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 Office Action is responsive to Applicant's amendment filed on 2 January 2026. Applicant’s amendment on 2 January 2026 amended Claims 1, 4, 8, 10, 12, 13, 15, 17, 19, and 20. Currently Claims 1-20 are pending and have been examined. The Examiner notes that the 101 rejection has been withdrawn.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2 January 2026 has been entered.
Examiner’s Note
The Examiner notes that based on a review of the amendments to the claim set and the applicable MPEP guidance, the amendments to the claims appear to overcome the 101 rejection because it dose not merely “apply” an abstract idea to a generic computer, but instead integrates any judicial exception into a practical application through specific technical improvements to oil refinery operations. The claim recites a particular technical solution involving mass and energy balance calculations and distillation columns, reconciled thermodynamic data, and a machine learning model specifically fin-tuned to prevent malfunctions of refinery components, these are not abstract concepts performable in the human mind but rather specific technical processes tied to industrial equipment. Most significantly, the claim concludes with a concrete physical action: adjusting industrial system operations by transmitting control signals from processors to modify the state of a valve or switch, which effects a transformation in the physical world and constitutes more than mere data gathering or manipulation. The physical control step, combined with the specific application to crude distillation unit operations and the prevention of equipment malfunctions, demonstrates that the claim is directed to an improvement in refinery technology rather than simply implementing an abstract concept on a computer. See MPEP 2106.05(a) (improvements to technology; MPEP 2106.04(d) (integrations into practical application through meaningful limitations beyond generally linking to a technological environment).
Response to Arguments
Applicant's arguments filed 2 January 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, 3, 4, 7, 8, 10, 11, 14, 15, 17, and 18 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).
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, using the mass and energy balance around each distillation column, reconciled data and thermodynamic properties for each distillation column (see; par. [0151] – par. [0160] of Alagappan teaches distillation tower (i.e. column) monitoring continuously while refining oil where the monitoring is of the chemical and thermal processes (i.e. thermodynamics properties), 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 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 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), and
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 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, 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 discloses the detecting abnormal event during the normal operations of refinery and chemical processes. However, Alagappan 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.
Robert 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 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 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 3, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr 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 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 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 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 7, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Charr 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 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 10, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr 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 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 14, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr 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 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 17, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr 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 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.
Claims 2, 5, 6, 9, 12, 13, 16, 19, and 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 2010, Adreas Jupke, Energy Efficient Unit Operations and Processes, Managing CO2 Emissions in the Chemical Industry, 2010 Wiley-VCH Verlag GmbH & Co, pp. 223-270 (hereafter Jupke).
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 Jupke 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,
Jupke teaches the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters (see; pg. 230 sec. 7.4.1 – pg. 231 of Jupke teaches provides basic principles on the balance of inputs and parameters such as reflux ratio (i.e. understanding properties to provide a specific performance level), pg. 224, sec. 7.2, par. 6 – managing input such as air, fuel, and processing).
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 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, 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, Jupke teaches energy efficient unit operations and processes 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.
Jupke discloses the field data comprises fuel gas properties and parameters, process feed parameters, and air intake parameters.
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 as taught by Jupke 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 Jupke 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 5, see discussion of claim 1 above, while Alagappan in view of Robert in further view of Jupke 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,
Jupke teaches providing, for display in a user interface, information about the decision (see; pg. 266, par. 3 – pg. 267, par. 1 of Jupke teaches real time monitoring and display, pg. 224 – 226, 7.2 based on current (i.e. reactive) and preventative data (i.e. proactive)).
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 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, 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, Jupke teaches energy efficient unit operations and processes 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.
Jupke 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 Jupke 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 Jupke 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 1 above, while Alagappan in view of Robert in further view of Charr 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 9, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr 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 12, see discussion of claim 8 above, while Alagappan in view of Robert in further view of Charr 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 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 16, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr 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 19, see discussion of claim 15 above, while Alagappan in view of Robert in further view of Charr 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 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
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625