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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/14/2026 has been entered.
Response to Arguments
The 35 USC 101 rejections are withdrawn due to amendments filed 4/16/2026.
Applicant's arguments regarding art rejections filed 4/16/2026 have been fully considered but they are not persuasive.
Applicant argues, “does not disclose any particular ANN training or execution architecture.” Remarks 11. Applicant doesn’t claim any particular ANN training or execution, just a generic ANN running on the same type of data that the ANN is trained on time series data. All of Susteata’s data is time series, see Susteata figure 1e-1h. Susteata paragraph 63 also teaches “a variety of suitable techniques for performing inference in connection with diagnostics/prognostics in accordance with the subject invention can be employed… For example, implicitly and/or explicitly classifiers can be utilized … suitable non-linear training schemes, neural networks…” Training on the same type of data that the trained model will be used for is how neural networks and non-linear training schemes work.
Applicant argues, “Moreover, Sustaeta describes variables used within an optimization objective function for evaluating process performance, such as minimizing energy cost per gallon pumped (Sustaeta, para [0123]). These variables are not disclosed as three distinct time-series inputs used to train or run an ANN for abnormal-behavior detection, nor are they described as labeled score data comprising criticality values. Sustaeta does not disclose or teach training an ANN using three separate categories of time-series data together with criticality-based score data, nor does it describe executing a trained ANN to output a criticality value indicative of abnormal behavior of a predicted observable process value at a predefined temporal distance, as expressly required by amended claim 1.” Remarks 11. Score data and criticality values aren’t a term of art and they don’t have a special meaning in the specification. For examination, “score data comprises… first criticality data” (claim 1) and is taught by operating limits (e.g., min/max speeds, min/max flows, min/max pump power levels, min/max pressures allowed, NPSHR values, and the like)…” Sustaeta para 209. The output “second criticality value” (claim 1) is taught by “future state” in Sustaeta paragraph 98.
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 1-14 are rejected under 35 U.S.C. 102(a)(1) as being described by US20090210081A1 to Sustaeta et al.
Susteata teaches claims 1 and 8. A method for finding an abnormal behavior of an industrial process, comprising: (Susteata para 86 “The predicted state or events can be, for example, quality of a product, production throughput, possible line failure, machine temperature, bearing failure, order arrival, feed stock quality, etc. “)
training a machine learning model by utilizing input data and score data, wherein the machine learning model is an artificial neural net, ANN, wherein the input data comprises: (Susteata para 63 “For example, implicitly and/or explicitly classifiers can be utilized in connection with performing a probabilistic or statistical based analysis/diagnosis/prognosis—… neural networks…” Neural networks are trained on the data similar to the data they ultimately run on. Therefore, When Susteata para 123 teaches running on certain types of data, the NN also trained on that same data structure but with different values.)
a first time-series of at least one observable process-value of the industrial process, (Susteata para 123 “if the operating objective is to minimize energy cost per gallon pumped then the objective function will include flow information, cost per kWh, and motor-drive power consumed…” power consumed is the obvservable value.)
a second time-series of at least one manipulated variable that influences the industrial process, and (Susteata para 123 “if the operating objective is to minimize energy cost per gallon pumped then the objective function will include flow information, cost per kWh, and motor-drive power consumed…” cost is manipulated.)
a third time-series of at least one internal variable of the industrial process; (Susteata para 123 “if the operating objective is to minimize energy cost per gallon pumped then the objective function will include flow information, cost per kWh, and motor-drive power consumed…” The internal variable is the flow information. This paragraph also shows that the number of input time series is over three.) and wherein the score data comprises:
a first criticality value of each of the at least one observable process-value indicative of the abnormal behavior of the industrial process, and (Susteata para 209 (emphasis added) “Setup information 2068 may be provided to the controller 2066, which may include operating limits (e.g., min/max speeds, min/max flows, min/max pump power levels, min/max pressures allowed, NPSHR values, and the like), such as are appropriate for a given pump 2004, motor 2006, and piping and process conditions.”)
a fourth time-series of at least one predicted observable process-value of the industrial process; (Susteata para 140 “predicting or anticipating a future state of the machine(s) 110 and/or the system 100 (e.g., and/or of a sub-system of which the motorized pump system 110 is a part).”)
running the trained machine learning model by applying the first time-series, the second time-series, and the third time-series to the trained machine learning model; and (Susteata para 140 “The data fusion system may be employed to derive system attribute information relating to any number of attributes according to measured attribute information (e.g., from the sensors) in accordance with the present invention. In this regard, the available attribute information may be employed by the data fusion system to derive attributes related to failed sensors, and/or to other performance characteristics of the machine(s) 110 and/or system 100 for which sensors are not available.” The measured attribute information includes the first second and third time series data from Susteata para 123, see above.)
outputting, by the trained machine learning model, an output value, comprising at least a second criticality value of the at least one predicted observable process-value indicative of the abnormal behavior of the industrial process in a predefined temporal distance; and (Susteata para 98 “the future state achieved could be optimal in some manner such as machinery operating cost, machinery lifetime, or mean time before failure for example.”)
generating, based on the second criticality value, an alarm signal to indicate the abnormal behavior of the industrial process, the alarm signal prompting adjustment of one or more setpoints of the industrial process or triggering of one or more safety systems associated with the industrial process. (Susteata para 192 “controllers MC1, MCN, and VC1 may determine desired operating points for the associated sub-systems according to performance characteristic information obtained from other controllers via the network 1602.” Susteata para 105 “Any discrepancy between the sampled value and the setpoint value will result in a change in the control action to the motor-pump system.” This action is a safety system and an adjustment of a setpoint.)
Susteata teaches claims 2 and 9. The method of claim 1, wherein the output value further comprises a scenario number of the industrial process, wherein the scenario number depends on at least one of the first time-series, the second time-series, and the third time-series. (Susteata para 222 “the energy optimization component 2104 can be augmented with a scenario search component that can generate a series of possible operating scenarios.” Susteata para 140 “The data fusion system may be employed to derive system attribute information relating to any number of attributes according to measured attribute information (e.g., from the sensors) in accordance with the present invention. In this regard, the available attribute information may be employed by the data fusion system to derive attributes related to failed sensors, and/or to other performance characteristics of the machine(s) 110 and/or system 100 for which sensors are not available.”)
Susteata teaches claims 3 and 10. The method of claim 1, wherein the output value further comprises a fifth time-series, which depends on at least one of the first time-series, the second time-series, and the third time-series. (Susteata para 223 “Profit optimization component 2106 can utilize data and information supplied by capacity management component 2102 and/or energy optimization component 2104 as well as data and information from a multiplicity of disparate other sources such as financial variables, quality components, supplier data, historical performance data, and the like. Profit optimization component 2106, based at least in part on the supplied data and information, can thereafter perform margin optimization.”)
Susteata teaches claims 4 and 11. The method of claim 1, wherein the output value further comprises the first criticality value of the at least one observable process-value. (Susteata para 140 “The data fusion system may be employed to derive system attribute information relating to any number of attributes according to measured attribute information (e.g., from the sensors) in accordance with the present invention. In this regard, the available attribute information may be employed by the data fusion system to derive attributes related to failed sensors, and/or to other performance characteristics of the machine(s) 110 and/or system 100 for which sensors are not available.”)
Susteata teaches claims 5 and 12. The method of claim 1, further comprising outputting a manipulated variable dependent on at least one of the first time-series and the third time-series. (Susteata para 186 “energy costs are to rise during peak daytime periods, the correlation engine may prescribe a slightly higher throughput during off-peak hours (e.g., less energy efficient during off-peak hours) in order to minimize operation during more costly peak energy cost periods.”)
Susteata teaches claims 6 and 13. The method of claim 1, further comprising the step of determining a temporal distance to a second criticality value that exceeds a predefined criticality value. (Susteata para 93 “ a process run involving a high-temperature and high pressure reaction or military mission over hostile territory of lengthy duration may indicate likely gearbox or engine failure before successful completion.” Predicting failure is the first critical value, the second critical value is the time to successful completion.)
Susteata teaches claims 7 and 14. The method of claim 1, further comprising the steps of: determining an increasing-velocity of the second criticality value; and (Susteata para 98 “the future state achieved could be optimal in some manner such as machinery operating cost, machinery lifetime, or mean time before failure for example.” Mean time between failure is velocity of failing, i.e. failures over unit time.)
outputting an alarm when the increasing-velocity exceeds a predefined criticality value. (Susteata para 192 “control information (e.g., setpoints, control outputs, alarm conditions, process limits . . . ), and performance characteristic information (e.g., related to life cycle cost information, efficiency information, life expectancy information, safety information, emissions information, operational cost information, MTBF information, noise information, vibration information, production requirements, delivery schedules, and the like). One or more of the individual controllers MC1, MCN, and VC1 may determine desired operating points for the associated sub-systems according to performance characteristic information…” This show outputting an alarm based on MTBF information, which is mean time between failure information. The fact that an alarm can be based on inferred MTBF is taught in Susteata para 235 “It should be noted in this context that the claimed matter automatically infers an event (e.g., alarm conditions, etc.) based at least in part on real-time input or incoming historical data rather than on human input.”)
Conclusion
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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