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 .
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
Applicant argues,
Here, pending claim 1 does not recite any mathematical formula or method of organizing human activity, and it cannot be performed mentally: it requires collecting and exploiting specific, multi-source industrial time series (observable, manipulated, internal) to train and run an ANN to produce a predictive "second criticality value" at a predefined future horizon-a computation that is not a pen and paper mental exercise.
Thus, on its face and under the USPTO's current guidance and Example 47, claim 1 does not recite any judicial exception, and is therefore eligible at Step 2A Prong One.
Remarks 8.
The mental concept is running a machine learning model and output a value from the model. The mathematical relationship is in dependent claims 6, 7, 13 and 14 – comparing values to a threshold value. Looking at input and outputting a fault indicator is a classical example of a process that is done by humans in an industrial process. The claims are directed to a judicial exception of an abstract idea of a mental concept under Step 2A prong one.
Applicant argues,
First, the inputs recited in claim 1 are not generic data. They are particular industrial process signals, that are observable process values, manipulated variables, and internal variables, that are tied to how an industrial process operates and is controlled. This meaningfully limits the claim to a specific industrial monitoring/control context.
Remarks 8.
The only particularity in the claims is all “industrial process[es]”. Claim 1. This is not a specific industrial process and therefore does not integrate the abstract idea into a practical application.
Applicant argues,
claim 1 is integrated into forward looking industrial outcome. The output is "a second criticality value" for a predicted observable process value that is "indicative of abnormal behavior ... in a predefined temporal distance." That is a future fault risk indicator that acts on industrial time scales, not a mere data display; by construction, it is designed for process safety/health management.
Remarks 8.
The elements “forward looking industrial outcome” is not specific and not claimed. Even if it were claimed that is every business prediction in every field that calls itself an industry. This is not an integration into a practical application under prong 2 of step 2a.
Applicant argues,
Third, claim 1 is in alignment with the rationale of Example 47. Claim 3 (a method) of Example 47, was deemed eligible even though it recites an abstract idea, because the claim integrates the exception into a practical application by improving network security (detecting malicious packets with a trained ANN). The same logic applies here: the claim improves the operation of an industrial process by enabling early, model-based abnormal behavior detection at a defined future horizon, which is a technical improvement in the industrial process domain.
Remarks 8-9.
Applicant has not claimed something so specific as improving network security, and applicant has not claimed a specific industrial application – just all of industry. This does not integrate the abstract idea into a practical application.
Applicant argues,
the reference does not disclose training an ANN using the required three separate time series and score data architecture of claim 1. Particularly, Sustaeta is deficient in teaching or describing 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, as recited in claim 1, as amended.
Remarks 10.
Susteata paragraph 63 teaches that “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. Susteata paragraph 123 teaches that the data that it’s models run on includes internal variables such as flow information and that the trained model runs on a third time series, “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…”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept without significantly more. The claims recite running a machine learning mode and outputting a value from the model – this is a mental concept. Claims 6, 7, 13 and 14 also recite establishing different thresholds and comparing values to the threshold, this is a mathematical concept. This judicial exception is not integrated into a practical application because it is merely linked to the technical field of industrial processes. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a “computer” is a generic computer. The training step is merely links the claims to machine learning because it is claimed with such generality.
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. (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.”)
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 “he 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
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142