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 .
Drawings
The drawings filed on 11/9/23 are accepted by the examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/22/23 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) mental steps involving modifying at least one constraint variable of the steady-state optimization problem; generating, based on the determined operational characteristics of the industrial process, at least one recommendation responsive to the operator question, these limitations as described in specification, wherein the performing the real-time simulation further includes comparing the determined updated value of the user-specified process variable to a current value of the user-specified process variable; wherein the at least one recommendation includes a recommendation for increasing or decreasing the user-specified process variable, wherein the performing the real-time simulation further includes comparing the determined updated value of the user-specified process variable to the target value; wherein the at least one recommendation includes a recommendation for setting the user-specified process variable to the target value, wherein the at least one recommendation includes at least one of: (i) changing a constraint variable for a MV of the MPC controller, (ii) changing a constraint variable for a CV of the MPC controller, (iii) activating a MV of the MPC controller, and (iv) activating a CV of the MPC controller, identifying a constraint variable of the at least one constraint variable, the identified constraint variable having an effect on the user-specified process variable, perturbing a value of a constraint variable of the at least one constraint variable; responsive to detecting that a value of the user-specified process variable has changed in the steady-state optimization problem based on the perturbing, determining that the constraint variable has an effect on the user-specified process variable; responsive to detecting that the value of the user-specified process variable has not changed in the steady-state optimization problem based on the perturbing, removing the constraint variable from the real-time simulation of the operational scenario, wherein the modifying the at least one constraint variable of the steady-state optimization problem includes: constructing an operator profile based on historical data for the at least one constraint variable; modifying the at least one constraint variable of the steady-state optimization problem based on the constructed operator profile, constructing the operator profile based on at least one of: (i) an operator high limit for a constraint variable of the at least one constraint variable, (ii) an operator low limit for a constraint variable of the at least one constraint variable, and (iii) an operator target value for a constraint variable of the at least one constraint variable, ranking, based on the scoring, the multiple recommendations (claims 1-3, 5-10, 13, 15-20) are recited in high level of generality constitutes as a mental process, such as an evaluation or judgement, that can be performed in the human mind. The claim(s) also recite(s) mathematical concepts of performing a real-time simulation of one or more operational scenarios of the industrial process using a steady-state optimization problem of the MPC controller to determine operational characteristics of the industrial process in each of the one or more operational scenarios, the performing the real-time simulation including, for each operational scenario: using the modified at least one constraint variable, determining an updated value of the user-specified process variable, wherein the determined operational characteristics of the industrial process include the determined updated value of the user-specified process variable, scoring the multiple recommendations; the scoring includes scoring each recommendation of the multiple recommendations based on at least one of: (i) minimizing an objective function of the MPC controller and (ii) an effect on the industrial process of applying the recommendation, simulating the one or more operational scenarios of the industrial process based on a real-time execution cycle of the MPC controller (claims 1, 10-13 and 19-20), these limitations as described in the specification constitutes details of mathematical calculations of the material model, physical properties, thus, it falls into the “mathematical concepts” group of abstract ideas see MPEP 2106.04(a)(2), (claims 19, 21-28, 32 and 34-35).
This judicial exception is not integrated into a practical application because the additional limitations of receiving, in memory of the processor, an operator question, the operator question relating to a user-specified process variable of a model predictive control (MPC) controller of an industrial process; wherein the operator question further relates to increasing or decreasing the user-specified process variable; wherein the operator question further relates to a target value for the user-specified process variable; wherein the user-specified process variable is (i) a manipulated variable (MV) of the MPC controller or (ii) a controlled variable (CV) of the MPC controller (claims 1-4, 13-15 and 20) represent mere data transmission which is an insignificant extrasolution activity. The processor, the memory and the computer-readable medium with computer code instruction (claims 1, 13 and 15-20) are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)). Accordingly, these additional element does not integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the insignificant extra-solution activity of data transmission is considered well-understood, routine, and conventional, see mpep 2106.05(d), infra applied prior art, references cited. The processor, the memory and the computer-readable medium with computer code instruction are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications, which cannot provide an inventive concept. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)).
Allowable Subject Matter
Claims 1, 13 and 20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Regarding claim 1, the prior art of record, US7949417 discloses solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation. US6056781 discloses a model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner. US6615090 discloses a diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter for a multi-variable function block associated with one or more devices or loops within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.
However, regarding claim 1, the combination of prior arts does not describe:
receiving, in memory of the processor, an operator question, the operator question relating to a user-specified process variable of a model predictive control (MPC) controller of an industrial process; performing a real-time simulation of one or more operational scenarios of the industrial process using a steady-state optimization problem of the MPC controller; modifying at least one constraint variable of the steady-state optimization problem; and using the modified at least one constraint variable, determining an updated value of the user-specified process variable, wherein the determined operational characteristics of the industrial process include the determined updated value of the user-specified process variable; and generating, based on the determined operational characteristics of the industrial process, at least one recommendation responsive to the operator question
Regarding claim 13, the prior art of record, US7949417 discloses solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation. US6056781 discloses a model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner. US6615090 discloses a diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter for a multi-variable function block associated with one or more devices or loops within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.
However, regarding claim 13, the combination of prior arts does not describe:
receive, in the memory, an operator question, the operator question relating to a user-specified process variable of a model predictive control (MPC) controller of an industrial process; perform a real-time simulation of one or more operational scenarios of the industrial process using a steady-state optimization problem of the MPC controller to determine operational characteristics of the industrial process in each of the one or more operational scenarios, by, for each operational scenario: modifying at least one constraint variable of the steady-state optimization problem; and using the modified at least one constraint variable, determining an updated value of the user-specified process variable, wherein the determined operational characteristics of the industrial process include the determined updated value of the user-specified process variable; and generate, based on the determined operational characteristics of the industrial process, at least one recommendation responsive to the operator question
Regarding claim 20, the prior art of record, US7949417 discloses solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation. US6056781 discloses a model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner. US6615090 discloses a diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter for a multi-variable function block associated with one or more devices or loops within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.
However, regarding claim 20, the combination of prior arts does not describe:
receive, in the memory, an operator question, the operator question relating to a user-specified process variable of a model predictive control (MPC) controller of an industrial process; perform a real-time simulation of one or more operational scenarios of the industrial process using a steady-state optimization problem of the MPC controller to determine operational characteristics of the industrial process in each of the one or more operational scenarios, by, for each operational scenario: modifying at least one constraint variable of the steady-state optimization problem; and using the modified at least one constraint variable, determining an updated value of the user-specified process variable, wherein the determined operational characteristics of the industrial process include the determined updated value of the user-specified process variable; and generate, based on the determined operational characteristics of the industrial process, at least one recommendation responsive to the operator question
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US20180032940 discloses a method of Key Performance Indicator (KPI) performance analysis. A dynamic Model Predictive Control (MPC) process model for an industrial process including measured variables (MVs) and controlled variables (CVs) for an MPC controller is provided. The MPC process model includes at least one KPI that is also included in a business KPI monitoring system for the industrial process. A future trajectory of the KPI and a steady-state (SS) value for the KPI are estimated. The future trajectory and SS value are used for determining dynamic relationships between key plant operating variables selected from the CVs and MVs, and the KPI. A performance of the KPI is analyzed including identifying at least one cause of a problem in the performance or exceeding the performance during operation of the industrial process from the dynamic relationships and a current value for at least a portion of the MVs.
US20040049299 discloses an integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated. Thereafter, during each scan of the process controller, the optimizer routine calculates the optimal operating target of each of the complete set of control and auxiliary variables and provides the determined target operating points for each of the selected subset of control and auxiliary variables to the model predictive control routine as inputs. The model predictive control routine determines changes in the manipulated variables for use in controlling the process from the target and measured values for each of the subset of the control and auxiliary variables and the M by M control matrix.
US20210116891 discloses systems and methods provide a new paradigm of Advanced Process Control that includes building and deploying APC seed models. Embodiments provide automated data cleansing and selection in model identification and adaption in multivariable process control (MPC) techniques. Rather than plant pre-testing onsite for building APC seed models, the embodiments help APC engineers to build APC seed models from existing plant historical data with self-learning automation and pattern recognition, AI techniques. Embodiments further provide “growing” and “calibrating” the APC seed models online with non-invasive closed loop step testing techniques. PID loops and associated SP, PV, and OPs are searched and identified. Only “informative moves” data is screened, identified, and selected among a long history of process variables for seed model development and MPC application. The seed models are efficiently developed while skipping the costly traditional pre-testing steps and minimizing the interferences to the subject production process.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON LIN whose telephone number is (571)270-3175. The examiner can normally be reached on Monday-Friday 9:30 a.m. – 6:00 p.m. PST.
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/JASON LIN/
Primary Examiner, Art Unit 2117