DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Final Office Action is responsive to Applicant's amendment filed on 22 June 2026. Applicant’s amendment on 22 June 2026 amended Claims 1, 2, 8, 9, 15, and 16. Currently Claims 1-20 are pending and have been examined. The Examiner notes that the 101 rejection was previously withdrawn in the Office Action filed 25 February 2026.
Response to Arguments
Applicant's arguments filed 22 June 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Alagappan et al. (U.S. Patent Publication 2007/0250292 A1) (hereafter Alagappan) in view of Robert et al. (U.S. Patent Publication 2022/0308533 A1) (hereafter Robert) in further view of Charr et al. (U.S. Patent Publication 2018/0364747 A1) (hereafter Charr) in further view of Modi et al. (U.S. Patent Publication 2019/0179271 A1) (hereafter Modi) in further view of Wouhaybi et al. (U.S. Patent Publication 2021/0325855 A1) (hereafter Wouhaybi).
Referring to Claim 1, Alagappan teaches a computer-implemented method, comprising:
receiving field data for distillation column feed streams and associated processes of a crude distillation unit of an oil refinery (see; par. [0151] of Alagappan teaches distillation tower (i.e. column) monitoring continuously while refining oil).
receiving maintenance history events and estimates per event of each distillation column of the oil refinery (see; par. [0151] of Alagappan teaches distillation tower (i.e. column) monitoring continuously while refining oil, par. [0101] where maintenance issues are recorded and used during future analysis, par. [0062] where the historical data is used to estimate the probability of an abnormal event).
performing, using the field data, mass and energy balance around each distillation column (see; par. [0151] of Alagappan teaches distillation tower (i.e. column) monitoring continuously while refining oil, par. [0081] where the data is used based on the masse energy balance from the collected data including historical data to determine an abnormal operation in the distillation tower (i.e. column)).
determining, based on executing the decision support model, information regarding operation of the oil refinery at a current performance level (see; par. [0319] of Alagappan teaches as part of creating a key performance indicator using measurements from outside abnormal operations then be used as key performance indicators moving forward).
Alagappan does not explicitly disclose the following limitations, however,
Robert teaches generating input parameters for a predictive model, the input parameters comprising the reconciled data, the thermodynamic properties for each distillation column, the maintenance history events, and the estimates per event (see; par. [0042] of Robert teaches a process parameter, par. [0025] using a predictive model, par. [0048] identifying and monitoring of distillation units, par. [0052] monitoring the heat and pressure (i.e. thermodynamics) of the process including distillation), and
executing, using the KPIs for each distillation column, a decision support model for the oil refinery, wherein the decision support model is based on activity-based information (see; par. [0046]-[0050] of Robert teaches KPIs for process and automations for optimizing configurations, par. [0025] using a predictive model, par. [0048] identifying and monitoring of distillation units (i.e. activity based), and then par. [0025] using predictive controls, par. [0048] based on the utilization of KPIs for oil and includes information regarding the multiple distillation columns), and
determining, based at least on the information regarding operation of the oil refinery, proactive data and predictive KPIs for distillation column estimate performance of each of the distillation columns (see; par. [0042] of Robert teaches a process parameters related to performance, par. [0046]-[0050] which include KPI for process and automations for optimizing configurations, par. [0048] identifying and monitoring of distillation units, and then par. [0025] using predictive controls, par. [0048] based on the utilization of KPIs for oil and includes information regarding the distillation columns), and
adjusting industrial system operations based on the proactive data and the predictive KPIs for distillation column estimate performance of each of the distillation columns, by transmitting control signals from one or more processors to the component of the oil refinery to at least partially modify the industrial system operations by modifying a state of a valve or a switch (see; par. [0026] of Robert teaches adjusting activators to control variables, par. [0025] based on a predictive model, par. [0048] that is based on KPIs related to the distillation units, par. [0042] where inputs and outputs of the process is controlled, par. [0048] and this involves for oil, and par. [0022] and the adjusting of activators control the valves).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan fails to disclose generating input parameters for a predictive model, the input parameters comprising the reconciled data, the thermodynamic properties for each distillation column, the maintenance history events, and the estimates per event, executing, using the KPIs for each distillation column, a decision support model for the oil refinery, wherein the decision support model is based on activity-based information, and determining, based at least on the information regarding operation of the oil refinery, proactive data and predictive KPIs for distillation column estimate performance of each of the distillation columns, and adjusting industrial system operations based on the proactive data and the predictive KPIs for distillation column estimate performance of each of the distillation columns, by transmitting control signals from one or more processors to the component of the oil refinery to at least partially modify the industrial system operations by modifying a state of a valve or a switch.
Robert discloses generating input parameters for a predictive model, the input parameters comprising the reconciled data, the thermodynamic properties for each distillation column, the maintenance history events, and the estimates per event, executing, using the KPIs for each distillation column, a decision support model for the oil refinery, wherein the decision support model is based on activity-based information, and determining, based at least on the information regarding operation of the oil refinery, proactive data and predictive KPIs for distillation column estimate performance of each of the distillation columns, and adjusting industrial system operations based on the proactive data and the predictive KPIs for distillation column estimate performance of each of the distillation columns, by transmitting control signals from one or more processors to the component of the oil refinery to at least partially modify the industrial system operations by modifying a state of a valve or a switch.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan generating input parameters for a predictive model, the input parameters comprising the reconciled data, the thermodynamic properties for each distillation column, the maintenance history events, and the estimates per event, executing, using the KPIs for each distillation column, a decision support model for the oil refinery, wherein the decision support model is based on activity-based information, and determining, based at least on the information regarding operation of the oil refinery, proactive data and predictive KPIs for distillation column estimate performance of each of the distillation columns, and adjusting industrial system operations based on the proactive data and the predictive KPIs for distillation column estimate performance of each of the distillation columns, by transmitting control signals from one or more processors to the component of the oil refinery to at least partially modify the industrial system operations by modifying a state of a valve or a switch as taught by Robert since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, and Robert teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Alagappan in view of Robert does not explicitly disclose the following limitation, however,
Charr teaches determining, by using the predictive model comprising a machine learning model trained to process the input parameters for each distillation column, key performance indicators (KPIs) for each distillation column, the predictive model processing the input parameters in combination with scenarios of industrial system operations, the machine learning model being fine-tuned to prevent a malfunction of a component of the oil refinery (see; par. [0112] of Charr teaches machine learning used to perform data analysis to prevent temperature excursions (i.e. malfunction) through models, par. [0110] and utilizes key performance indicators as part of the modeling).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Charr teaches incipient temperature excursion mitigation and control in a chemical process plant or refinery and as it is comparable in certain respects to Alagappan and Robert which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan and Robert discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan and Robert fails to disclose determining, by using the predictive model comprising a machine learning model trained to process the input parameters for each distillation column, key performance indicators (KPIs) for each distillation column, the predictive model processing the input parameters in combination with scenarios of industrial system operations, the machine learning model being fine-tuned to prevent a malfunction of a component of the oil refinery.
Charr discloses determining, by using the predictive model comprising a machine learning model trained to process the input parameters for each distillation column, key performance indicators (KPIs) for each distillation column, the predictive model processing the input parameters in combination with scenarios of industrial system operations, the machine learning model being fine-tuned to prevent a malfunction of a component of the oil refinery.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan and Robert the determining, by using the predictive model comprising a machine learning model trained to process the input parameters for each distillation column, key performance indicators (KPIs) for each distillation column, the predictive model processing the input parameters in combination with scenarios of industrial system operations, the machine learning model being fine-tuned to prevent a malfunction of a component of the oil refinery as taught by Charr since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, Robert, and Charr teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Alagappan in view of Robert in further view of Charr does not explicitly disclose the following limitation, however,
Modi teaches the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters (see; par. [0069] of Modi teaches a feed component value of at the column (i.e. feed parameters and air intake), including par. [0014] parameters), and
determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column (see; par. [0014] of Modi teaches efficiency parameter that represents a degree of separation, and par. [0048] based on a determination of a mass balance for the columns).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Charr teaches incipient temperature excursion mitigation and control in a chemical process plant or refinery and as it is comparable in certain respects to Alagappan and Robert which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Modi teaches a computer system and method for the dynamic construction and online deployment of an operation-centric first-principles process model for predictive analytics and as it is comparable in certain respects to Alagappan, Robert, and Charr which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan, Robert, and Charr discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan, Robert, and Charr fails to disclose the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
Modi discloses the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan, Robert, and Charr the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column as taught by Modi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, Robert, Charr, and Modi teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Alagappan in view of Robert in further view of Charr in further view of Modi does not explicitly disclose the following limitation, however,
Wouhaybi teaches the machine learning model being collaboratively trained with a system controller to minimize errors in industrial plant operation adjustments (see; par. [0059] of Wouhaybi teaches machine learning model used to control and minimize errors in the industrial plant).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Charr teaches incipient temperature excursion mitigation and control in a chemical process plant or refinery and as it is comparable in certain respects to Alagappan and Robert which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Modi teaches a computer system and method for the dynamic construction and online deployment of an operation-centric first-principles process model for predictive analytics and as it is comparable in certain respects to Alagappan, Robert, and Charr which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Wouhaybi teaches methods and apparatus for time sensitive networking coordinated transfer learning in industrial settings and as it is comparable in certain respects to Alagappan, Robert, Charr, and Modi which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan, Robert, Charr, and Modi discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan, Robert, Charr, and Modi fails to disclose the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
Wouhaybi discloses the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan, Robert, Charr, and Modi the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column as taught by Wouhaybi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, Robert, Charr, Modi, and Wouhaybi teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan in view of Robert in further view of Charr does not explicitly disclose a method having the limitations of, however,
Modi teaches passively obtained data and actively retrieved data in response to triggering signals activating data collection, wherein the obtained data and the actively retrieved data are tagged for serialization (see; par. [0089] of Modi teaches a trigger to signals, par. [0049] with a mapping function that tags the configuration (i.e. sterilization)).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Charr teaches incipient temperature excursion mitigation and control in a chemical process plant or refinery and as it is comparable in certain respects to Alagappan and Robert which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Modi teaches a computer system and method for the dynamic construction and online deployment of an operation-centric first-principles process model for predictive analytics and as it is comparable in certain respects to Alagappan, Robert, and Charr which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan, Robert, and Charr discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan, Robert, and Charr fails to disclose the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
Modi discloses the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan, Robert, and Charr the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters, and determining, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties corresponding to separation efficiency of each distillation column as taught by Modi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, Robert, Charr, and Modi teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan does not explicitly disclose a method having the limitations of, however,
Robert teaches the predictive KPIs comprise an energy consumption per unit mass of feed though the distillation column (see; par. [0020] of Robert teaches monitoring the energy usage a part of, par. [0048] based on KPIs of the distillation column, par. [0050] where the process is optimized).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan an event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan fails to disclose the predictive KPIs comprise an energy consumption per unit mass of feed though the distillation column.
Robert discloses the predictive KPIs comprise an energy consumption per unit mass of feed though the distillation column.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan the predictive KPIs comprise an energy consumption per unit mass of feed though the distillation column as taught by Robert since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, and Robert teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan further discloses a method having the limitations of,
performing data cleansing on the field data and the maintenance history events and estimates per event (see; par. [0382]-[0384] of Alagappan teaches updating parameter data based on current event performance, par. [0101] where maintenance issues are recorded and used during future analysis, par. [0062] where the historical data is used to estimate the probability of an abnormal event).
Referring to Claim 5, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan further discloses a method having the limitations of,
generating a decision whether to continue operating the oil refinery at the current performance level (see; par. [0220]-[0227] of Alagappan teaches an example of making modifications to either stay with current performance or make modifications to the operating changes).
Alagappan in view of Robert in further view of Charr does not explicitly disclose the following limitation, however,
Modi teaches providing, for display in a user interface, information about the decision (see; par. [0032] of Modi teaches a display in a user interface that drives real time decision making).
The Examiner notes that Alagappan teaches similar to the instant application teaches event detection technology to delayed coking unit as part of an oil refining process. Specifically, Alagappan discloses the detecting abnormal event during the normal operations of refinery and chemical processes it is therefore viewed as analogous art in the same field of endeavor. Additionally, Robert teaches identifying key performance indicators for industrial process including oil processing and as it is comparable in certain respects to Alagappan event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Charr teaches incipient temperature excursion mitigation and control in a chemical process plant or refinery and as it is comparable in certain respects to Alagappan and Robert which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Modi teaches a computer system and method for the dynamic construction and online deployment of an operation-centric first-principles process model for predictive analytics and as it is comparable in certain respects to Alagappan, Robert, and Charr which event detection technology to delayed coking unit as part of an oil refining process as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Alagappan, Robert, and Charr discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan, Robert, and Charr fails to disclose providing, for display in a user interface, information about the decision.
Modi discloses providing, for display in a user interface, information about the decision.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Alagappan, Robert, and Charr providing, for display in a user interface, information about the decision as taught by Modi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Alagappan, Robert, Charr, and Modi teach the collecting and analysis of data in order to manage the costing of intermediate products of a crude distillation in a refinery resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 5 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan further discloses a method having the limitations of,
generating, based on the information regarding operation of the oil refinery, alerts and advisories for display in the user interface (see; par. [0038] of Alagappan teaches an alert and indication of the nature of the refining process).
Referring to Claim 7, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches the method above, Alagappan further discloses a method having the limitations of,
the proactive data comprise suggestions for actions to performed at the oil refinery (see; par. [0066] of Alagappan teaches the monitoring provides a warning using drill down capabilities).
Referring to Claim 8, Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium. Claim 8 recites the same or similar limitations as those addressed above in claim 1, Claim 8 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 9, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 9 recites the same or similar limitations as those addressed above in claim 2, Claim 9 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 10, see discussion of claim 8 above, while A Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 10 recites the same or similar limitations as those addressed above in claim 3, Claim 10 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 11, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 11 recites the same or similar limitations as those addressed above in claim 4, Claim 11 is therefore rejected for the same or similar limitations as set forth above in claim 4.
Referring to Claim 12, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 12 recites the same or similar limitations as those addressed above in claim 5, Claim 12 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 13, see discussion of claim 12 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 13 recites the same or similar limitations as those addressed above in claim 6, Claim 13 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Referring to Claim 14, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a non-transitory, computer-readable medium above Claim 14 recites the same or similar limitations as those addressed above in claim 7, Claim 14 is therefore rejected for the same or similar limitations as set forth above in claim 7.
Referring to Claim 15, Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system. Claim 15 recites the same or similar limitations as those addressed above in claim 1, Claim 15 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exception;
one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations (see; Abstract of Alagappan teaches a processing device to manage the detection of events in the fuel refining process).
Referring to Claim 16, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system above Claim 16 recites the same or similar limitations as those addressed above in claim 2, Claim 16 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 17, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system above Claim 17 recites the same or similar limitations as those addressed above in claim 3, Claim 17 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 18, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system above Claim 18 recites the same or similar limitations as those addressed above in claim 4, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 4.
Referring to Claim 19, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system above Claim 19 recites the same or similar limitations as those addressed above in claim 5, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 20, see discussion of claim 19 above, while Alagappan in view of Robert in further view of Charr in further view of Modi in further view of Wouhaybi teaches a computer implemented system above Claim 20 recites the same or similar limitations as those addressed above in claim 6, Claim 20 is therefore rejected for the same or similar limitations as set forth above in claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al. (U.S. Patent Publication 2020/0201292 A1) discloses a method for data collection and frequency analysis with self-organized functionality.
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.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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