DETAILED ACTION
This action is filed in response to the application filed on 10/23/2023.
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
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 10/23/2023 and 2/26/2024. These IDS have been considered.
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. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claim 11 recite the following abstract concepts in BOLD of:
A method of detecting wear rate of the equipment at a pumping system for a well, comprising:
receiving first measurement data associated with or from a first equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system; and
estimating a wear rate associated with the first equipment based on an operational assessment of the downhole environment.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 discloses a method and Claim 11 discloses a system.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The step of estimating a wear rate can be interpreted as either performing mathematics or a mental process depending on one's interpretation of the limitation.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes the claimed methods and system are not tied to a particular machine or apparatus, and thus do not represent an improvement to another technology or technical field. Examiner notes the preamble of Claims 1 and 11 recite a pumping system for a well, but those elements simply indicate a field of use and not a particular machine. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation disclosing the receipt of first measurement data recites necessary data gathering and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Furthermore Claim 11 recites the additional elements of a storage system and a processor, however these are generic computer elements and are not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-10 and 12-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2 and 12 further limit the mental process of estimating in Claim 1 without significantly more.
Claims 3 and 13 teach performing the abstract idea of estimating using a machine learning model. Examiner notes these claims amount to mere instructions to implement the abstract idea of Claim 1 on a computer which does not amount to significantly more. See MPEP 2106.05(f), “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art"),” and see 2106.05(f)(2) “Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).”
Claims 4-6 and 14-16 further limit both the performance of mathematics and mental process elements of the abstract idea of estimating without significantly more.
Claims 7 and 17 further limit the abstract idea of performing mathematics without significantly more.
Claims 8-10 and 18-20 teach displaying data which does not integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), displaying analysis/results is considered extra solution activity. See MPEP 2106.05(g) “Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55”, see also MPEP 2106.05(h), As a whole the claim itself is analogous to the Electric Power Group decision in which it was determined that “ Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).”
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 1-4, 8, 11-14, and 18 rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US11078774B2) in view of Beck (US20210071508A1).
Regarding Claim 1, Gupta discloses a method of detecting wear rate of the equipment at a pumping system for a well (e.g. see [col 3 lines 13-15] “An aspect of the present disclosure provides a method for predicting and preventing electrical submersible pump (ESP) trip and failure events”), comprising:
receiving first measurement data associated with or from a first equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system (e.g. see [Col 3 lines 50-57] “in another aspect, the real-time data includes information of the plurality of decision variables across surface, wellbore, and downhole gauges, and on whether a trip or failure occurred. (15) In yet another aspect, the plurality of decision variables include at least one or more of the following: flowline pressure, wellhead pressure, wellhead temperature, motor current, pump intake pressure, pump discharge pressure”); and
estimating a wear rate associated with the first equipment based on an operational assessment of the downhole environment (e.g. see. [Fig. 16] and [Col 16 lines 44-59] “The bars in FIG. 16 represent the stable operating range for each of the 22 variables. Since there is significant difference between magnitudes of various variables, the operating value is expressed in percentage. The line graphs represent the reading of the various variables at the time of two separate trip events, Trip 1 and Trip 2. (134) This plot is useful to distinguish between variables operating within the stable range and those operating outside their stable ranges and show how far are some of the variables operating outside their stable ranges in real time for every time step. Controllable variables such as choke and motor current can be reset such that all the variables get adjusted to lie within the stable bands. This plot is significant in enabling effective decision making and can also be extended to monitor the behavior of variables in real-time operations.”).
Furthermore, in the same field of endeavor, Beck teaches estimating a wear rate associated with the first equipment based on an operational assessment of the downhole environment (e.g. see [0047-0048] “At 302, input data associated with operation of an ESP may be received by a deep learning model. The deep learning model may receive the input data via one or more of the communication network and/or sensors. The input data may take various forms including one or more of operating conditions, application input, supervisory input, model inputs, a goal set, and a classification state.[0048] The deep learning model models flows and geologic properties of a specific well/reservoir and/or determines operating parameters or operating conditions for a specific type ESP. At 304, the deep learning model may determine an output associated with control of the ESP based on the one or more input data associated with meeting fluid production goals of the wellbore and well system, with minimal downtime due to failure of an ESP. The output may take the form of one or more of operating parameters, operating conditions, and/or a classification state of an ESP.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the wear detection of Gupta with that of Beck for the purpose of determining the state of downhole pumps with the advantage of knowing which part of the pump specifically has had a change in performance.
Regarding Claim 2, Gupta and Beck teach the limitations of Claim 1. Gupta further discloses wherein estimating the wear rate comprises analyzing previous operational data associated with the first equipment (e.g. see [Col. 4 lines 40-46] “The system includes at least one processor used to control a software algorithm to: collect data, in real-time, from a well and an ESP positioned in a well via a plurality of sensors, manipulate historical data to determine and evaluate a plurality of decision variables, determining a stable operating range for each of the plurality of decision variables”).
Regarding Claim 3, Gupta and Beck teach the limitations of Claim 1. Gupta further discloses wherein the wear rate comprises a plurality of wear rates associated with the first equipment, and wherein a machine learning model is configured to generate the wear rate based at least in part on the first measurement data (e.g. see [Col 11 line 65-Col 12 line 5] “From the real-time data, a stable time period is identified when no trip or failure event is observed and all the 22 variables are operating at a stable value. FIG. 6 is an example plot of four such variables operating at a stable value for the entire time range. This data set matrix of t time steps or batches and p variables (22) is chosen as the X matrix and normalized and fed into the PCA model. (92) The second step is running a robust PCA model.”).
Regarding Claim 4, Gupta and Beck teach the limitations of Claim 3. Gupta further discloses receiving at least one forecasted parameter associated with future operation of the first equipment (e.g. see [Col 14 lines 61-63] “The predictive model is enhanced to determine the contribution of each of the 22 variables towards an abnormal impending event”).
Gupta does not explicitly disclose predicting an estimated extraction schedule based on an estimated wear rate associated with the at least one forecasted parameter. In the same field of endeavor, Beck teaches predicting an estimated extraction schedule based on an estimated wear rate associated with the at least one forecasted parameter (e.g. see [0050] “For example, if the operating condition output is fluid production of reservoir which does not meet production goals in a month, the operating condition output may be used to control the ESPs to increase pumping to meet the production goals.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the forecasted parameter of Gupta with the estimated extraction schedule of Beck for the purpose of detecting wear in well equipment with the advantage of determining how the equipment wear impacts overall production rates.
Regarding Claim 8, Gupta and Beck teach the limitations of Claim 1. Gupta further discloses displaying information related to a lifecycle of the first equipment to an operator of the well based on the wear rate (e.g. see [Col 18 lines 44-53] “This system can give the operator a medium to early detect deviation of ESP behavior from normal, integrate capability, and allow ample time for inspection and action that could mitigate or altogether avoid anomalies. If the predictive KPI shows a trend towards an impending failure, the system would generate an alarm and alert field personnel, production engineers, and data scientist via email or a mobile device, at any time of day. The engineering team can then quickly diagnose the issue and take appropriate corrective actions”).
Regarding Claim 11, Gupta discloses a system for detecting equipment wear at a pumping system for a well (e.g. see [col 3 lines 20-23] “More particularly, the present disclosure relates to a system and method for detecting, diagnosing, and correcting trips or failures of electrical submersible pumps.”), comprising:
a storage configured to store instructions and a processor configured to execute the instructions (e.g. see [Col 4 lines 28-29] “The system includes at least one processor used to control a software algorithm”)and cause the processor to:
receive first measurement data associated with or from a first equipment submersed into a downhole environment during a portion of a current operation for extracting materials from the downhole environment at a pumping system (e.g. see [Col 3 lines 50-57] “in another aspect, the real-time data includes information of the plurality of decision variables across surface, wellbore, and downhole gauges, and on whether a trip or failure occurred. (15) In yet another aspect, the plurality of decision variables include at least one or more of the following: flowline pressure, wellhead pressure, wellhead temperature, motor current, pump intake pressure, pump discharge pressure”); and
estimate a wear rate associated with the first equipment based on an operational assessment of the downhole environment (e.g. see. [Fig. 16] and [Col 16 lines 44-59] “The bars in FIG. 16 represent the stable operating range for each of the 22 variables. Since there is significant difference between magnitudes of various variables, the operating value is expressed in percentage. The line graphs represent the reading of the various variables at the time of two separate trip events, Trip 1 and Trip 2. (134) This plot is useful to distinguish between variables operating within the stable range and those operating outside their stable ranges and show how far are some of the variables operating outside their stable ranges in real time for every time step. Controllable variables such as choke and motor current can be reset such that all the variables get adjusted to lie within the stable bands. This plot is significant in enabling effective decision making and can also be extended to monitor the behavior of variables in real-time operations.”).
Furthermore, in the same field of endeavor, Beck teaches a storage configured to store instructions and a processor configured to execute the instructions e.g. see [0028] “One or more of the motor controller 104 and centralized computer system 112 have a respective processor 122, 126, memory 124, 128, and deep learning model 134, 136”) to cause the processor to:
estimate a wear rate associated with the first equipment based on an operational assessment of the downhole environment (e.g. see [0047-0048] “At 302, input data associated with operation of an ESP may be received by a deep learning model. The deep learning model may receive the input data via one or more of the communication network and/or sensors. The input data may take various forms including one or more of operating conditions, application input, supervisory input, model inputs, a goal set, and a classification state.[0048] The deep learning model models flows and geologic properties of a specific well/reservoir and/or determines operating parameters or operating conditions for a specific type ESP. At 304, the deep learning model may determine an output associated with control of the ESP based on the one or more input data associated with meeting fluid production goals of the wellbore and well system, with minimal downtime due to failure of an ESP. The output may take the form of one or more of operating parameters, operating conditions, and/or a classification state of an ESP.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the wear detection of Gupta with that of Beck for the purpose of determining the state of downhole pumps with the advantage of knowing which part of the pump specifically has had a change in performance.
Regarding Claim 12, Gupta and Beck teach the limitations of Claim 11. Gupta further discloses wherein estimating the wear rate comprises analyzing previous operational data associated with the first equipment (e.g. see [Col. 4 lines 40-46] “The system includes at least one processor used to control a software algorithm to: collect data, in real-time, from a well and an ESP positioned in a well via a plurality of sensors, manipulate historical data to determine and evaluate a plurality of decision variables, determining a stable operating range for each of the plurality of decision variables”).
Regarding Claim 13, Gupta and Beck teach the limitations of Claim 11. Gupta further discloses wherein the wear rate comprises a plurality of wear rates associated with the first equipment, and wherein a machine learning model is configured to generate the wear rate based at least in part on the first measurement data (e.g. see [Col 11 line 65-Col 12 line 5] “From the real-time data, a stable time period is identified when no trip or failure event is observed and all the 22 variables are operating at a stable value. FIG. 6 is an example plot of four such variables operating at a stable value for the entire time range. This data set matrix of t time steps or batches and p variables (22) is chosen as the X matrix and normalized and fed into the PCA model. (92) The second step is running a robust PCA model.”).
Regarding Claim 14, Gupta and Beck teach the limitations of Claim 13. Gupta further discloses receive at least one forecasted parameter associated with future operation of the first equipment (e.g. see [Col 14 lines 61-63] “The predictive model is enhanced to determine the contribution of each of the 22 variables towards an abnormal impending event”).
Gupta does not explicitly disclose predict an estimated extraction schedule based on an estimated wear rate associated with the at least one forecasted parameter. In the same field of endeavor, Beck teaches predict an estimated extraction schedule based on an estimated wear rate associated with the at least one forecasted parameter (e.g. see [0050] “For example, if the operating condition output is fluid production of reservoir which does not meet production goals in a month, the operating condition output may be used to control the ESPs to increase pumping to meet the production goals.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the forecasted parameter of Gupta with the estimated extraction schedule of Beck for the purpose of detecting wear in well equipment with the advantage of determining how the equipment wear impacts overall production rates.
Regarding Claim 18, Gupta and Beck teach the limitations of Claim 11. Gupta further discloses display information related to a lifecycle of the first equipment to an operator of the well based on the wear rate (e.g. see [Col 18 lines 44-53] “This system can give the operator a medium to early detect deviation of ESP behavior from normal, integrate capability, and allow ample time for inspection and action that could mitigate or altogether avoid anomalies. If the predictive KPI shows a trend towards an impending failure, the system would generate an alarm and alert field personnel, production engineers, and data scientist via email or a mobile device, at any time of day. The engineering team can then quickly diagnose the issue and take appropriate corrective actions”).
Claims 5 and 15 rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US11078774B2) in view of Beck (US20210071508A1) and Eslinger (US10753192B2).
Regarding Claim 5, Gupta and Beck teach the limitations of Claim 4. Gupta does not explicitly disclose wherein the estimated extraction schedule identifies an estimated failure date of the first equipment.
In the same field of endeavor, Beck teaches wherein the estimated extraction schedule identifies and estimated failure date of the first equipment (e.g. see [0051] “The deep learning model may output a prediction of an operating condition at some future time. To illustrate, the deep learning model may output an operating condition such as flow rate or gas lock an hour in the future or a day in the future”).
Also in the same field of endeavor, Eslinger also teaches wherein the estimated extraction schedule identifies an estimated failure date of the first equipment (e.g. see [Col 8 lines 56-59] “For example, the methodologies may be used to predict not simply imminent potential failure but also the time to failure throughout the life of the pumping system”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the extraction schedule of Gupta as modified by Beck with the estimated failure date of Beck and Eslinger for the purpose of detecting the wear of a downhole pump with the advantage of additional information to allow a user to judge the urgency of any system deterioration.
Regarding Claim 15, Gupta and Beck teach the limitations of Claim 14. Gupta does not explicitly disclose wherein the estimated extraction schedule identifies an estimated failure date of the first equipment.
In the same field of endeavor, Beck teaches wherein the estimated extraction schedule identifies and estimated failure date of the first equipment (e.g. see [0051] “The deep learning model may output a prediction of an operating condition at some future time. To illustrate, the deep learning model may output an operating condition such as flow rate or gas lock an hour in the future or a day in the future”).
Also in the same field of endeavor, Eslinger also teaches wherein the estimated extraction schedule identifies an estimated failure date of the first equipment (e.g. see [Col 8 lines 56-59] “For example, the methodologies may be used to predict not simply imminent potential failure but also the time to failure throughout the life of the pumping system”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the extraction schedule of Gupta as modified by Beck with the estimated failure date of Beck and Eslinger for the purpose of detecting the wear of a downhole pump with the advantage of additional information to allow a user to judge the urgency of any system deterioration.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US11078774B2) in view of Beck (US20210071508A1) and Chung (US20210224669 A1).
Regarding Claim 6, Gupta and Beck teach the limitations of Claim 4. Gupta as modified by Beck does not explicitly disclose wherein the estimated extraction schedule includes a confidence level of the estimated extraction schedule.
In the same field of endeavor, Chung teaches wherein the estimated extraction schedule includes a confidence level of the estimated extraction schedule (e.g. see [0046] “Thus, the accuracy metric may be used to validate or provide a confidence level in the predicted production data provided by the production prediction engine 106”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the extraction schedule of Gupta as modified by Beck with the confidence level of Chung for the purpose of determining the future production of the well with the advantage of an additional statistical metric to ensure the accuracy of the production determination.
Regarding Claim 16, Gupta and Beck teach the limitations of Claim 14. Gupta as modified by Beck does not explicitly disclose wherein the estimated extraction schedule includes a confidence level of the estimated extraction schedule.
In the same field of endeavor, Chung teaches wherein the estimated extraction schedule includes a confidence level of the estimated extraction schedule (e.g. see [0046] “Thus, the accuracy metric may be used to validate or provide a confidence level in the predicted production data provided by the production prediction engine 106”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the extraction schedule of Gupta as modified by Beck with the confidence level of Chung for the purpose of determining the future production of the well with the advantage of an additional statistical metric to ensure the accuracy of the production determination.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US11078774B2) in view of Beck (US20210071508A1) and in further view of Hande (US11480952 B2).
Regarding Claim 7, Gupta and Beck teach the limitations of Claim 3. Gupta as modified by Beck does not explicitly disclose determine a mean time to failure associated with the first equipment based on the plurality of wear rates and a statistical model.
In the same field of endeavor, Hande teaches determine a mean time to failure associated with the first equipment based on the plurality of wear rates and a statistical model (e.g. see [Col 21 lines 62-64] “Further, the reliability based mean remaining life is determined at act 512.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the wear rate and model of Gupta as modified by Beck with the mean time to failure of Hande for the purpose of determining the deterioration of well equipment with the advantage of additional information to allow a user to make an informed choice on what remedy the system needs.
Regarding Claim 17, Gupta and Beck teach the limitations of Claim 13. Gupta as modified by Beck does not explicitly disclose determine a mean time to failure associated with the first equipment based on the plurality of wear rates and a statistical model.
In the same field of endeavor, Hande teaches determine a mean time to failure associated with the first equipment based on the plurality of wear rates and a statistical model (e.g. see [Col 21 lines 62-64] “Further, the reliability based mean remaining life is determined at act 512.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the wear rate and model of Gupta as modified by Beck with the mean time to failure of Hande for the purpose of determining the deterioration of well equipment with the advantage of additional information to allow a user to make an informed choice on what remedy the system needs.
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US11078774B2) in view of Beck (US20210071508A1) and in further view of Kanfar (US20240093596A1).
Regarding Claim 9, Gupta and Beck teach the limitations of Claim 8. While Gupta teaches a production curve (e.g. see [Col 10 lines 33-36]), Gupta as modified by Beck does not explicitly disclose displaying a performance curve corresponding to real-time predictions of the first equipment; and in response to an input related to a change in the performance curve, mapping the input to modify the performance curve.
In the same field of endeavor, Kanfar teaches displaying a performance curve corresponding to real-time predictions of the first equipment (e.g. see [0009] “in one or more implementations, at least one computing device generates a pump performance curve for each of the plurality of electrical submersible pumps. Further, the at least one computing device applies at least some of the information provided by sensors and the information associated with the respective model of each of the plurality of electrical submersible pumps to the pump performance curve to estimate the second gross rate of each of the plurality of wells”); and
in response to an input related to a change in the performance curve, mapping the input to modify the performance curve (e.g. see [0027] ‘Further, specific equations, formulas, and fitting for calculating the pump performance curve associated with an electrical submersible pump and using the real time data are provided to determine well gross rate accurately, and for validating the determined gross rate”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the real time predictions of the first equipment with the performance curves of Kanfar for the purpose of modeling the predicted performance of the equipment with the advantage of a graphical display.
Regarding Claim 10, Gupta, Beck, and Kanfar teach the limitations of Claim 9. Gupta does not explicitly disclose displaying a predicted material extraction schedule based on the input to modify the performance curve. In the same field of endeavor, Kanfar teaches displaying (e.g. see [0040] “FIGS. 4A and 4B are example screen displays that are included in a user interface 400, in accordance with an implementation of the present disclosure. User interface 400 includes collections of graphical screens controls and displays that enable a user to view and interact within information representing the compliance of various rates”) predicted material extraction schedule based on the input to modify the performance curve. (e.g. see [0028] “Once the data are inserted into applied in the machine learning application, gross rate can be predicted and, thereafter, compared to available and actual existing rates”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the real time predictions of the first equipment with the performance curves of Kanfar for the purpose of modeling the predicted performance of the equipment with the advantage of a graphical display.
Regarding Claim 19, Gupta and Beck teach the limitations of Claim 18. While Gupta teaches a production curve (e.g. see [Col 10 lines 33-36]), Gupta as modified by Beck does not explicitly disclose display a performance curve corresponding to real-time predictions of the first equipment; and in response to an input related to a change in the performance curve, map the input to modify the performance curve.
In the same field of endeavor, Kanfar teaches display a performance curve corresponding to real-time predictions of the first equipment (e.g. see [0009] “in one or more implementations, at least one computing device generates a pump performance curve for each of the plurality of electrical submersible pumps. Further, the at least one computing device applies at least some of the information provided by sensors and the information associated with the respective model of each of the plurality of electrical submersible pumps to the pump performance curve to estimate the second gross rate of each of the plurality of wells”); and
in response to an input related to a change in the performance curve, map the input to modify the performance curve (e.g. see [0027] ‘Further, specific equations, formulas, and fitting for calculating the pump performance curve associated with an electrical submersible pump and using the real time data are provided to determine well gross rate accurately, and for validating the determined gross rate”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the real time predictions of the first equipment with the performance curves of Kanfar for the purpose of modeling the predicted performance of the equipment with the advantage of a graphical display.
Regarding Claim 20, Gupta, Beck, and Kanfar teach the limitations of Claim 19. Gupta does not explicitly disclose display a predicted material extraction schedule based on the input to modify the performance curve. In the same field of endeavor, Kanfar teaches display (e.g. see [0040] “FIGS. 4A and 4B are example screen displays that are included in a user interface 400, in accordance with an implementation of the present disclosure. User interface 400 includes collections of graphical screens controls and displays that enable a user to view and interact within information representing the compliance of various rates”) predicted material extraction schedule based on the input to modify the performance curve. (e.g. see [0028] “Once the data are inserted into applied in the machine learning application, gross rate can be predicted and, thereafter, compared to available and actual existing rates”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the real time predictions of the first equipment with the performance curves of Kanfar for the purpose of modeling the predicted performance of the equipment with the advantage of a graphical display.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20230114088A1 teaches predicting an estimated extraction schedule (e.g. see [Fig. 4] and [0049] “In addition, FIG. 4 shows a required target graph 402 and a predicted target graph 404. The target graph 402 illustrates the expected results for a target criterion, such as maximized oil recovery (i.e. estimated extraction schedule), minimized water cut, or maximized NPV. The predicted target graph 404 illustrates the results of the MPC operation. In some instances, the predictions should converge to expected values.”) based on an estimated wear rate associated with the at least one forecasted parameter (e.g. see [Fig. 4] and [0047] “each control step t, a Model Predictive Control (MPC) controller measures the current state of the system, X(t). The MPC controller is an advanced mathematical method of system optimization, which is used to control a process while satisfying a set of constraints. In this example, the control is performed by the control vector, and the constraints are defined by a data-driven model. Then, to predict the parameter value for the particular time t+1, the production control system 214 uses the data-driven model 208 to derive, at time t, an a priori state estimate for the next time step, t+1,”).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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