DETAILED ACTION
Claims 1-20 (filed 10/07/2024) have been considered in this action. Claims 1-20 are newly filed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2, 6 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 21, 24 of U.S. Patent No. 12,111,634. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are obvious variants of one another. The below analysis shows how similar limitations from the instant application compare with the above U.S. Patent:
Application 18/908,486
Claim 2
U.S. Patent No. 12,111,634
Claim 21
A method comprising: monitoring intermediate data and correlation data;
A method of controlling a thermal system of an industrial process, the thermal system including a heater system and a process control system configured to generate an output control for the heater system based on intermediate data generated by the process control system, the method comprising:
storing historical data sets associated with the intermediate data and historical data sets associated with the correlation data; wherein the intermediate data includes one or more of proportional gain data, integral gain data, or derivative gain data.
associating the intermediate data with correlation data, wherein the correlation data is associated with the output control, and wherein the intermediate data comprises one or more of: proportional gain data, integral gain data, or derivative gain data;
generating a first model that defines a relationship between the historical data sets associated with the intermediate data and the historical data sets associated with the correlation data;
generating a supervised learning model that defines a relationship between the intermediate data and the correlation data;
comparing the first model with a second model defining a nominal relationship between real-time data inputs associated with the intermediate data and real-time data inputs associated with the correlation data;
comparing the model with a nominal model defining a nominal relationship between the intermediate data and the correlation data;
predicting a state of a heater system based on the comparison between the first model and the second model;
predicting a state of the heater system based on the comparison between the nominal model and the model;
and selectively performing a corrective action based on the predicted state of the heater system.
and selectively performing a corrective action based on the predicted state of the heater system, wherein the corrective action includes controlling power to the heater system based on the predicted state of the heater system.
Application 18/908,486
Claim 6
U.S. Patent No. 12,111,634
Claim 24
The method of Claim 1, wherein the comparison of the first model with the second model further comprises: determining a deviation between the first model and the second model, wherein the predicted state of the heater system is further based on the deviation, and wherein the deviation includes a proportional gain deviation, an integral gain deviation, or a derivative gain deviation.
The method of claim 21, wherein the predicted state of the heater system is further based on a deviation between the nominal model and the model, and wherein the deviation includes a proportional gain deviation, an integral gain deviation, or a derivative gain deviation.
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.
Step 1:
Claim(s) 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea without significantly more. The claims are directed to the statutory category of invention of a method.
Step 2A Prong One:
Claim(s) 1-8 are method claims directed to (A) Mental processes capable of being performed in the human mind with the aid of pen and paper or are Mathematical concepts describing a mathematical relationship. The acts of monitoring intermediate and correlation data, generating a first model that defines a relationship between historical data sets, comparing the first model with a second model defining a nominal relationship, predicting a state based on the comparison between the models, and selectively performing a corrective action based on the predicted state from claim 1 are directed to abstract ideas in the form of mental processing steps. For example, a person in their mind could formulate modeled states or relationships between the data sets as observed/monitored using their own mental faculties, and predict what was going to happen with a system on the basis of comparing those modeled states. The selective performance of a control action requires performance being done selectively, which based on the BRI includes doing nothing, which is mentally capable of being performed by a person (i.e. if condition X occurs, do nothing). The acts of generating a first model that defines a relationship between historical sets and comparing the first model with a second model defining a nominal relationship are mathematical concepts in the form of mathematical relationships such as taking a difference (comparing) or formulating a mathematical model from data (generating a model).
Step 2A Prong Two:
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination because the additional elements, which are recited at a high level of generality, provide conventional functions that do not add meaningful limits to practicing the abstract idea.
Claim 1 recites, in part, a heater system and associated intermediate data and correlation data. These limitations describe the concept of (A), which corresponds to the concepts identified as abstract ideas by the courts, the limitation described is extra solution activity and amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application”, (see MPEP 2106.05(h): “..vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group”).
Claim 1 further recites, in part, storing of historical data sets. These limitations relate to the acts of necessary data gathering that the courts have found afford insignificant extra-solution activity to the claims “see MPEP 2106.05(g):… “As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that "[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula". 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was "post-solution activity").”
The abstract idea described in claim 1 is not meaningfully different than those abstract ideas found by the courts, therefor the claim is considered to be directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. The claim recites the additional elements of relating the prediction to a field of use in heaters being tangentially included, and performing necessary data gathering that fails to significantly change the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves another technology. Their collective functions merely provide conventional computer implementations and functions.
Dependent claims 2-8 are drawn to additional elements that merely indicate a field of use of the abstract idea without significantly more. For example, claims 2-5 and 7 further defines what a particular type of data is or define certain “wherein” clauses that fail to afford any additional steps performed in the method, and only attempt to further link the claims to mere fields of use to apply the abstract ideas. Claims 6 and 8 relate to steps that are further defining steps which are capable of practically being performed in the human mind because they recite actions that are simple enough to be performed in the human mind with pen and paper. The scope of These limitations are considered to be drawn to the abstract idea without adding significantly more.
Claims 1-8 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claims 9-14 and 15-20 recite limitations that, albeit in different statutory categories of invention (claims 9-14 are a machine or system, claims 15-20 are a product of manufacture or computer-readable media) recite similar limitations to one of claims 1-8. Accordingly, a similar analysis under 35 U.S.C. 101 as applied to claims 1-8 can be applied to claims 9-14 and 15-20 respectively. Accordingly, claims 9-14 and 15-20 are rejected under 35 U.S.C. 101 for similar reasons as applied to claims 1-8.
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 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because based on the broadest reasonable interpretation of the claims, the claims can be considered directed towards software per se.
Claim 9 recites in its preamble and first limitation “A system comprising: a process control system configured to:…”. Based upon the BRI, a process control system includes software only or software per se. The claim does not recite any hardware or structural elements that define what a “process control system” actually entails, and thus because the BRI includes software per se, the claims can be considered directed towards software per se.
Claims 10-14 are dependent upon claim 9, and thus inherit the rejection of claim 9 under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “correlation data” in claims 1, 9 and 15 is a relative term which renders the claim indefinite. The term “correlation data” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Applicant’s specification states that “[0046] the correlation module 202 is configured to obtain the intermediate data (e.g., proportional gain values, integral gain values, derivative gain values) and the correlation data (e.g., the internal process control input, the output control and/or the external heater control input)”. The scope of what “correlation data” is in unclear because the metes and bounds of the term are unclear. Applicant is seemingly attempting to encompass any and all “internal process control” and “output control” data which could broadly be defined as any internal data utilized in a process control system. As shown in figures 1-3, internal process control input is reflected as several different and seemingly unrelated data collection points in various forms of block diagrams that are exemplary process control systems. It cannot be determined from the specification alone if this term attempts to subsume all form of internal data in process control systems as correlation data. The scope of this term is unclear because PHOSITA would not understand from the examples given what the scope of an “internal process control input” or “output control” actually is, as there is no clear definition for how these terms would relate to process control system that are different from those provided in the figures. This term is further confusing because correlation data is not a term that is well-known in the art, and is seemingly a made-up term by the applicant that attempts to incorporate all forms of internal process control data (i.e. any data that is input or output from a block in a process control system). For the sake of compact prosecution, the examiner shall consider correlation data to mean any data associated or correlated with a controller, which includes any form of data acted on by a controller.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 9-11, 13, 15-17 and 19 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Sharpe (US 20060020423, hereinafter Sharpe).
In regards to Claim 1, Sharpe discloses “A method comprising: monitoring intermediate data and correlation data” ([0059] Generally speaking, the blocks 80 and 82 or sub-elements of these blocks, collect data, such a process variable data, within the device in which they are located...the block 82 includes a set of four SPM blocks or units SPM1-SPM4 which may collect process variable or other data within the transmitter and perform one or more statistical calculations on the collected data to determine, for example, a mean, a median, a standard deviation, etc. of the collected data. [0061] The term "process gain" may be used to refer to a change in a process variable (PV) resulting from some change in a load variable (LV) associated with the PV. For instance, a process gain may be a final, steady-state change in the PV resulting from a 0.1% change, a 1% change, a 5% change, a 10% change, etc., in the LV. The term "process gain" also may be used to refer to a ratio of a steady-state value of the PV at a given value of the LV. The PV could be an input to or an output from a controller (e.g., a PID controller), for example. The LV could be some other process variable associated with the PV, or it could be a control output (OP), for example. For many control loops and controllers, the actual process gain can be expected to vary over the range of the LV. For example, valve linearization techniques are often applied to approximate a linear gain from closed to fully open so that the PID algorithm (a linear controller) will work properly across the full range of a valve. Some controllers may utilize a Proportional-Integral-Derivative (PID) control algorithm to generate the OP; wherein the gain data is intermediate data, and the control output data is correlation data) “storing historical data sets associated with the intermediate data and historical data sets associated with the correlation data;” ([0054] database 78 may be connected to the communication bus 76 to operate as a data historian that collects and stores configuration information as well as on-line process variable data, parameter data, status data, and other data associated with the process controllers 60 and field devices 64 and 66 within the process plant 10. Thus, the database 78 may operate as a configuration database to store the current configuration, including process configuration modules, as well as control configuration information for the process control system 54 as downloaded to and stored within the process controllers 60 and the field devices 64 and 66. Likewise, the database 78 may store historical abnormal situation prevention data, including statistical data collected by the field devices 64 and 66 within the process plant 10 or statistical data determined from process variables collected by the field devices 64 and 66) “generating a first model that defines a relationship between the historical data sets associated with the intermediate data and the historical data sets associated with the correlation data” ([0069] SPM blocks can be used to generate statistical data associated with a process gain of a control loop. This statistical data may be useful in detecting, predicting, preventing, mitigating, etc., an abnormal situation associated with the control loop. The statistical data associated with the control loop that may be generated based on data generated by SPM blocks may, for example, be compared to an expected PV for a given OP or LV, an expected OP for a given PV or LV, an expected change in the PV for a given change in the OP or LV, an expected change in the OP for a given change in the PV or LV, etc. Many other types of statistical data associated with the control loop may also be generated using the SPM blocks and may be compared to many-different types of data. The statistical data generated using the SPM blocks can be used to detect, predict, prevent, mitigate, etc., an abnormal situation. [0070] One technique that may be used to determine data associated with the process gain of a control loop may include operating the control loop across an operating region and collecting data points associated with the process gain. With a control loop including a valve for example, PV data could be collected as an OP signal is varied to cycle a valve from a fully closed position to a fully open position (or vice versa). Then, the collected data could be used to generate expected PV values for different OP values. As just one example, any of a variety of suitable algorithms (e.g., a least squares algorithm) could be used to fit a curve to the collected data of actual PV values for different OP values. The relationship of expected PV values for given OP values (or vice versa) for a control loop may be referred to as the signature of the control loop. [0077] The subsystem 350 may comprise an OP generator 354 to cause the OP to vary during the Learn mode. The OP generator 354 may be implemented using the PID 154 (FIG. 3), for instance. As just one example, the SP input to the PID 154 could be varied to cause the OP to vary. Thus, the OP generator 354 may comprise a SP generator and a PID, for example) “comparing the first model with a second model” ([0085] At a block 408, rules may be applied to some or all of the statistical data generated at the block 404 to determine if a statistically significant process gain deviation has occurred. For example, statistical data generated at the block 404 may be compared with a control loop signature and/or confidence intervals. Rules, such as rules similar to SPC rules, may be applied. For example, it may be determined if a mean of a PV fell outside of a confidence interval. Also, it may be determined if a particular number of PV mean values fell below the signature, or if a particular number of PV mean values fell above the signature. As further examples, it may be determined if a process gain fell outside of a confidence interval, a particular number of process gain values fell below the signature, or if a particular number of process gain values fell above the signature. [0087] FIG. 11 is an example graph 420 illustrating various values during an interval of operation of a control loop and may help illustrate the operation of the method 400 of FIG. 10. In the graph 420, a median 422 of a PV during an interval of operation of the control loop is shown. Also, a median 423 of an LV is shown. These medians may be calculated at the block 404 of FIG. 10, for example. Further, the graph shows an expected value 424 of the PV and the confidence interval 426a, 426b during the interval of operation. As can be seen in the graph 420, the expect value 424 of the PV varies as the median 423 of the LV varies. In a region generally indicated by the circle 429, the median 422 of the PV falls below the lower bound 426b of the confidence interval and continuously falls below the expected value 424 for a period of time. Thus an alert may be generated (blocks 408 and 412 of FIG. 10) or some other action may be taken) “a second model defining a nominal relationship between real-time data inputs associated with the intermediate data and real-time data inputs associated with the correlation data” ([0083] Referring again to FIGS. 7 and 8, in some implementations it may be preferable to collect PV, LV, process gain, etc., data during normal operation of the control loop (i.e., without directly causing the OP, SP, PV, LV, etc., to vary beyond that due to normal control loop operation). Thus, it may be preferable in some implementations to omit the OP generator 354 and the control signal generator 384. Or, the OP generator 354 and the control signal generator 384 each may merely comprise a normally operating PID algorithm implemented by a controller) “predicting a state of a heater system based on the comparison between the first model and the second model” ([0085] At a block 408, rules may be applied to some or all of the statistical data generated at the block 404 to determine if a statistically significant process gain deviation has occurred. For example, statistical data generated at the block 404 may be compared with a control loop signature and/or confidence intervals. Rules, such as rules similar to SPC rules, may be applied. For example, it may be determined if a mean of a PV fell outside of a confidence interval. Also, it may be determined if a particular number of PV mean values fell below the signature, or if a particular number of PV mean values fell above the signature. As further examples, it may be determined if a process gain fell outside of a confidence interval, a particular number of process gain values fell below the signature, or if a particular number of process gain values fell above the signature. [0087] FIG. 11 is an example graph 420 illustrating various values during an interval of operation of a control loop and may help illustrate the operation of the method 400 of FIG. 10. In the graph 420, a median 422 of a PV during an interval of operation of the control loop is shown. Also, a median 423 of an LV is shown. These medians may be calculated at the block 404 of FIG. 10, for example. Further, the graph shows an expected value 424 of the PV and the confidence interval 426a, 426b during the interval of operation. As can be seen in the graph 420, the expect value 424 of the PV varies as the median 423 of the LV varies. In a region generally indicated by the circle 429, the median 422 of the PV falls below the lower bound 426b of the confidence interval and continuously falls below the expected value 424 for a period of time. Thus an alert may be generated (blocks 408 and 412 of FIG. 10) or some other action may be taken. [0099] The abnormal situation detector 488 could be configured to detect one or more abnormal situations associated with a control loop and/or process plant entity associated with the control loop (e.g., a unit operation such as a heater, a compressor, a column, a drum, etc.) based at least on the indicator when the PV, LV, or calculated process gain substantially deviates from the expected behavior. The abnormal situation detector 488 could comprise an expert system, for example, or any other suitable system for detecting an abnormal situation in a process plant; wherein abnormal is a predicted state of the system) “and selectively performing a corrective action based on the predicted state of the heater system” ([0086] At a block 412, an alert may be generated if it is determined at the block 408 that a statistically significant process gain deviation has occurred. Additionally or alternatively, some action may be taken such as making process plant adjustments, adjusting control signal values, shutting equipment down, initializing additional diagnostic procedures, etc.).
Claims 9 and 15 are directed towards substantially similar subject matter as claim 1, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 1, and thus claims 9 and 15 can be considered rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) in view of Sharpe using similar analysis as applied to claim 1.
In regards to Claim 2, Sharpe further teaches “The method of Claim 1, wherein the intermediate data includes one or more of proportional gain data, integral gain data, or derivative gain data” ([0061] The term "process gain" also may be used to refer to a ratio of a steady-state value of the PV at a given value of the LV. The PV could be an input to or an output from a controller (e.g., a PID controller), for example. The LV could be some other process variable associated with the PV, or it could be a control output (OP), for example. For many control loops and controllers, the actual process gain can be expected to vary over the range of the LV. For example, valve linearization techniques are often applied to approximate a linear gain from closed to fully open so that the PID algorithm (a linear controller) will work properly across the full range of a valve. Some controllers may utilize a Proportional-Integral-Derivative (PID) control algorithm to generate the OP. For example, typical continuous or batch processes in various industries are controlled using PID control algorithms to manipulate valves that control process flows of some kind. A typical PID algorithm is tuned to respond to a deviation in a PV input to the algorithm from a setpoint (SP) or target input. [0064] In the control loop 150 of FIG. 3, a process gain may be determined as a final, steady-state change in the PV input to the PID algorithm 154 resulting from some change in the control OP of the PID algorithm 154. [0016] Proportional-Integral-Derivative (PID) loop monitoring systems (e.g. DeltaV Inspect from Emerson Process Management, Loop Doctor from Matrikon, and Loop Scout from Honeywell) generate statistical data associated with a control loop. With PID loop monitoring systems, the generated statistical data is used to detect problems with the control loop such as high variability, limited control action, incorrect controller mode, and bad inputs. Also, PID loop tuning systems calculate process and controller gains, time constants and tuning factors and can be used to detect and correct problems with the control loop).
Claims 10 and 16 are directed towards substantially similar subject matter as claim 2, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 2, and thus claims 10 and 16 can be considered rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) in view of Sharpe using similar analysis as applied to claim 2.
In regards to Claim 3, Sharpe further teaches “The method of Claim 2, wherein the correlation data is associated with an output control of the heater system generated by a process control system and wherein the output control is based on a sum of the proportional gain data, the integral gain data, and the derivative gain data” ([0061] The term "process gain" may be used to refer to a change in a process variable (PV) resulting from some change in a load variable (LV) associated with the PV. For instance, a process gain may be a final, steady-state change in the PV resulting from a 0.1% change, a 1% change, a 5% change, a 10% change, etc., in the LV. The term "process gain" also may be used to refer to a ratio of a steady-state value of the PV at a given value of the LV. The PV could be an input to or an output from a controller (e.g., a PID controller), for example. The LV could be some other process variable associated with the PV, or it could be a control output (OP), for example. For many control loops and controllers, the actual process gain can be expected to vary over the range of the LV. For example, valve linearization techniques are often applied to approximate a linear gain from closed to fully open so that the PID algorithm (a linear controller) will work properly across the full range of a valve. Some controllers may utilize a Proportional-Integral-Derivative (PID) control algorithm to generate the OP. For example, typical continuous or batch processes in various industries are controlled using PID control algorithms to manipulate valves that control process flows of some kind. A typical PID algorithm is tuned to respond to a deviation in a PV input to the algorithm from a setpoint (SP) or target input; wherein the output of a PID is the sum of proportional, derivative, and integral gain data (i.e. gains and error are used to determine each element, as is well-known)).
Claims 11 and 17 are directed towards substantially similar subject matter as claim 3, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 3, and thus claims 11 and 17 can be considered rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) in view of Sharpe using similar analysis as applied to claim 3.
In regards to Claim 6, Sharpe further teaches “The method of Claim 1, wherein the comparison of the first model with the second model further comprises: determining a deviation between the first model and the second model, wherein the predicted state of the heater system is further based on the deviation, and wherein the deviation includes a proportional gain deviation, an integral gain deviation, or a derivative gain deviation” ([0086] At a block 412, an alert may be generated if it is determined at the block 408 that a statistically significant process gain deviation has occurred. Additionally or alternatively, some action may be taken such as making process plant adjustments, adjusting control signal values, shutting equipment down, initializing additional diagnostic procedures, etc. [0090] It is believed that the detection of statistically significant process gain related deviations, such as those described above, may provide an early indicator of process problems. Early knowledge about a process gain change may be able to provide time for the operator to analyze the problem before it becomes critical. Additionally, detection of statistically significant process gain related deviations may help with root cause analysis and/or provide an inference tool to base recommended operator responses... A help screen associated with the alert could be provided that would lead the operator to a historical trend of the PV vs. OP showing actual, expected loop signature and confidence intervals with statistical alarm points shown (e.g., in a different color). [0106] The above discussed methods and systems can be used with a variety of control loops, equipment, units, etc., in process plants associated with various industries. As just one example, the above methods and techniques may be used with a feed heater with a control loop for adjusting furnace fuel. In this example, the temperature setpoint and the normal operating region for the temperature process variable may be relatively constant and independent of charge rates. The steady-state value for the temperature controller output (e.g., fuel flow) may be more related to unit charge rate than the temperature process variable. In this case, the process gain could be determined as being the heater charge (LV) in relation to the temperature controller output (OP). As just one example, a short-term process gain deviation might be of interest and may cause a process gain alert to be generated).
Claims 13 and 19 are directed towards substantially similar subject matter as claim 6, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 6 and thus claims 13 and 19 can be considered rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) in view of Sharpe using similar analysis as applied to claim 6.
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 4-5, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sharpe as applied to claims 3, 11 and 17 above, and further in view of Krystad et al. (US 20170082683, hereinafter Krystad).
In regards to Claim 4, Sharpe teaches the method of claim 3 as incorporated above.
Sharpe further teaches “…and wherein the intermediate data includes loop data of the cascade control system” ([0069] SPM blocks can be used to generate statistical data associated with a process gain of a control loop. This statistical data may be useful in detecting, predicting, preventing, mitigating, etc., an abnormal situation associated with the control loop. The statistical data associated with the control loop that may be generated based on data generated by SPM blocks may, for example, be compared to an expected PV for a given OP or LV, an expected OP for a given PV or LV, an expected change in the PV for a given change in the OP or LV, an expected change in the OP for a given change in the PV or LV, etc. Many other types of statistical data associated with the control loop may also be generated using the SPM blocks and may be compared to many-different types of data. The statistical data generated using the SPM blocks can be used to detect, predict, prevent, mitigate, etc., an abnormal situation. For example, if an actual PV or an actual change in the PV deviates from the expected PV or the expected change in the PV, respectively, by some degree or amount, an alarm or alert may be generated, further analysis may be performed, a control parameter may be adjusted, etc. [0082] Expected values of the process gain vs. different values of a PV or an LV may be generated based on the generated signature for a control loop. Further, confidence intervals or some other type of interval may be generated for the process gain. Any of a variety of suitable techniques may be used to generate expect values and intervals for a process gain of a control loop. FIG. 9 is an example graph 390 of expected values of a process gain of a control loop versus an LV of a control loop. In particular, a line 392 indicates the expected values of the process gain, and the lines 394a and 394b may indicate an interval such as a confidence interval; wherein the gain data is the intermediate data).
Sharpe fails to teach “The method of Claim 3, wherein the process control system includes a cascade control system having a primary controller and a secondary controller”.
Krystad teaches “The method of Claim 3, wherein the process control system includes a cascade control system having a primary controller and a secondary controller” ([0032] a control circuit 304 further comprises a second PID controller 33b and an outlet temperature sensor T1 as shown in FIG. 5. The second PID controller 33b is coupled to the first PID controller 33a….The above configuration represents a cascaded PID control loops, with the first PID controller 33a controlling the inner or secondary control loop, while the second PID controller 33b controlling the outer or primary control loop.).
It would have been obvious to a person having ordinary skill in the art before the priority date of the instant invention to have utilized the methods of Sharpe in which a PID loop has its gain values extracted and utilized for model comparison against the expected/predicted gain values of a normal PID loop, with the use of a cascaded control system by which multiple PID control loops are nested into a primary and secondary controller as taught by Krystad, because it would be considered taking the known methods of Sharpe, and applying them to the known system configuration of Krystad in a predictable way that achieves predictable results. For example, by collecting the gain data from both primary and second PID control loops of Krystad, the methods of Sharpe could be applied for abnormal operation detection when comparing normal models to real-time models.
In regards to Claim 5, the combination of Sharpe and Krystad teaches the method as incorporated by claim 4 above. Krystad further teaches “The method of Claim 4, wherein the output control is based on a sum of proportional gain data of the secondary controller, integral gain data of the secondary controller, and derivative gain data of the secondary controller” ([0032] a control circuit 304 further comprises a second PID controller 33b and an outlet temperature sensor T1 as shown in FIG. 5. The second PID controller 33b is coupled to the first PID controller 33a….The above configuration represents a cascaded PID control loops, with the first PID controller 33a controlling the inner or secondary control loop, while the second PID controller 33b controlling the outer or primary control loop; wherein a PID loop is well-known to output based on a sum of proportional gain data, integral gain data and derivative gain data using error (feedback-setpoint) multiplied by each respective gain).
Claims 12 and 18 are directed towards substantially similar subject matter as claim 5, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 5, and thus claims 12 and 18 can be considered rejected under 35 U.S.C. 103 over Sharpe in view of Krystad using similar analysis as applied to claim 5.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sharpe as applied to claim 1 above, and further in view of Yamamoto et al. (US 20140203821, hereinafter Yamamoto).
In regards to Claim 7, Sharpe teaches the methods as incorporated by claim 1 above.
Sharpe further teaches “The method of Claim 1, wherein the comparison of the first model and the second model indicates a change in a thermal response of the heater system” ([0099] The abnormal situation detector 488 could be configured to detect one or more abnormal situations associated with a control loop and/or process plant entity associated with the control loop (e.g., a unit operation such as a heater, a compressor, a column, a drum, etc.) based at least on the indicator when the PV, LV, or calculated process gain substantially deviates from the expected behavior. The abnormal situation detector 488 could comprise an expert system, for example, or any other suitable system for detecting an abnormal situation in a process plant. [0101] FIG. 16 is an example screen display 500 associated with a heater. The display 500 includes a graphical depiction 504 of a heater in a process plant. In this example, a steady state operation of the heater has been detected. In response, an operator may be prompted whether to turn on a process gain analysis via a window 506. If the operator selects, via a button 508, to turn on process gain analysis, process gain analysis for one or more control loops associated with the heater unit may commence. As another example, an operator may be prompted and/or may be able to easily turn learning mode ON or OFF for all or particular process gain analysis subsystems associated with a specific piece of equipment or process unit (e.g., a heater unit). [0102] In some implementations, a system may provide standard criteria (e.g., expert system rules) that may be used to identify similar problems for multiple control loops associated with similar process control systems. For example, if a process plant includes multiple heater units, a common set of criteria associated with monitoring process gains could be provided for each similar control loop in the heater units. But signatures and/or confidence intervals for each control loop in the heater units could be determined individually. Optionally, a user could modify or customize the common set of criteria as applied to some subset of the heater units; [0106] As just one example, the above methods and techniques may be used with a feed heater with a control loop for adjusting furnace fuel. In this example, the temperature setpoint and the normal operating region for the temperature process variable may be relatively constant and independent of charge rates. The steady-state value for the temperature controller output (e.g., fuel flow) may be more related to unit charge rate than the temperature process variable. In this case, the process gain could be determined as being the heater charge (LV) in relation to the temperature controller output (OP). As just one example, a short-term process gain deviation might be of interest and may cause a process gain alert to be generated; wherein a change from a steady state response to an abnormal response on the basis of controller gain model comparison of a heater is considered a thermal response).
Sharpe fails to teach “and wherein the change in the thermal response includes a heater insulation breakdown or a fluid accumulation associated with the heater system”.
Yamamoto teaches “and wherein the change in the thermal response includes a heater insulation breakdown or a fluid accumulation associated with the heater system” ([0007] In the plasma processing apparatus, a substrate temperature at the time of plasma processing may be controlled at a high response speed. In general, a heater system is employed in which a heating element configured to generate heat by energization is embedded in the electrostatic chuck or the susceptor so as to control joule heat generated by the heating element. In such a heater system, when noise of a high frequency wave introduced into a heater power feeding line through the heating element embedded in the susceptor reaches a heater power source, the operation or performance of the heater power source may be impaired.[0074] Within the high frequency power source unit 10, the high frequency oscillator 22 and the power amplifier 24 generate a high frequency wave of a predetermined frequency at a variable power. Then, the power source control unit 26 variably controls a gain of the high frequency power amplifier 24 such that a comparison error is close to zero by comparing the progressive wave detection signal MV.sub.f output from the directional coupler 28 to the reference waveform signal WS output from the reference waveform generator 12. In this manner, a high frequency pulse V.sub.RF having a rhomboid waveform defined by the reference waveform signal WS and a peak value V.sub.max may be obtained from the high frequency power source unit 10 (FIG. 5C) [0079] In another exemplary embodiment of the monitor display form, the observed waveform data of a test object applied voltage V.sub.UT may be sent to the main control unit 20 from the digital type oscilloscope 16. The main control unit 20 may determine whether the wave form of the test object applied voltage V.sub.UT is bent in the middle thereof (on the reversed taper-shaped inclined portion EN) through a signal processing using a predetermined algorithm based on the observed waveform data, so as to determine whether there is insulation failure or dielectric breakdown. Further, the main control unit 20 may read the voltage value of the bent point on the reversed taper-shaped inclined portion EN of the waveform through the signal processing using a predetermined algorithm so as to determine an insulation failure voltage V.sub.J or a dielectric breakdown voltage V.sub.K. Then, information of a monitoring result may be displayed and output on the control panel 18).
It would have been obvious to a person having ordinary skill in the art before the priority of the instant application to have taken the methods of Sharpe which determine from a learned normal and abnormal process model that correlates process PID loop gains and input and output variables with normal and abnormal thermal response of a heater as taught by Sharpe, to further include observing what occurs during a heater’s insulation breakdown so that the determination of abnormal thermal response also includes data for when insulation breaks down as taught by Yamamoto because it can be considered taking a known method of determining when insulation breakdown occurs in accordance with a comparison and using it to modify the methods of Sharpe in a known way that achieves the predicable result of determining when a thermal insulation breakdown occurs in a heater.
Claims 8, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sharpe as applied to claims 1, 9 and 15 above, and further in view of Miller (US 20080183427, hereinafter Miller).
In regards to Claim 8, Sharpe teaches the method as incorporated by claim 1 above.
Sharpe further teaches “The method of Claim 1, wherein the selective performance of the corrective action further comprises: controlling power to the heater system based on the predicted state of the heater system” ([0086] At a block 412, an alert may be generated if it is determined at the block 408 that a statistically significant process gain deviation has occurred. Additionally or alternatively, some action may be taken such as making process plant adjustments, adjusting control signal values, shutting equipment down, initializing additional diagnostic procedures, etc.) “wherein the corrective action includes broadcasting an alert based on the predicted state of the heater system” ([0107] FIG. 19 illustrates the example screen display 500 during a process gain analysis system for the heater. If a process gain deviation associated with the heater is detected, an alert could be generated. For instance, a window 530 could be displayed informing the operator that a process gain deviation had been detected. [0109] Similar alerts could be generated and/or window could be displayed when a process gain associated with the heater falls above a confidence interval, continuously falls below expected values for a selected period of time, falls above expected values for a selected period of time, etc. Process gain analysis may be used with a variety of control loops associated with a heater such as those involving pass flows, temperatures, fuel, total heater charge, airflow, O2, etc.).
Sharpe fails to teach “wherein the alert indicates a material accumulation within a conduit communicatively coupled to a process control system, or a nominal thermal deviation of a resistive heating element communicatively coupled to the process control system” ([0054] There are a number of possible platforms to implement these statistical methods and detection. In particular, these conditions may be detected as part of a transmitter advanced diagnostics block disposed within a valve or a transmitter associated with the heat exchanger 100, a temperature sensor/transmitter, a level sensor/transmitter, a pressure sensor/transmitter, etc. In particular, a diagnostic block may be trained to detect or determine a baseline thermal resistance, when the system is healthy, and then may monitor the mean value of the thermal resistance and any other appropriate parameters after establishing the baseline. On the other hand, monitoring and detection could be achieved using an SPM block in a transmitter or other field device with a simple threshold logic. That is, the SPM block could monitor the thermal resistance to determine the mean, the standard deviation. etc. for and compare these statistical measures to a pre-established threshold (which may be set by a user or which may be based on a baseline statistical measure computed from measurements of the appropriate process variables during a training period). Also, if desired, host level software run in a user interface device or other computing device connected to the field devices, such as an advanced diagnostic block explorer or expert, maybe used to set and monitor normal and abnormal values and to perform abnormal situation detection based on the concepts described above).
It would have been obvious to a person having ordinary skill in the art before the priority date of the instant application to have taken the known methods of broadcasting an alert that says a resistive thermal element is of a nominal thermal deviation, and incorporating those elements into the process control methods of Sharpe which utilize gain and output data to correlate when normal and abnormal operations of a heater occurs and outputs an alert so that the alert of Sharpe includes information about resistive thermal elements breaking down. By combining these elements, it can be considered taking the known methods of Miller of alerting to resistive thermal heater breakdown, and applying them to the alerts of Sharpe in a known way that achieves predictable results.
Claims 14 and 20 are directed towards substantially similar subject matter as claim 8, albeit in different statutory categories of invention. Sharpe teaches the use of computer hardware ([0057]) for implementing the steps of claim 8, and thus claims 14 and 20 can be considered rejected under 35 U.S.C. 103 over Sharpe in view of Miller using similar analysis as applied to claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhao et al. (US 20210116891) – teaches using machine learning and artificial intelligence to build a seed model for a process control system using historical process control data
Kephart et al. (US 20210011466) – teaches the use of a simulated process control system that operates in parallel with the real-time system to predict future abnormal situations
Maturana et al. (US 20150277399) – teaches a method for gain tuning of PID controllers using historical data that optimizes control loop functionality
Caldwell et al. (US 20070078533) – teaches using adaptive control techniques by identifying process models from historical and real-time feedback data for determining gain, delay, etc. in process loops
Wojsznis et al. (US 7113834) – teaches a system with multiple process models for a PID control loop with different parameters that get adapted across a range of conditions
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/JONATHAN MICHAEL SKRZYCKI/ Examiner, Art Unit 2116
