DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 30-JUN-2023.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/14/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 11/13/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/07/2025 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 an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
selecting a range of values for one or more process variable input parameters associated with the at least one asset;
The “selecting” is interpreted as performing an evaluation using judgement. The “range of values” are selected from observed values of the “variable input parameters”.
simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant;
The “simulating operation” is interpreted as performing the evaluation repetitively. The “range of values of simulated output parameters” can be interpreted as calculating and evaluating limits for the system.
plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot;
Performing the method for “plotting” using a “parallel coordinates plot” is an evaluation.
identifying, from the parallel coordinates plot, a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values; and
The “sub-range of the selected range of values” is done by “identifying”, which is interpreted as performing a judgement or opinion when “identifying” the “sub-range”. The “optimum range of values” are determined by performing an evaluation of the “process variable input parameters”.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
Post solution activity and is well-understood, routine, and conventional activity:
controlling the at least one asset based on the one or more process variable input parameters in the identified sub-range.
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(d) “Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")”
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. “The method of claim 1, further comprising collecting information about the industrial plant to identify the one or more process variable input parameters and the optimum range of values of the simulated output parameters.”
The “collecting information” is observing additional information regarding the “industrial plant” where the observation is interpreted as a mental process. (Step 2A Prong 1).
3. “The method of claim 2, wherein collecting information about the industrial plant comprises executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description.”
Further defining the input as “at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description” does not change the input as part of an observation, which is a mental process. (Step 2A Prong 1).
4. “The method of claim 1, wherein controlling the at least one asset based on the one or more process variable input parameters in the identified sub-range comprises setting one or more alarm limits configured to maintain operation of the at least one asset such that the industrial plant produces output parameters within the optimum range of values.”
The “setting one or more alarm limits” is interpreted as post solution activity and to be well-understood, routine, and conventional. The limitation is analyzed in Step 2A Prong 2 and the analysis for well-understood, routine, and conventional under Step 2B in claim 1 remains the same.
5. The method of claim 4, wherein the optimum range of values of the output parameters produced by the industrial plant represent an efficient operating region for the industrial plant.
The “optimum range” is determined based on evaluation or based on judgement of the “efficient operation region” (Step 2A Prong 1).
6. “The method of claim 1, wherein simulating operation of the industrial plant comprises providing the process variable input parameters in the selected range of values as inputs to a digital twin of the industrial plant and producing, by the digital twin, the simulated output parameters based on the provided process variable input parameters.”
The “digital twin” is an abstract model of the “industrial plant” and is used in “simulation” where the simulation can be a form of evaluation. The “digital twin” is observing the current “variable input parameters” and evaluating the parameters to determine the current state of the “industrial plant”. (Step 2A Prong 1).
7. “The method of claim 6, wherein the simulated outputs parameters produced by the digital twin correspond to the output parameters associated with the industrial plant.”
The “simulated outputs” and the “output parameters” are set to “correspond”, which can reasonably be done in the mind by performing an observation of the two sets of data and evaluating if the data “correspond”. (Step 2A Prong 1).
8. “The method of claim 1, wherein the output parameters associated with the industrial plant include at least one of: an amount of chemical production and a rate of chemical production with at least one optimum alarm limit and efficient operating range.”
The “amount” and the “rate of chemical production” are observed values. The “alarm limit” and the “efficient operating range” are determined based on judgement. (Step 2A Prong 1).
9. “The method of claim 1, wherein the identified process variable input parameters include at least one of: a reactor temperature and a reactor pressure.”
The “reactor temperature” and “reactor pressure” are observed values. (Step 2A Prong 1).
10. “The method of claim 1, wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises reducing nuisance alarms resulting from the simulated output parameters.”
The “reducing nuisance alarms” is based on the “identifying the sub-range”, which amounts to selecting an alarm level. The alarm level is based on performing an evaluation. (Step 2A Prong 1).
11. “The method of claim 1, wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters.”
The “boundary condition” are determined based on “identifying”. The “first operating state” and “second operating state” are observed and an evaluation is performing using judgement to select the “boundary condition”. (Step 2A Prong 1).
Claim 12 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
selecting a range of values for one or more process variable input parameters associated with the at least one asset;
The “selecting” is interpreted as performing an evaluation using judgement. The “range of values” are selected from observed values of the “variable input parameters”.
simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant;
The “simulating operation” is interpreted as performing the evaluation repetitively. The “range of values of simulated output parameters” can be interpreted as calculating and evaluating limits for the system.
identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters;
The “boundary condition between a first operating state and a second operating state” is observed. A person of ordinary skill in the art could reasonably perform the “identifying” by performing an evaluation based on judgement.
identifying a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and
The evaluation continues to determine if the “sub-range” is within the “optimum range”. The “selected range of values” is observed and the “identifying” is based on judgement of the observed values.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
Post solution activity and is well-understood, routine, and conventional activity:
setting one or more alarm limits configured to maintain operation of the at least one asset such that the industrial plant produces output parameters within the optimum range of values based on the one or more process variable input parameters in the identified sub-range.
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(d) “Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")”
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
13. “The method of claim 12, further comprising collecting information about the industrial plant to identify the one or more process variable input parameters and the optimum range of values of the simulated output parameters.”
The “collecting information” is observing additional information regarding the “industrial plant” where the observation is interpreted as a mental process. (Step 2A Prong 1).
14. “The method of claim 13, wherein collecting information about the industrial plant comprises executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description.”
Further defining the input as “at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description” does not change the input as part of an observation, which is a mental process. (Step 2A Prong 1).
15. “The method of claim 12, wherein the optimum range of values of the output parameters produced by the industrial plant represent an efficient operating region for the industrial plant.”
The “optimum range” is determined based on evaluation or based on judgement of the “efficient operation region” (Step 2A Prong 1).
16. “The method of claim 12, wherein simulating operation of the industrial plant comprises providing the process variable input parameters in the selected range of values for the one or more process variable input parameters as inputs to a digital twin of the industrial plant and producing, by the digital twin, the simulated output parameters based on the provided process variable input parameters.”
The “digital twin” is an abstract model of the “industrial plant” and is used in “simulation” where the simulation can be a form of evaluation. The “digital twin” is observing the current “variable input parameters” and evaluating the parameters to determine the current state of the “industrial plant”. (Step 2A Prong 1).
17. “The method of claim 16, wherein the simulated outputs parameters produced by the digital twin correspond to the output parameters associated with the industrial plant.”
The “simulated outputs” and the “output parameters” are set to “correspond”, which can reasonably be done in the mind by performing an observation of the two sets of data and evaluating if the data “correspond”. (Step 2A Prong 1).
18. “The method of claim 12, wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises reducing nuisance alarms resulting from the simulated output parameters.”
The “reducing nuisance alarms” is based on the “identifying the sub-range”, which amounts to selecting an alarm level. The alarm level is based on performing an evaluation. (Step 2A Prong 1).
19. “The method of claim 12, wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot, and further comprising identifying, from the parallel coordinates plot, the sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within the optimum range of values.”
Performing the method for “plotting” using a “parallel coordinates plot” is an evaluation. The “sub-range of the selected range of values” is done by “identifying”, which is interpreted as performing a judgement or opinion when “identifying” the “sub-range”. (Step 2A Prong 1).
Claim 20 (Statutory Category – System/Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
select a range of values for one or more process variable input parameters associated with the at least one industrial asset;
The “selecting” is interpreted as performing an evaluation using judgement. The “range of values” are selected from observed values of the “variable input parameters”.
simulate operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant;
The “simulating operation” is interpreted as performing the evaluation repetitively. The “range of values of simulated output parameters” can be interpreted as calculating and evaluating limits for the system.
plot the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot;
Performing the method for “plotting” using a “parallel coordinates plot” is an evaluation.
identify, from the parallel coordinates plot, at least one boundary condition for efficient operation between a first operating state and a second operating state as represented by the simulated output parameters;
The “boundary condition between a first operating state and a second operating state” is observed. A person of ordinary skill in the art could reasonably perform the “identifying” by performing an evaluation based on judgement.
identify a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and
The evaluation continues to determine if the “sub-range” is within the “optimum range”. The “selected range of values” is observed and the “identifying” is based on judgement of the observed values.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
Post solution activity and is well-understood, routine, and conventional activity:
set one or more alarm limits for the at least one industrial asset via the controller, the alarm limits configured to maintain operation of the at least one industrial asset such that the industrial plant produces output parameters within the optimum range of values based on the one or more process variable input parameters in the identified sub-range.
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
at least one industrial asset;
at least one controller communicatively coupled to the asset;
a processor; and
one or more memory devices coupled to the processor, the memory devices storing processor-executable instructions that, when executed, configure the processor to:
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(d) “Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")”
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
Claim Rejections - 35 USC § 103
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 1-2, 4-7, 9-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Thomsen et al., U.S. Patent Application Publication 2021/0397174 A1 (hereinafter ‘Thomsen’) in view of
Bhatti et al., “Intelligent Fault Diagnosis Mechanism for Industrial Robot Actuators using Digital Twin Technology” [2021] (hereinafter ‘Bhatti’) further in view of
LAMEGO et al., U.S. Patent Application Publication 2021/0022676 A1 (hereinafter ‘LAMEGO’).
Regarding Claim 1: A method for operating at least one asset of an industrial plant in an optimized operating region, the method comprising:
Thomsen teaches selecting a range of values for one or more process variable input parameters associated with the at least one asset; ([0198] Thomsen “…When the instruction 3202 is set to log according to variable change, the instruction 3202 causes the identified BIDT property to be logged each time a linked process variable changes value or state…” [0096] Thomsen “…Accordingly, the Rate BIDT 604 will not generate a velocity value that is outside the range defined by the defined maximum and minimum values, and may generate an error or alarm output if the measured velocity value exceeds the defined maximum or falls below the defined minimum…”)
Thomsen teaches simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant; ([0180] Thomsen “…Since digital twin 2306 models both automation and mechanical characteristics of an industrial asset, the digital twin 2306 can be used to simulate expected behaviors of the industrial asset ( e.g., responses to control inputs in terms of movement, speed, temperatures, flows, fill levels, fluid mechanics, product displacements, etc.) in connection with testing control programs or device configurations…”)
Thomsen teaches controlling the at least one asset based on the one or more process variable input parameters in the identified sub-range. ([0054] Thomsen “…FIG. 1 is a block diagram of an example industrial control environment 100. In this example, a number of industrial controllers 118 are deployed throughout an industrial plant environment to monitor and control respective industrial systems or processes relating to product manufacture, machining, motion control, batch processing, material handling, or other such industrial functions. Industrial controllers 118 typically execute respective control programs to facilitate monitoring and control of industrial devices 120 making up the controlled industrial assets or systems (e.g., industrial machines). One or more industrial controllers 118 may also comprise a soft controller executed on a personal computer or other hardware platform, or on a cloud platform…”)
Thomsen does not appear to explicitly disclose
plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot;
identifying, from the parallel coordinates plot,
However, Bhatti teaches plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot; and identifying, from the parallel coordinates plot, (Fig. 9 and Pg. 5 right col 1st paragraph Bhatti “…The performance of the Gaussian Naïve Bayes classifier is further delineated in Fig. 9 showing the true positive rate, the false negative rate of the model as well as a parallel coordinates view of the data and predictions…”)
PNG
media_image1.png
332
738
media_image1.png
Greyscale
Thomsen and Bhatti are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant as disclosed by Thomsen by plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot as disclosed by Bhatti.
One of ordinary skill in the art would have been motivated to make this modification in order to incorporate smart monitoring in to industrial equipment as discussed on pg. 2 left col 2nd paragraph “This study focuses on providing an architecture suitable for furthering the goal of Industry 4.0, which is to involve smart monitoring and manufacturing in standard industrial equipment and processes…”
Thomsen and Bhatti do not appear to explicitly disclose
…a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values; and
However, LAMEGO teaches …a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values; and ([0313] LAMEGO “…FIG. 41A depicts a data flow diagram of an example method of detailing the optimization process involving a series of indexed models. Element 4100 is an indexed model (modeli). Element 4100 is indexed by the index variable "i", which transforms a sequence of discrete values, sub-ranges, n-tuples of discrete values, or n-tuples of sub-range values used to index a particular series of indexed models into an integer sequence that also indexes the same series of indexed models. The index variable "i" provides a convenient way of generalizing the notion of indexing. For instance, multidimensional sub-ranges or grids can be easily mapped into a sequence of integer values represented by the index variable "i"…”)
Thomsen, Bhatti, and LAMEGO are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the identifying, from the parallel coordinates plot as disclosed by Thomsen and Bhatti by a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values as disclosed by LAMEGO.
One of ordinary skill in the art would have been motivated to make this modification in order to determine the real-time states and to update data that is displayed, shared, or stored, such as with alarms, as discussed in [0015] by LAMEGO “…In another aspect, a hybrid system that employs state space representation for the measurement algorithms. A hybrid system is a system that exhibits both continuous and discrete dynamic behavior. In some embodiments, data in the hybrid system is processed in a discrete-time periods. In such embodiments, data processing is performed for each epoch ( e.g., each discrete time period). The method includes initializing one or more constants and the input, output, and current state variables (i.e., state variables defined in current epoch) using the one or more initialized constants and/or one or more default values. Real-time data is received by the host device from the monitoring device and the next states and outputs are calculated from the one or more inputs, the current states, and the one or more constants. The current states are then updated from the calculated next states. The data that is displayed, shared, and/or stored is updated, along with any alarms, fuel gauges (e.g., battery icon), and/or notifications…”
Regarding Claim 2: Thomsen, Bhatti, and LAMEGO teach The method of claim 1, further comprising
Thomsen teaches collecting information about the industrial plant to identify the one or more process variable input parameters and the optimum range of values of the simulated output parameters. ([0058] Thomsen “…The industrial controllers 118 can also store persisted data values that can be referenced by the control program and used for control decisions, including but not limited to measured or calculated values representing operational states of a controlled machine or process ( e.g., tank levels, positions, alarms, etc.) or captured time series data that is collected during operation of the automation system ( e.g., status information for multiple points in time, diagnostic occurrences, etc.). Similarly, some intelligent devices-including but not limited to motor drives, instruments, or condition monitoring modules-may store data values that are used for control and/or to visualize states of operation. Such devices may also capture time-series data or events on a log for later retrieval and viewing…”)
Regarding Claim 4: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Thomsen teaches wherein controlling the at least one asset based on the one or more process variable input parameters in the identified sub-range comprises setting one or more alarm limits configured to maintain operation of the at least one asset such that the industrial plant produces output parameters within the optimum range of values. ([0141] Thomsen “…FIG. 20 illustrates an example methodology 2000 for configuring and utilizing BIDT data tags in an industrial controller for delivery of industrial data to a visualization system. Initially, at 2002, one or more data tags are defined on an industrial device, where the data tags conform to one or more basic information data types (BIDTs), and the BIDTs comprise at least one of a rate BIDT, a state BIDT, an odometer BIDT, or an event BIDT. Rate BIDT data tags can represent an integer or real value of a measured rate of a metric associated with the industrial asset or device. State BIDT data tags can represent a current state of an industrial asset or device ( e.g., a machine, a production line, a motor drive, etc.). Odometer BIDT data tags can represent cumulative quantities associated with an industrial asset (e.g., a cumulative quantity with a rollover value, or a quantity over a defined time interval). Event BIDT data types can represent instantaneous or persistent event associated with an industrial asset (e.g., a push-button event, a sensor event, a safety device event, and alarm event, etc.)…”)
Regarding Claim 5: Thomsen, Bhatti, and LAMEGO teach The method of claim 4,
LAMEGO teaches wherein the optimum range of values of the output parameters produced by the industrial plant represent an efficient operating region for the industrial plant. ([0306] LAMEGO “…Next, one or more parameter constants for a series of indexed models are initialized at block 4002. The parameter constants for the series of indexed models can define dimensions, number of equations, type of equations, equation parameters, operating regions, variable sizes and types, limits, thresholds, topology, architecture, algorithm configurations, and/or constants from the mathematical operator(s) from the indexed models that is used for initialization and calculation of the series of indexed models…”)
Regarding Claim 6: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Thomsen teaches wherein simulating operation of the industrial plant comprises providing the process variable input parameters in the selected range of values as inputs to a digital twin of the industrial plant and producing, by the digital twin, the simulated output parameters based on the provided process variable input parameters. ([0161] Thomsen “…The BIDT-based type system shared by the asset model 422 and the mechanical model 2304 creates a mapping of properties between the two models, allowing the mechanical formulas or transformations defined by the mechanical model 2304 to be applied to measured contextualized automation data to yield additional real-time or historical behavior or response data for the industrial asset (including but not limited to forces, positions, orientations, shapes, or temperatures of mechanical components). The combined asset model 422 and mechanical model 2304-with properties linked via common BIDT references----can serve as a mechatronic model or digital twin 2306 of the industrial asset capable of generating more comprehensive information about the industrial asset than either of the two models can produce individually. In general, the digital twin 2306 comprises multiple disparate models of the industrial asset (the asset model 422 and the mechanical model 2304) that interact to simulate the behavior of the industrial asset…”)
Regarding Claim 7: Thomsen, Bhatti, and LAMEGO teach The method of claim 6,
Thomsen teaches wherein the simulated outputs parameters produced by the digital twin correspond to the output parameters associated with the industrial plant. ([0161] Thomsen “…In general, the digital twin 2306 comprises multiple disparate models of the industrial asset (the asset model 422 and the mechanical model 2304) that interact to simulate the behavior of the industrial asset…”)
Regarding Claim 9: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Thomsen teaches wherein the identified process variable input parameters include at least one of: a reactor temperature and a reactor pressure. ([0055] Thomsen “…Industrial devices 120 may include both input devices that provide data relating to the controlled industrial systems to the industrial controllers 118, and output devices that respond to control signals generated by the industrial controllers 118 to control aspects of the industrial systems. Example input devices can include telemetry devices (e.g., temperature sensors, flow meters, level sensors, pressure sensors, etc.), manual operator control devices (e.g., push buttons, selector switches, etc.), safety monitoring devices (e.g., safety mats, safety pull cords, light curtains, etc.), and other such devices. Output devices may include motor drives, pneumatic actuators, signaling devices, robot control inputs, valves, and the like…”)
Regarding Claim 10: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Thomsen teaches wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises reducing nuisance alarms resulting from the simulated output parameters. ([0213] Thomsen “…The link defined at step 3706 ensures that the first BIDT data tag and the one or more interlinked second BIDT data tags are logged in a synchronous manner, ensuring that all the interlinked values have common time-stamps. This can improve accuracy of time-based analysis of the historical BIDT data by eliminating the need to interpolate an estimated value of a BIDT tag corresponding to a timestamp of an event…” [0096] Thomsen “…Metadata of an Event BIDT 608 associated with a component of the filling machine can define an input address or data tag representing a state of a device (e.g., a push-button, a photo-sensor, etc.) that determines the event, or an alarm data tag corresponding to an alarm whose state (e.g., Abnormal, Normal, Acknowledged, Unacknowledged, etc.) determines the event…”)
Regarding Claim 11: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Bhatti teaches
PNG
media_image2.png
779
627
media_image2.png
Greyscale
wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters. (Fig. 7A-C and Pg. 5 left col 2nd paragraph Bhatti “…The two types of faults when simulated on the DT in no-load condition between 2.5-10 seconds giving the current and torque profiles as seen in Fig. 7…”)
Regarding Claim 12: A method for operating at least one asset of an industrial plant in an optimized operating region, the method comprising:
Thomsen teaches selecting a range of values for one or more process variable input parameters associated with the at least one asset; ([0198] Thomsen “…When the instruction 3202 is set to log according to variable change, the instruction 3202 causes the identified BIDT property to be logged each time a linked process variable changes value or state…” [0096] Thomsen “…Accordingly, the Rate BIDT 604 will not generate a velocity value that is outside the range defined by the defined maximum and minimum values, and may generate an error or alarm output if the measured velocity value exceeds the defined maximum or falls below the defined minimum…”)
Thomsen teaches simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant; ([0180] Thomsen “…Since digital twin 2306 models both automation and mechanical characteristics of an industrial asset, the digital twin 2306 can be used to simulate expected behaviors of the industrial asset ( e.g., responses to control inputs in terms of movement, speed, temperatures, flows, fill levels, fluid mechanics, product displacements, etc.) in connection with testing control programs or device configurations…”)
Thomsen teaches setting one or more alarm limits configured to maintain operation of the at least one asset such that the industrial plant produces output parameters within the optimum range of values based on the one or more process variable input parameters in the identified sub-range. ([0141] Thomsen “…FIG. 20 illustrates an example methodology 2000 for configuring and utilizing BIDT data tags in an industrial controller for delivery of industrial data to a visualization system. Initially, at 2002, one or more data tags are defined on an industrial device, where the data tags conform to one or more basic information data types (BIDTs), and the BIDTs comprise at least one of a rate BIDT, a state BIDT, an odometer BIDT, or an event BIDT. Rate BIDT data tags can represent an integer or real value of a measured rate of a metric associated with the industrial asset or device. State BIDT data tags can represent a current state of an industrial asset or device ( e.g., a machine, a production line, a motor drive, etc.). Odometer BIDT data tags can represent cumulative quantities associated with an industrial asset (e.g., a cumulative quantity with a rollover value, or a quantity over a defined time interval). Event BIDT data types can represent instantaneous or persistent event associated with an industrial asset (e.g., a push-button event, a sensor event, a safety device event, and alarm event, etc.)…”)
Thomsen does not appear to explicitly disclose
identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters;
PNG
media_image3.png
708
570
media_image3.png
Greyscale
However, Bhatti teaches identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters; (Fig. 7A-C and Pg. 5 left col 2nd paragraph Bhatti “…The two types of faults when simulated on the DT in no-load condition between 2.5-10 seconds giving the current and torque profiles as seen in Fig. 7…”)
Thomsen and Bhatti are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant as disclosed by Thomsen by identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters as disclosed by Bhatti.
One of ordinary skill in the art would have been motivated to make this modification in order to incorporate smart monitoring in to industrial equipment as discussed on pg. 2 left col 2nd paragraph “This study focuses on providing an architecture suitable for furthering the goal of Industry 4.0, which is to involve smart monitoring and manufacturing in standard industrial equipment and processes…”
Thomsen and Bhatti do not appear to explicitly disclose
identifying a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and
However, LAMEGO teaches identifying a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and ([0313] Bhatti “…FIG. 41A depicts a data flow diagram of an example method of detailing the optimization process involving a series of indexed models. Element 4100 is an indexed model (modeli). Element 4100 is indexed by the index variable "i", which transforms a sequence of discrete values, sub-ranges, n-tuples of discrete values, or n-tuples of sub-range values used to index a particular series of indexed models into an integer sequence that also indexes the same series of indexed models. The index variable "i" provides a convenient way of generalizing the notion of indexing. For instance, multidimensional sub-ranges or grids can be easily mapped into a sequence of integer values represented by the index variable "i"…”)
Thomsen, Bhatti, and LAMEGO are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant as disclosed by Thomsen and Bhatti by identifying a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary as disclosed by LAMEGO.
One of ordinary skill in the art would have been motivated to make this modification in order to determine the real-time states and to update data that is displayed, shared, or stored, such as with alarms, as discussed in [0015] by LAMEGO “…In another aspect, a hybrid system that employs state space representation for the measurement algorithms. A hybrid system is a system that exhibits both continuous and discrete dynamic behavior. In some embodiments, data in the hybrid system is processed in a discrete-time periods. In such embodiments, data processing is performed for each epoch ( e.g., each discrete time period). The method includes initializing one or more constants and the input, output, and current state variables (i.e., state variables defined in current epoch) using the one or more initialized constants and/or one or more default values. Real-time data is received by the host device from the monitoring device and the next states and outputs are calculated from the one or more inputs, the current states, and the one or more constants. The current states are then updated from the calculated next states. The data that is displayed, shared, and/or stored is updated, along with any alarms, fuel gauges (e.g., battery icon), and/or notifications…”
Regarding Claim 13: Thomsen, Bhatti, and LAMEGO teach The method of claim 12, further comprising
Thomsen teaches collecting information about the industrial plant to identify the one or more process variable input parameters and the optimum range of values of the simulated output parameters. ([0058] Thomsen “…The industrial controllers 118 can also store persisted data values that can be referenced by the control program and used for control decisions, including but not limited to measured or calculated values representing operational states of a controlled machine or process ( e.g., tank levels, positions, alarms, etc.) or captured time series data that is collected during operation of the automation system ( e.g., status information for multiple points in time, diagnostic occurrences, etc.). Similarly, some intelligent devices-including but not limited to motor drives, instruments, or condition monitoring modules-may store data values that are used for control and/or to visualize states of operation. Such devices may also capture time-series data or events on a log for later retrieval and viewing…”)
Regarding Claim 15: Thomsen, Bhatti, and LAMEGO teach The method of claim 12,
LAMEGO teaches wherein the optimum range of values of the output parameters produced by the industrial plant represent an efficient operating region for the industrial plant. ([0306] LAMEGO “…Next, one or more parameter constants for a series of indexed models are initialized at block 4002. The parameter constants for the series of indexed models can define dimensions, number of equations, type of equations, equation parameters, operating regions, variable sizes and types, limits, thresholds, topology, architecture, algorithm configurations, and/or constants from the mathematical operator(s) from the indexed models that is used for initialization and calculation of the series of indexed models…”)
Regarding Claim 16: Thomsen, Bhatti, and LAMEGO teach The method of claim 12,
Thomsen teaches wherein simulating operation of the industrial plant comprises providing the process variable input parameters in the selected range of values for the one or more process variable input parameters as inputs to a digital twin of the industrial plant and producing, by the digital twin, the simulated output parameters based on the provided process variable input parameters. ([0161] Thomsen “…The BIDT-based type system shared by the asset model 422 and the mechanical model 2304 creates a mapping of properties between the two models, allowing the mechanical formulas or transformations defined by the mechanical model 2304 to be applied to measured contextualized automation data to yield additional real-time or historical behavior or response data for the industrial asset (including but not limited to forces, positions, orientations, shapes, or temperatures of mechanical components). The combined asset model 422 and mechanical model 2304-with properties linked via common BIDT references----can serve as a mechatronic model or digital twin 2306 of the industrial asset capable of generating more comprehensive information about the industrial asset than either of the two models can produce individually. In general, the digital twin 2306 comprises multiple disparate models of the industrial asset (the asset model 422 and the mechanical model 2304) that interact to simulate the behavior of the industrial asset…”)
Regarding Claim 17: Thomsen, Bhatti, and LAMEGO teach The method of claim 16,
Thomsen teaches wherein the simulated outputs parameters produced by the digital twin correspond to the output parameters associated with the industrial plant. ([0161] Thomsen “…In general, the digital twin 2306 comprises multiple disparate models of the industrial asset (the asset model 422 and the mechanical model 2304) that interact to simulate the behavior of the industrial asset…”)
Regarding Claim 18: Thomsen, Bhatti, and LAMEGO teach The method of claim 12,
Thomsen teaches wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises reducing nuisance alarms resulting from the simulated output parameters. ([0213] Thomsen “…The link defined at step 3706 ensures that the first BIDT data tag and the one or more interlinked second BIDT data tags are logged in a synchronous manner, ensuring that all the interlinked values have common time-stamps. This can improve accuracy of time-based analysis of the historical BIDT data by eliminating the need to interpolate an estimated value of a BIDT tag corresponding to a timestamp of an event…” [0096] Thomsen “…Metadata of an Event BIDT 608 associated with a component of the filling machine can define an input address or data tag representing a state of a device (e.g., a push-button, a photo-sensor, etc.) that determines the event, or an alarm data tag corresponding to an alarm whose state (e.g., Abnormal, Normal, Acknowledged, Unacknowledged, etc.) determines the event…”)
Regarding Claim 19: Thomsen, Bhatti, and LAMEGO teach The method of claim 12,
Bhatti teaches wherein identifying the sub-range of the selected range of values for the one or more process variable input parameters comprises plotting the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot, and further comprising (Fig. 9 and Pg. 5 right col 1st paragraph Bhatti “…The performance of the Gaussian Naïve Bayes classifier is further delineated in Fig. 9 showing the true positive rate, the false negative rate of the model as well as a parallel coordinates view of the data and predictions…”)
PNG
media_image1.png
332
738
media_image1.png
Greyscale
LAMEGO teaches identifying, from the parallel coordinates plot, the sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within the optimum range of values. ([0313] LAMEGO “…FIG. 41A depicts a data flow diagram of an example method of detailing the optimization process involving a series of indexed models. Element 4100 is an indexed model (modeli). Element 4100 is indexed by the index variable "i", which transforms a sequence of discrete values, sub-ranges, n-tuples of discrete values, or n-tuples of sub-range values used to index a particular series of indexed models into an integer sequence that also indexes the same series of indexed models. The index variable "i" provides a convenient way of generalizing the notion of indexing. For instance, multidimensional sub-ranges or grids can be easily mapped into a sequence of integer values represented by the index variable "i"…”)
Regarding Claim 20: A system comprising:
Thomsen teaches at least one industrial asset; ([0059] Thomsen “…Industrial assets and their associated industrial assets can generate large amounts of information during operation…”)
Thomsen teaches at least one controller communicatively coupled to the asset; ([0057] Thomsen “…Industrial automation systems often include one or more human-machine interfaces (HMis) 114 that allow plant personnel to view telemetry and status data associated with the automation systems, and to control some aspects of system operation. HMis 114 may communicate with one or more of the industrial controllers 118 over a plant network 116, and exchange data with the industrial controllers to facilitate visualization of information relating to the controlled industrial processes on one or more pre-developed operator interface screens…”)
Thomsen teaches a processor; and one or more memory devices coupled to the processor, the memory devices storing processor-executable instructions that, when executed, configure the processor to: ([0067] Thomsen “…Industrial device 302 can include a program execution component 304, an I/0 control component 306, a BIDT configuration component 308, a BIDT publishing component 310, a networking component 312, a user interface component 314, one or more processors 318, and memory 320…”)
Thomsen teaches select a range of values for one or more process variable input parameters associated with the at least one industrial asset; ([0198] Thomsen “…When the instruction 3202 is set to log according to variable change, the instruction 3202 causes the identified BIDT property to be logged each time a linked process variable changes value or state…” [0096] Thomsen “…Accordingly, the Rate BIDT 604 will not generate a velocity value that is outside the range defined by the defined maximum and minimum values, and may generate an error or alarm output if the measured velocity value exceeds the defined maximum or falls below the defined minimum…”)
Thomsen teaches simulate operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant; ([0180] Thomsen “…Since digital twin 2306 models both automation and mechanical characteristics of an industrial asset, the digital twin 2306 can be used to simulate expected behaviors of the industrial asset ( e.g., responses to control inputs in terms of movement, speed, temperatures, flows, fill levels, fluid mechanics, product displacements, etc.) in connection with testing control programs or device configurations…”)
Thomsen teaches set one or more alarm limits for the at least one industrial asset via the controller, the alarm limits configured to maintain operation of the at least one industrial asset such that the industrial plant produces output parameters within the optimum range of values based on the one or more process variable input parameters in the identified sub-range. ([0141] Thomsen “…FIG. 20 illustrates an example methodology 2000 for configuring and utilizing BIDT data tags in an industrial controller for delivery of industrial data to a visualization system. Initially, at 2002, one or more data tags are defined on an industrial device, where the data tags conform to one or more basic information data types (BIDTs), and the BIDTs comprise at least one of a rate BIDT, a state BIDT, an odometer BIDT, or an event BIDT. Rate BIDT data tags can represent an integer or real value of a measured rate of a metric associated with the industrial asset or device. State BIDT data tags can represent a current state of an industrial asset or device ( e.g., a machine, a production line, a motor drive, etc.). Odometer BIDT data tags can represent cumulative quantities associated with an industrial asset (e.g., a cumulative quantity with a rollover value, or a quantity over a defined time interval). Event BIDT data types can represent instantaneous or persistent event associated with an industrial asset (e.g., a push-button event, a sensor event, a safety device event, and alarm event, etc.)…”)
Thomsen does not appear to explicitly disclose
plot the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot;
identify, from the parallel coordinates plot, at least one boundary condition for efficient operation between a first operating state and a second operating state as represented by the simulated output parameters;
However, Bhatti teaches plot the process variable input parameters and the simulated output parameters on at least one parallel coordinates plot; (Fig. 9 and Pg. 5 right col 1st paragraph Bhatti “…The performance of the Gaussian Naïve Bayes classifier is further delineated in Fig. 9 showing the true positive rate, the false negative rate of the model as well as a parallel coordinates view of the data and predictions…”)
PNG
media_image1.png
332
738
media_image1.png
Greyscale
PNG
media_image3.png
708
570
media_image3.png
Greyscale
Bhatti teaches identify, from the parallel coordinates plot, at least one boundary condition for efficient operation between a first operating state and a second operating state as represented by the simulated output parameters; (Fig. 7A-C and Pg. 5 left col 2nd paragraph Bhatti “…The two types of faults when simulated on the DT in no-load condition between 2.5-10 seconds giving the current and torque profiles as seen in Fig. 7…”)
Thomsen and Bhatti are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the simulating operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant as disclosed by Thomsen by identifying at least one boundary condition between a first operating state and a second operating state as represented by the simulated output parameters as disclosed by Bhatti.
One of ordinary skill in the art would have been motivated to make this modification in order to incorporate smart monitoring in to industrial equipment as discussed on pg. 2 left col 2nd paragraph “This study focuses on providing an architecture suitable for furthering the goal of Industry 4.0, which is to involve smart monitoring and manufacturing in standard industrial equipment and processes…”
Thomsen and Bhatti do not appear to explicitly disclose
identify a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and
However, LAMEGO teaches identify a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition; and ([0313] LAMEGO “…FIG. 41A depicts a data flow diagram of an example method of detailing the optimization process involving a series of indexed models. Element 4100 is an indexed model (modeli). Element 4100 is indexed by the index variable "i", which transforms a sequence of discrete values, sub-ranges, n-tuples of discrete values, or n-tuples of sub-range values used to index a particular series of indexed models into an integer sequence that also indexes the same series of indexed models. The index variable "i" provides a convenient way of generalizing the notion of indexing. For instance, multidimensional sub-ranges or grids can be easily mapped into a sequence of integer values represented by the index variable "i"…”)
Thomsen, Bhatti, and LAMEGO are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the simulate operation of the industrial plant based on the process variable input parameters to produce a range of values of simulated output parameters of the industrial plant as disclosed by Thomsen and Bhatti by identify a sub-range of the selected range of values for the one or more process variable input parameters for which the simulated output parameters are within an optimum range of values based on the at least one boundary condition as disclosed by LAMEGO.
One of ordinary skill in the art would have been motivated to make this modification in order to determine the real-time states and to update data that is displayed, shared, or stored, such as with alarms, as discussed in [0015] by LAMEGO “…In another aspect, a hybrid system that employs state space representation for the measurement algorithms. A hybrid system is a system that exhibits both continuous and discrete dynamic behavior. In some embodiments, data in the hybrid system is processed in a discrete-time periods. In such embodiments, data processing is performed for each epoch ( e.g., each discrete time period). The method includes initializing one or more constants and the input, output, and current state variables (i.e., state variables defined in current epoch) using the one or more initialized constants and/or one or more default values. Real-time data is received by the host device from the monitoring device and the next states and outputs are calculated from the one or more inputs, the current states, and the one or more constants. The current states are then updated from the calculated next states. The data that is displayed, shared, and/or stored is updated, along with any alarms, fuel gauges (e.g., battery icon), and/or notifications…”
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over
Thomsen et al., U.S. Patent Application Publication 2021/0397174 A1 (hereinafter ‘Thomsen’) in view of
Bhatti et al., “Intelligent Fault Diagnosis Mechanism for Industrial Robot Actuators using Digital Twin Technology” [2021] (hereinafter ‘Bhatti’) further in view of
LAMEGO et al., U.S. Patent Application Publication 2021/0022676 A1 (hereinafter ‘LAMEGO’) further in view of
Schulz et al., United State Patent 12,222,709 B2 (hereinafter ‘Schulz’).
Regarding Claim 3: Thomsen, Bhatti, and LAMEGO teach The method of claim 2, wherein collecting information about the industrial plant comprises
Thomsen, Bhatti, and LAMEGO do not appear to explicitly disclose
executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description.
However, Schulz teaches executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description. (Col 1 lines 28-34 Schulz “…The requirements of the production process are thereby extracted from different types of process design documents like piping & instrumentation diagrams, signal tables, textual requirements, etc. These process design documents are in tum created based on the original knowledge and intent of the process expert, e.g. a chemical engineer…” Col 6 lines 15-21 Schulz “…The extraction is preferably based on machine learning and/or knowledge extraction techniques from Natural Language Processing. The control narratives are parsed and entities of interest are recognized from the parsed text. If the input is not in text-format, the text is extracted from control diagrams by parsing and Optical Symbol/Character Recognition…”)
Thomsen, Bhatti, LAMEGO, and Schulz are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the selecting a range of values for one or more process variable input parameters associated with the at least one asset as disclosed by Thomsen, Bhatti, and LAMEGO by executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description as disclosed by Schulz.
One of ordinary skill in the art would have been motivated to make this modification in order to better design systems when determining alarm and event systems ad discussed in col 2 lines 4-19 “…This is achieved in an iterative dialog between optimization experts within the automation engineering company and the chemical/process experts. The issues of lost information can also be seen considering the alarm and event systems in an operational plant. While it is a particular purpose of process alarms to alert operators of KPI-critical process conditions, creating a quality alarm system is only feasible with exactly that process knowledge which today is lost in the engineering process, and it means added cost which many customers initially do not want to pay. As a result, operators may face large amounts of alarms with often unclear suggested actions, so-called alarm floods, which diminishes their ability to react appropriately and operate the process efficiently also under non-standard conditions…”
Regarding Claim 14: Thomsen, Bhatti, and LAMEGO teach The method of claim 13, wherein collecting information about the industrial plant comprises
Thomsen, Bhatti, and LAMEGO do not appear to explicitly disclose
executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description.
However, Schulz teaches executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description. (Col 1 lines 28-34 Schulz “…The requirements of the production process are thereby extracted from different types of process design documents like piping & instrumentation diagrams, signal tables, textual requirements, etc. These process design documents are in tum created based on the original knowledge and intent of the process expert, e.g. a chemical engineer…” Col 6 lines 15-21 Schulz “…The extraction is preferably based on machine learning and/or knowledge extraction techniques from Natural Language Processing. The control narratives are parsed and entities of interest are recognized from the parsed text. If the input is not in text-format, the text is extracted from control diagrams by parsing and Optical Symbol/Character Recognition…”)
Thomsen, Bhatti, LAMEGO, and Schulz are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the selecting a range of values for one or more process variable input parameters associated with the at least one asset as disclosed by Thomsen, Bhatti, and LAMEGO by executing one or more machine learning algorithms on a process input, and wherein the process input comprises at least one of: a process flow diagram (PFD), a piping and instrumentation diagram (P&ID), a heating and material balance (H&MB) document, and a process description as disclosed by Schulz.
One of ordinary skill in the art would have been motivated to make this modification in order to better design systems when determining alarm and event systems ad discussed in col 2 lines 4-19 “…This is achieved in an iterative dialog between optimization experts within the automation engineering company and the chemical/process experts. The issues of lost information can also be seen considering the alarm and event systems in an operational plant. While it is a particular purpose of process alarms to alert operators of KPI-critical process conditions, creating a quality alarm system is only feasible with exactly that process knowledge which today is lost in the engineering process, and it means added cost which many customers initially do not want to pay. As a result, operators may face large amounts of alarms with often unclear suggested actions, so-called alarm floods, which diminishes their ability to react appropriately and operate the process efficiently also under non-standard conditions…”
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over
Thomsen et al., U.S. Patent Application Publication 2021/0397174 A1 (hereinafter ‘Thomsen’) in view of
Bhatti et al., “Intelligent Fault Diagnosis Mechanism for Industrial Robot Actuators using Digital Twin Technology” [2021] (hereinafter ‘Bhatti’) further in view of
LAMEGO et al., U.S. Patent Application Publication 2021/0022676 A1 (hereinafter ‘LAMEGO’) further in view of
Habibi et al., U.S. Patent Application Publication 2015/0330872 A1 (hereinafter ‘Habibi’).
Regarding Claim 8: Thomsen, Bhatti, and LAMEGO teach The method of claim 1,
Thomsen, Bhatti, and LAMEGO do not appear to explicitly disclose
wherein the output parameters associated with the industrial plant include at least one of: an amount of chemical production and a rate of chemical production with at least one optimum alarm limit and efficient operating range.
However, Habibi teaches wherein the output parameters associated with the industrial plant include at least one of: an amount of chemical production and a rate of chemical production with at least one optimum alarm limit and efficient operating range. ([0046] Habibi “…Examples of chemical plant and refinery process parameters and properties that relate to a facilities performance and which are monitored through the use of sensors include but are not limited to pressure, temperature, flow rate, pressure drop, production, consumption, composition, decomposition, physical or chemical characteristics, combustion, volume, yield, energy, work, oxidation state, precipitation, reactions, pH, boiling point, characterization factors, vapor pressure, viscosity, 0 API, enthalpy, flash point, and pour content. Moreover, according to the novel system and methods disclosed herein, these as well as other process or facility parameters and properties associated with a facility may be monitored, controlled and analyzed by utilizing sensors through which the sensed data and associated new metrics are developed or determined and analyzed for improved plant operator situational awareness and to improve process/facility reliability, safety and profitability…”)
Thomsen, Bhatti, LAMEGO, and Habibi are analogous art because they are from the same field of endeavor, warning systems.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the selecting a range of values for one or more process variable input parameters associated with the at least one asset as disclosed by Thomsen, Bhatti, and LAMEGO by wherein the output parameters associated with the industrial plant include at least one of: an amount of chemical production and a rate of chemical production with at least one optimum alarm limit and efficient operating range as disclosed by Habibi.
One of ordinary skill in the art would have been motivated to make this modification in order to have correct alarm performance and maintain safety procedures ad discussed in [0011] by Habibi “…In some embodiments, the reporting of one or more ASOP metrics of interest are made readily available to and/or displayed for the operating personnel in a customizable dashboard environment to reflect the current and/or historical performance over a single or multiple time period(s ). In some embodiments, theASOP metric comprises at least one of the Alarm System Performance Metric (ASPM), Operator Loading Metric (OLM), Controllability Performance Metric (CLPM), Proximity to Safety System Metric (PSSM), Demand on Safety Systems Metric (DSSM), and Control System Integrity Metric (CSIM). The ASOP metric may be used collectively or individually or a subset thereof as measures of overall automation safety and operations performance…”
Conclusion
Claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN E JOHANSEN whose telephone number is (571)272-8062. The examiner can normally be reached M-F 9AM-3PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at 5712723652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN E JOHANSEN/Examiner, Art Unit 2187
Prosecution Insights