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 .
Status of the Claims
Claims 1, 3-5, 7-8, 11, 13-15, and 17-18 are rejected under 35 U.S.C. 102(a)(2).
Claims 2, 6, 10, 12, 16, and 20 are rejected under 35 U.S.C. 103.
Claims 9 and 19 are objected to for depending from a rejected base claim.
Claims 1-20 are objected to for minor informalities.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
In line 9 of claim 1, “context data associate with a current condition” should read “context data associated with a current condition”. The same correction is necessary in line 6 of claim 11. Claims 2-10 and 12-20 are objected to due to their dependencies.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 7-8, 11, 13-15, and 17-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PATHAK (US 2022/0123552 A1).
Regarding Claim 1, PATHAK teaches an adaptive power grid management system, (¶ 28, 34-36, Figs. 1-2: The disclosure is directed to adaptive diagnosis of power anomalies in a power network.)
the system comprises: a network device database; (¶ 38: “The reporting module 234 may access database 236 to obtain operational relationships between machines and/or other elements in the hierarchy of power distribution of the facility 200”. A database 236 (Fig. 2) is a network device database.
a network adapter, (¶ 75: “The power network 1900 may include a receiver 1970 and/or a transmitter 1980 that communicate with a facility, power metering devices, users, operators, and/or a display device configured to provide fault information.”)
and a processor coupled to the network device database and the network adapter, the processor being configured to: (¶ 79, Fig. 2, backend includes a processor coupled to the databases and adapters. See Fig. 19.)
train, with a machine learning algorithm using historical device signals and historical context information associated with a network of devices, a context model; (Fig. 3A model 308 is trained with a machine learning algorithm 306 using historical device signals and context information associated with the network devices, such as power usage. See ¶ 41-42.)
receive signals from the network of devices; (¶ 38, 40-41: Signals such as the current active power from the network are received.)
determine, based on the context model, context data associate with a current condition of the network of devices; (¶ 41, 47, Fig. 3A, 3C: The trained context model 308/316 is used to determine deviations from a baseline or anomalies of the network of devices, which is context data associated with current condition of the network devices.)
determine a formation plan based on the context data, the formation plan comprises tasks to be carried out by one or more devices in the network of devices; (Fig. 3C classification/ severity 338, suggested action items 344, and alarm notification 342 are a formation plan that is determined based on the context (i.e. anomaly) data determined by the machine learning model. See ¶ 30, 47: Actions are recommended to mitigate faults in the network.)
configure one or more scout applications based on the formation plan and device information stored in the network device database; and cause, via the network adapter, the one or more scout applications to be executed by the one or more devices in the network of devices. (¶ 30, 36, 38, 46: A reporting module (scout application) reports the anomaly and executes actions based on the recommended actions based on the classified anomalies. The reporting of the anomalies includes fault isolation, as discussed in ¶ 11, 15, 51, 58, and 72, to determine the location (i.e. meter, device) where the fault is occurring.)
Claim 11 is directed to a method but otherwise recites the same limitations as claim 1. Claim 11 is therefore rejected for the same reasoning discussed above.
Regarding Claim 3, PATHAK further teaches wherein the context data comprises a set of select vital attributes each associated with a severity degree. (¶ 46-47: The context data is classified as an anomaly type and severity level and whether it is critical (vital) or not.)
Claim 13 recites the same limitations as claim 3 and is rejected for the same reasoning discussed above.
Regarding Claim 4, PATHAK further teaches wherein the context data comprises a control area identifier (¶ 68-69: An uncertainty bound (control area) is determined from the context data), a timing identifier (¶ 68-69: A temporal patten is determined in the context data), and an alert condition identifier (¶ 47: An alert condition is determined from the context data.).
Claim 14 recites the same limitations as claim 4 and is rejected for the same reasoning discussed above.
Regarding Claim 5, PATHAK further teaches wherein the formation plan is determined based on: matching the context data with context patterns in a context pattern database; (¶ 29-30, 41, 52-53: Patterns in the historical power consumption data are stored in a database and used to train the machine learning model. The trained model is then used to match current data with the learned patterns.)
separating the context data into contextual abstraction panels each corresponding to a separate problem domain perspective; (¶ 46-47: The context data is classified into different anomaly types with severity levels and determined criticality (i.e. separated into “contextual abstraction panels each corresponding to a separate problem domain perspective”))
and determining the tasks and the one or more devices associated with each contextual abstraction panel. (¶ 9, 46, 47, 67, Fig. 2 repair action database 232: The anomaly type is associated with a repair action that is taken responsive to isolating the anomaly (i.e. determining the location, or device, responsible for the fault.)
Claim 15 recites the same limitations as claim 5 and is rejected for the same reasoning discussed above.
Regarding Claim 7, PATHAK further teaches wherein configuring the one or more scout applications comprises selecting a stored scout application matching the formation plan. (¶ 9, 46: Historical faults are mapped to their remedies or repair actions in a stored database. The repair action (scout application) is therefore selected matching the formation plan, which identified the fault type, severity level, and criticality, as discussed in the rejection of claim 1 and illustrated in Fig. 3C.)
Claim 17 recites the same limitations as claim 7 and is rejected for the same reasoning discussed above.
Regarding Claim 8, PATHAK further teaches wherein the one or more scout applications are each assigned a role and is configured based on the role. (¶ 9, 46-47, 54: The type of the anomaly determines what repair action (i.e. scout application) is taken. The different repair actions are therefore configured based on a role, i.e. the type and criticality of the anomaly. This role also determines whether an alarm notification is sent, so an alarm is also configured based on the determined anomaly type and criticality.)
Claim 18 recites the same limitations as claim 8 and is rejected for the same reasoning discussed above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 6, 10, 12, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over PATHAK (US 2022/0123552 A1) in view of YAN (US 2018/0262525 A1).
Regarding Claim 2, PATHAK teaches all the limitations of claim 1, on which claim 2 depends.
PATHAK does not explicitly teach wherein the one or more devices comprises field agent devices associated with one or more of a power plant, a solar farm, a windfarm, a digital substation, a microgrid controller, and an electric vehicle charging station.
However, YAN, which is similarly directed to managing a power grid to protect from anomalies, teaches wherein the one or more devices comprises field agent devices associated with one or more of a power plant, a solar farm, a windfarm, a digital substation, a microgrid controller, and an electric vehicle charging station. (¶ 30, 54: Alerts are transmitted via a field agent system associated with a plant. “Digital fault recorders” also reads on digital substations.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the monitoring of a power network using a trained machine learning model to detect abnormalities in network devices taught by PATHAK by including or applying the methods to network devices associated with a power plant or digital substation as taught by YAN. Since PATHAK teaches a hierarchy of devices in a network (see Fig. 1), it would have been obvious to apply the methods to any type of power distribution network, such as for a power plant. As suggested by YAN (¶ 4), combination of such features would allow for improved systems and methods to protect an electric power grid from malicious attacks.
Claim 12 recites the same limitations as claim 2 and is rejected for the same reasoning discussed above.
Regarding Claim 6, PATHAK teaches all the limitations of claim 1, on which claim 6 depends.
PATHAK does not explicitly teach wherein the formation plan is determined based on a state machine comprising a list of steps and trigger conditions determined based on the context data.
However, YAN, which is similarly directed to managing a power grid to protect from anomalies, teaches wherein the formation plan is determined based on a state machine comprising a list of steps and trigger conditions determined based on the context data. (¶ 22, 30: An electric power grid control system evaluates a plurality of “decision boundaries” (trigger conditions) from streamed data sources nodes, including sensors and actuators (context data). Based on a decision boundary being satisfied, different actions (formation plan) are taken, including modifying parameters or shutting down devices.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the monitoring of a power network using a trained machine learning model to detect abnormalities in network devices taught by PATHAK by determining a plan for corrective action based on a list of steps and trigger conditions determined from the learned context data as taught by YAN. As taught by YAN (¶ 33), incorporating such trigger conditions would “help to create a threat zone in a multi-dimensional feature space”. This would aid a developer in ensuring the safety of the power grid (YAN, ¶ 4). The combination of these features therefore would have produced advantageous and predictable results.
Claim 16 recites the same limitations as claim 6 and is rejected for the same reasoning discussed above.
Regarding Claim 10, PATHAK teaches all the limitations of claim 1, on which claim 10 depends.
PATHAK does not explicitly teach wherein the processor is further configured to simulate the formation plan based on updated signals from the network of devices prior to causing the one or more scout applications to be executed by the one or more devices in the network of devices.
However, YAN, which is similarly directed to managing a power grid to protect from anomalies, teaches wherein the processor is further configured to simulate the formation plan based on updated signals from the network of devices prior to causing the one or more scout applications to be executed by the one or more devices in the network of devices. (¶ 30: “a software application might be automatically triggered to capture data and/or isolate possible causes” ¶ 50, 52: Different possible threat scenarios for a power network are simulated before the decisions, such as the actions discussed in ¶ 30, are executed.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the monitoring of a power network using a trained machine learning model to detect abnormalities in network devices and perform repair actions taught by PATHAK by simulating the potential plans prior to causing them to be executed as taught by YAN. YAN (¶ 52) teaches “This may help characterize and rank the threats from the perspective of a large-scale power system phenomenon.” YAN (¶ 34) also suggests such methods would allow the system to more accurately learn about the physical process and threat, protecting the grid in an automatic and accurate manner (¶ 21). The combination of these features therefore would have produced advantageous and predictable results.
Claim 20 recites the same limitations as claim 10 and is rejected for the same reasoning discussed above.
Allowable Subject Matter
Claims 9 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
San Andres (US 2013/0218357 A1) teaches generating switching plans based on a network model generated from power system events. (Fig. 2, ¶ 15-20)
Meaghar (US 2017/0046458 A1) teaches real-time analytics of a power microgrid, including a virtual representation of a topology of the microgrid. (Abstract, Fig. 3)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI RAFAT OKASHA whose telephone number is (571)272-0675. The examiner can normally be reached M-F 10-6 EST.
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/RAMI R OKASHA/Primary Examiner, Art Unit 2118
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