DETAILED ACTION
This action is responsive to applicant’s communication filed 07/03/2026.
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 .
Status of the Claims
Claims 1-3, 5-12, and 19-20 are rejected under 35 U.S.C. 102(a)(1).
Claims 4 and 13-18 are rejected under 35 U.S.C. 103.
Claim 21 is objected to for depending from a rejected base claim.
Response to Arguments
Due to the amendments to the title, the objection to the specification made in the previous office action has been withdrawn.
Due to the amendments to the claims, the 35 U.S.C. 101 rejections made in the previous office action have been withdrawn.
Applicant’s arguments regarding the prior art over the amendments to the independent claims have been fully considered but are not persuasive.
Applicant generally argues that Shapiro “at best discloses update of calculation results” and fails to disclose or suggest update or optimization of model parameters. However, Shapiro explicitly discusses the optimization of manipulated variable values and of coefficients of an objective function used in an optimization and scheduling module (i.e. a planning model), the manipulated variables and coefficients (i.e. model parameters) also corresponding to parameters used to generate and update a process model (i.e. a simulation model). Shapiro therefore discloses the features required by the amendments to the independent claims. A detailed discussion of the relevant portions of Shapiro (¶ 61-63, 66-72, and 75-79) that discuss the updating of model parameters as required by the claims is included in the updated 35 U.S.C. 102(a)(1) rejections below.
Claim Rejections - 35 USC § 102
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 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 1-3, 5-12, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SHAPIRO (US 2013/0317629 A1).
Regarding Claim 1, SHAPIRO teaches a system comprising comprising at least one processor including a non-transient program configured to execute processes of: (¶ 20: “The disclosed invention consists of continuous real time dynamic process simulation running in parallel to real process, automatic coefficient adjustment of dynamic and static process models, automatic construction of transfer functions, determination of globally optimal operating point specific to current conditions, provision of additional optimal operating scenarios through a variety of unit combinations, and calculation of operational forecasts in accordance with planned production.” The simulation would be run on a computing system having a processor and memory.)
simulating operation of at least a portion of a production site, based on a simulation model of the at least a portion of the production site; (¶ 22, 34, 39, 53, 75: A Process Model simulates a production site having a plurality of processing units.)
monitoring actual operation of the at least a portion of the production site; (¶: 36, 39, 73-74: Actual measured data from the production site is obtained from a distributed control system that provides the measured data to the different components, including the simulator and planning optimizer.)
calibrating the simulation model, based on a difference between the simulated operation and the actual operation; (¶ 23, 75-76, 78-79, Fig. 7: Based on the difference between simulated process values and actual measured values of the production site, a calibration of the Process Model, i.e. the simulation, is performed by adjusting coefficients used in the simulation.)
and updating a planning model used to generate a production plan for the production site by updating parameters of the planning model having a correspondence with parameters of the simulation model, (¶ 61-64, 66-72, Fig. 6: An optimization and scheduling module, which together are equivalent to the claimed “planning model”, includes coefficients, constraints, demand, and manipulated variable values (i.e. parameters) that are adjusted and optimized (i.e. updated) based on real-time data. The manipulated variables and adjusted coefficients are parameters of this planning model that have a correspondence with parameters of the simulation model since the manipulated variables and coefficients are used in the creation of the simulation model (see ¶ 38-39, Process Model 310). The “advised scenarios”, which are fed into the scheduling module by the optimization module result in “advised schedules”, which is a production plan for the production site.
See ¶ 72: “This algorithm repeats and updates itself until incremental improvements are no longer financially viable. Finally, the Scheduling Module 611 provides the optimal schedule and forecast 613 to the Operator 61”.)
based on parameters of the simulation model calibrated based on a difference between the simulation operation and the actual operation, in accordance with the calibration of the simulation model. (¶ 75-79: “Process simulation occurs concurrently with the live process… Online Simulation Module 710 provides simulated process variables (SV) 715 for every time scan corresponding to measured variable values 78… The ultimate goal of the algorithm is to automatically adjust process model coefficients to reflect current operating mode and ensure model accuracy at any given time… the difference between the smoothed measured variable value and its corresponding simulated variable value dP 716 is minimized… The coefficients of function are thus adjusted by the module 717 to reflect the most accurate relationship between the simulated variable values and the measured variable values. The adjusted coefficients 718 are provided to the Plant Model 719 and overall algorithm repeats whenever process changes occur”.
The plant/process model has its coefficients calibrated by minimizing a difference between simulated variable values and actual measured values. These adjusted coefficients are then provided to the optimization and scheduling modules for updating of the modules and producing the advised schedules as discussed above. Updating of the optimization/scheduling (i.e. planning) module is therefore based on this calibration of the parameters of the process or simulation model.)
Claim 19 is directed to a method and Claim 20 is directed to a recording medium but they otherwise recite the same limitations as claim 1. Claim 19 and Claim 20 are therefore rejected using the same reasoning discussed above.
Regarding Claim 2, SHAPIRO further teaches wherein the planning model is a linear programming model. (¶ 61: The “optimization module”, which is the planning model, uses linear programming to optimize the process plan and scheduling.)
Regarding Claim 3, SHAPIRO further teaches wherein the updating updates at least one coefficient in a first-order expression used in the linear programming model. (¶ 24, 44, 53-55, 61, 66: The optimization model, which in an embodiment uses linear programming, uses first order functions. The optimization model initially uses coefficients provided by the operator but is then updated with adjusted coefficients provided by the “model construction module”.)
Regarding Claim 5, SHAPIRO further teaches, wherein the simulating simulates the operation of the at least a portion of the production site by inputting an update parameter for updating the planning model into the simulation model that has been calibrated, and the updating updates the planning model based on a simulation result obtained using the update parameter. (¶ 62, 66, 72, Fig. 6: Optimal manipulated variable values (see ¶ 39) provided to an operator are ”an update parameter”. The process repeats and updates itself. The simulation model uses initial coefficients but is calibrated with adjusted coefficients. On another iteration, the updated manipulated variable would be input into a calibrated simulation model. The results of the simulation are then used to update the optimization of the process plan, and the process continues to repeat until the determined improvement is no longer viable.)
Regarding Claim 6, SHAPIRO further teaches wherein the simulating simulates the operation of the at least a portion of the production site by inputting an update parameter for updating the planning model into the simulation model that has been calibrated, and the updating updates the planning model based on a simulation result obtained using the update parameter. (¶ 62, 66, 72, Fig. 6: Optimal manipulated variable values (see ¶ 39) provided to an operator are ”an update parameter”. The process repeats and updates itself. The simulation model uses initial coefficients but is calibrated with adjusted coefficients. On another iteration, the updated manipulated variable would be input into a calibrated simulation model. The results of the simulation are then used to update the optimization of the process plan, and the process continues to repeat until the determined improvement is no longer viable.)
Regarding Claim 7, SHAPIRO further teaches wherein the simulation model is a steady state model. (¶ 39: The process model is a collection of equations in steady-state working conditions that describe the interdependencies between process units and process variables. The simulation model is therefore a steady state model. Also see ¶ 45, which further teaches that the simulation model of the process is a steady state model.)
Regarding Claim 8, SHAPIRO further teaches wherein the simulation model is a steady state model. (¶ 39: The process model is a collection of equations in steady-state working conditions that describe the interdependencies between process units and process variables. The simulation model is therefore a steady state model. Also see ¶ 45, which further teaches that the simulation model of the process is a steady state model.)
Regarding Claim 9, SHAPIRO further teaches wherein the simulating simulates operation of one process unit at the production site. (¶ 34, 39: The Process Model, which is a component of the simulation, describe the interdependences between process units and process variables. Optimal setpoints are determined for each component of the plant. Operation of each process unit is therefore simulated.)
Regarding Claim 10, SHAPIRO further teaches wherein the simulating simulates operation of one process unit at the production site. (¶ 34, 39: The Process Model, which is a component of the simulation, describe the interdependences between process units and process variables. Optimal setpoints are determined for each component of the plant. Operation of each process unit is therefore simulated.)
Regarding Claim 11, SHAPIRO further teaches wherein the simulating simulates operation of a group of a plurality of process units at the production site. (¶ 20-22: “A system and method of Advanced Process Control for optimal operation of multi-unit plants in large scale processing and power generation industries is provided… provision of additional optimal operating scenarios through a variety of unit combinations… The uniqueness of the proposed invention lies in the method and apparatus for accomplishing this for large scale multi-unit systems”. The real time dynamic process simulation includes a variety of process unit combinations.)
Regarding Claim 12, SHAPIRO further teaches wherein the simulating simulates operation of a group of a plurality of process units at the production site. (¶ 20-22: “A system and method of Advanced Process Control for optimal operation of multi-unit plants in large scale processing and power generation industries is provided… provision of additional optimal operating scenarios through a variety of unit combinations… The uniqueness of the proposed invention lies in the method and apparatus for accomplishing this for large scale multi-unit systems”. The real time dynamic process simulation includes a variety of process unit combinations.)
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 4 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over SHAPIRO (US 2013/0317629 A1) in view of NANDIGAM (US 2016/0147202 A1).
Regarding Claim 4, SHAPIRO teaches all the limitations of claim 3, on which claim 4 depends.
SHAPIRO does not explicitly teach wherein the planning model and the simulation model include a common parameter, and the updating updates the at least one coefficient corresponding to the common parameter.
However, NANDIGAM, which is similarly directed to simulation and resource planning in an industrial environment, teaches wherein the planning model and the simulation model include a common parameter, and the updating updates the at least one coefficient corresponding to the common parameter. (¶ 10, 35, 45: The planner directly incorporates a set of model parameters directly updated from the runtime model into the planning model. The “runtime model” is a simulation. The planning model and the runtime model share at least one common parameter, as the runtime model updates these parameters and feeds the updated parameters to the planning model. ¶ 62, 66-69: A parameter being tuned (i.e. “Y” in Formula 1) is the common parameter that is updated by updating coefficients designed to minimize the error between the actual measurement of the parameter and the predicted measurement of the parameter.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by including a common parameter between the simulation and planning models that is updated by adjusting coefficients to reduce an error with an actual measurement of the parameter as taught by NANDIGAM. Since the references are similarly directed to optimization of planning models based on comparisons between simulations and actual plant data, the combination would have yielded predictable results. As taught by NANDIGAM (¶ 35, 39, 45), using this technique to update model parameters important to the planning of a refinery process would increase the efficiency of the plant and aide in quality control by producing a more accurate plan that more closely matches real plant behavior.
Regarding Claim 17, SHAPIRO teaches all the limitations of claim 1, on which claim 17 depends.
While SHAPIRO suggests use of the disclosure for multi-unit plants, including oil pipelines and hydrocarbon processing plants (¶ 5), SHAPIRO does not explicitly teach wherein the production site includes a refinery that produces a plurality of petroleum products by refining crude oil.
However, NANDIGAM, which is similarly directed to simulation and resource planning in an industrial environment, teaches wherein the production site includes a refinery that produces a plurality of petroleum products by refining crude oil. (¶ 35, 38, Fig. 1 and ¶ 39, 44-45 and Fig. 2C: A production site for refining crude oil and producing refinery end products is disclosed. The system includes a planning model and a plurality of runtime models for one or more of the reactors used in the production site. The runtime models are simulation models. A process of tuning the parameters of the runtime models based on comparisons to the real-world plant and thereby updating the planning model is illustrated in Fig. 2C.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by applying the techniques to a crude oil refining production plant as taught by NANDIGAM. Since SHAPIRO (¶ 5) at least suggests a refinery as a production plant applicable to its teachings, the combination would have yielded predictable results. NANDIGAM (¶ 1-2) also teaches such an implementation would assist planner in creating high-quality plans in the face of unforeseen circumstances, which is a common problem in the oil refining industry.
Regarding Claim 18, SHAPIRO in view of NANDIGAM further teaches wherein the at least the portion of the production site includes at least one of a crude distillation unit, vacuum distillation unit, naphtha hydrotreating unit, catalytic reforming unit, benzene extraction unit, kerosene hydrotreating unit, diesel desulfurization unit, heavy oil desulfurization unit, fluid catalytic cracking unit, FCC gasoline desulfurization unit, thermal cracking unit, hydrocracker unit, or asphalt production unit. (NANDIGAM, Fig. 1, ¶ 38: The production site includes crude oil refining components, including fluid catalytic cracking units, hydrocrackers, catalytic reforming units, and naphtha hydrotreating units.)
It would have been further obvious for the process units taught by SHAPIRO to include the industrial equipment taught by NANDIGAM for the same reasoning discussed in the rejection of claim 17.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over SHAPIRO (US 2013/0317629 A1) in view of HORN (US 2018/0046155 A1).
Regarding Claim 13, SHAPIRO teaches all the limitations of claim 1, on which claim 13 depends.
SHAPIRO does not teach wherein the simulation model is calibrated when the difference exceeds a predetermined threshold value.
However, HORN, which is similarly directed to building a process model of a petrochemical plant, including simulating the plant and tuning the simulation, teaches wherein the simulation model is calibrated when the difference exceeds a predetermined threshold value. (¶ 45: If a difference between the output of a process model simulation and actual plant operations is above a threshold value, a process model is designated for further tuning, i.e. calibration.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by calibrating the simulation model when a difference between the simulated and actual data is above a threshold as taught by HORN. Since the references are similarly directed to improved simulation and operation of a production plant, such as for an oil refining process, the combination would have yielded predictable results. Implementing a threshold to determine when additional tuning of the process simulation is needed would have improved the fitness of the simulation and thus optimize refinery operations, as taught by HORN (¶ 2, 13).
Regarding Claim 14, SHAPIRO teaches all the limitations of claim 2, on which claim 14 depends.
SHAPIRO does not teach wherein the simulation model is calibrated when the difference exceeds a predetermined threshold value.
However, HORN, which is similarly directed to building a process model of a petrochemical plant, including simulating the plant and tuning the simulation, teaches wherein the simulation model is calibrated when the difference exceeds a predetermined threshold value. (¶ 45: If a difference between the output of a process model simulation and actual plant operations is above a threshold value, a process model is designated for further tuning, i.e. calibration.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by calibrating the simulation model when a difference between the simulated and actual data is above a threshold as taught by HORN. Since the references are similarly directed to improved simulation and operation of a production plant, such as for an oil refining process, the combination would have yielded predictable results. Implementing a threshold to determine when additional tuning of the process simulation is needed would have improved the fitness of the simulation and thus optimize refinery operations, as taught by HORN (¶ 2, 13).
Regarding Claim 15, SHAPIRO teaches all the limitations of claim 1, on which claim 15 depends.
SHAPIRO does not teach further comprising: detecting deterioration or improvement of the at least the portion of the production site, based on a parameter that has been calibrated in the simulation model.
However, HORN, which is similarly directed to building a process model of a petrochemical plant, including simulating the plant and tuning the simulation, teaches further comprising: detecting deterioration or improvement of the at least the portion of the production site, based on a parameter that has been calibrated in the simulation model. (¶ 65-69, Fig. 3: After a simulation model for the production site has been tuned (i.e. calibrated), a scoring model is used to determine a degree of trustworthiness of the simulation, including examining performance trends, both declines and improvements. Based on determined deterioration or improvement of the production site, including yields, production rates, and product properties, the simulation model is further tuned.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by detecting improvement or deterioration of a production site using trend data after calibrating the simulation model in order to determine further calibrations as taught by HORN. Since the references are similarly directed to improved simulation and operation of a production plant, such as for an oil refining process, the combination would have yielded predictable results. HORN teaches multiple benefits of such an implementation that would have been considered advantageous to a person of ordinary skill in the art, including long-term sustainability of plant performance, improved training of technical personnel, automation of business processes, and making optimization of business processed more predictable (¶ 70-74).
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over SHAPIRO (US 2013/0317629 A1) in view of STRAIN (US 2007/0050070 A1).
Regarding Claim 16, SHAPIRO teaches all the limitations of claim 1, on which claim 16 depends.
SHAPIRO does not teach further comprising: judging whether to change a structure of the planning model, based on a difference between the production plan and the actual operation.
However, STRAIN, which is similarly directed to improved planning at a production site, teaches further comprising: judging whether to change a structure of the planning model, based on a difference between the production plan and the actual operation. (¶ 46, 84, 89, 100-103: An “exploring module” is a “judging section” that determines whether changes to a production plan are necessary based on a difference between “the planned operation and actual events”. Based on this comparison, feedback is provided for real-time scheduling changes, including updating of start times for future events.)
Before the effective filing date of the invention, it would have been obvious to one
of ordinary skill in the art to modify the updating of a planning model based on comparisons between a simulation and actual plant data taught by SHAPIRO by changing the planning model, when necessary, based on a difference between a production plan and actual events during production as taught by STRAIN. Since the references are similarly directed to optimization of production plans for an industrial site, the combination would have yielded predictable results. As taught by STRAIN, such a feature would allow for constant feedback and control that would ensure that the planning module constantly adheres to and monitors regulatory limits in real time (¶ 84).
Allowable Subject Matter
Claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Strain (US 2007/0050070 A1) at best teaches “changing a structure of a planning model when… the planning model differs from the actual operation” (see the rejection of claim 16), but Strain does not teach the combined condition of the simulated operation matching the actual operation while the planning model differs from the actual operation. Such a feature was not found in the prior art of record or otherwise reasonably obvious over a combination of the teachings of the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Adra (US 12,579,340 B2) teaches dynamically updating multiple simulation models when there are changes to a facility based on real time data. (Abstract, Fig. 2)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMI RAFAT OKASHA whose telephone number is (571)272-0675. The examiner can normally be reached M-F 10-6 EST.
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/RAMI R OKASHA/Primary Examiner, Art Unit 2118
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