DETAILED ACTION
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 .
This action is in response to the applicant’s communication filed on 10/03/2024
Claims 1-20 are pending
Claim Objections
Claim 2 objected to because of the following informalities: “using an observer and data points for generate the trajectory” is grammatically incorrect and obscures the intended relationship between the observer, the data points, and the trajectory. Applicant is advised to amend the limitation to recite “using an observer to generate data points for the trajectory” or other language of similar scope, consistent with paragraph [0023] of the specification. Appropriate correction is required.
Claim 12 objected to because of the following informalities: “wherein the controller is configured to,” should be changed to “wherein the controller is configured to:”. Appropriate correction is required.
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.
Claim(s) 1-9 and 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claim 1, at step 1, the claim recites a method comprising a combination of “receiving…”, “generating…”, “comparing…”, and “identifying…”, and therefore is a process, which is a statutory category of invention.
At step 2A, prong one, the claim recites “generating a trajectory from the data that predicts performance of a part on the flow control”; “comparing the trajectory to a model trajectory”; and “identifying a failure mode in response to a relationship between the trajectory and the model trajectory.”
The above limitations, under their broadest reasonable interpretation, recite mathematical concepts and/or mental processes. For example, the claim recites generating a data representation that predicts performance, comparing the generated data representation with model data, evaluating the relationship between the generated and model data, and identifying a failure mode based on the evaluation. These limitations amount to organizing, comparing, and evaluating information and making a judgment concerning whether the evaluated information indicates a failure. Thus, the claim recites an abstract idea, namely mathematical concepts and/or mental processes of analyzing flow-control operating data by generating and comparing trajectories to identify a failure mode. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III).
At step 2A, prong two, this judicial exception is not integrated into a practical application. The additional limitations of “receiving data from sensors, the data defining conditions on a flow control” merely gather the data upon which the abstract analysis is performed. Limiting the gathered and analyzed data to conditions on a flow control merely limits the abstract idea to a particular technological environment or field of use. The additional limitation of “generating an output in response to the relationship, the output relating to the failure mode” merely reports or conveys the result of the abstract analysis. The claim does not require using the identified failure mode or generated output to adjust, deactivate, repair, or otherwise alter operation of the flow control. Accordingly, the claim does not integrate the abstract idea into a practical application. See MPEP 2106.04(d), 2106.05(g), and 2106.05(h).
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the sensors merely collect operating data, the flow control merely identifies the technological environment and subject of the analysis, and the output merely reports the result of the analysis. These elements perform their ordinary functions and do not provide an inventive concept. The remaining limitations are part of the abstract idea itself because they recite generating, comparing, and evaluating trajectory information to identify a failure mode. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus, claim 1 is not patent eligible.
Regarding independent claim 12, the claim recites substantively the same abstract idea identified in claim 1 above; and recites substantively similar additional elements (a device for performing the abstract idea) and is ineligible for the same reasons as those indicated in the analysis of claim 1 above.
Regarding dependent claim 2, the additional limitation of “using an observer and data points for generate the trajectory” merely further specifies the data-processing technique used to generate the trajectory. The claim does not recite a particular observer equation, physical-model implementation, or resulting control of the flow control. Thus, the limitation is part of the abstract data analysis and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 3 and 19, the additional limitation of “wherein the trajectory includes data points interpolated from data from the sensors” merely recites mathematically interpolating data points from collected information. This limitation is part of the mathematical concepts and data analysis identified above and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 4 and 13, the additional limitation of “wherein the model trajectory defines performance of the part under nominal operating conditions” merely further defines the information represented by the model trajectory. The limitation does not require applying the identified failure mode to change operation of the flow control and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 5 and 14, the additional limitation of “wherein the model trajectory comprises an upper boundary and a lower boundary” merely recites mathematical boundaries defining the model information. This limitation is part of the mathematical concepts identified above and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 6 and 15, the additional limitation of “wherein the model trajectory defines an area that represents outer performance limits for the part” merely further defines a mathematical area or range represented by the model trajectory. This limitation is part of the mathematical concepts and data analysis identified above and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 7 and 16, the additional limitation that the model-trajectory area “corresponds with data aggregated from a plurality of simulations of performance of the part” merely recites aggregating simulation data to generate the model information. This limitation further specifies the abstract mathematical modeling and data-analysis process and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claim 8, the additional limitation that the plurality of simulations are “done with samples of uncertain parameters” merely recites performing mathematical simulations using sampled parameter values. This limitation is part of the mathematical concepts identified above and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claim 17, the additional limitation that the plurality of simulations are “done with samples of data from the sensors” merely specifies the data used in performing the simulations. This limitation further defines the information used in the abstract mathematical modeling process and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea.
Regarding dependent claims 9 and 18, the additional limitation that “the model trajectory corresponds to simulations done remote from the flow control” merely specifies the location at which the abstract simulation and data-processing operations are performed. Performing the abstract idea remotely merely limits the abstract idea to a particular computing location and does not integrate the abstract idea into a practical application or add significantly more than the abstract idea. See MPEP 2106.05(h).
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.
Claim(s) 1, 3-6, 9, 12-15, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genta USPGPUB 2012/0290261 A1 (hereinafter Genta) in view of Friman et al. USPGPUB 2022/0333714 A1 (hereinafter Friman).
Regarding claim 1, Genta teaches a method (Par. [0034], “a plurality of methods is described for determining plausible causal factors for valve actuator performance deviations, malfunctions and failure(s).”), comprising:
receiving data from sensors (Par. [0047], “receiving a plurality of input signals from a plurality of sensors”), the data defining conditions on a flow control (Par. [0036], “each signal represents a condition of a different operational variable associated with the actuator”; Par. [0076], “The controller 130 can receive operational conditions (i.e., variables) from various sensors”; Par. [0003], “the primary function of a valve actuator is to transmit the necessary mechanical force to the valve stem to move the valve member in order to produce a predetermined effect on the fluid that passes through one or more ports of the valve body.”);
generating a trajectory from the data that predicts performance of a part on the flow control (Par. [0414], “the calculated time difference is used to sort vectors of identical assembly by increasing time-difference. As a result, the trajectories of the vectors as a function of time can be identified”; Par. [0209], “simulate the behavior of the actuator in future operating conditions such that the likelihood or proximity (i.e., temporally) to a deviation, malfunction or failure can be predicted based on the simulated scenario defined by time-series of inputs applied to the input channels.” – Genta generates the claimed trajectory by sorting actuator-operation vectors over time. The trajectory predicts performance of the actuator because Genta uses time-series input data to simulate future actuator behavior and predict the likelihood or timing of actuator deviation, malfunction, or failure. The actuator corresponds to a part on the flow control because it is a component of the valve/flow-control system whose future deviation, malfunction, or failure is predicted.);
Genta does not explicitly teach comparing the trajectory to a model trajectory;
identifying a failure mode in response to a relationship between the trajectory and the model trajectory; and
generating an output in response to the relationship, the output relating to the failure mode.
However, Friman teaches comparing the trajectory to a model trajectory (Fig. 8, Par. [0060], “In FIG. 8, the embedded tracking digital twin 211 may track an error or difference between the simulated pneumatic actuator pressure psim (dashed line) and the measured pneumatic actuator pressure pmeas (solid line). During normal operation of the valve assembly, such as within the time period t1-t2, the simulated actuator pressure psim follows quite accurately the measured pneumatic actuator pressure Pmeas, i.e. the trend lines substantially overlap” – the measured actuator pressure trend corresponds to the trajectory, and the simulated digital-twin pressure trend corresponds to the model trajectory.);
identifying a failure mode in response to a relationship between the trajectory and the model trajectory (Fig. 8, Par. [0061], “However, during a fault in the valve assembly, the simulated actuator pressure psim and the measured pneumatic actuator pressure Pmeas begin to diverge, such as shortly after time instant t2 in FIG. 8. This due to that the simulation model 212 of the physical valve assembly still simulates the unfaulty valve assembly and not the actual faulty valve assembly. The tracking and model parameter update block 213 detects the divergence (error, difference, deviation) and begins in real time to determine an updated value for one or more fault-related simulation model parameters of the embedded tracking digital twin in a sense that the error or difference is decreased. In the example of FIG. 8, during the timer period t2-t3, the tracking and model parameter update block 213 gradually increases the diameter of the leakage hole in the simulation model so that the simulated pneumatic actuator pressure psim and the measured pneumatic actuator pressure Pmeas converge and finally overlap the time instant t3. The situation is stabilized and no further updating of simulation parameters will be needed during the time period t3-t4. The increased physical value of the fault-related parameter "Leakage hole diameter" directly indicates a leakage fault in the pneumatic actuator 3” – The divergence between the measured and simulated trajectories corresponds to the relationship, and the parameter updated in response to that divergence identifies the leakage failure mode.); and
generating an output in response to the relationship (Par. [0058], “The tracking and model parameter update block 213 may compare the simulated and measured values to track an error or difference between the results of simulated operation of the digital twin and the real operation of the physical asset. In the case it is determined that the error or difference can be decreased by adjusting or updating value or values of one or more of the fault-related simulation model parameters, the model parameters in the simulation model in the simulation block 212 may be updated accordingly …The microprocessor system, preferably the embedded tracking digital twin 211 may store and/or generate fault alarms and reports on faults in the valve assembly based on values of the fault-related simulation model parameters in a memory of the microprocessor system 21.” – Friman uses the simulated vs. measured relationship to update the fault-related parameter and generates the alarm or report based on that updated parameter), the output relating to the failure mode (Fig. 8, Par. [0061], “The increased physical value of the fault-related parameter "Leakage hole diameter" directly indicates a leakage fault in the pneumatic actuator 3. The specific fault-related parameter also gives clear maintenance-relevant information, a physical diameter of the leakage hole” – The alarm or report is based on a parameter that identifies and describes the actuator leakage failure mode.).
Genta and Friman are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to diagnosing faults or failures in valve systems using sensed valve or actuator operating data.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator fault analysis method, as taught by Genta, and incorporate comparing the generated actuator performance trajectory with a simulated digital twin model trajectory and identify a specific valve assembly failure mode from the difference between measured and simulated operation, as taught by Friman.
One of ordinary skill in the art would have been motivated to improve identification of the particular valve assembly part that has failed or will fail next as suggested by Friman (Par. [0050]).
Regarding claim 3, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the trajectory includes data points interpolated from data from the sensors (Par. [0058], “In embodiments, the embedded tracking digital twin may track an error or difference between the at least one simulated measurement result representing a simulated result of a control action (such as the simulated measured valve position and/or the simulated measured pneumatic actuator pressure), and the at least one corresponding real physical measurement result (such as the measured valve position and/or the measured pneumatic actuator pressure), and adjust a value of the at least one fault-related simulation model parameter of the embedded tracking digital twin in a sense that the error or difference is decreased, in real-time during operation of the valve assembly”; Par. [0062], “The past values of the fault related simulation model parameter (the leakage hole diameter in this example) create a historian trend of the development of the fault … In embodiments, the microprocessor system 21, preferably the embedded tracking digital twin 211 may, based on the past behavior of the fault or the parameter, interpolate the future trend of a fault-related simulation model parameter mathematically.” – Friman generates the past parameter values by adjusting the model parameter based on the difference between simulated results and corresponding measured valve-position or actuator-pressure sensor values, and mathematically interpolates future data points from those past values to form the future trajectory).
Regarding claim 4, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the model trajectory defines performance of the part under nominal operating conditions (Fig. 8, Par. [0060], “During normal operation of the valve assembly, such as within the time period t1-t2, the simulated actuator pressure psim follows quite accurately the measured pneumatic actuator pressure Pmeas, i.e. the trend lines substantially overlap. The tracking and model parameter update block 213 observes the simulated and measured values are substantially equal, i.e. there is no error, difference or deviation and no need to adjust any of the simulation model parameters” – The pneumatic actuator corresponds to the claimed part on the flow control, the simulated actuator pressure trend corresponds to the model trajectory, and the normal-operation period corresponds to nominal operating conditions because the simulated model represents expected non-faulty actuator performance with no error, difference, or deviation before the fault-related divergence occurs.).
Regarding claim 5, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta further teaches wherein the model trajectory comprises an upper boundary and a lower boundary (Par. [0415], “In a deterministic process where two variables are correlated by a physical law and where such variables vary in a predictable manner within a time frame, it is expected that repetitions of a particular experiment or test used for training will demark specific trajectories of the variables in the N-dimensional space. Such trajectories may be recognized by the Valve Actuator Fault Analyzer System as valid relationships among the variables, provided that a confidence level is reached.”; Par. [0420], “The process of trajectory identification is repeated for the other groups of vectors for all the vector dimensions of the vectors listed in TABLE XVI. Further, the vectors found within valid trajectories along with the value of their variables, their upper/lower limits and time-layers are identified and stored in TABLE XVII.” – Genta’s valid trajectory is a learned and stored trajectory established from repeated training tests and therefore corresponds to the model trajectory, and its stored upper and lower limits correspond to the upper and lower boundaries.).
Regarding claim 6, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta further teaches wherein the model trajectory defines an area that represents outer performance limits for the part (Par. [0417], “Thereafter, the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step. If the density is equal or higher than a predetermined value (e.g., 95%) of the maximum possible amount of vectors (points) "MQV R" in the region of the trajectory, then the trajectory is accepted as valid and the sequence of vectors that form such trajectory is stored in the non-volatile memory.”; Par. [0419], “If the experiment is repeated at identical conditions, the end of travel will be reached at a specific torque value plus/minus a tolerance. Similarly, the trajectory of the torque will follow a specific path plus/minus a tolerance”; Par. [0420], “the vectors found within valid trajectories along with the value of their variables, their upper/lower limits and time-layers are identified and stored in TABLE XVII.” – The actuator corresponds to the claimed “part” on the flow control because the actuator is a component of the valve/flow-control system whose torque performance is being modeled. Genta’s valid torque trajectory corresponds to the model trajectory. The boundaries/region of the valid trajectory, plus/minus tolerance, and stored upper/lower limits define a bounded area of acceptable actuator torque performance. The bounded area represents out performance limits for the actuator because values within the bounded area are accepted as valid trajectory behavior, while values outside the bounded area fall outside the learned acceptable limits.).
Regarding claim 9, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the model trajectory corresponds to simulations done remote from the flow control (Par. [0051], “Simulations, fault detections and fault predictions can be made inside a valve positioner or controller or in a remote computing entity or distributed between a valve positioner and one or more remote computing entity”).
Regarding claim 12, Genta teaches a flow control (Par. [0003], “the primary function of a valve actuator is to transmit the necessary mechanical force to the valve stem to move the valve member in order to produce a predetermined effect on the fluid that passes through one or more ports of the valve body.”), comprising:
a valve body housing a closure member and a seat (Par. [0002], “The valve includes a valve body that houses a valve member which interfaces with a valve seat formed along the interior surface of the valve body to form a leak-tight seal when the valve member is fully closed” – the valve member is interpreted as a closure member);
a valve stem coupled to the closure member (Par. [0002], “A valve stem is joined to or contacts the valve member and is used to transmit motion to control the position of the internal valve member with respect to the valve seat” – the valve stem is coupled to the valve member/closure member because it is joined to or contacts the valve member and transmits motion to move the valve member relative to the seat.);
an actuator coupled to the valve stem (Par. [0002], “External to the valve body, the stem is attached to a handle or other controlling device, such as a valve actuator to thereby adjust the stem and positioning of the valve member relative to the valve seat”; Par. [0075], “the induction motor 112 rotates a first gear 116 coupled thereon, which in turn rotates a second gear 118 that is coupled to the valve stem 106”);
a controller coupled to the actuator (Par. [0076], “the fault analyzer (controller) 130 is provided to control the operation of the motor 112, as well as provide fault analysis in accordance with the present invention”; Par. [0077], “The controller 130 is electrically coupled to the motor driver 120, which regulates the rotational speed, torque, as well as the direction of rotation of the motor 112.”); and
sensors coupled to the controller (Par. [0076], “The controller 130 can receive operational conditions (i.e., variables) from various sensors”),
wherein the controller is configured to (Par. [0076], “The fault analyzer 130 can be any microcontroller that includes memory for storing data and programs which are executable by one or more processors (e.g., microprocessor). The programs include routines that monitor, analyze and display information pertaining to the state of operation of the actuator system 100”),
receive data from sensors (Par. [0076], “The controller 130 can receive operational conditions (i.e., variables) from various sensors”), the data defining conditions on the flow control (Par. [0036], “each signal represents a condition of a different operational variable associated with the actuator” – the operational variables define conditions of the valve actuator and flow-control assembly.); and
generate a trajectory from the data that predicts performance of a part on the flow control (Par. [0414], “the calculated time difference is used to sort vectors of identical assembly by increasing time-difference. As a result, the trajectories of the vectors as a function of time can be identified”; Par. [0209], “simulate the behavior of the actuator in future operating conditions such that the likelihood or proximity (i.e., temporally) to a deviation, malfunction or failure can be predicted based on the simulated scenario defined by time-series of inputs applied to the input channels.” – Genta generates the claimed trajectory by sorting actuator-operation vectors over time. The trajectory predicts performance fo the actuator because Genta uses time-series input data to simulate future actuator behavior and predict the likelihood or timing of actuator deviation, malfunction, or failure. The actuator corresponds to a part on the flow control because it is a component of the valve/flow-control system whose future deviation, malfunction, or failure is predicted.).
Genta does not explicitly teach compare the trajectory to a model trajectory;
identify a failure mode in response to a relationship between the trajectory and the model trajectory; and
generate an output in response to the relationship, the output relating to the failure mode.
However, Friman teaches compare the trajectory to a model trajectory (Fig. 8, Par. [0060], “In FIG. 8, the embedded tracking digital twin 211 may track an error or difference between the simulated pneumatic actuator pressure psim (dashed line) and the measured pneumatic actuator pressure pmeas (solid line). During normal operation of the valve assembly, such as within the time period t1-t2, the simulated actuator pressure psim follows quite accurately the measured pneumatic actuator pressure Pmeas, i.e. the trend lines substantially overlap” – the measured actuator pressure trend corresponds to the trajectory, and the simulated digital-twin pressure trend corresponds to the model trajectory.);
identify a failure mode in response to a relationship between the trajectory and the model trajectory (Fig. 8, Par. [0061], “However, during a fault in the valve assembly, the simulated actuator pressure psim and the measured pneumatic actuator pressure Pmeas begin to diverge, such as shortly after time instant t2 in FIG. 8. This due to that the simulation model 212 of the physical valve assembly still simulates the unfaulty valve assembly and not the actual faulty valve assembly. The tracking and model parameter update block 213 detects the divergence (error, difference, deviation) and begins in real time to determine an updated value for one or more fault-related simulation model parameters of the embedded tracking digital twin in a sense that the error or difference is decreased. In the example of FIG. 8, during the timer period t2-t3, the tracking and model parameter update block 213 gradually increases the diameter of the leakage hole in the simulation model so that the simulated pneumatic actuator pressure psim and the measured pneumatic actuator pressure Pmeas converge and finally overlap the time instant t3. The situation is stabilized and no further updating of simulation parameters will be needed during the time period t3-t4. The increased physical value of the fault-related parameter "Leakage hole diameter" directly indicates a leakage fault in the pneumatic actuator 3” – The divergence between the measured and simulated trajectories corresponds to the relationship, and the parameter updated in response to that divergence identifies the leakage failure mode.); and
generate an output in response to the relationship (Par. [0058], “The tracking and model parameter update block 213 may compare the simulated and measured values to track an error or difference between the results of simulated operation of the digital twin and the real operation of the physical asset. In the case it is determined that the error or difference can be decreased by adjusting or updating value or values of one or more of the fault-related simulation model parameters, the model parameters in the simulation model in the simulation block 212 may be updated accordingly …The microprocessor system, preferably the embedded tracking digital twin 211 may store and/or generate fault alarms and reports on faults in the valve assembly based on values of the fault-related simulation model parameters in a memory of the microprocessor system 21.” – Friman uses the simulated vs. measured relationship to update the fault-related parameter and generates the alarm or report based on that updated parameter), the output relating to the failure mode (Fig. 8, Par. [0061], “The increased physical value of the fault-related parameter "Leakage hole diameter" directly indicates a leakage fault in the pneumatic actuator 3. The specific fault-related parameter also gives clear maintenance-relevant information, a physical diameter of the leakage hole” – The alarm or report is based on a parameter that identifies and describes the actuator leakage failure mode).
Genta and Friman are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to diagnosing faults or failures in valve systems using sensed valve or actuator operating data.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator fault analysis method, as taught by Genta, and incorporate comparing the generated actuator performance trajectory with a simulated digital twin model trajectory and identify a specific valve assembly failure mode from the difference between measured and simulated operation, as taught by Friman.
One of ordinary skill in the art would have been motivated to improve identification of the particular valve assembly part that has failed or will fail next as suggested by Friman (Par. [0050]).
Regarding claim 13, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the model trajectory defines performance of the
part under nominal operating conditions (Fig. 8, Par. [0060], “During normal operation of the valve assembly, such as within the time period t1-t2, the simulated actuator pressure psim follows quite accurately the measured pneumatic actuator pressure Pmeas, i.e. the trend lines substantially overlap. The tracking and model parameter update block 213 observes the simulated and measured values are substantially equal, i.e. there is no error, difference or deviation and no need to adjust any of the simulation model parameters” – The pneumatic actuator corresponds to the claimed part on the flow control, the simulated actuator pressure trend corresponds to the model trajectory, and the normal-operation period corresponds to nominal operating conditions because the simulated model represents expected non-faulty actuator performance with no error, difference, or deviation before the fault-related divergence occurs.).
Regarding claim 14, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta further teaches wherein the model trajectory comprises an upper boundary and a lower boundary (Par. [0415], “In a deterministic process where two variables are correlated by a physical law and where such variables vary in a predictable manner within a time frame, it is expected that repetitions of a particular experiment or test used for training will demark specific trajectories of the variables in the N-dimensional space. Such trajectories may be recognized by the Valve Actuator Fault Analyzer System as valid relationships among the variables, provided that a confidence level is reached.”; Par. [0420], “The process of trajectory identification is repeated for the other groups of vectors for all the vector dimensions of the vectors listed in TABLE XVI. Further, the vectors found within valid trajectories along with the value of their variables, their upper/lower limits and time-layers are identified and stored in TABLE XVII.” – Genta’s valid trajectory is a learned and stored trajectory established from repeated training tests and therefore corresponds to the model trajectory, and its stored upper and lower limits correspond to the upper and lower boundaries.).
Regarding claim 15, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta further teaches wherein the model trajectory defines an area that represents outer performance limits for the part (Par. [0417], “Thereafter, the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step. If the density is equal or higher than a predetermined value (e.g., 95%) of the maximum possible amount of vectors (points) "MQV R" in the region of the trajectory, then the trajectory is accepted as valid and the sequence of vectors that form such trajectory is stored in the non-volatile memory.”; Par. [0419], “If the experiment is repeated at identical conditions, the end of travel will be reached at a specific torque value plus/minus a tolerance. Similarly, the trajectory of the torque will follow a specific path plus/minus a tolerance”; Par. [0420], “the vectors found within valid trajectories along with the value of their variables, their upper/lower limits and time-layers are identified and stored in TABLE XVII.” – The actuator corresponds to the claimed “part” on the flow control because the actuator is a component of the valve/flow-control system whose torque performance is being modeled. Genta’s valid torque trajectory corresponds to the model trajectory. The boundaries/region of the valid trajectory, plus/minus tolerance, and stored upper/lower limits define a bounded area of acceptable actuator torque performance. The bounded area represents out performance limits for the actuator because values within the bounded area are accepted as valid trajectory behavior, while values outside the bounded area fall outside the learned acceptable limits.).
Regarding claim 18, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the model trajectory corresponds to simulations
done remote from the flow control (Par. [0051], “Simulations, fault detections and fault predictions can be made inside a valve positioner or controller or in a remote computing entity or distributed between a valve positioner and one or more remote computing entity”).
Regarding claim 19, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Friman further teaches wherein the trajectory includes data points interpolated from data from the sensors (Par. [0058], “In embodiments, the embedded tracking digital twin may track an error or difference between the at least one simulated measurement result representing a simulated result of a control action (such as the simulated measured valve position and/or the simulated measured pneumatic actuator pressure), and the at least one corresponding real physical measurement result (such as the measured valve position and/or the measured pneumatic actuator pressure), and adjust a value of the at least one fault-related simulation model parameter of the embedded tracking digital twin in a sense that the error or difference is decreased, in real-time during operation of the valve assembly”; Par. [0062], “The past values of the fault related simulation model parameter (the leakage hole diameter in this example) create a historian trend of the development of the fault … In embodiments, the microprocessor system 21, preferably the embedded tracking digital twin 211 may, based on the past behavior of the fault or the parameter, interpolate the future trend of a fault-related simulation model parameter mathematically.” – Friman generates the past parameter values by adjusting the model parameter based on the difference between simulated results and corresponding measured valve-position or actuator-pressure sensor values, and mathematically interpolates future data points from those past values to form the future trajectory).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genta USPGPUB 2012/0290261 A1 (hereinafter Genta) in view of Friman et al. USPGPUB 2022/0333714 A1 (hereinafter Friman), and further in view of Clausen USPGPUB 2007/0295924 A1 (hereinafter Clausen).
Regarding claim 2, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
The combination of Genta and Clausen teaches using an observer (Clausen, Par. [0008], “an observer adapted, based on a reference, to determine a model output which is indicative of a theoretically correct position of the valve member relative to the housing”) and data points for generate the trajectory (Genta, Par. [0416], “The vectors resulting from the previous step are sorted by increasing time-layers. Thus, the different possible trajectories of the variables are identified.”; Par. [0417], “the density of vectors (points) within the boundaries of the trajectory is calculated” – the vectors or points correspond to the data points used to identify the trajectory.).
Genta, Friman, and Clausen are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to detecting faults in valve or fluid control systems using sensed operating data and model-based analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate an observer-generated model output as a valve variable data point for generating the trajectory, as taught by Clausen.
One of ordinary skill in the art would have been motivated to improve detection of restricted or otherwise incorrect valve member movement, as suggested by Clausen (Par. [0014]).
Claim(s) 7-8, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genta USPGPUB 2012/0290261 A1 (hereinafter Genta) in view of Friman et al. USPGPUB 2022/0333714 A1 (hereinafter Friman), and further in view of Tryon, III et al. USPGPUB 2003/0004679 A1 (hereinafter Tryon).
Regarding claim 7, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
The combination of Genta and Tryon teaches wherein the model trajectory describes an area (Genta, Par. [0417], “the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step”; Par. [0419], “the trajectory of the torque will follow a specific path plus/minus a tolerance” – Genta’s bounded torque-trajectory region corresponds to the area) that corresponds with data aggregated from a plurality of simulations (Tryon, Par. [0087], “Monte Carlo simulation methods were used to develop the full CDF of the capacity portion of the response surface at step 120. For each MC simulation, random values of Gcrit and E11 were generated based on their respective statistical distribution types and respective statistical parameters. With each set Gcrit and E11 values generated, the capacity portion of the response surface equation was computed. Following that, a histogram analysis was performed to develop the CDF curve” – Tryon produces data for each Monte Carlo simulation and statistically aggregates the simulation data to form the CDF.) of performance of the part (Genta, Par. [0419], “For example, if the experiment used for training consists of an electrical actuator that closes a valve having 200 Kg stem weight in 15 seconds at 50° C. ambient temperature, such actuator will reach the end of travel at a specific torque value. If this experiment is repeated at identical conditions, the end of travel will be reached at a specific torque value plus/minus a tolerance. Similarly, the trajectory of the torque will follow a specific path plus/minus a tolerance.” – trajectory of the torque is interpreted as performance of the valve actuator).
Genta, Friman, and Tryon are analogous art because they contain functional similarities. They all relate to predicting or diagnosing component performance and failure using model-based analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate generating the data defining the model trajectory area by aggregating results from a plurality of Monte Carlo simulations, as taught by Tryon.
One of ordinary skill in the art would have been motivated to improve the accuracy of component-failure predictions, as suggested by Tryon (Par. [0029]).
Regarding claim 8, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
The combination of Genta and Tryon teaches wherein the model trajectory describes an area (Genta, Par. [0417], “the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step”; Par. [0419], “the trajectory of the torque will follow a specific path plus/minus a tolerance” – Genta’s bounded torque-trajectory region corresponds to the area) that corresponds with data aggregated from a plurality of simulations done with samples of uncertain parameters (Tryon, Par. [0087], “Monte Carlo simulation methods were used to develop the full CDF of the capacity portion of the response surface at step 120. For each MC simulation, random values of Gcrit and E11 were generated based on their respective statistical distribution types and respective statistical parameters. With each set Gcrit and E11 values generated, the capacity portion of the response surface equation was computed. Following that, a histogram analysis was performed to develop the CDF curve for the capacity portion of the response surface equation” – Each Monte Carlo simulation samples uncertain parameters from their statistical distributions, computes a performance result using those samples, and aggregates the simulation results through histogram analysis to form the CDF.).
Genta, Friman, and Tryon are analogous art because they contain functional similarities. They all relate to predicting or diagnosing component performance and failure using model-based analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate generating the data defining the model trajectory area by aggregating results from a plurality of Monte Carlo simulations performed using random samples of uncertain parameters, as taught by Tryon.
One of ordinary skill in the art would have been motivated to improve the accuracy of component-failure predictions, as suggested by Tryon (Par. [0029]).
Regarding claim 16, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
The combination of Genta and Tryon teaches wherein the model trajectory describes an area (Genta, Par. [0417], “the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step”; Par. [0419], “the trajectory of the torque will follow a specific path plus/minus a tolerance” – Genta’s bounded torque-trajectory region corresponds to the area) that corresponds with data aggregated from a plurality of simulations (Tryon, Par. [0087], “Monte Carlo simulation methods were used to develop the full CDF of the capacity portion of the response surface at step 120. For each MC simulation, random values of Gcrit and E11 were generated based on their respective statistical distribution types and respective statistical parameters. With each set Gcrit and E11 values generated, the capacity portion of the response surface equation was computed. Following that, a histogram analysis was performed to develop the CDF curve” – Tryon produces data for each Monte Carlo simulation and statistically aggregates the simulation data to form the CDF.) of performance of the part (Genta, Par. [0419], “For example, if the experiment used for training consists of an electrical actuator that closes a valve having 200 Kg stem weight in 15 seconds at 50° C. ambient temperature, such actuator will reach the end of travel at a specific torque value. If this experiment is repeated at identical conditions, the end of travel will be reached at a specific torque value plus/minus a tolerance. Similarly, the trajectory of the torque will follow a specific path plus/minus a tolerance.” – trajectory of the torque is interpreted as performance of the valve actuator).
Genta, Friman, and Tryon are analogous art because they contain functional similarities. They all relate to predicting or diagnosing component performance and failure using model-based analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate generating the data defining the model trajectory area by aggregating results from a plurality of Monte Carlo simulations, as taught by Tryon.
One of ordinary skill in the art would have been motivated to improve the accuracy of component-failure predictions, as suggested by Tryon (Par. [0029]).
Regarding claim 17, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
The combination of Genta and Tryon teaches wherein the model trajectory describes an area (Genta, Par. [0417], “the density of vectors (points) within the boundaries of the trajectory is calculated. The boundary of the trajectory is determined by the start and end time-layers of the vectors of the previous step”; Par. [0419], “the trajectory of the torque will follow a specific path plus/minus a tolerance” – Genta’s bounded torque-trajectory region corresponds to the area.) that corresponds with data aggregated from a plurality of simulations done with samples of data from the sensors (Tryon, Par. [0092], “Based on the analysis frequency, previously determined to be two flight cycles, once the sensors gathered the values of the directly sensed variables, values of the inferred variables were randomly generated in step 130 using MC methods and random values of Gcrit and E11 and were generated based on their respective statistical distribution types and respective statistical parameters. For each set of directly sensed data, several sets of the inferred variables were generated.”; Par. [0093], “For each simulation, if G>Gcrit, a failure counter was incremented by one. For example, let us assume that for each set of Pmax and Ø sensed, M sets of the inferred variables were generated. Among those M sets, for n sets (n≤M), G was greater than Gcrit. Then the probability of failure would be n/M.” – Tryon performs multiple Monte Carlo simulations for each set of sensed Pmax and Ø data and aggregates the simulation results by counting the simulations indicating failure and calculating the failure probability n/M.)
Genta, Friman, and Tryon are analogous art because they contain functional similarities. They all relate to predicting or diagnosing component performance and failure using model-based analysis.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate generating the data defining the model trajectory area by aggregating results from a plurality of Monte Carlo simulations performed for sets of sensor data, as taught by Tryon.
One of ordinary skill in the art would have been motivated to improve the accuracy of component-failure predictions, as suggested by Tryon (Par. [0029]).
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genta USPGPUB 2012/0290261 A1 (hereinafter Genta) in view of Friman et al. USPGPUB 2022/0333714 A1 (hereinafter Friman), and further in view of Florentino et al. USPGPUB 2017/0343968 A1 (hereinafter Florentino).
Regarding claim 10, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta and Friman do not explicitly teach wherein the output deactivates the flow control.
However, Florentino teaches wherein the output deactivates the flow control (Par. [0030], “method includes automatically shutting down the control element if the standard deviation ratio and the correlation coefficient ratio respectively exceeds the preset standard deviation ratio threshold and the preset correlation coefficient ratio threshold”; Par. [0142], “if a valve in a particular process is set as Warning or Uncertain, the system or IA operation can reroute a process to another controller, final control element, and process that have been identified as being Good. In some embodiments, the system or IA operation may automatically deactivate or shut down the final control element of the combination of controller, final control element, and process upon the determination of Warning or Uncertain.” – Florentino’s Warning or Uncertain determination corresponds to an output identifying an abnormal operating condition of the final control element, and the output causes the final control element, corresponding to the flow control, to be automatically deactivated or shut down.).
Genta, Friman, and Florentino are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to diagnosing abnormal conditions or failures in control valves using valve operating data.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator fault analysis method, as taught by Genta and Friman, and incorporate automatically deactivating or shutting down the flow control in response to the identified failure condition, as taught by Florentino.
One of ordinary skill in the art would have been motivated to improve control stabilization and safe operation of control elements, as suggested by Florentino (Par. [0003]).
Regarding claim 20, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta and Friman do not explicitly teach wherein the output deactivates the flow control.
However, Florentino teaches wherein the output deactivates the flow control (Par. [0030], “method includes automatically shutting down the control element if the standard deviation ratio and the correlation coefficient ratio respectively exceeds the preset standard deviation ratio threshold and the preset correlation coefficient ratio threshold”; Par. [0142], “if a valve in a particular process is set as Warning or Uncertain, the system or IA operation can reroute a process to another controller, final control element, and process that have been identified as being Good. In some embodiments, the system or IA operation may automatically deactivate or shut down the final control element of the combination of controller, final control element, and process upon the determination of Warning or Uncertain.” – Florentino’s Warning or Uncertain determination corresponds to an output identifying an abnormal operating condition of the final control element, and the output causes the final control element, corresponding to the flow control, to be automatically deactivated or shut down.).
Genta, Friman, and Florentino are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to diagnosing abnormal conditions or failures in control valves using valve operating data.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator fault analysis method, as taught by Genta and Friman, and incorporate automatically deactivating or shutting down the flow control in response to the identified failure condition, as taught by Florentino.
One of ordinary skill in the art would have been motivated to improve control stabilization and safe operation of control elements, as suggested by Florentino (Par. [0003]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Genta USPGPUB 2012/0290261 A1 (hereinafter Genta) in view of Friman et al. USPGPUB 2022/0333714 A1 (hereinafter Friman), and further in view of Esposito USPGPUB 2015/0013786 A1 (hereinafter Esposito).
Regarding claim 11, the combination of Genta and Friman teaches all the limitations of the base claims as outlined above.
Genta and Friman do not explicitly teach wherein the output causes the flow control to operate in a reduced function mode.
However, Esposito teaches wherein the output causes the flow control to operate in a reduced function mode (Par. [0027], “The operating signal 270 from the processing component 218 can cause the signal switching component 252 to change between the operating states 260, 262. Examples of the operating signal 270 can have one or more assigned parameters (e.g., voltage, current, etc.). These assigned parameters can change, e.g., in response to failure of the processing component 218 and/or changes in operation of the control valve 202 … In response to the low level, the signal switching component 252 may enter the second operating state 262, which causes the by-pass component 220 to operate in the second mode to conduct the input power signal from the signal conditioning component 250 to the converter component 214.”; Par. [0019], “If a failure occurs, i.e., if the processing component 118 fails and/or other operating deviations of the control valve 102 (FIG. 1) are detected, the by-pass component 120 can operate in a second mode that permits the by-pass component 120 to conduct the input control signal, or a derivation thereof, directly to the valve components … This configuration maintains operation of control valve 102 (FIG. 1) to modulate the flow of fluid, but without the processing and functionality of the processing component 118.” – the operating signal is the output that causes the valve positioner to enter the second mode, in which the control valve continues controlling fluid flow with reduced functionality because the processing component is bypassed.).
Genta, Friman, and Esposito are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to monitoring and controlling control valves or valve actuators in connection with detected operating failures.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above valve actuator diagnostic method, as taught by Genta and Friman, and incorporate generating an operating signal that causes the control valve to enter a bypass operating mode during a failure condition, as taught by Esposito.
One of ordinary skill in the art would have been motivated to improve continued operation of the control valve during component failure and reduce downtime, as suggested by Esposito (Par. [0006]).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cummings et al. [USPGPUB 2009/0306830 A1] teaches a logic unit for a valve that generates an indication signal when the cycle parameter exhibits a predetermined variance from the expected cycle parameter.
Hershey et al. [USPGPUB 2017/0286572 A1] teaches an apparatus may implement a digital twin of a twinned physical system such that one or more sensors to sense values of one or more designated parameters of the twinned physical system.
Conclusion
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/PETER XU/ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119
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