DETAILED ACTION
Claims 1-20 (filed 06/27/2024) have been considered in this action. Claims 1-20 are newly filed.
Specification
The disclosure is objected to because of the following informalities:
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: SYSTEMS, APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR MONITORING ADVANCED PROCESS CONTROL ASSETS USING PERFORMANCE INDICATORS WITH RESPECT TO UPTIME DATA
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 7-11, and 15-19 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) 1-3 and 7-8 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) 1-3 and 7-8 are method claims directed to limitations that under the BRI are directed to steps which can be identified as Mathematical concepts, or Mental processes capable of being performed in the human mind with the aid of pen and paper. The acts of identifying a set of one or more performance indicators, generating a set of one or more weighted performance indicators, and generating a performance health score by aggregating the performance indicators with uptime data from claim 1 are directed to abstract ideas. For example, the acts of identifying and generating are processes that are directed to mental processing steps capable of being performed in the human mind. An aggregating can be for example determining a sum, which under the BRI constitutes a mathematical concept. For example, paragraph [0101] describes how aggregated performance indicator and uptime are utilized as a mathematical formula for generating the aggregated performance indicator. For further example, paragraph [0086]-[0103] provide various mathematical formulas for generating weighted performance indicators, thus showing the mathematical formulas being elicited by the claimed limitations.
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, the additional elements of a computer implemented method for monitoring advanced process assets, and initiating performance of one or more prediction-based actions based on the performance health score. These additional elements under the BRI are mere instructions to apply an exception without significantly more, which amounts to little more than a recitation of the words “apply it”. The identified limitations only recite the idea of a solution or outcome without claiming details of how a solution is accomplished (see MPEP 2106.05(f): “…The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)”. For example, the use of computers are recited at a high level of generality such that the claimed use of computers are superficial as the claims cannot be said to be improving any form of computer or computer technology as the use of computers is generic and does not require any form of specialized computer function to operate. The computer is merely used as a tool to perform the abstract ideas. The use of “performance of one or more prediction-based action” can be considered a generic statement that amounts to “apply it”, as there is no particular action claimed, and this statements covers all solutions and can be considered mere instructions to apply an exception without significantly more.
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 a computer implemented method for monitoring advanced process assets, and initiating performance of one or more prediction-based actions based on the performance health score. 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. Aside from being claimed as “by one or more processors” the use of computers is superficial in that they are merely being used as a tool to accomplish the performance of abstract ideas.
Dependent claims 1-3 and 7-8 are drawn to process that further describe abstract idea concepts, or which are additional elements that fail to offer significantly more to the claim. For example, claims 2 and 3 and 8 describe elements which can further be considered abstract ideas in the form of processes that can be performed mentally, or which further describe mathematical concepts or formula. The use of weighting and applying weights is a mathematical concept in and of itself, and furthermore is a simple enough mathematical concept that it can be performed mentally with the aid of pen and paper. The specifying of 5 different types of performance indicators in claim 3, for which only one is required to be used (set of one or more) can be considered to further specify a type of mathematical formula or type of mental processing performed. These various types are not claimed in any further limiting way aside from by name, such that these differently named performance indicators can be considered just that, different in name only without any technical features because there are no technical features being claimed. The step of generating actionable insight data is again a process which can be considered a mental processing step, as there is no particular solution being presented as to how the actionable insight data is utilized to afford a particular solution to a problem, but instead covers every solution. These limitations are considered to be drawn to the abstract idea without adding significantly more. Claims 7-8 specify additional elements that fail to offer significantly more and can be further considered a version of “apply it”. Much like in Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016) the additional elements of claims 7-8 in which data is rendered or provided to a user is insignificant extra-solution activity that fails to afford to the claim anything more.
Claims 1-3, 7 and 8 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claims 9-11 and 15-19, while directed towards a different statutory category of invention as claims 1-3, 7 and 8, are directed towards similar limitations as those in claims 1-3, 7 and 8. A similar analysis under 35 U.S.C. 101 can be applied from claims 1-3, 7 and 8 to claims 9-11 and 15-19. Accordingly, claims 9-11 and 15-19 are rejected as being directed towards abstract ideas without significantly more under 35 U.S.C 101, as applied to claims 1-3, 7 and 8 above to the corresponding limitations.
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 1-5, 7-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Michalscheck et al. (US 20160282858, hereinafter Michalscheck) in view of Leudtke (US 20150127121, hereinafter Leudtke).
In regards to Claim 1, Michalscheck teaches “A computer-implemented method for monitoring advanced process control assets, the computer-implemented method comprising: identifying, by one or more processors, a set of one or more performance indicators for an asset” ([0004] a method for assessing industrial machine quality includes receiving, via a processor, data related to one or more machine scoring factors associated with a performance of a machine. The data is received from one or more sensors associated with the machine and the one or more machine scoring factors relate to characteristics of the machine comprising maintenance, production, network quality, asset quality, workflow, predictive problem solving, environmental impact, or sustainability. The method also includes converting, via the processor, the data into a first score for the machine based on the machine scoring factors.[0088] FIG. 8 is a flow diagram of a method 110 for generating a score based on machine scoring factors, in accordance with embodiments presented herein. Although the following description of the method 110 is provided with reference to the processor 36 of the computing device 26, it should be noted that the method 110 may be performed by other processors disposed on other devices that may be capable of communicating with the cloud-based computing system 28, the industrial automation equipment 16, or other components associated with the industrial application 24.) “generating, by the one or more processors, a set of one or more weighted performance indicator scores corresponding to the set of one or more performance indicators” ([0070] For the industrial application 24, data may be received for a set of scoring factors related to maintenance of equipment used to perform the industrial application 24 (e.g., whether equipment is placed offline due to a detected condition), production metrics of the industrial application 24 (e.g., whether production rate is satisfactory), network accessibility of the industrial application 24 (e.g., whether network quality is satisfactory), asset quality or ratings of the equipment used to perform the industrial application 24 (e.g., whether a part has been recalled), and so forth. Based on the data received related to the scoring factors, the processor 36 may determine one or more scores for the respective item being evaluated (block 84). That is, the processor 36 may convert or transform the data received from the sensors 18, camera, or input by the technician 70 to a performance score for respective items of the industrial enterprise hierarchy 50. The score for any item of the industrial enterprise hierarchy 50 may be determined based on a scoring function that includes summing various weighted variables (e.g., scoring factors). In some embodiments, each of the scoring factors may be assigned a respective score and those respective scores may be added to produce the overall score for the component. It should be noted that some scoring factors may be weighted differently to influence the overall score more heavily than other scoring factors. The weights may be multipliers used to increase or decrease the weight of an associated factor; wherein the scores are indicators) “generating, by the one or more processors and based on the set of one or more performance indicators and uptime data, a performance health score for the asset by aggregating the set of one or more performance indicators with respect to the uptime data;” ([0097] As mentioned above, the processor 36 may generate an overall performance score for the industrial automation equipment 16 based on the machine scoring factors (block 128). To generate the performance score, the scoring function may sum the scores associated with each machine scoring factor. In addition, the machine scoring factors may be weighted differently depending on which machine scoring factor is more important to a user. For example, if maintenance of the industrial automation equipment 16 is the most important machine scoring factor, then a higher weight than the other weights may be assigned to the maintenance scoring factor. [0073] For example, to generate a score for the maintenance scoring factor, a number of points may be added to the score by the processor 36 based on data received that indicates that a machine employed by the industrial application 24 is placed offline after a certain condition is detected. In another example, a number of points may be added to the score for the maintenance scoring factor based on a percentage of time the machinery is shutdown; [0104] Further, as illustrated, “machine 1” includes a score of three “stars,” which is calculated by the scoring function by summing the various weighted machine scoring factors 148-162. Each machine scoring factor 148-162 includes its own individual score, as described above) “and initiating performance of one or more prediction-based actions based on the performance health score” ([0098] The processor 36 may also control the operation of at least one industrial automation component 34 based on the score (block 130). For example, if the industrial automation equipment 16 receives a score below a threshold, then the processor 36 may execute a preventative action, such as powering down industrial automation equipment 16, causing the industrial automation equipment 16 to save energy by running certain industrial automation equipment 16 at non-peak hours, and the like. In contrast, if the industrial automation equipment 16 receives a score above a threshold, then the processor 36 may continue operating the machinery of the industrial automation equipment 16 normal. [0099] Further, in some embodiments, the processor 36 may accumulate each score for the industrial automation equipment 16 to show the trends in the rating of the machinery so that recommendations for increasing the rating may be made. For example, the computing device 26 and/or cloud-based computing system 28 may recommend that alternatives to lockout-tagout be put in place when the machinery is taken offline more than a threshold number of times in a specified time period. In some embodiments, the recommendations may increase the machinery's score when followed, which may increase the score for the organization 54).
Michalscheck fails to teach the use of an uptime in determining scores/indicators. Michaelscheck suggests that shutdowns and offline time can be utilized in the determination of scores, which while related, is not explicitly an uptime. Michalscheck is therefore does not explicitly teach “generating, by the one or more processors and based on the set of one or more performance indicators and uptime data, a performance health score for the asset by aggregating the set of one or more performance indicators with respect to the uptime data”.
Leudtke teaches “generating, by the one or more processors and based on the set of one or more performance indicators and uptime data, a performance health score for the asset by aggregating the set of one or more performance indicators with respect to the uptime data” ([0096] FIG. 4c illustrates the unit time model. The unit time model comprises total time, available time, available production time and operating time as the line time model. Further, the unit time model comprises production time. [0097] Production time should be understood to be operating time except for equipment stops. Equipment stops should be understood stops caused by the unit itself, also referred to as machine unit specific stops; [0099] Referring to FIG. 5, by having a processing system divided in lines and machine units as suggested above and by having time periods as suggested above, a method 500 for determining a performance indicator performed in a control device may be implemented. [0100] A first step 502 may be to receive a request for determining a performance indicator. In the request the time period and MDO data to be used may be specified or, alternatively, the performance indicator may be used as an ID for retrieving this information from a memory or a database. [0101] In a subsequent step 504, when having the request, a performance indicator level can be determined. Which level to choose may be part of the request or, alternatively, the performance indicator may be used to retrieve the information. How many levels may vary depending on the complexity of the processing system, but for a food processing system the three levels presented above--processing system, line or machine unit level--may be used;[0103] In a step 508, when knowing which time period to use, relevant time period data may be retrieved from e.g. a time period database. The time period database may be configured to continuously update by retrieving information from the different machine units and/or the lines. More particularly, information on changes from one time period to another may be sent to the time period database. [0104] In a step 510, it is decided which MDO to use. This information may be included in the request or can be retrieved from an external database when knowing which performance indicator to determine. This step may be performed either before or in parallel with the steps 506 and/or 508. [0105] In a step 512, when knowing the MDO to use, relevant MDO values are retrieved from e.g. a MDO database. As the time period database, the MDO database may be continuously updated by retrieving data from measurement devices placed in the lines and/or in the machine units. In order to retrieve relevant MDO values points in time may be used to link the MDO values to the time period. [0106] In a step 514, when having the time period value and the MDO value, the performance indicator is determined. The actual processing may be performed in a control device connected to the processing system, or alternatively, if the processing requires significant processing power or if the actual calculation should be kept secret, the actual processing may be made by an external processing device; wherein fig. 4c shows that production time is uptime, as it is the total time minus idle time, planned shutdown, other stops and equipment stops and the MDOs utilized for the indicator at the points in time that correspond with the uptime are the aggregated set of performance indicators for generating health score).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the method of generating an aggregate health score for an asset on the basis of different machine performance indicators/scores being aggregated together as taught by Michalscheck with the use of using the uptime of Leudtke to only determine such scores/indicators for machinery when it is actually in use by aggregating the indicator values with the time data for when the machine is actually in production (i.e. uptime) because it would gain the stated benefit of Leudtke ([0087] Most often performance indicators are related to time in some way. In order to make sure that reliable performance indicators are determined it is necessary to make sure that a correct time period is considered). This is further suggested by Michalscheck as they take into consideration shutdown and offline time, as uptime is just the opposite of shutdown and offline time. By combining these elements, it can be considered taking the know use of uptime data with corresponding performance data during that uptime data for determining a performance indicator as taught by Leudtke, and using it to improve the determination of aggregate performance health in a known way that achieves predictable results.
In regards to Claim 9 and 17, the analysis of claim 1 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claims 9 and 17. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claims 9 and 17 are rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 1.
In regards to Claim 2, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 1 above. Michalscheck further teaches “The computer-implemented method of claim 1, further comprising assigning a weight to each performance indicator of the set of one or more performance indicators” ([0070] For the industrial application 24, data may be received for a set of scoring factors related to maintenance of equipment used to perform the industrial application 24 (e.g., whether equipment is placed offline due to a detected condition), production metrics of the industrial application 24 (e.g., whether production rate is satisfactory), network accessibility of the industrial application 24 (e.g., whether network quality is satisfactory), asset quality or ratings of the equipment used to perform the industrial application 24 (e.g., whether a part has been recalled), and so forth. Based on the data received related to the scoring factors, the processor 36 may determine one or more scores for the respective item being evaluated (block 84). That is, the processor 36 may convert or transform the data received from the sensors 18, camera, or input by the technician 70 to a performance score for respective items of the industrial enterprise hierarchy 50. The score for any item of the industrial enterprise hierarchy 50 may be determined based on a scoring function that includes summing various weighted variables (e.g., scoring factors). In some embodiments, each of the scoring factors may be assigned a respective score and those respective scores may be added to produce the overall score for the component. It should be noted that some scoring factors may be weighted differently to influence the overall score more heavily than other scoring factors. The weights may be multipliers used to increase or decrease the weight of an associated factor).
In regards to Claim 10 and 18, the analysis of claim 2 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claims 10 and 18. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claims 10 and 18 are rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 2.
In regards to Claim 3, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 2 above. Michalscheck further teaches “The computer-implemented method of claim 2, wherein the set of one or more performance indicators comprise (i) usefulness performance indicator, (ii) criticality performance indicator, (iii) value performance indicator, (iv) acceptance performance indicator, and (v) reliability performance indicator” ([0073] One or more scores may also be determined for machinery of the industrial application 24. For example, a score may be determined for each of the following machine scoring factors of the industrial application 24: maintenance, production, network, asset, work flow, predictive problems, sustainable, and/or environmental. To generate a score for each scoring factor, a number of points may be associated with events and/or properties related to the scoring factors and the points may be summed by the processor 36 based on data received as the events occur and/or the properties are met to generate a score for the respective scoring factor; [0088-0097] including production scoring factor (value performance indicator), maintenance scoring factory (acceptance performance indicator), asset scoring factor (reliability performance indicator), predictive problem scoring factor (usefulness performance indicator), workflow scoring factor (criticality performance indicator)).
In regards to Claim 11 and 19, the analysis of claim 3 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claims 11 and 19. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claims 11 and 19 are rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 3.
In regards to Claim 4, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 3 above. Michalscheck further teaches “The computer-implemented method of claim 3, wherein generating the set of one or more weighted performance indicator scores comprises generating, for a sampling period, a weighted usefulness score corresponding to the usefulness performance indicator by: determining, based on one more control variable conditions, a control variable usefulness score for each control variable of one or more control variables associated with the asset; aggregating the control variable usefulness score for each control variable to generate an aggregated control variable usefulness score; determining a manipulated variable count associated with the asset; determining a cumulative usefulness score by determining a ratio of the aggregated control variable usefulness score to the manipulated variable count; and applying the corresponding assigned weight to the cumulative usefulness score” (Michalscheck teaches a plurality of weighted scores that correspond to weighted performance indicators in [0088]-[0097], and because the claim requires only one of the type of performance indicators of claim 3, it can be considered that the scope of this claim is covered when another performance indicator different from usefulness performance indicator utilized. In other words, these limitations are contingent limitations that provide an option of which performance indicator is utilized because 5 different types are specified by claim 3, yet only “one or more” is required by the instant claim, and thus for example when that performance indicator is a reliability performance indicator, the instant claim does not further limit the invention)
In regards to Claim 12 and 20, the analysis of claim 4 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claims 12 and 20. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claims 12 and 20 are rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 4.
In regards to Claim 5, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 4 above. Michalscheck further teaches “The computer-implemented method of claim 4, wherein generating a control variable usefulness score for a control variable comprises: determining, for the sampling period, a control variable count based on a set of one or more control variable constraint conditions of the one or more control variable conditions by determining whether the control variable satisfies at least one control variable constraint condition from the set of one or more control variable constraint conditions; determining, for the sampling period, a control variable status count based on a set of one or more control variable status conditions of the one or more control variable conditions by determining whether the control variable satisfies the one or more control variable status conditions; and determining a ratio of the control variable count to the control variable status count” (Michalscheck teaches a plurality of weighted scores that correspond to weighted performance indicators in [0088]-[0097], and because the claim requires only one of the type of performance indicators of claim 3, it can be considered that the scope of this claim is covered when another performance indicator different from usefulness performance indicator utilized. In other words, these limitations are contingent limitations that provide an option of which performance indicator is utilized because 5 different types are specified by claim 3, yet only “one or more” is required by the instant claim, and thus for example when that performance indicator is a reliability performance indicator, the instant claim does not further limit the invention).
In regards to Claim 13, the analysis of claim 5 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claim 13. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claim 13 is rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 5.
In regards to Claim 7, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 1 above. Michalscheck further teaches “The computer-implemented method of claim 1, wherein initiating the performance of one or more prediction-based actions comprises causing rendering of a user interface comprising one or more representations of the performance health score” ([0061] The computer instructions may be configured to shutdown the industrial automation equipment 34, control the industrial automation equipment 34 via remotely accessing the HMI(s) 12 and/or automation controller 14, view data passed between the components of the control and monitoring system 10, track the performance of the technician 70 and/or the industrial automation equipment 34 of the industrial hierarchy 50, determine a score indicative of the performance of the technician 70 and/or the components, and/or display the scores via a visualization on a display screen of the computing device 26, among other things).
In regards to Claim 15, the analysis of claim 7 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claim 15. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claim 15 is rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 7.
In regards to Claim 8, the combination of Michalscheck and Leudtke teaches the method of monitoring process control as incorporated by claim 1 above. Michalscheck further teaches “The computer-implemented method of claim 1, wherein initiating the performance of one or more prediction-based actions comprises: generating actionable insight data based on the performance health score; and providing the actionable insight data to a user device” ([0099] Further, in some embodiments, the processor 36 may accumulate each score for the industrial automation equipment 16 to show the trends in the rating of the machinery so that recommendations for increasing the rating may be made. For example, the computing device 26 and/or cloud-based computing system 28 may recommend that alternatives to lockout-tagout be put in place when the machinery is taken offline more than a threshold number of times in a specified time period. In some embodiments, the recommendations may increase the machinery's score when followed, which may increase the score for the organization 54. [0061] The computer instructions may be configured to shutdown the industrial automation equipment 34, control the industrial automation equipment 34 via remotely accessing the HMI(s) 12 and/or automation controller 14, view data passed between the components of the control and monitoring system 10, track the performance of the technician 70 and/or the industrial automation equipment 34 of the industrial hierarchy 50, determine a score indicative of the performance of the technician 70 and/or the components, and/or display the scores via a visualization on a display screen of the computing device 26, among other things. [0077] Further, in some embodiments, the processor 36 may accumulate each score for the technician 70 and/or the machinery to show the trends in the rating of the technician and/or machinery so that recommendations for increasing the rating may be made. For example, the computing device 26 and/or the cloud-based computing system 28 may recommend certain retraining for the technician 70 if the technician 70 is continuously slow at performing tasks. In addition, the computing device 26 and/or cloud-based computing system 28 may recommend that alternatives to lockout-tagout be put in place when the machinery is taken offline more than a threshold number of times in a specified time period. As may be appreciated, providing recommendations to increase the ratings of individual components (e.g., technician, machinery) may enable a self-analyzing system that enables users to increase the rating for the organization 54 as a whole if the recommendations for the components are followed; wherein a recommendation is an actionable insight).
In regards to Claim 16, the analysis of claim 8 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claim 16. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claim 16 is rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 8.
Claim(s) 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Michalscheck and Leudtke as applied to claim 1 above, and further in view of Zhang et al. (US 20170016354, hereinafter Zhang).
In regards to Claim 6, the combination of Michalscheck and Leudtke teaches the method of monitoring a process control asset as incorporated by claim 1 above.
The combination of Michalscheck and Leudtke fail to teach “The computer-implemented method of claim 1, wherein generating the set of one or more weighted performance indicator scores comprises generating, for a sampling period, a weighted criticality score at least in part by: mapping an objective into manipulated variables; and determining ratio of the manipulated variables that have gradients to a count of manipulated variables”.
Zhang teaches “The computer-implemented method of claim 1, wherein generating the set of one or more weighted performance indicator scores comprises generating, for a sampling period, a weighted criticality score at least in part by” ([0016] In accordance with the present principles, systems and methods are provided for optimizing Key Performance Indicator (KPI) values in a production system by incorporating a nonparametric framework uses both variable dependencies and manifold regularization. These systems and methods can improve the quantity or quality of the output in production systems. As long as an objective can be clearly defined and measured, the provided systems and methods can be used to tune control variables to maximize the output KPI values) “mapping an objective into manipulated variables; and” ([0018] In an embodiment, a nonparametric estimation is built that relates the input and output variables together. The relationship between the input and output variables takes into account the dependency structures of the input variables, either by a pre-discovery process or through domain knowledge.[0019] In an embodiment, manifold regularization is added together with a loss function to estimate an input-output map; wherien inputs are considered manipulated variables) “determining ratio of the manipulated variables that have gradients to a count of manipulated variables” ([0054] At 250, after the input-out mapping function, ƒ, is estimated, a gradient ascent method is used to determine the local optimal of the KPI values 140. The system uses the determined local optimal of the KPI values 140 to generate optimal control variables, at 255. [0055] At 260, the optimal control variables are recommended to a user via, e.g., a graphical user interface 160. [0056] In an embodiment, the online optimizer 155 locates an optimal control variable, C(t)*, by maximizing the function estimated in the second function estimator 130 as well as the value of the environmental variables 150 in the online estimator 145.).
It would have been obvious to a person having ordinary skill in the art before the effective file date of the claimed invention to have modified the method of monitoring a process control asset which includes a generating of a criticality performance indicator as taught by Michalscheck and Leudtke with the use of a method for generating a performance indicator on the basis of mapping particular variables to an objective and determining a ratio of those inputs that have gradients to a total as taught by Zhang because it would gain the stated benefit of Zhang, namely that it would increase production output ([0008]). It can be considered that the method for determination of a criticality performance indicator as taught by Zhang could be incorporated into the system of Michalscheck in a known way that achieves predictable results that would improve the system in a similar way.
In regards to Claim 14, the analysis of claim 6 in view of Michalscheck and Leudtke can be applied to the corresponding limitations from claim 14. Michalscheck teaches the methods are performed by computers with processor and memory (computing device 26 in Fig. 3) and thus the related computer hardware and non-transitory computer readable medium are implied by Michalscheck. Accordingly, claim 14 is rejected under 35 U.S.C. 103 in view of Michalscheck and Leudtke using similar analysis as applied to claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Vale et al. (US 20240310793) – teaches performance indicators for a digital twin of an industrial asset, including the useful life (usefulness) determination of the asset
Bulut et al. (US 20220129560) – teaches a method for determining health indicators for computers including criticality factors/indicators
Chapin (US 20200118053) – teaches performance indicators for models of automation systems using digital twins
Taneja et al. (US 20180232084) – teaches performance and health indicators for an automation plant using real-time calculations of the health indicators from sensor data
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/JONATHAN MICHAEL SKRZYCKI/Examiner, Art Unit 2116
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