DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 03/26/2026.
Status of Claims
Claims 8 and 18 have been cancelled.
Claims 1, 6, 9, 11, 16, and 19-20 have been amended.
Claims 1-7, 9-17, and 19-20 are now pending.
Response to Arguments
Applicant's arguments filed on 03/26/2026 regarding the 35 U.S.C. 101, 102, and 103 rejections of the claims have been fully considered. The Applicant argues the following:
(1) As per the 101 rejection, the Applicant argues, in summary, that the pending claims are not directed to an abstract idea and the claim elements integrate the alleged abstract idea into a practical application. The pending claims implements the alleged judicial exception in conjunction with a particular machine, provide an improvement to the controllers of an industrial process control and automation system as identified in paragraphs [0031]-[0036] of the Applicant's specification, and amount to significantly more than the alleged abstract idea.
The Examiner finds the Applicant’s argument persuasive. Therefore, the 35 U.S.C. 101 rejection is withdrawn.
(2) As per the 102/103 rejections, the Applicant argues that Naduthota and Sayyarodsari does not teach, suggest, or render obvious all of the features of amended claims 1, 11, and 20.
The Examiner respectfully disagrees. The Examiner submits that Naduthota alone does not teach all of the features of amended claims 1, 11, and 20. However, Naduthota in combination with Sayyarodsari teach all of the features of amended claims 1, 11, and 20 as shown in the updated claim mapping below. Therefore, the previous 35 U.S.C. 102 rejection is withdrawn and a new 35 U.S.C. 103 rejection is entered. See details below.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 9-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Naduthota et al. (US 2015/0309506 A1) in view of Sayyarodsari et al. (US 2023/0061688 A1).
As per claims 1, 11, and 20 (Currently Amended), Naduthota teaches a computer-implemented method for categorizing performance of proportional-integral-derivative (PID) controller in a process control system, the method comprising; an apparatus for categorizing performance of a proportional- integral-derivative (PID) controller in a process control system, the apparatus comprising: at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to; and at least one non-transitory computer-readable storage medium having computer-executable instructions stored thereon that when executed by at least one processor, cause the processor to: (Naduthota e.g. This disclosure relates to an apparatus and method for providing a generalized continuous performance indicator ([0001] and [0005]). In a second embodiment, an apparatus includes at least one memory configured to store information identifying multiple diagnostic indicators associated with at least a portion of an industrial process system. The apparatus also includes at least one processing device configured to combine the diagnostic indicators to form a generalized indicator [0007]. In a third embodiment, a non-transitory computer readable medium embodies a computer program. The computer program includes computer readable program code for obtaining multiple diagnostic indicators associated with at least a portion of an industrial process system. The computer program also includes computer readable program code for combining the diagnostic indicators to form a generalized indicator [0008]. FIG. 1 illustrates an example industrial process control and automation system 100 according to this disclosure [0022]. Each controller 106 could, for example, represent simple Proportional Integral Derivative (PID) controllers that are part of a distributed control system, a multivariable controller, or advanced controllers such as model predictive controllers. As a particular example, each controller 106 could represent a computing device running a real-time operating system (Fig. 1 and [0025]). FIG. 2 illustrates an example device 200 for implementing a generalized indicator tool in an industrial control and automation system according to this disclosure [0049]. FIG. 11 illustrates an example method 1100 for analyzing at least part of a process system according to this disclosure. The method 1100 is described as being performed by the device 200 of FIG. 2 operating in the system 100 of FIG. 1 [0085].)
Naduthota teaches receiving, by one or more processors, plant data from one or more process plants, wherein the plant data comprises a PID controller data associated with at least one PID controller; (Naduthota e.g. FIG. 1 illustrates an example industrial process control and automation system 100 according to this disclosure [0022]. Each controller 106 could, for example, represent simple Proportional Integral Derivative (PID) controllers that are part of a distributed control system, a multivariable controller, or advanced controllers such as model predictive controllers [0025]. The machine-level controllers 114 could log information collected or generated by the controllers 106, such as measurement data from the sensors 102 a or control signals for the actuators 102 b [0028]. The operator stations 116 could allow users to review the operational history of the sensors 102 a and actuators 102 b using information collected by the controllers 106 and/or the machine-level controllers 114 [0029].)
Naduthota teaches generating, by one or more processors and using a performance analysis model, performance diagnostic data for a PID controller by applying KPI data to one or more performance diagnosis rules, wherein each performance diagnosis rule is defined at least in part by one or more threshold values corresponding to one or more KPIs for the PID controller; (Naduthota e.g. Controllers are often designed to maintain process variables at desired reference points known as setpoints. A system designed to control a process variable may be called a control loop [0002]. In a typical scenario, the performance of each control loop can be rated against multiple Key Performance Indicators (KPIs) [0004]. Each controller 106 could, for example, represent simple Proportional Integral Derivative (PID) controllers that are part of a distributed control system, a multivariable controller, or advanced controllers such as model predictive controllers (Fig. 1 and [0025]). As shown in FIG. 11, at operation 1105, the device receives a plurality of diagnostic indicators for at least part of a process system. Each diagnostic indicator has a value, and that value may represent the value of a key performance indicator [0086]. The OSI, RPI, and Error Standard Deviation may be key performance indicators (KPIs). The Oscillation Index element may be a value between zero and one, where values approaching zero represent less oscillation and values approaching one represent more oscillations and more oscillations that are regular. The Relative Performance Index is a ratio between a benchmark response speed and an actual response speed. The Error Standard Deviation is a measure of a variability in a controller, which can provide an indication of how aggressive the controller is (Fig. 3 and [0057]). Each KPI can be associated with a band that defines its acceptable range of operation. For example, acceptable ranges of the KPIs could be an OSI between 0.0-0.5, an RPI between 0.4-2.5, and a std-dev between 0-2% of PV mean value. In other embodiments, these ranges may be different [0058]. At any point in time, the overall performance rating for a control loop can be determined based on which of these indicators are within their acceptable operating ranges [0059]. At operation 1110, the device identifies a set of rules for each outcome of the plurality of diagnostic indicators. In some embodiments, the rules may be the same as or similar to rules 1-8 of FIG. 5C [0086]. At operation 1115, the device performs fuzzy logic with a min-max implication scheme for each rule of the set of rules to form a set of results [0087]. As shown in FIG. 5A, the CPI system 500 includes inputs 505-515, a fuzzy logic block 520, and an output 525 [0064]. The inputs 505-515 may be KPIs, such as RPI, OSI, and error standard deviation values. The inputs 505-515 are processed by the fuzzy logic block 520. Each input can be processed according to its predefined acceptable range [0065]. The fuzzy logic block 520 operates to generate a generalized CPI value as the output 525. The generalized CPI can be defined based upon the existing discrete levels of the overall performance rating. The types of input and output functions (such as triangular, Gaussian, and the like) can be selected to suit the properties of the corresponding index [0065]. In FIG. 10B, in the continuous color scale 1010, a system may degrade quickly enough to notify an operator, such as by an alarm or warning. For example, a distance 1015 may represent the degradation of the system over a period of a single day, and this distance can be compared to a threshold to identify a problem. If, for example, the degradation at the distance 1015 is a percentage of 22%, the system may alert an operator if the threshold is 20%. Note, however, that other distances, thresholds, and periods of time may be used [0083].)
Naduthota teaches generating, by the one or more processors, performance issue classification for the PID controller based on the performance diagnostic data; (Naduthota e.g. A generalized index can be used in control loop monitoring tools to provide a qualitative indication of an overall loop performance (such as Excellent, Fair, Poor, etc.) based on the values of various Key Performance Indicators (KPIs) (which may also be known as diagnostic indicators) [0041]. For example, the generalized indicator tool 144 may provide a quantitative measure associated with the overall performance of a control loop so as to provide a continuous status about the loop's performance (referred to as a continuous performance index or CPI) [0043]. The generalized indicator tool 144 may provide a generalized CPI at all levels in a plant hierarchy (such as from unit level to enterprise level) in order to provide a better indication of overall performance to concerned authorities [0044]. For example, the generalized indicator tool 144 may provide a mechanism to give early warning to concerned authorities about an impending deterioration based upon a rate of ongoing performance degradation, thereby enabling corrective action to be taken [0044]. For alarm management applications, where the objective is to manage the alarms generated within in plant, the generalized indicator tool can provide a continuous status of overall alarm performance. A color gradient-based progressive indication of plant's overall alarm performance enables pre-emptive actions in case of gradual degradation [0046].)
Naduthota teaches generating, by the one or more processors, a performance rating classification for the PID controller by applying the performance rating data derived from the KPI data to one or more performance rating classification rules; (Naduthota e.g. A generalized index can be used in control loop monitoring tools to provide a qualitative indication of an overall loop performance (such as Excellent, Fair, Poor, etc.) based on the values of various Key Performance Indicators (KPIs) (which may also be known as diagnostic indicators) [0041]. FIGS. 5A through 5C illustrates an example continuous performance index (CPI) system 500 according to this disclosure [0064]. As shown in FIG. 5A, the CPI system 500 includes inputs 505-515, a fuzzy logic block 520, and an output 525 [0064]. The inputs 505-515 are processed by the fuzzy logic block 520. Each input can be processed according to its predefined acceptable range. The fuzzy logic block 520 operates to generate a generalized CPI value as the output 525 [0065]. In some embodiments, the fuzzy logic block 520 can use a Fuzzy Associative Memory (FAM) table 530, which is shown in FIG. 5B. The FAM table 530 defines the rules governing the overall implementation logic, such as “if-then” rules. For example, if the RPI 535 is good, the OSI 540 is good, and the std-dev 545 is good, the generalized CPI 550 is Excellent [0066]. In some embodiments, the importance of various input indices towards the overall performance rating can be considered while defining the fuzzy rules. For example, rules 536 indicate that the OSI 540 index is more critical when defining the overall performance of the control loop compared to the other two indices. In other examples, other KPIs may be more important [0066]. In some embodiments, the fuzzy logic block 520 uses Mamdani-based fuzzy logic based on a min-max implication scheme to combine inputs 560-570 for each rule 1-8 as shown in FIG. 5C [0067].)
Naduthota teaches determining, by the one or more processors, an improvement action for the PID controller by applying the performance issue classification and the performance rating classification to one or more PID categorization rules, wherein the improvement action corresponds to a PID controller category of a plurality of PID controller categories; and (Naduthota e.g. The generalized indicator tool 144 can support various other features. For example, the generalized indicator tool 144 may provide a mechanism to give early warning to concerned authorities about an impending deterioration based upon a rate of ongoing performance degradation, thereby enabling corrective action to be taken. As another example, the generalized indicator tool 144 may provide a mechanism to display predictive performance of the loop in the near future, thereby enabling the operator to take pre-emptive measures to avert further degradation [0044]. FIG. 3 illustrates an example indicator table 300 according to this disclosure. As shown in FIG. 3, the indicator table 300 may include a controller category 305, a Relative Performance Index (RPI) range 310, an Oscillation Index (OSI) range 315, and an Error Standard Deviation (std-dev) range 320 [0057]. The indicator table 300 may combine these KPIs into qualitative indicators, such as excellent, good, fair, poor, and the like [0058]. As another example, the generalized indicator tool 144 may provide a mechanism to display predictive performance of the loop in the near future, thereby enabling the operator to take pre-emptive measures to avert further degradation [0044]. FIGS. 8 and 9 illustrate example plant progressions according to this disclosure. As shown in FIG. 8, a plant progression 800 may be an expression of the generalized CPI of all of the areas of a plant over the course of a period of time [0074]. In some embodiments, the progressions 800 and 900 can be used to help analyze the status of a plant or portion thereof over a period of time, issue initial warnings, and enable pre-emptive actions in situations of gradual degradation [0080]. FIGS. 10A and 10B illustrate example instances for triggering an alarm according to this disclosure [0082]. In FIG. 10A, at a mark 1005, the degradation of a system may reach a point at which an operator is notified, such as by alarm or warning. Note that the mark 1005 can be set to any suitable point or points [0082].)
Naduthota teaches initiating, by the one or more processors, and based on the improvement action, performance of one or more control operations… (Naduthota e.g. The generalized indicator tool 144 can support various other features. For example, the generalized indicator tool 144 may provide a mechanism to give early warning to concerned authorities about an impending deterioration based upon a rate of ongoing performance degradation, thereby enabling corrective action to be taken [0044]. As another example, the generalized indicator tool 144 may provide a mechanism to display predictive performance of the loop in the near future, thereby enabling the operator to take pre-emptive measures to avert further degradation [0044]. FIGS. 10A and 10B illustrate example instances for triggering an alarm according to this disclosure. In FIG. 10A, at a mark 1005, the degradation of a system may reach a point at which an operator is notified, such as by alarm or warning. Note that the mark 1005 can be set to any suitable point or points [0082].)
Naduthota does not explicitly teach, however, Sayyarodsari teaches one or more control operations comprising automatically adjusting one or more operating parameters of at least one of: the PID controller, an advanced process control (APC) controller associated with the PID controller, or a plantwide optimizer (PWO) controller associated with the PID controller, herein the automatic adjustment modifies control of at least one process variable in the process plant. (Sayyarodsari e.g. The present disclosure generally relates to control systems and, more particularly, to control systems performing nonintrusive monitoring and diagnostics in industrial automation environments with electromechanical machinery, production lines, conveyer systems, and the like [0001]. Generally, a control system may facilitate performance of an industrial automation process by controlling operation of one or more automation devices. Additionally, the control system may monitor performance of the process to determine whether the process is operating as desired. When not operating as desired, the control system may also perform diagnostic operations on the process to determine potential causes of the undesired operation [0002]. The local control system 42 may include a control/monitoring device or automation controller adapted to interface with the industrial automation devices 20 or other components that may monitor and control various types of the industrial automation devices 20 [0034]. In certain embodiments, one or more properties of the industrial automation devices 20 may be monitored and controlled by certain equipment for regulating control variables used to operate the industrial automation devices 20 [0043]. For example, the sensors 16 may monitor various properties of the industrial automation devices 20 and may provide data to the local control system 42, which may adjust operations of the industrial automation equipment 50, respectively [0043]. In certain embodiments, one or more properties of the industrial automation devices 20 may be monitored and controlled by certain equipment for regulating control variables used to operate the industrial automation devices 20. For example, the sensors 16 may monitor various properties of the industrial automation devices 20 and may provide data to the local control system 42, which may adjust operations of the industrial automation equipment 50, respectively [0043]. n some embodiments, the processor 74 may automatically perform certain operational adjustments to the industrial automation devices 20 in response to the distinguishable features 114 matching one of the known clusters 120. That is, certain operational states that correspond to particular known clusters 120 may be associated with undesired states of operation. As such, upon detecting of the respective industrial automation devices 20 operating in these states, the processor 74 may automatically perform certain operational adjustments (e.g., slow down, power off) to ensure that the industrial system 10 continues to operate and to minimize an amount of wear that the respective industrial automation devices 20 may experience (Figs. 2-4 and [0088]).)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Naduthota’s diagnostic indicator process to include initiating corrective actions such as an adjustment to controller parameters as taught by Sayyarodsari in order to provide efficient operations for industrial equipment (Sayyarodsari e.g. [0026]).
As per claims 2 and 12 (Original), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 1 and the apparatus of claim 11, Naduthota teaches further comprising: receiving plant data from one or more process plants, the plant data comprising PID data for the PID controller; and generating the KPI data for the PID controller based on the PID data. (Naduthota e.g. FIG. 1 illustrates an example industrial process control and automation system 100 according to this disclosure [0022]. Each controller 106 could, for example, represent simple Proportional Integral Derivative (PID) controllers that are part of a distributed control system, a multivariable controller, or advanced controllers such as model predictive controllers [0025]. The machine-level controllers 114 could log information collected or generated by the controllers 106, such as measurement data from the sensors 102 a or control signals for the actuators 102 b [0028]. The operator stations 116 could allow users to review the operational history of the sensors 102 a and actuators 102 b using information collected by the controllers 106 and/or the machine-level controllers 114 [0029]. Controllers are often designed to maintain process variables at desired reference points known as setpoints. A system designed to control a process variable may be called a control loop [0002]. In a typical scenario, the performance of each control loop can be rated against multiple Key Performance Indicators (KPIs) [0004]. Conventional industrial facilities can include hundreds or thousands of control loops with their associated controllers. As a result, it is often difficult to determine which control loops are experiencing problems, especially when a problem in one control loop adversely affects other control loops [0041]. A generalized index can be used in control loop monitoring tools to provide a qualitative indication of an overall loop performance (such as Excellent, Fair, Poor, etc.) based on the values of various Key Performance Indicators (KPIs) (which may also be known as diagnostic indicators) [0041]. Each diagnostic indicator has a value, and the generalized indicator is associated with a position on a continuous scale ([0006]-[0008]). In accordance with this disclosure, at least one component of the system 100 implements or otherwise provides a generalized indicator tool 144. The generalized indicator tool 144 helps to provide a better indication of the status of equipment in a control loop [0042]. FIG. 3 illustrates an example indicator table 300 according to this disclosure. As shown in FIG. 3, the indicator table 300 may include a controller category 305, a Relative Performance Index (RPI) range 310, an Oscillation Index (OSI) range 315, and an Error Standard Deviation (std-dev) range 320 [0057]. The Oscillation Index element may be a value between zero and one, where values approaching zero represent less oscillation and values approaching one represent more oscillations and more oscillations that are regular. The Relative Performance Index is a ratio between a benchmark response speed and an actual response speed. The Error Standard Deviation is a measure of a variability in a controller, which can provide an indication of how aggressive the controller is. The OSI, RPI, and Error Standard Deviation may be key performance indicators (KPIs) (Fig. 3 and [0057]).)
As per claims 3 and 13 (Original), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 2 and the apparatus of claim 12, Naduthota teaches further comprising: generating the performance rating data for the PID controller by comparing each of one or more KPI values from the KPI data to a corresponding threshold value (Naduthota e.g. Each KPI can be associated with a band that defines its acceptable range of operation. For example, acceptable ranges of the KPIs could be an OSI between 0.0-0.5, an RPI between 0.4-2.5, and a std-dev between 0-2% of PV mean value. In other embodiments, these ranges may be different [0058]. At any point in time, the overall performance rating for a control loop can be determined based on which of these indicators are within their acceptable operating ranges [0059]. FIGS. 5A through 5C illustrates an example continuous performance index (CPI) system 500 according to this disclosure [0064].)
As per claims 4 and 14 (Original), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 2 and the apparatus of claim 11, Naduthota does not explicitly teach, however, Sayyarodsari teaches wherein the performance diagnostic data for the PID controller comprises one or more detected faults associated with PID controller. (Sayyarodsari e.g. The present disclosure generally relates to control systems and, more particularly, to control systems performing nonintrusive monitoring and diagnostics in industrial automation environments with electromechanical machinery, production lines, conveyer systems, and the like [0001]. Generally, a control system may facilitate performance of an industrial automation process by controlling operation of one or more automation devices. Additionally, the control system may monitor performance of the process to determine whether the process is operating as desired. When not operating as desired, the control system may also perform diagnostic operations on the process to determine potential causes of the undesired operation [0002]. By way of introduction, FIG. 1 illustrates an example industrial automation system 10 employed by a food manufacturer. However, it should be noted that although the example industrial automation system 10 of FIG. 1 is directed at a food manufacturer, the present embodiments described herein may be employed within any suitable industry, such as automotive, mining, hydrocarbon production, manufacturing, and the like [0027]. The industrial automation devices 20 may include controllers, input/output (I/O) modules, motor control centers, motors, human machine interfaces (HMIs), operator interfaces, contactors, starters, sensors 16, actuators, conveyors, drives, relays, protection devices, switchgear, compressors, sensor, actuator, firewall, network switches (e.g., Ethernet switches, modular-managed, fixed-managed, service-router, industrial, unmanaged, etc.) and the like ([0029] and [0038]). The local control system 42 may include a control/monitoring device or automation controller adapted to interface with the industrial automation devices 20 or other components that may monitor and control various types of the industrial automation devices 20 [0034]. To facilitate controlling operation and/or performing other functions, the local control system 42 may include one or more controllers, such as one or more model predictive control (MPC) controllers, one or more proportional-integral-derivative (PID) controllers, one or more neural network controllers, one or more fuzzy logic controllers, or any combination thereof [0051]. Referring now to FIG. 4 , a data flow diagram of a process 100 is illustrated to detail a manner in which audio data acquired by the audio sensor 82 is analyzed to determine whether industrial automation devices 20 or at least a portion of the industrial automation system 10 is operating in an expected state or an anomalous state. As used herein, an anomalous state may correspond to unexpected states of operation in which one or more industrial automation devices 20 are operating inefficiently, ineffectively, or the like. The anomalous states may be indicative of a problem in the respective devices that may correspond to a wear condition, a need for maintenance, a likelihood of a fault or stop in operation, or the like [0068].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Naduthota’s diagnostic indicator process to include analysis of performance data to detect faults associated with automation devices (e.g. controllers) as taught by Sayyarodsari in order to provide efficient operations for industrial equipment (Sayyarodsari e.g. [0026]).
As per claims 5 and 15 (Original), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 4 and the apparatus of claim 14, Naduthota teaches wherein the performance analysis model comprises a rules-based model. (Naduthota e.g. FIGS. 5A through 5C illustrates an example continuous performance index (CPI) system 500 according to this disclosure [0064]. As shown in FIG. 5A, the CPI system 500 includes inputs 505-515, a fuzzy logic block 520, and an output 525 [0064]. The fuzzy logic block 520 operates to generate a generalized CPI value as the output 525. The generalized CPI can be defined based upon the existing discrete levels of the overall performance rating. The types of input and output functions (such as triangular, Gaussian, and the like) can be selected to suit the properties of the corresponding index [0065]. In some embodiments, the fuzzy logic block 520 can use a Fuzzy Associative Memory (FAM) table 530, which is shown in FIG. 5B. The FAM table 530 defines the rules governing the overall implementation logic, such as “if-then” rules. For example, if the RPI 535 is good, the OSI 540 is good, and the std-dev 545 is good, the generalized CPI 550 is Excellent [0066].)
As per claims 6 and 16 (Currently Amended), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 1 and the apparatus of claim 11, Naduthota teaches wherein the improvement action is generated using the performance analysis model, where the performance analysis model defines the one or more PID categorization rules (Naduthota e.g. The generalized indicator tool 144 can support various other features. For example, the generalized indicator tool 144 may provide a mechanism to give early warning to concerned authorities about an impending deterioration based upon a rate of ongoing performance degradation, thereby enabling corrective action to be taken [0044]. For alarm management applications, where the objective is to manage the alarms generated within in plant, the generalized indicator tool can provide a continuous status of overall alarm performance. A color gradient-based progressive indication of plant's overall alarm performance enables pre-emptive actions in case of gradual degradation [0046]. Each KPI can be associated with a band that defines its acceptable range of operation. For example, acceptable ranges of the KPIs could be an OSI between 0.0-0.5, an RPI between 0.4-2.5, and a std-dev between 0-2% of PV mean value. In other embodiments, these ranges may be different [0058]. At any point in time, the overall performance rating for a control loop can be determined based on which of these indicators are within their acceptable operating ranges [0059]. FIGS. 10A and 10B illustrate example instances for triggering an alarm according to this disclosure [0082]. In FIG. 10A, at a mark 1005, the degradation of a system may reach a point at which an operator is notified, such as by alarm or warning. Note that the mark 1005 can be set to any suitable point or points [0082]. In FIG. 10B, in the continuous color scale 1010, a system may degrade quickly enough to notify an operator, such as by an alarm or warning. For example, a distance 1015 may represent the degradation of the system over a period of a single day, and this distance can be compared to a threshold to identify a problem. If, for example, the degradation at the distance 1015 is a percentage of 22%, the system may alert an operator if the threshold is 20%. Note, however, that other distances, thresholds, and periods of time may be used [0083].) Naduthota does not explicitly teach, however, Sayyarodsari teaches to initiate the automatic adjustment of operating parameters (Sayyarodsari e.g. The local control system 42 may include a control/monitoring device or automation controller adapted to interface with the industrial automation devices 20 or other components that may monitor and control various types of the industrial automation devices 20 [0034]. Here, the industrial automation devices 20 may receive data from the associated devices and use the data to perform their respective operations more efficiently. For example, a controller of a motor drive may receive data regarding a temperature of a connected motor and may adjust operations of the motor drive based on the data [0039]. In some embodiments, the processor 74 may automatically perform certain operational adjustments to the industrial automation devices 20 in response to the distinguishable features 114 matching one of the known clusters 120. That is, certain operational states that correspond to particular known clusters 120 may be associated with undesired states of operation. As such, upon detecting of the respective industrial automation devices 20 operating in these states, the processor 74 may automatically perform certain operational adjustments (e.g., slow down, power off) to ensure that the industrial system 10 continues to operate and to minimize an amount of wear that the respective industrial automation devices 20 may experience [0088].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Naduthota’s diagnostic indicator process to include initiating corrective actions such as an adjustment to controller parameters as taught by Sayyarodsari in order to provide efficient operations for industrial equipment (Sayyarodsari e.g. [0026]).
As per claims 7 and 17 (Original), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 1 and the apparatus of claim 11, Naduthota teaches wherein the PID controller is associated with one or more of an APC controller or a PWO controller. (Naduthota e.g. The system 100 also includes various controllers 106. The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 106 may use measurements from one or more sensors 102 a to control the operation of one or more actuators 102 b. These controllers 106 could interact with the sensors 102 a, actuators 102 b, and other field devices via the I/O module(s) 104. A second set of controllers 106 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 106 could be used to perform additional functions (Fig. 1 and [0023]). Controllers 106 are often arranged hierarchically in a system. For example, different controllers 106 could be used to control individual actuators, collections of actuators forming machines, collections of machines forming units, collections of units forming plants, and collections of plants forming an enterprise (Fig. 1 and [0024]). Each controller 106 includes any suitable structure for controlling one or more aspects of an industrial process. At least some of the controllers 106 could, for example, represent proportional-integral-derivative (PID) controllers or multivariable controllers, such as Robust Multivariable Predictive Control Technology (RMPCT) controllers or other types of controllers implementing model predictive control (MPC) or other advanced predictive control (Fig. 1 and [0025]).)
As per claim 9 and 19 (Currently Amended), Naduthota in view of Sayyarodsari teach the computer-implemented method of claim 1 and the apparatus of claim 11, wherein initiating the performance of one or more prediction-based operations comprises generating statistical data for each PID controller category of the plurality of Controller categories based on the PID controllers assigned to each PID controller category; and causing rendering of a user interface comprising one or more representation of the statistical data, wherein the user interface indicates the improvement action (Naduthota e.g. One or more operator stations 116 are coupled to the networks 112. The operator stations 116 represent computing or communication devices providing user access to the machine-level controllers 114, which could then provide user access to the controllers 106 (and possibly the sensors 102 a and actuators 102 b) [0029]. In addition, the operator stations 116 could receive and display warnings, alerts, or other messages or displays generated by the controllers 106 or the machine-level controllers 114 [0029]. The generalized indicator tool 144 may provide a mechanism to display predictive performance of the loop in the near future, thereby enabling the operator to take pre-emptive measures to avert further degradation [0044]. FIGS. 8 and 9 illustrate example plant progressions according to this disclosure. As shown in FIG. 8, a plant progression 800 may be an expression of the generalized CPI of all of the areas of a plant over the course of a period of time [0074]. FIGS. 10A and 10B illustrate example instances for triggering an alarm according to this disclosure [0082]. FIG. 11 illustrates an example method 1100 for analyzing at least part of a process system according to this disclosure. The method 1100 is described as being performed by the device 200 of FIG. 2 operating in the system 100 of FIG. 1. However, the method 1100 could be used by any suitable device and in any suitable system [0085]. At operation 1130, the device displays the generalized indicator, which represents a value on a continuous scale. The continuous scale can include a color gradient, and the specific generalized indicator may appear as a color based on the position of the generalized indicator along the color gradient [0087].) and indicates the associated automatic adjustment of the one or more operating parameters (Sayyarodsari e.g. The industrial automation devices 20 may include controllers, input/output (I/O) modules, motor control centers, motors, human machine interfaces (HMIs), operator interfaces, contactors, starters, sensors 16, etc. ([0029] and [0038]). In FIG. 2, the local control system 42 is illustrated being communicatively coupled to a human machine interface (HMI) 52 [0034]. The local control system 42 may include a control/monitoring device or automation controller adapted to interface with the industrial automation devices 20 or other components that may monitor and control various types of the industrial automation devices 20 [0034]. In some embodiments, the supervisory control system 40 may provide centralized control over operation of the industrial automation application. For example, the supervisory control system 40 may enable centralized communication with a user (e.g., operator). To facilitate, the supervisory control system 40 may include the display 86 to facilitate providing information to the user. For example, display 86 may display visual representations of information, such as process data, selected features, expected operational parameters, and/or relationships there between [0052]. As such, the display 86 may serve as a user interface to communicate with the sensor 16. The display 86 may display a graphical user interface (GUI) for operating the sensor 16 [0061].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Naduthota’s industrial process control system and automation system to include displaying the automatic adjustment of the one or more operating parameters and display interfaces as taught by Sayyarodsari in order to employ improved methods for analyzing data acquired by the sensor devices to provide efficient operations for industrial equipment (Sayyarodsari e.g. [0026]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ayanna Minor whose telephone number is (571)272-3605. The examiner can normally be reached M-F 9am-5 pm.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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