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 .
Information Disclosure Statement
The information disclosure statement (IDS) filed 01/15/2026 has been considered.
Drawings
The drawings are objected to because Figure 3 fails to comply with 37 CFR 1.84(o), which reads. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
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 GAS FLARING PEAK PREDICTION.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 are directed to a method. Claims 10-20 are directed to a machine or article of manufacture.
Regarding claim 1:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“A method comprising: identifying historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period;”
“identifying historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period;”
“configuring a flare peak prediction model using the historical flaring data and the historical plant component operations data;”
“generating flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model;”
The limitations of identifying historical flaring data, identifying historical plant component operations, configuring a flare peak prediction model and generating flare peak prediction data can be reasonably performed with the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants”
“initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data.”
The limitation of receiving real-time plant operations data falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of initiating performance of one or more flare peak prediction responsive actions falls under mere instructions to apply an exception.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of receiving real-time plant operations data and initiating performance of one or more flare peak prediction responsive actions represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants”, US 20170102694 A1 to Enver et al. (herein “Enver”) describes “In some configurations, data engines are connected to various communication links within the process plant or otherwise are connected to the process plant so as to have a view of or window into the real-time data transmitted during the execution or control of the process within the plant” (Paragraph 11). US 20090149981 A1 to Evans et al. (herein “Evans”) describes “Such a model is used by an online monitoring system to monitor the continual operation of a chemical manufacturing facility or refinery in real-time from a remote location” (abstract).
With regards to “initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data”, Enver describes “diagnosis of a fault's root cause, and carrying out corrective actions that bring the instrument, controller, process, and/or equipment that is a source of the fault back to normal operations” (Paragraph 83). Evans describes “The various outputs and actions, A1, A2, and A3 illustrated in FIG. 1 can include an overall process control monitor and status update, the generation of alerts (such as when a temperature falls below a certain, prescribed range), and a variety of response actions (such as adjusting a flow rate, shutting off a condenser, or manually attending to an alert).” (Paragraph 25)
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claim 2, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of processing the historical flaring data and the historical plant component operations data by performing a labeling technique can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 3, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein configuring the flare peak prediction model comprises determining one or more flare peaks by performing at least one of a plurality of peak prediction operations and generating flare peak signature data can be reasonably performed with the human mind/with pen and paper and thus fall under the ”Mental Processes” grouping of abstract ideas. The limitation of training the flare peak prediction model using the flare peak signature data falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 4, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein a first peak prediction operation of the plurality of peak prediction operations comprises: performing a binary classification technique using the historical flaring data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 5, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein a second peak prediction operation of the plurality of peak prediction operations comprises: performing a multiclass classification technique using the historical flaring data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 6, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein a third peak prediction operation of the plurality of peak prediction operations comprises: determining a predicted flaring rate by performing a regression technique using the historical flaring data; and performing one or more optimization techniques using the predicted flaring rate fall under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 7, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein a fourth peak prediction operation of the plurality of peak prediction operations comprises: determining a predicted flaring rate by performing a multivariate time series forecasting technique using the historical flaring data; and performing one or more optimization techniques using the predicted flaring rate fall under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 8, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein initiating performance of the one or more flare peak prediction responsive actions comprises: generating a flare peak prediction interface component, wherein the flare peak prediction interface component comprises the flare peak prediction data; and causing the flare peak prediction interface component to be rendered to a flare peak prediction interface of a computing device fall under post-solution activity and are thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 9, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein initiating performance of the one or more flare peak prediction responsive actions comprises: causing actuation of at least one of the one or more components of the first plant falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 10, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein initiating performance of the one or more flare peak prediction responsive actions comprises: transmitting a flare peak prevention action instruction to the first plant falls under post-solution activity and is thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 11:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“identify historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period;”
“identify historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period;”
“configure a flare peak prediction model using the historical flaring data and the historical plant component operations data;”
“generate flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model;”
The limitations of identifying historical flaring data, identifying historical plant component operations, configuring a flare peak prediction model and generating flare peak prediction data can be reasonably performed with the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“An apparatus comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:”
“receive real-time plant operations data representing real-time operations of a first plant of the one or more plants;”
“initiate performance of one or more flare peak prediction responsive actions based on the flare peak prediction data.”
The limitation of an apparatus comprising memory and one or more processors communicatively coupled to the memory describes a generic system and thus falls under mere instructions to apply an exception (MPEP 2106.05(f)). The limitation of receiving real-time plant operations data falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of initiating performance of one or more flare peak prediction responsive actions falls under mere instructions to apply an exception.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of receiving real-time plant operations data and initiating performance of one or more flare peak prediction responsive actions represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “An apparatus comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:”, Enver describes “a processor that is communicatively coupled to the big data storage node, and a program memory that is communicatively coupled to the processor” (Paragraph 20). US 20190195525 A1 to Varghese et al. (herein “Varghese”) describes “As depicted in FIG. 3, the system 300 comprises one or more hardware processor elements 302 (e.g., a central processing unit (CPU), a microprocessor, or a multi-core processor), a memory 304, e.g., random access memory (RAM) and/or read only memory (ROM)” (Paragraph 82).
With regards to “receive real-time plant operations data representing real-time operations of a first plant of the one or more plants”, Enver describes “In some configurations, data engines are connected to various communication links within the process plant or otherwise are connected to the process plant so as to have a view of or window into the real-time data transmitted during the execution or control of the process within the plant” (Paragraph 11). Evans describes “Such a model is used by an online monitoring system to monitor the continual operation of a chemical manufacturing facility or refinery in real-time from a remote location” (abstract).
With regards to “initiate performance of one or more flare peak prediction responsive actions based on the flare peak prediction data”, Enver describes “diagnosis of a fault's root cause, and carrying out corrective actions that bring the instrument, controller, process, and/or equipment that is a source of the fault back to normal operations” (Paragraph 83). Evans describes “The various outputs and actions, A1, A2, and A3 illustrated in FIG. 1 can include an overall process control monitor and status update, the generation of alerts (such as when a temperature falls below a certain, prescribed range), and a variety of response actions (such as adjusting a flow rate, shutting off a condenser, or manually attending to an alert).” (Paragraph 25)
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Regarding claim 12, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein to configure the flare peak prediction model comprises the one or more processors being configured to determine one or more flare peaks by performing at least one of a plurality of peak prediction operations and generate peak signature data can be reasonably performed with a human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas. The limitation of training the flare peak prediction model using the flare peak signature data falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 13, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein a first peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to: perform a binary classification technique using the historical flaring data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 14, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein a second peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to: perform a multiclass classification technique using the historical flaring data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 15, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein a third peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to: determine g a predicted flaring rate by performing a regression technique using the historical flaring data; and perform one or more optimization techniques using the predicted flaring rate fall under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 16, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein a fourth peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to: determine a predicted flaring rate by performing a multivariate time series forecasting technique using the historical flaring data; and perform one or more optimization techniques using the predicted flaring rate fall under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 17, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to: generate a flare peak prediction interface component, wherein the flare peak prediction interface component comprises the flare peak prediction data; and cause the flare peak prediction interface component to be rendered to a flare peak prediction interface of a computing device fall under post-solution activity and are thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 18, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to: cause actuation of at least one of the one or more components of the first plant falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 19, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to: transmit a flare peak prevention action instruction to the first plant falls under post-solution activity and is thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 20:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“identifying historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period;”
“identifying historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period;”
“configuring a flare peak prediction model using the historical flaring data and the historical plant component operations data;”
“generating flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model;”
The limitations of identifying historical flaring data, identifying historical plant component operations, configuring a flare peak prediction model and generating flare peak prediction data can be reasonably performed with the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, configures the computer program product for:”
“receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants;”
“initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data.”
The limitation of a computer program product comprising at least one non-transitory computer-readable storage medium having computer program code describes a generic system and thus falls under mere instructions to apply an exception (MPEP 2106.05(f)). The limitation of receiving real-time plant operations data falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of initiating performance of one or more flare peak prediction responsive actions falls under mere instructions to apply an exception.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of receiving real-time plant operations data and initiating performance of one or more flare peak prediction responsive actions represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor, configures the computer program product for:”, Enver describes “the program memory may comprise a tangible, non-transitory storage medium storing instructions” (Paragraph 20). US 20190195525 A1 to Varghese et al. (herein “Varghese”) describes “In one embodiment, instructions and data for the present module or process 305 for operating heating and cooling equipment via a communications network (e.g., a software program comprising computer-executable instructions) can be loaded into memory 304 and executed by hardware processor element 302” (Paragraph 85).
With regards to “receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants”, Enver describes “In some configurations, data engines are connected to various communication links within the process plant or otherwise are connected to the process plant so as to have a view of or window into the real-time data transmitted during the execution or control of the process within the plant” (Paragraph 11). Evans describes “Such a model is used by an online monitoring system to monitor the continual operation of a chemical manufacturing facility or refinery in real-time from a remote location” (abstract).
With regards to “initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data”, Enver describes “diagnosis of a fault's root cause, and carrying out corrective actions that bring the instrument, controller, process, and/or equipment that is a source of the fault back to normal operations” (Paragraph 83). Evans describes “The various outputs and actions, A1, A2, and A3 illustrated in FIG. 1 can include an overall process control monitor and status update, the generation of alerts (such as when a temperature falls below a certain, prescribed range), and a variety of response actions (such as adjusting a flow rate, shutting off a condenser, or manually attending to an alert).” (Paragraph 25)
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
The examiner notes that amending the independent claims (claim 1, 11, and 20) to more precisely recite what “initiating/initiate performance of one or more flare peak responsive actions” entails – such as “sending commands consisting of equipment setpoints” – would serve to overcome the 101 rejection.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 8-12, 15, and 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20170102694 A1 to Enver et al. (herein "Enver").
Regarding claim 1, Enver teaches a method (Paragraph 11) comprising:
identifying historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period (Paragraph 400 “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
identifying historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period (Paragraph 400 “The example process data includes a process flow measurement or signal 1100 obtained from a unit in the flare system of the refinery during a particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
configuring a flare peak prediction model using the historical flaring data and the historical plant component operations data (Paragraph 401 “The identification of the change in the process flow signal 1100 as a leading indicator for the flare event 1104 may be determined, for example, by using data analysis techniques such as PCA, cross-correlation, PLS regression, etc. This change/leading indicator may be captured, identified, or defined; a new signal or process variable corresponding to the identified change/leading indicator may be generated, defined, or created; and the time-series output of the new process variable may be determined by utilizing a rolling FFT”);
receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”);
generating flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”); and
initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data (Paragraph 401 “Accordingly, by monitoring the time-series data of the new process variable as determined by the rolling FFT for the presence of a leading indicator, an impending flare may be determined, and a corresponding warning may be presented to a user (e.g., an engineer, a process operator, etc.) so that preventative and/or mitigating actions may be taken”).
Regarding claim 2, Enver teaches the method of claim 1, further comprising:
processing the historical flaring data and the historical plant component operations data by performing a labeling technique (Paragraph 337 “As an example, the anomaly discovery block may output the times at which various anomalies occur in various data sets”, Paragraph 400 “The example process data includes a process flow measurement or signal 1100 obtained from a unit in the flare system of the refinery during a particular time interval”, “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval“, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104). Looking at FIG. 7B, it is apparent that the behavior of the process flow signal 1100 changes prior to the flare event 1104“ where it is clear that anomalous data is identified/labelled as such)
Regarding claim 3, Enver teaches the method of claim 1, wherein configuring the flare peak prediction model comprises:
determining one or more flare peaks associated with the one or more plants that occurred during the first time period by performing at least one of a plurality of peak prediction operations using the historical flaring data using the historical flaring data (Paragraph 400 “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval”);
generating flare peak signature data based on the one or more flare peaks and the historical plant component operations data (Paragraph 401 “This change/leading indicator may be captured, identified, or defined; a new signal or process variable corresponding to the identified change/leading indicator may be generated, defined, or created; and the time-series output of the new process variable may be determined by utilizing a rolling FFT”); and
training the flare peak prediction model using the flare peak signature data (Paragraph 159 “After an off-line data module (or portion thereof) has been evaluated, the results of the evaluation are able to be reviewed and scrutinized by the user, and the draft model (or portion thereof) can be modified accordingly and reevaluated until a satisfactory result has been achieved“, Paragraph 340 “In the latter case, the explore block 604g may allow the user to visualize the output of the PLS model (which may include a predicted value of a variable) and compare the output with the actual values of that variable (from the block 604d) that were used to create the PLS model. The user may be able to determine, by looking at graphs of the data, whether the PLS model is accurately representing the process such that it has some predictive value in the process” where the process of evaluating the model’s accuracy and modifying it constitutes training).
Regarding claim 6, Enver teaches the method of claim 3, wherein a third peak prediction operation of the plurality of peak prediction operations comprises:
determining a predicted flaring rate by performing a regression technique using the historical flaring data (Paragraph 401 “PLS regression”); and
performing one or more optimization techniques using the predicted flaring rate (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 8, Enver teaches the method of claim 1, wherein initiating performance of the one or more flare peak prediction responsive actions comprises:
generating a flare peak prediction interface component, wherein the flare peak prediction interface component comprises the flare peak prediction data (Paragraph 417 “In some cases….in obtained time-series data”); and
causing the flare peak prediction interface component to be rendered to a flare peak prediction interface of a computing device (Paragraph 417 “In some cases….in obtained time-series data”).
Regarding claim 9, Enver teaches the method of claim 1, wherein initiating performance of the one or more flare peak prediction responsive actions comprises:
causing actuation of at least one of the one or more components of the first plant (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 10, Enver teaches the method of claim 1, wherein initiating performance of the one or more flare peak prediction responsive actions comprises:
transmitting a flare peak prevention action instruction to the first plant (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 11, Enver teaches an apparatus (abstract “system”) comprising memory and one or more processors communicatively coupled to the memory (Paragraph 20 “a processor that is communicatively coupled to the big data storage node, and a program memory that is communicatively coupled to the processor”), the one or more processors configured to:
identify historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period (Paragraph 400 “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
identify historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period (Paragraph 400 “The example process data includes a process flow measurement or signal 1100 obtained from a unit in the flare system of the refinery during a particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
configure a flare peak prediction model using the historical flaring data and the historical plant component operations data (Paragraph 401 “The identification of the change in the process flow signal 1100 as a leading indicator for the flare event 1104 may be determined, for example, by using data analysis techniques such as PCA, cross-correlation, PLS regression, etc. This change/leading indicator may be captured, identified, or defined; a new signal or process variable corresponding to the identified change/leading indicator may be generated, defined, or created; and the time-series output of the new process variable may be determined by utilizing a rolling FFT”);
receive real-time plant operations data representing real-time operations of a first plant of the one or more plants (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”);
generate flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”); and
initiate performance of one or more flare peak prediction responsive actions based on the flare peak prediction data (Paragraph 401 “Accordingly, by monitoring the time-series data of the new process variable as determined by the rolling FFT for the presence of a leading indicator, an impending flare may be determined, and a corresponding warning may be presented to a user (e.g., an engineer, a process operator, etc.) so that preventative and/or mitigating actions may be taken”).
Regarding claim 12, Enver teaches the apparatus of claim 11, wherein to configure the flare peak prediction model comprises the one or more processors being configured to:
determine one or more flare peaks associated with the one or more plants that occurred during the first time period by performing at least one of a plurality of peak prediction operations using the historical flaring data using the historical flaring data (Paragraph 400 “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval”);
generate flare peak signature data based on the one or more flare peaks and the historical plant component operations data (Paragraph 401 “This change/leading indicator may be captured, identified, or defined; a new signal or process variable corresponding to the identified change/leading indicator may be generated, defined, or created; and the time-series output of the new process variable may be determined by utilizing a rolling FFT”); and
train the flare peak prediction model using the flare peak signature data (Paragraph 159 “After an off-line data module (or portion thereof) has been evaluated, the results of the evaluation are able to be reviewed and scrutinized by the user, and the draft model (or portion thereof) can be modified accordingly and reevaluated until a satisfactory result has been achieved”, Paragraph 340 “In the latter case, the explore block 604g may allow the user to visualize the output of the PLS model (which may include a predicted value of a variable) and compare the output with the actual values of that variable (from the block 604d) that were used to create the PLS model. The user may be able to determine, by looking at graphs of the data, whether the PLS model is accurately representing the process such that it has some predictive value in the process” where the process of evaluating the model’s accuracy and modifying it constitutes training).
Regarding claim 15, Enver teaches the apparatus of claim 12, wherein a third peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to
determine a predicted flaring rate by performing a regression technique using the historical flaring data (Paragraph 401 “PLS regression”); and
perform one or more optimization techniques using the predicted flaring rate (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 17, Enver teaches the apparatus of claim 11, wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to:
generate a flare peak prediction interface component, wherein the flare peak prediction interface component comprises the flare peak prediction data (Paragraph 417 “In some cases….in obtained time-series data”); and
cause the flare peak prediction interface component to be rendered to a flare peak prediction interface of a computing device (Paragraph 417 “In some cases….in obtained time-series data”).
Regarding claim 18, Enver teaches the apparatus of claim 11, wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to: cause actuation of at least one of the one or more components of the first plant (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 19, Enver teaches the apparatus of claim 11, wherein to initiate performance of the one or more flare peak prediction responsive actions comprises the one or more processors being configured to: transmit a flare peak prevention action instruction to the first plant (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Regarding claim 20, Enver teaches a computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon that, in execution with at least one processor (Paragraph 20 “The program memory may comprise a tangible, non-transitory storage medium storing instructions that, when executed by the processor…predict an aspect of the operation of the process control environment”), configures the computer program product for:
identifying historical flaring data representing flaring associated with one or more plants, wherein the historical flaring data is associated with a first time period (Paragraph 400 “Additionally, the example process data includes another signal 1102 which is indicative of the flare pressure of the flare system over the same particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
identifying historical plant component operations data representing historical operations of one or more components of the one or more plants, wherein the historical plant component operations data is associated with the first time period (Paragraph 400 “The example process data includes a process flow measurement or signal 1100 obtained from a unit in the flare system of the refinery during a particular time interval”, Paragraph 401 “FIG. 7B shows the same signals 1100 and 1102 for a different time interval during which a flare event 1104 in the signal 1102 has occurred (e.g., the flare pressure corresponding to signal 1102 has built up and exceeded a pressure limit, thus causing the flare event 1104)”);
configuring a flare peak prediction model using the historical flaring data and the historical plant component operations data (Paragraph 401 “The identification of the change in the process flow signal 1100 as a leading indicator for the flare event 1104 may be determined, for example, by using data analysis techniques such as PCA, cross-correlation, PLS regression, etc. This change/leading indicator may be captured, identified, or defined; a new signal or process variable corresponding to the identified change/leading indicator may be generated, defined, or created; and the time-series output of the new process variable may be determined by utilizing a rolling FFT”);
receiving real-time plant operations data representing real-time operations of a first plant of the one or more plants (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”);
generating flare peak prediction data by applying the real-time plant operations data to the flare peak prediction model (Paragraph 406 “However, once the new process variables (e.g., frequencies, principal components, and/or other higher-order data of interest) are identified and defined, the rolling FFT analytic may be performed on streaming real-time data from the on-line process plant”); and
initiating performance of one or more flare peak prediction responsive actions based on the flare peak prediction data (Paragraph 401 “Accordingly, by monitoring the time-series data of the new process variable as determined by the rolling FFT for the presence of a leading indicator, an impending flare may be determined, and a corresponding warning may be presented to a user (e.g., an engineer, a process operator, etc.) so that preventative and/or mitigating actions may be taken”).
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.
Claim(s) 4-5 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Enver further in light of US 20190195525 A1 to Varghese et al. (herein “Varghese”).
Regarding claim 4, Enver teaches the method of claim 3.
Enver does not teach wherein a first peak prediction operation of the plurality of peak prediction operations comprises:
performing a binary classification technique using the historical flaring data.
However, Varghese teaches using a binary classification technique on historical data for predictions (Paragraph 72 “binary classifier”).
While Varghese does not specifically teach applying a binary classification technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a binary classification technique for prediction is applicable to any predictive model.
Enver and Varghese are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Varghese. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on binary classification) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using binary classification.
Regarding claim 5, Enver teaches the method of claim 3.
Enver does not teach wherein a first peak prediction operation of the plurality of peak prediction operations comprises:
performing a multiclass classification technique using the historical flaring data.
However, Varghese teaches using a multiclass classification technique on historical data for prediction (Paragraph 72 “multi-class classifier”).
While Varghese does not specifically teach applying a multiclass classification technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a multiclass classification technique for prediction is applicable to any predictive model.
Enver and Varghese are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Varghese. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on multiclass classification) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using multiclass classification.
Regarding claim 13, Enver teaches the apparatus of claim 12.
Enver does not teach wherein a first peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to:
perform a binary classification technique using the historical flaring data.
However, Varghese teaches using a binary classification technique on historical data for predictions (Paragraph 72 “binary classifier”).
While Varghese does not specifically teach applying a binary classification technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a binary classification technique for prediction is applicable to any predictive model.
Enver and Varghese are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Varghese. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on binary classification) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using binary classification.
Regarding claim 14, Ener teaches the apparatus of claim 14.
Enver does not teach wherein a second peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to:
perform a multiclass classification technique using the historical flaring data.
However, Varghese teaches using a multiclass classification technique on historical data for prediction (Paragraph 72 “multi-class classifier”).
While Varghese does not specifically teach applying a multiclass classification technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a multiclass classification technique for prediction is applicable to any predictive model.
Enver and Varghese are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Varghese. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on multiclass classification) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using multiclass classification.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Enver in light of US 20230038977 A1 to Ditto et al. (herein "Ditto").
Regarding claim 7, Enver teaches the method of claim 3.
Enver also teaches performing one or more optimization techniques using the predicted flaring rate (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Enver does not teach wherein a fourth peak prediction operation of the plurality of peak prediction operations comprises:
determining a predicted flaring rate by performing a multivariate time series forecasting technique using the historical flaring data;
However, Ditto teaches using a multivariate time series forecasting technique on historical data for predictions (Paragraph 60 “During the training phase the machine learning algorithm is applied to known data values, such as data values previously received, and referred to as training data”, Paragraph 62 “multivariate time series forecast algorithm”).
While Ditto does not specifically teach applying a multivariate time series forecasting technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a multivariate time series forecasting classification technique for prediction is applicable to any predictive model, including flaring peak prediction.
Enver and Ditto are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Ditto. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on multivariate time series forecasting) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using multivariate time series forecasting.
Regarding claim 16, Enver teaches the apparatus of claim 12.
Enver also teaches: perform one or more optimization techniques using the predicted flaring rate (Paragraph 418 “For example, the data analytics system…in conjunction with the process plant”).
Enver does not teach wherein a fourth peak prediction operation of the plurality of peak prediction operations comprises the one or more processors being configured to:
determine a predicted flaring rate by performing a multivariate time series forecasting technique using the historical flaring data;
However, Ditto teaches using a multivariate time series forecasting technique on historical data for predictions (Paragraph 60 “During the training phase the machine learning algorithm is applied to known data values, such as data values previously received, and referred to as training data”, Paragraph 62 “multivariate time series forecast algorithm”).
While Ditto does not specifically teach applying a multivariate time series forecasting technique to flaring peak prediction, Enver already teaches a system for flaring peak prediction. It is the examiner’s interpretation that the principle of using a multivariate time series forecasting classification technique for prediction is applicable to any predictive model, including flaring peak prediction.
Enver and Ditto are both analogous to the claimed invention because both are in the field of developing predictive models. It would be obvious to one of ordinary skill in the art to incorporate the system of Enver with the system of Ditto. Such a combination would merely be combining prior art elements (a system for flaring peak prediction, and a system that performs predictions based on multivariate time series forecasting) according to known methods to yield predictable results, where the predictable result would be a system for flaring peak prediction that can predict flaring peaks using multivariate time series forecasting.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM XIANG ZHANG whose telephone number is (571)272-1276. The examiner can normally be reached M-F (8:30 AM - 5 PM).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 5712722748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/W.X.Z./ Examiner, Art Unit 2117
/ROBERT E FENNEMA/ Supervisory Patent Examiner, Art Unit 2117
Prosecution Insights