more clients”. ¶ [0322], “module 1104 to provide recommendations to users, based on the detected conditions within the meters or IEDs…The recommendation may be sent by action module 1106 when the expected life remaining for the component is below a predetermined threshold.”);
determine a communications and cyber security (CCS) risk for the at least one secondary asset based on the security analytics parameter (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0284], “Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).”); and
determine…the at least one secondary asset based on the life cycle risk and the CCS risk (¶ [0142], “the networks may be configured to adhere to various cyber security standards to minimize the number of successful cyber security attacks. The cyber security standards apply to devices, IEDs, computers and computer networks.” ¶ [0164], “Another example may be for the processor or communication module of the IED to upload its current operational status, such as uptime, security status, and internal health checks, to the server every minute, such that the server can keep track of the health of the IED.” ¶ [0284], “Action module 1106 may further use the detected attempts of intrusion or tampering to increase a security state (e.g., require more factors of authentication at an IED or facility) to reduce the risk of intrusion or tampering until the intrusion or tampering is otherwise dealt with.” ¶ [0295], “Another set of data that may be stored in library 1102 and used as inputs to such algorithms in module 1104 may be Security Logs of login and secure action attempts to a meter…When an intrusion attempt is detected, action module 1106 may send an alert to one or more clients and/or send a control signal to increase a security state (e.g., require additional points of authentication for access to an IED or facility and/or shut down an IED or facility).” ¶ [0322], “module 1104 to provide recommendations to users, based on the detected conditions within the meters or IEDs…The recommendation may be sent by action module 1106 when the expected life remaining for the component is below a predetermined threshold.”)
Matan in view of Koval teaches the limitations above. However, Matan in view of Koval do not explicitly teach determine a ranking for the at least one secondary asset.
Greenberg teaches determine a ranking for the at least one secondary asset (¶ [0053], “Block 608 represents ranking the plurality of network gateways maintained in the hash table.”)
Matan, Koval, and Greenberg are analogous art to the claimed invention because they are concerning with interface with network of devices (i.e., same field of endeavor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having Matan in view of Koval and Greenberg before them to modify the intelligent grid operating system of Matan in view of Koval to incorporate the function of determining a ranking of Greenberg. One of ordinary skill in the art would have combined the elements as claimed by known methods as disclosed by Greenberg (¶ [0048]-[0062]), because the function of determining a ranking does not depend on the intelligent grid operating system. That is the function of determining a ranking performs the same function independent on which interface it is incorporated onto, and therefore, the result of the combination would have been predictable to one of ordinary skill in the art. The motivation to combine would have been to determine desirability of a network gateway as a route as suggested by Greenberg (¶ [0053]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2021/0296897 (Cruickshank, III) – discloses method and system for optimize load shaping for optimizing production and consumption of energy.
CN 102725933 (Cherian) – discloses system for dynamic management and control distributed energy within a power grid.
RU 2733063 (Brombach) – discloses method of controlling electrical distribution network having a plurality of adjustment zones.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONG-SHUNE CHUNG whose telephone number is (571) 270-5817. The examiner can normally be reached on M-F (9-5) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman, can be reached at telephone number 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/MONG-SHUNE CHUNG/
Primary Examiner, Art Unit 2118
Prosecution Insights