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 .
The three months suspension approved on 11/26/2025 has expired. Applicant has three months to respond to what’s on record.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 6, 15 recite the limitation “wherein providing quantification of expected utility and variance associated with each possible process control move is used to generate risk-adjusted optimization of process controls by providing precise quantification of the expected utility and variance associated with each possible process control move". It is not described how this limitation is related to other claimed limitations. “providing quantification of expected utility…” was not mentioned in claim language before this limitation. The term “wherein” make this limitation confusion. Secondly, the wording “by providing precise quantification of the expected utility and variance associated with each possible process control move" is duplicated with “wherein providing quantification of expected utility and variance associated with each possible process control move”.
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 1 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Brooks et al. US 2017/0205781 (hereinafter Brooks) in view of Wilson et al. US 2002/0032499 (hereinafter Wilson).
Regarding claim 1, Brooks teaches: a method for predictive control of a system, comprising steps of:
injecting randomized controlled signals in subsystems of the system (Fig. 1, [0016] - - provide signal rejections to utility grid);
ensuring the signal injections occur within normal operational ranges and constraints ([0017] - - the signal injection is done by manipulating existing grid controls within the range of permissible states for that grid control; the permissible states are normal operational ranges);
monitoring performance of the system or the subsystems in response to the controlled signals (Fig. 1, [0016] - - sensor data are response signals);
computing confidence intervals about the causal relationships between the system or the subsystems performance and the controlled signals (Fig. 6, [0038] - - compute confidence intervals);
using computed confidence intervals to predict an expected change in performance caused by changes in the controlled signals (Fig. 6, [0038] -[0042] - - compute a predicted response to the signal injection using confidence intervals); and
selecting optimal signals that iteratively improve the system and subsystems performance ([0005] - - optimize grid parameters; [0058] - - process optimization efforts).
providing quantification of the variance associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0021] - - variances of multiple different grid parameters; [0005] - - optimize grid parameters).
But Brooks does not explicitly teach: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls.
However, Wilson teaches: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0037] - - the sensitivity values in the form of a Jacobian matrix; the sensitivity values are quantification of utility; the optimizer determines parameters using the sensitivity values).
Brooks and Wilson are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Brooks, and incorporating optimization using quantification of utility, as taught by Wilson.
One of ordinary skill in the art would have been motivated to do this modification in order to improve product quality, as suggested by Wilson ([0014]).
Regarding claim 2, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the controlled signals comprise set points, time delays and gain parameters of proportional controllers, integral controllers, derivative controllers, and combinations of controllers ([0017] - - the signal injection is manipulating existing grid controls and implementing one particular state, the particular state is set point).
Regarding claim 3, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the normal operational ranges comprise a multidimensional space of possible control states generated based on control information and operational constraints ([0017] - - the range of permissible states for that grid control).
Regarding claim 4, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the selecting step further comprises selecting the optimal signals based upon external data ([0058] - - process optimization efforts such as adjusting flow rates or controlling reactive power levels to support transmission while minimizing waste; waste is external data).
Regarding claim 5, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: at time T the method predicts future possible states of the system at time T+t under different control signals and selects the optimal control signals that maximizes system performance at T+t, then iteratively repeats this process (Fig. 7, [0059] - - predicting the effects of a signal injection on the grid; iteratively implement signal injections and provide information that improves the selection of signal injections by predicting the effects of a signal injection on the grid).
Regarding claim 6, Brooks teaches: a method for predictive control of a system, comprising steps of:
providing signal injections for subsystems of the system (Fig. 1, [0016] - - provide signal rejections to utility grid);
receiving response signals corresponding with the signal injections (Fig. 1, [0016] - - sensor data are response signals);
measuring a utility of the response signals (Fig. 1, [0016] - - associate sensor data with particular signal injections; the association relationship is a utility);
accessing data relating to operation of the system or the subsystems ([0016] - - models of sensor response is data relating to operation of the system(grid)); and
modifying the data based upon the utility of the response signals (Fig. 1, [0016] - - the associated data is used to update models of sensor response; updating is modifying).
selecting optimal signals that iteratively improve the system and subsystems performance ([0005] - - optimize grid parameters; [0058] - - process optimization efforts);
providing quantification of the variance associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0021] - - variances of multiple different grid parameters; [0005] - - optimize grid parameters).
But Brooks does not explicitly teach: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls.
However, Wilson teaches: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0037] - - the sensitivity values in the form of a Jacobian matrix; the sensitivity values are quantification of utility; the optimizer determines parameters using the sensitivity values).
Brooks and Wilson are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Brooks, and incorporating optimization using quantification of utility, as taught by Wilson.
One of ordinary skill in the art would have been motivated to do this modification in order to improve product quality, as suggested by Wilson ([0014]).
Regarding claim 7, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the signal injections comprise set points, time delays and gain parameters of proportional controllers, integral controllers, derivative controllers, and combinations of controllers ([0017] - - the signal injection is manipulating existing grid controls and implementing one particular state, the particular state is set point).
Regarding claim 8, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the accessing step comprises accessing a look-up table ([0040] - - a table describes the relationship between a particular signal injection and its effects on sensor responses.).
Regarding claim 9, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the signal injections have a spatial reach (Fig. 1 - - spatial reach of signal injections).
Regarding claim 10, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the signal injections have a temporal reach (Fig. 1 - - temporal reach of signal injections).
Regarding claim 11, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the signal injections have multiple temporal reaches at different time intervals ([0048] - - temporal reaches using current data and historical data).
Regarding claim 12, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
Brooks further teaches: the modifying step further comprises modifying the data based upon external data ([0044] - - sensor data from the sensor lying outside all of the spatial reach areas is external data; OR [0052] - - rejecting model whose predictions deviate from the actual value by more than an error threshold amount; the threshold is external data; rejecting model is modifying the model).
Claims 13 – 18 are rejected under 35 U.S.C. 103 as being unpatentable over Brooks et al. US 2017/0205781 (hereinafter Brooks) in view of Wilson et al. US 2002/0032499 (hereinafter Wilson) and further in view of ABBASZADEH et al. US 2020/0125978 (hereinafter ABBASZADEH).
Regarding claim 13, the combination of Brooks and Wilson teaches all the limitations of the base claims as outlined above.
But the combination of Brooks and Wilson does not explicitly teach: the data comprises a causal model stored as a set of Jacobian and hessian matrices.
However, ABBASZADEH teaches: the data comprises a causal model stored as a set of Jacobian and hessian matrices ([0087] - - [0089] - - Jacobian and hessian matrices).
Brooks, Wilson and ABBASZADEH are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Brooks and Wilson, and incorporating Jacobian and hessian matrices, as taught by ABBASZADEH.
One of ordinary skill in the art would have been motivated to do this modification in order to detecting attacks automatically, as suggested by ABBASZADEH ([0002]).
Regarding claim 14, the combination of Brooks, Wilson and ABBASZADEH teaches all the limitations of the base claims as outlined above.
ABBASZADEH further teaches: updating the model includes modifying or updating coefficients of the matrices ([0086] - - the features is calculated over a sliding window of the signal time series. The features are coefficients and coefficients are modified over different sliding windows).
Brooks, Wilson and ABBASZADEH are combinable for the same rationale as set forth.
Regarding claim 15, Brooks teaches: a method for self-calibrated model predictive control of a system, comprising steps of:
injecting N randomized controlled signals in subsystems of the system (Fig. 1, [0016] - - provide signal rejections to utility grid);
ensuring the signal injections occur within normal operational ranges and constraints ([0017] - - the signal injection is done by manipulating existing grid controls within the range of permissible states for that grid control; the permissible states are normal operational ranges);
monitoring M responses of the system or the subsystems to the controlled signals (Fig. 1, [0016] - - sensor data are response signals);
computing confidence intervals (Fig. 6, [0038] - - compute confidence intervals);
using a model predictive control algorithm to predict an expected change in performance caused by changes in the controlled signals (Fig. 6, [0038] -[0042] - - compute a predicted response to the signal injection using the model);
and selecting optimal signals that iteratively improve the system and subsystems performance based upon the expected change in performance predicted by the model predictive control algorithm ([0005] - - optimize grid parameters; [0058] - - process optimization efforts);
providing quantification of the variance associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0021] - - variances of multiple different grid parameters; [0005] - - optimize grid parameters).
But Brooks does not explicitly teach: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls.
However, Wilson teaches: providing quantification of the expected utility associated with each possible process control move is used to generate risk-adjusted optimization of process controls ([0037] - - the sensitivity values in the form of a Jacobian matrix; the sensitivity values are quantification of utility; the optimizer determines parameters using the sensitivity values).
Brooks and Wilson are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Brooks, and incorporating optimization using quantification of utility, as taught by Wilson.
One of ordinary skill in the art would have been motivated to do this modification in order to improve product quality, as suggested by Wilson ([0014]).
But the combination of Brooks and Wilson does not explicitly teach:
computing confidence intervals about first-order partial derivatives of the system responses with respect to the signal injections;
using a model predictive control algorithm to predict based on the NxM matrix of first-order derivatives an expected change in performance caused by changes in the controlled signals;
However, ABBASZADEH teaches: computing confidence intervals about first-order partial derivatives of the system responses with respect to the signal injections ([0115] - - confidence level of the HMM mode estimate);
using a model predictive control algorithm to predict based on the NxM matrix of first-order derivatives an expected change in performance caused by changes in the controlled signal ([0087] - - [0089] - - Jacobian and hessian matrices are NxM matrix).
Brooks, Wilson and ABBASZADEH are analogous art because they are from the same field of endeavor. They all relate to control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Brooks and Wilson, and incorporating Jacobian and hessian matrices, as taught by ABBASZADEH.
One of ordinary skill in the art would have been motivated to do this modification in order to detecting attacks automatically, as suggested by ABBASZADEH ([0002]).
Regarding claim 16, the combination of Brooks, Wilson and ABBASZADEH teaches all the limitations of the base claims as outlined above.
ABBASZADEH further teaches: the using step comprises using the NxM matrix of 2nd - order derivatives ([0088] - - Hessian matrices are 2nd - order derivatives).
Brooks, Wilson and ABBASZADEH are combinable for the same rationale as set forth.
Regarding claim 17, the combination of Brooks, Wilson and ABBASZADEH teaches all the limitations of the base claims as outlined above.
ABBASZADEH further teaches: using the NxM matrix of Nth- order derivatives ([0089] - -
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is Nth-order derivatives).
Brooks and ABBASZADEH are combinable for the same rationale as set forth.
Regarding claim 18, the combination of Brooks, Wilson and ABBASZADEH teaches all the limitations of the base claims as outlined above.
ABBASZADEH further teaches: using the NxM matrix of time- varying derivatives ([0088] - - Hessian matrices of time varying elements).
Brooks, Wilson and ABBASZADEH are combinable for the same rationale as set forth.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Brooks et al. US 2017/0205781 (hereinafter Brooks) in view of Wilson et al. US 2002/0032499 (hereinafter Wilson) and ABBASZADEH et al. US 2020/0125978 (hereinafter ABBASZADEH) and further in view of Malikopoulos et al. US 2009/0306866 (hereinafter Malikopoulos).
Regarding claim 19, the combination of Brooks, Wilson and ABBASZADEH teaches all the limitations of the base claims as outlined above.
But the combination of Brooks, Wilson and ABBASZADEH does not explicitly teach: optimally balances an explore for updating the derivative estimates versus an exploit for letting the model predictive control algorithm decide what action to take based on the current derivative estimates.
However, Malikopoulos teaches: optimally balances an explore for updating the derivative estimates versus an exploit for letting the model predictive control algorithm decide what action to take based on the current derivative estimates. ([0342] - - exploration-exploitation dilemma: balance between an exhaustive exploration of the environment and the exploitation of the learned policy is fundamental to reach nearly optimal solutions).
Brooks, Wilson, ABBASZADEH and Malikopoulos are analogous art because they are from the same field of endeavor. They all relate to power control system.
Therefore before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Brooks, Wilson and ABBASZADEH, and incorporating balancing between exploration and exploitation, as taught by Malikopoulos.
One of ordinary skill in the art would have been motivated to do this modification in order to reach optimal solutions, as suggested by Malikopoulos ([0342]).
Conclusion
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/YUHUI R PAN/Primary Examiner, Art Unit 2116