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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: Claim 1 recites “system”, “artificial intelligence model construction platforms” and “synthetic disturbance platform”.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: [0029], [0111], Fig 1 and 24.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-15, 17, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abbaszadeh et al (US 2019/0056722 A1), hereinafter Abbaszadeh, in view of Koudal et al (US 2019/0236489 A1), hereinafter Koudal.
Claim 1. A system associated with an industrial asset, comprising:
a system node data store containing historical system node data associated with normal operation of the industrial asset;
Abbaszadeh: [0028] “The system 100 may include a “normal space” data source 120 storing, for each of a plurality of monitoring nodes 110, a series of normal values over time that represent normal operation of an industrial asset (e.g., collected from actual monitoring node 110 data as illustrated by the dashed line in FIG. 1).”
a plurality of artificial intelligence model construction platforms, coupled to the system node data store, to receive the historical system node data and to each automatically construct a data-driven, dynamic artificial intelligence model associated with the industrial asset based on received system node data,
Abbaszadeh: [0004] “… A normal space data source may store data, for each of a plurality of monitoring nodes (e.g., sensor nodes, actuator nodes, controller nodes, software nodes, etc.), a series of normal values (also referred to as a “data stream”) from monitoring nodes that represent normal operation of an industrial asset. In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. …”
[0070] “The extracted features may then be used to train a classification decision boundary using machine learning. Any classification method might be used, such as SVM, deep learning neural networks, or extreme learning machines with linear or nonlinear kernels.”
wherein the plurality of artificial intelligence models are interconnected and simultaneously trained;
Abbaszadeh [0064] “Each identified dynamic model can be simulated independently or in an interconnected fashion with other identified dynamic models. ...”
a synthetic disturbance platform to inject at least one synthetic disturbance into the plurality of artificial intelligence models to create, for each of a plurality of monitoring nodes, a series of synthetic disturbance monitoring node values over time that represent simulated abnormal operation of the industrial asset.
Abbaszadeh: [0004] “… A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.
Abbaszadeh does not appear to explicitly disclose create a digital twin of the industrial asset;
However, Koudal discloses wherein the plurality of artificial intelligence models are interconnected and simultaneously trained to create a digital twin of the industrial asset; on [0034] “Thus, some embodiments described herein may create a learning digital twin system of each and every part across a company (or a set of companies), which may enable designers, managers, sourcing, material managers, and/or engineers to automatically search, rationalize, and find the best part for a given purpose at the lowest economic cost to the company. By using advanced artificial intelligence and search technology, some embodiments may permit the automatic amalgamation of disparate, disjoined, “dirty” data and systems to be used to create a learning knowledge base. User interaction and automatic interaction with multiple systems of data may provide continuous learning to constantly improve the information available to each user in the system. The learning may include not only data and information about parts, products, and subsystems, but may also include contextual information about users, communities, networks. …”
Abbaszadeh and Koudal are analogous art because they are from the “same field of endeavor” industrial assets analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abbaszadeh and Koudal before him or her, to modify the system of Abbaszadeh to include the digital twin feature of Koudal because this combination improves performance of the system.
The suggestion/motivation for doing so would have been Koudal [0004] It would therefore be desirable to provide systems and methods to efficiently and accurately manage industrial asset parts using learning algorithms.
Therefore, it would have been obvious to combine Abbaszadeh and Koudal to obtain the invention as specified in the instant claim(s).
Regarding Claim 18 and 20 the same ground of rejection is made as discussed above for substantially similar rationale of Claim 1.
Claim 2. The system of claim 1, wherein the monitoring nodes include at least one of: (i) a sensor node, (ii) a critical sensor node, (iii) an actuator node, (iv) a controller node, and (v) a key software node.
Abbaszadeh [0004] According to some embodiments, a system to protect an industrial asset from cyber-attacks may include monitoring nodes (also referred to as “threat points”). Monitoring nodes might be signals from critical sensors, controller nodes, actuators, and/or key software nodes to be used to monitor occurrences of cyber-threats or attacks. A normal space data source may store data, for each of a plurality of monitoring nodes (e.g., sensor nodes, actuator nodes, controller nodes, software nodes, etc.), a series of normal values (also referred to as a “data stream”) from monitoring nodes that represent normal operation of an industrial asset.
Claim 3. The system of claim 1, wherein the digital twin is associated with at least one of: (i) control-oriented modeling, (ii) control design, (iii) state estimation, (iv) a dynamic system simulation, and (v) a fault/anomaly simulation.
Koudal [0029] “At 220, the system may execute at least one search or analysis algorithm to evaluate learning models in a digital twin industrial part modeling platform. The digital twin industrial part modeling platform may, for example, contain a plurality of learning models, each learning model describing “characteristics” of an industrial part available to be incorporated into an industrial asset. As used herein, a “characteristic” might include, by way of examples, a part identifier, a part name, a part description, a part image (e.g., a picture or Computer Aided Design (“CAD”) file), design details, a part geometry (e.g., a part shape), cost information, supplier information, geographic location data, a manufacturing technique (e.g., an additive manufacturing technique), a manufacturing material, part availability, related bills of material, related drawings, quality control data (e.g., reliability information), etc”
Claim 4. The system of claim 1, wherein at least one data-driven, dynamic artificial intelligence model is built in state-space and using one of black-box and grey-box system identification techniques.
Abbaszadeh [0046] “FIG. 7 is a data-driven model creation method according to some embodiments. At S710, the system may receive system node data associated with the industrial asset. At S720, the system may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. According to some embodiments, the data-driven, dynamic system model is built in state-space and using one of black-box and grey-box system identification techniques.”
Claim 5. The system of claim 4, wherein at least one of the data-driven, dynamic artificial intelligence models are associated with at least one of: (i) a plant model of the industrial asset, (ii) a controller model of the industrial asset, and (iii) a sub-controller model of the industrial asset.
Abbaszadeh [0046] “… Moreover, note that a plurality of data-driven, dynamic system models might be automatically constructed and at least one of the data-driven, dynamic system models might be associated with a plant model of the industrial asset, a controller model of the industrial asset, and/or a sub-controller model of the industrial asset.”
Claim 6. The system of claim 1, wherein at least one of the data-driven, dynamic artificial intelligence models is associated with a linear or nonlinear model.
Abbaszadeh [0047] “According to some embodiments, the data-driven, dynamic system model is associated with a linear or non-linear model.”
Claim 7. The system of claim 1, wherein at least one of the data-driven, dynamic artificial intelligence models is associated with a model order automatically selected by at least one of the artificial intelligence model construction platforms.
Abbaszadeh [0056] “…. According to some embodiments, dynamic models are built in state-space using data-driven (black-box) or grey-box system identification techniques. Note that each dynamic model could be linear or nonlinear based on the fit to the data. The order of the model might, according to some embodiments, be selected automatically using Hankel norm analysis or any other model order selection technique. The parameters of the model may be estimated using any system identification method such as PE, N4SID, ERA, etc.”
Claim 8. The system of claim 7, wherein the selection is performed via a Hankel norm analysis.
Abbaszadeh [0047] “… Moreover, the data-driven, dynamic system model might be associated with an order that was automatically selected by the system model construction platform (e.g., via a Hankel norm analysis).”
Claim 9. The system of claim 7, wherein parameters of the at least one data-driven, dynamic artificial intelligence model is estimated via a system identification method associated with at least one of: (i) Prediction Error (“PE”) minimization, (ii) subspace methods, (iii) Subspace State Space System Identification (“N4SID”), and (iv) Eigen-system Realization Algorithm (“ERA”) techniques.
Abbaszadeh [0056] “…. According to some embodiments, dynamic models are built in state-space using data-driven (black-box) or grey-box system identification techniques. Note that each dynamic model could be linear or nonlinear based on the fit to the data. The order of the model might, according to some embodiments, be selected automatically using Hankel norm analysis or any other model order selection technique. The parameters of the model may be estimated using any system identification method such as PE, N4SID, ERA, etc.”
Claim 10. The system of claim 1, wherein at least one of the data-driven, dynamic artificial intelligence models is associated with one of: (i) a Single-Input, Single-Output (“SISO”) model, (ii) a Single-Input, Multiple-Output (“SIMO”) model, (ii) a Multiple-Input, Single-Output (“MISO”) model, and (iv) a Multiple-Input, Multiple-Output (“MIMO”) model.
Abbaszadeh [0047] “… According to some embodiments, parameters of the data-driven, dynamic system model are estimated via a system identification method associated with Prediction Error (“PE”) minimization, subspace methods, Subspace State Space System Identification (“N4SID”), and/or Eigen-system Realization Algorithm (“ERA”) techniques. Note that the data-driven, dynamic system model might be associated with, for example, a Single-Input, Single-Output (“SISO”) model, a Single-Input, Multi-Output (“SIMO”) model, a Multi-Input, Single-Output (“MISO”) model, and/or a Multi-Input, Multi-Output (“MIMO”) model.”
Claim 11. The system of claim 1, further comprising; a disturbance detection model creation computer, coupled to the system node data store, to:
(i) receive the series of normal monitoring node values and generate a set of normal feature vectors, (ii) receive the series of synthetic disturbance monitoring node values and generate a set of disturbance feature vectors, and (iii) automatically calculate and output at least one decision boundary for a disturbance detection model based on the set of normal feature vectors and the set of disturbance feature vectors.
Abbaszadeh [0051] “FIG. 10 illustrates a model creation method that might be performed by some or all of the elements of the system 900 described with respect to FIG. 9. At S1010, the system may retrieve, for each of a plurality of monitoring nodes, a series of normal values over time that represent normal operation of the industrial asset and a set of normal feature vectors may be generated. At S1020 the system may retrieve, for each of the plurality of monitoring nodes, a series of synthetic attack values over time that represent attacked operation of the industrial asset and a set of attacked feature vectors may be generated. At S1030, a decision boundary may be automatically calculated and output for a threat detection model based on the sets of normal feature vectors, attacked feature vectors, and fault feature vectors. According to some embodiments, the decision boundary might be associated with a line, a hyperplane, a non-linear boundary separating normal space from attacked space, and/or a plurality of decision boundaries.”
Claim 12. The system of claim 11, further comprising: a disturbance detection computer, coupled to the plurality of monitoring nodes, to:
(i) receive the series of current monitoring node values and generate a set of current feature vectors, (ii) access the disturbance detection model having the at least one decision boundary, and (iii) execute the at least one disturbance detection model and transmit a disturbance alert based on the set of current feature vectors and the at least one decision boundary,
Abbaszadeh [0034] “At S210, the system may receive, from a plurality of monitoring nodes, a series of current values over time that represent a current operation of an industrial asset. The system may also generate, based on the received series of current values, a set of current feature vectors. At S220, a threat detection model may be accessed including at least one decision boundary. At S230, the model may be executed and a threat alert may be transmitted based on the set of current feature vectors and the decision boundary when appropriate (e.g., when a cyber-attack is detected). According to some embodiments, one or more response actions may be performed when a threat alert is transmitted.”
wherein the disturbance alert indicates whether a monitoring node status is normal or abnormal.
Abbaszadeh [0050] “…The threat detection model 955 may, for example, monitor streams of data from the monitoring nodes 910 comprising data from sensor nodes, actuator nodes, and/or any other critical monitoring nodes (e.g., monitoring nodes MN.sub.1 through MN.sub.N) and automatically output a threat alert (e.g., indicating that operation of the industrial asset is normal or attacked) to one or more remote monitoring devices 970 when appropriate (e.g., for display to a user).”
Claim 13. The system of claim 12, wherein the set of current feature vectors includes at least one of: (i) a local feature vector associated with a particular monitoring node, and (ii) a global feature vector associated with a plurality of monitoring nodes.
Abbaszadeh [0069] “… The type and number of features for each monitoring node, might be optimized using domain-knowledge, feature engineering, or ROC statistics. The local features for each monitoring node may be stacked to create the global feature vector. The features may be normalized and the dimension of the global feature vector can then be further reduced using any dimensionality reduction technique such as PCA. The features may be calculated over a sliding window of the signal time series.”
Claim 14. The system of claim 12, wherein the at least one decision boundary is associated with at least one of: (i) a line, (ii) a hyperplane, and (iii) a nonlinear boundary.
Abbaszadeh [0051] “… At S1030, a decision boundary may be automatically calculated and output for a threat detection model based on the sets of normal feature vectors, attacked feature vectors, and fault feature vectors. According to some embodiments, the decision boundary might be associated with a line, a hyperplane, a non-linear boundary separating normal space from attacked space, and/or a plurality of decision boundaries.”
Claim 15. The system of claim 12, wherein the at least one decision boundary exists in a multi-dimensional space and is associated with at least one of: (i) a dynamic model, (ii) design of experiment data, (iii) machine learning techniques, (iv) a two-class or multi-class support vector machine, (v) a full factorial process, (vi) Taguchi screening, (vii) a central composite methodology, (viii) a Box-Behnken methodology, (ix) real-world operating conditions, (x) a full-factorial design, (xi) a screening design, and (xii) a central composite design.
Abbaszadeh [0038] “… According to some embodiments, appropriate decision boundaries may be constructed in a multi-dimensional space using a data set which is obtained via scientific principles associated with Design of Experiments (“DoE”) techniques. Moreover, multiple algorithmic methods (e.g., support vector machines or machine learning techniques) may be used to generate decision boundaries. Since boundaries may be driven by measured data, defined boundary margins may help to create a threat zone in a multi-dimensional feature space. Moreover, the margins may be dynamic in nature and adapted based on a transient or steady state model of the equipment and/or be obtained while operating the system as in self-learning systems from the incoming data stream.”
Claim 17. The system of claim 12, wherein at least one normal or synthetic attack/disturbance monitoring node value is obtained by running design of experiments on an industrial control system associated with at least one of: (i) a turbine, (ii) a gas turbine, (iii) a wind turbine, (iv) an engine, (v) a jet engine, (vi) a locomotive engine, (vii) a refinery, (viii) a power grid, and (ix) an autonomous vehicle.
Abbaszadeh [0039] “… The series of normal values might be obtained, for example, by running DoE on an industrial control system associated with a power turbine, a jet engine, a locomotive, an autonomous vehicle, etc.”
Claim(s) 16, 19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abbaszadeh et al (US 2019/0056722 A1), hereinafter Abbaszadeh, in view of Koudal et al (US 2019/0236489 A1), hereinafter Koudal and further in view of El Ferik et al (US 2017/0139423 A1), hereinafter El Ferik.
Claims 16, 19 and 21
Abbaszadeh and Koudal do not appear to explicitly disclose wherein a robustness analysis is performed for the plurality of data-driven, dynamic artificial intelligence models to compute uncertainty bounds using at least one of: (i) Lipschitz bounds, (ii) one-sided Lipschitz bounds, and (iii) incremental quadratic bounds.
However, El Ferik discloses wherein a robustness analysis is performed for the plurality of data-driven, dynamic artificial intelligence models to compute uncertainty bounds using at least one of: (i) Lipschitz bounds, (ii) one-sided Lipschitz bounds, and (iii) incremental quadratic bounds on [0048-0050] Table 2.
Abbaszadeh, Koudal and El Ferik are analogous art because they are from the “same field of endeavor” machine analysis.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abbaszadeh, Koudal and El Ferik before him or her, to modify the system of Abbaszadeh to include the digital twin feature of Koudal and the constraint tightening feature of El Ferik because this combination improves performance of the system.
The suggestion/motivation for doing so would have been El Ferik [0049] Constraint tightening (19)-(20) is novel as it is the first time that such a variety of uncertainty contributions have been considered simultaneously. Remarkably, the external input is not a constant or random unknown as is usually assumed, but herein it is considered to evolve according to an uncertain nonlinear map. Besides, estimation errors and prediction errors are also considered. This leads to very general bounds on prediction error, which can be specialized to specific cases (e.g. perfect measurement will mean ξx.fwdarw.0). Also worthy of note is the fact that we have not considered the model mismatch to be state-dependent, as it does not have obvious practical application in mobile robotics. In fact, if the system is very nonlinear, one cannot expect modeling error to reduce with state, as in many cases larger state amplitude offers better model fidelity. Moreover, the FHOCP is recursively feasible.
Therefore, it would have been obvious to combine Abbaszadeh, Koudal and El Ferik to obtain the invention as specified in the instant claim(s).
Conclusion
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189
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