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 Claims
Claims 1-20 are currently pending in this application in response to the claim amendments filed on January 21, 2026. All independent claims 1, 17, and 20 have been amended with newly introduced subject matter.
Response to Applicant’s Remarks
With respect to 35 U.S.C. §103 rejections:
Applicant’s remarks filed 01/21/2026 have been fully considered but are moot because at least the independent claims 1, 17, and 20 have been amended adding new subject matter that change the scope of the original claimed invention; therefore, the prior rejections have been withdrawn. Upon further consideration, see the new ground(s) of rejection is made as shown below.
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, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 4, 6-7, 9-11, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (“Artificial Intelligence-Based Approach for Forced Oscillation Source Detection and Classification,” 2020 8th International Conference on Condition Monitoring and Diagnosis (CMD), Phuket, Thailand, 2020, pp.186-189) in view of Liu et al. (US 20220034947 A1).
With respect to claims 1, 17 and 20, Chan teaches a method, a computing system, and a non-transitory computer-readable medium comprising instructions which when read by a processor cause a computer to perform a method comprising (Oscillation source locating methods, figs.1-4, machine learning and training to locate the source of forced oscillation, section II, p.187):
receiving measurements from one or more sensors on a power grid, the measurements comprising data of an oscillation within the power grid (observation of forced oscillations caused by resonance within power grids, Abstract; “signal” received [fig.1] or new data obtained [fig.3] and observed via Phasor measurement units, p.186)
determining, via execution of one or more machine learning models, a candidate set of power system components disposed on the power grid that are candidates for being a source of the oscillation based on the received measurements (Oscillation source locating methods according to fig.1 where “signal” is processed with AI-based method (ABM) leverages machine learning (ML) and training for a variety of operating conditions and expands upon a starting decision tree DT0 with ML as well as expounds upon minimum-volume-enclosing characteristic ellipsoids, among other techniques, to validate/improve upon the ECBM, HMM, LBM, and SIGMOR, fig.1 and p.187; AI engine with untrained model, trained model, pre-trained model, and optimized model, fig.4); and
identifying, via execution of an optimization model and subsequent to the determining, a component from among the candidate set of power system components which is the source of the oscillation based on the received measurements, wherein the optimization model receives as input the candidate set produced by the one or more machine learning models (a variety of oscillatory source identification techniques with an artificial intelligence-based approach for a more optimal oscillation source location detection as well as a higher resolution analysis of anomalous oscillatory behavior classification, Abstract, last 6 lines; Fig. 1, which is informed by Fig. 3, is shown as an input into the training layer. Eventually, the Trained Model becomes a Pre-Trained Model, and “Fine-Tuning” can be achieved by Continuous Back Propagation and optimizing at certain training layers, such as the optimality of the Prony analysis linear prediction model (PALMP) order (<.3 of the data length [13]) as well as the previously discussed decimation. The Pre-Trained Model is then further optimized when the New Dataset is ingested. To avoid over-fitting, the Pre-Trained Model of the CNN can also serve as a feature extractor for which the features can be fed into a SVM. Collectively, the described constituent components constitute the prototype AI engine underpinning the AI ODCS, fig.3-4 and p.189, column 1; fig.4 teaches where the output of pre-trained model is routed to an input of the optimized model).
With respect to claims 1, 17 and 20, Chan teaches of “alert” when new data obtained is determined to be an “abrupt change” (Chan: fig.3). But Chan is not clear to teach the step of “displaying, via a user interface, information about the identified component”.
However, it is known by Liu to teach a method, a computing system, and a non-transitory computer-readable medium comprising instructions which when read by a processor cause a computer to perform a method comprising (Liu: figs.1-8, processor 208/300, figs.2-3, with program code embodied in the CRM, [0018]): receiving measurements from one or more sensors on a power grid (Liu: phasor measurement units {PMUs} 118a-c of each substations 116a-c within distribution grid 110 of main power grid 102, fig.1 and [0042]), the measurements comprising data of an oscillation within the power grid (Liu: locate the geographic source of a forced oscillation event based on the mode angle analysis of the PMUs' measurements, [0038]; forced oscillation signal can be observed in both frequency and phase angle measurements of the power system signal, [0048]); determining, a candidate set of power system components disposed on the power grid that are candidates for being a source of the oscillation based on the received measurements (Liu: determine the dominant mode frequency of the forced oscillation signal component of the power system signal, as well as the mode angle and magnitude of the dominant mode at different geographic areas corresponding to different PMU locations, [0038]);
identifying, via execution of an optimization model, a component from among the candidate set of power system components which is the source of the oscillation based on the received measurements (Liu: the geographic location of the source of the forced oscillation event may be determined to be the location of the PMU that has the most leading mode angle associated therewith relative to other ones of the PMUs… through application of a least squares regression analysis to the mode angles associated with PMUs in geographic proximity to the PMU associated with the most leading mode angle along with the rate of change of the mode angle with distance, [0055]); and displaying, via a user interface, information about the identified component (Liu: enabling operating personnel to monitor and control the main power grid via GUI of DMS 114, fig.1 and [0041]; Identification of the source of a forced oscillation event may allow operators of the power system to better investigate and determine potential causes of the event, [0072]).
Because Liu is also directed to determining the source of the oscillation in the power grid (Liu: fig.1; Chan: fig.1), it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of displaying, via a user interface, information about the identified component as taught by Liu with the system and method for determining the source of the oscillation in the power grid as taught by Chan for the purpose to allow operators of the power system to better investigate and determine potential causes of the event…actions may be taken with respect to the design and engineering of the power system to reduce the risk of further forced oscillation events and/or reduce their impact on power system's ability to deliver power reliably (Liu: [0072]).
With respect to claim 2, Chan and Liu combined teaches wherein the determining the candidate set of power system components comprises determining at least one bus on the power grid as the source of the oscillation (Chan: ascertain source location for small/weak signal instabilities within any bulk power systemLiu: locate the geographic source of a forced oscillation event based on the mode angle analysis of the PMUs' measurements…the power system signal data collected form the PMUs to determine the dominant mode frequency of the forced oscillation signal component of the power system signal, as well as the mode angle and magnitude of the dominant mode at different geographic areas corresponding to different PMU locations…mode angle may be unwrapped geographically based on the location of each PMU, and the geographic location with the most leading mode angle may be considered as the source of the forced oscillation event…the location of the forced oscillation event may be pinpointed even further by performing a least squares regression analysis to the mode angles associated with PMUs in geographic proximity to the PMU associated with the most leading mode angle along with the rate of change of the mode angle with distance, [0038]; determining the geographic source location of a forced oscillation event in a power system network, [0066], the power system network 100 as shown in fig.1 teaches at least one PMU on the power system network is the source of the forced oscillation event, [0066]) via execution of the one or more machine learning models (Chan: Artificial Intelligence-Based Approach for Forced Oscillation Source Detection and Classification {title and abstract} of power systems {abstract} subdivided into local, inter-area, and system-wide {lines 10-11 of col.2 of p.186}, at an interconnected substation {line 24 of col.2 of p.187}).
With respect to claim 4, Chan and Liu combined teaches wherein the identifying comprises routing an output of the one or more machine learning models to an input of the optimization model (Chan: output of pre-trained model is routed to an input of the optimized model, fig.4).
With respect to claim 6, Chan and Liu combined teaches wherein the one or more machine learning models comprise a feature extraction model which identifies features within the measurements, and a machine learning model which predicts the plurality of power system components based on the features identified by the feature extraction model (Chan: pre-trained model serves as a feature extractor for which the features can be fed into a SVM, col.1 of p.189).
With respect to claim 7, Chan and Liu combined teaches wherein the one or more machine learning models comprise a plurality of different machine learning models which generate a plurality of candidate sets of power system components as the source location of the oscillation, respectively, and a fusion model1 which combines the plurality of candidate sets of power system components to generate the candidate set (Chan: AI engine with untrained model, trained model, pre-trained model, and optimized model, fig.4; optimized model takes in output of the pre-trained model {transfer learning} and new dataset, fig.4 and p.189).
With respect to claim 9, Chan and Liu combined teaches wherein the optimization model comprises a discrete and continuous parameter co-search algorithm (Chan: discrete-time and continuous time, p.187-188).
With respect to claim 10, Chan and Liu combined teaches wherein a discrete parameter of the discrete and continuous parameter co-search algorithm comprises one or more of an area name, a bus name/number, a controller type, and an asset type (Chan: subdivided into local, inter-area, and system-wide {lines 10-11 of col.2 of p.186}, at an interconnected substation {line 24 of col.2 of p.187}).
With respect to claim 11, Chan and Liu combined teaches wherein a continuous parameter discrete and continuous parameter co-search algorithm comprises one or more of an oscillation frequency (Chan: compute oscillation frequency, fig.3 and p.188), a damping ratio (Chan: compute damping ratio, fig.3 and p.187-188), a start time, and an end time.
With respect to claim 16, Chan and Liu combined teaches wherein the determining further comprises determining the candidate set based on a dynamic power system model (Liu: high-precision time synchronization may allow comparing measured values (synchrophasors) from different substations distant to each other and drawing conclusions regarding the system state and dynamic events, [0042]) and a network model of the power grid (facilitate communication between the DMS 114 processor and the PMUs 118a, 118b, and 118c of FIG. 1 over the network 120 and to facilitate communication of the geographic source location of a forced oscillation event and data and other information associated therewith to one or more entities, [0065], determining the geographic source location of a forced oscillation event in a power system network [0066]).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (“Artificial Intelligence-Based Approach for Forced Oscillation Source Detection and Classification,” 2020 8th International Conference on Condition Monitoring and Diagnosis (CMD), Phuket, Thailand, 2020, pp.186-189) in view of Liu et al. (US 20220034947 A1) and further in view of Sen et al. (US 2014/0281645-A1).
With respect to claims 12-13, Chan and Liu combined does not appear to teach wherein the discrete and continuous parameter co-search algorithm comprises a combinatory optimization algorithm and one or more of a Kalman filtering algorithm, a nonlinear least square algorithm, and an evolutional algorithm.
However, it is known by Sen to teach a power grid system (Sen: figs.1-2 and [0002]; Chan: title, abstract, and figs.1-4) applying a combinatory optimization algorithm (Sen: combinatorial optimization algorithms, [0073,0078]) and one or more of a Kalman filtering algorithm, a nonlinear least square algorithm, and an evolutional algorithm (evolutionary game algorithms, [0073]).
Because Sen is also directed to determining a fault/disturbance in a power system (Sen: [0002,0007]; Chan: fig.1; Liu: fig.1), it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of a combinatory optimization algorithm and one or more of a Kalman filtering algorithm, a nonlinear least square algorithm, and an evolutional algorithm as taught by Sen with the system to determine a fault/disturbance in a power system as taught by Chan and Liu for the purpose to make dynamic predictions and to provide an optimal response as a feedback-loop for each consumption device modeled as a quantum candidate (Sen: [0073]).
Allowable Subject Matter
Claims 3, 5, 8, and 14-15 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.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record, taken alone or in combination, fails to disclose or render obvious, which makes the following claims allowable over the prior art:
With respect to claim 3/2/1, wherein the identifying the component comprises selecting a power system component from among a plurality of power system components that are attached to the determined at least one bus on the power grid, as the source of the oscillation via execution of the optimization model.
With respect to claim 5/1, wherein the method further comprises simulating a plurality of oscillations on the power grid to generation simulation results and training the one or more machine learning models based on the simulation results.
With respect to claim 8/1, wherein the one or more machine learning models comprise a plurality of different machine learning models which each predict a respective candidate set of power system components as the source location of the oscillation, and the method further comprises assigning weights to the plurality of different machine learning models, and combining the respective candidate sets of power system components based on the assigned weights to generate the candidate set.
With respect to claim 14/1, wherein the displaying comprises displaying an identifier of a bus of the source of the oscillation, an identifier of the target component, a type of the target component, and a geographical area of the target component.
Claim 15 is allowed due to its dependency on claim 14.
Conclusion
The additional prior arts made of record and have not been relied upon are considered pertinent to applicant's disclosure as follows: CN_112751345_A.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN (CINDY) D KHUU whose telephone number is (571)272-8585. The examiner can normally be reached on Monday-Friday 8a-8p.
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/HIEN D KHUU/Primary Examiner, Art Unit 2116 March 30, 2026
1 A fusion model is interpreted as a model that combines transferred learning with new dataset, as disclosed in fig.4 of Chan.
Prosecution Insights