DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/25/2024 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Claim Objections
Claims 7 and 9 are objected to because of the following informalities:
In claim 7 line 1, “wherein calculating the performance curve” should read “wherein determining the performance curve” in accordance with apparent antecedent relation to the manner in which the “performance curve” is generated in claim 1.
In claim 9 line 1, “the initial performance curve of the first equipment” lacks sufficient antecedent basis. In one option, claim 9 may be amended to depend from claim 8, which recites “an initial performance curve,” and claim 8 amended to recharacterize “an initial performance curve” as “an initial performance curve of the first equipment.” Alternatively, claim 9 may be amended to replace “the initial performance curve of the first equipment” with “an initial performance curve of the first equipment.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Independent claim 11, substantially representative also of claim 1, recites:
“[a] system for providing performance feedback of an extraction facility, comprising:
a storage configured to store instructions;
a processor configured to execute the instructions and cause the processor to:
receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment;
determine a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and
provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 11 recites a system and claim 1 recites a method and each therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 11 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and/or the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
The recited function
“determine a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity,” may be performed as mental processes.
Determining a performance curve based on the first measurement data, in which the performance curve identifies an extraction rate of the material with respect to efficiency and head capacity, may be performed via mental processes such as evaluation of the measurement data (possibly aided by pen-and-paper) and judgement in determining corresponding performance curve points.
The recited function “determine a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity” in claim 11 is further determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)) because as explained for example in Applicant’s specification, using measurement data to determine a performance curve that identifies an extraction rate with respect to efficiency and head capacity is fundamentally characterized by mathematical relations/calculations (polynomial/quadratic equations) and therefore constitutes mathematical relationships.
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 11 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including “a storage configured to store instructions,” “a processor configured to execute the instructions and cause the processor to” (execute functions including functions falling within the judicial exception), “receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment,” “provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted step or a device for implementing the highlighted steps such as a signal processing device or a generic computer. For example, storage configured to store instructions and a processor configured to execute the instructions for implementing the recited functions represent ordinary, high-level computer processing components for implementing the functions falling within the judicial exception, and therefore constitute insignificant extra solution activity that fails to integrate the judicial exception into a practical application. The function “provide the performance curve to an operator of the run” represents an ordinary, high-level data processing output function having no particularized functional relation to the steps falling within the judicial exception, and therefore represents extra solution activity failing to integrate the judicial exception into a practical application. The characterization “for real-time feedback associated with performance of equipment in the downhole environment” conveys an intended result/purpose that does not significantly characterize the structure/function of the system recited in claim 11 and therefore also fails to integrate the judicial exception into a practical application. Receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment represent high-level data collection and therefore constitute extra solution activity that fails to integrate the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements are configured and implemented in a conventional rather than a particularized manner of implementing monitoring and control for downhole pumps.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 11 does not include any such transformation or reduction. Instead, claim 11 as a whole entails receiving input information (measurement data from equipment), applying standard processing techniques (processor and associated instruction storage) to the information to determine performance curve information with the additional elements failing to provide a meaningful integration of the abstract idea in an application that transforms an article to a different state. Instead, the additional elements, individually and in combination, represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 11 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 11 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in claim 11 constitute extra solution activity and therefore do not result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Moricca (US 2014/0039836 A1) and Lu (CN112503000B), each of which teach substantially the same data processing configuration for monitoring and analyzing downhole pump operations.
As explained in the grounds for rejecting claim 11 under 102, Moricca teaches “a storage configured to store instructions,” “a processor configured to execute the instructions and cause the processor to” (execute functions including functions falling within the judicial exception), and “provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment,” as does Lu (page 4, Detailed Description, paragraphs beginning with “Among them, the SCADA” and “A Distributed Control system (DCS)” describing using a computer (inherently entails a processor for executing instructions stored in memory); and page 3, Disclosure of the Invention, paragraph beginning with “Optionally, the centrifugal pump energy management and control system further includes” describing display of real-time pump operations data).
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Independent claim 11 is therefore not patent eligible under 101.
Claim 1 includes substantially the same combination of elements as claim 11 that fall within the judicial exception and includes no further significant additional elements that either integrate the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception such that claim 1 is likewise not patent eligible under 101.
Claims 2-10 depending from claim 1, and claims 12-20 depending from claim 11 provide additional features/steps that are part of an expanded algorithm that includes the abstract idea of the respective independent claim (Step 2A, Prong One). None of dependent claims 2-10 and 12-20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to the independent claims and for the following reasons.
Claim 2, substantially representative also of claim 12, recites “predicting a second measurement from a second equipment submersed into the downhole environment during the run” and “wherein the performance curve is further based on the second measurement,” each of which falls within the mental processes exception because each may be performed via mental processes (e.g., evaluation and judgement). The feature of “using a first machine learning model” to predict the second measurement data represents conventional, routine data processing functionality (program instructions configured to implement machine learning) to implement the function falling within the judicial exception, and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claims 3 and 4, substantially representative of claims 13 and 14, respectively, recite training the first machine learning model using data that directly corresponds to the type of information that the model is used to eventually process following training. Therefore, the training data characterizations in claims 3-4 and 13-14 represent routine, conventional data processing techniques (training ML models) using conventionally selected training data, such that these elements constitute extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 5, substantially representative also of claim 15, recites “identify classifications of the downhole environment” and “predict the second measurement based on training data similar to the downhole environment” each of which falls within the mental processes exception because each may be performed via mental processes (e.g., evaluation and judgement). The use of a “multi-label classifier” as the machine learning model to perform the functions represents conventional, routine data processing functionality (program instructions configured to implement multi-classifier machine learning classification, which was known in the art prior to the effective filing date) to implement the function falling within the judicial exception, and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 6, substantially representative also of claim 16, recites “determining that a third equipment becomes unavailable during the run” and “predicting third measurement data associated with the third equipment during a time that the third equipment is unavailable,” each of which falls within the mental processes exception because each may be performed via mental processes (e.g., evaluation and judgement).
Claim 7, substantially representative also of claim 17, recites that “calculating the performance curve based on the first measurement data” includes “predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material” which falls within the mental processes exception because it may be performed via mental processes (e.g., evaluation and judgement). The element “predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material” is further found to fall within the mathematical relations subcategory of the mathematical concepts exception because as explained in Applicant’s specification the maximum and minimum values may be determined via statistical calculations, which are fundamentally characterized by mathematical relations/calculations. Claim 7 further recites that the minimum value, the maximum value, and the actual value of the efficiency “are provided to the operator,” which represents routine, conventional data processing activity having no particularized functional relation to the elements falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 8, substantially representative also of claim 18, further recites “before beginning of the run, an initial performance curve is generated,” which, similarly to the “determining a performance curve” element in claim 1 may be performed via mental processes and further falls within the mathematical concepts exception. Just as the initial performance curve may be generated via mental processes and/or via mathematical concepts, in like manner it may be changed. (Examiner notes that, as set forth in the grounds for rejecting claim 8 under 102, “the performance curve changes based on measurement data during the run,” conveys an intended result of unspecified activity that does not further limit the function of the recited method and is therefore not given patentable weight.
Claim 9, substantially representative also of claim 19, only characterizes the source/nature of the information used to generate the initial performance curve and therefore also falls within the same judicial exception.
Claim 10, substantially representative also of claim 20, further recites “estimating a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment,” which falls within the mental processes judicial exception because it may be performed via mental processes (e.g., evaluation of information such as downhole environment information and judgement to estimate a corresponding recommended flow rate with the intended purpose of minimizing equipment deterioration). The Examiner notes that, as explained in the grounds for rejecting claim 10 under 102 set forth below, “to minimize deterioration of the equipment” conveys an intended result/purpose that does not further limit the functions implemented by the recited method and is therefore not given patentable weight.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8, 10-11, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moricca (US 2014/0039836 A1).
As to claim 1, Moricca teaches “[a] method of providing performance feedback of an extraction facility (Abstract disclosing method for monitoring to optimize operations of ESP; FIG. 4A and 4B; method performed by system 300 in FIG. 3) comprising:
receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment (Abstract disclosing collecting measured data representative of state of ESP within a well; [0012] and [0014] monitoring ESP operations including collecting downhole measurement data; [0023] data acquisition system 310 acquired the measurement data);
determining a performance curve based on the first measurement data (Abstract performance curve for ESP generated using updated model that is based on the measured data; [0023] performance curve(s) generated based on nodal model, which is based on the measured data), the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity (FIG. 2E performance curve (as interpreted in view of Applicant’s specification to entail a collection of curves) relates liquid rate (extraction rate of ESP) to head in feet (capacity) and efficiency (ops range including best efficiency)); and
providing the performance curve to an operator of the run (FIG. 3 depicting graphical display that may be viewed by a user; [0023] data generated by matched model) for real-time feedback associated with performance of equipment in the downhole environment ([0023] ESP data may include real-time monitoring data such that the feedback provided by the displayed curved effectively constitutes real-time feedback).”
As to claim 8, Moricca teaches “[t]he method of claim 1, wherein, before beginning the run, an initial performance curve is generated (FIG. 4 blocks 410 (model is updated), 412 (corresponding performance curves generated that per block 414 constitute updated performance curves), and the performance curve changes based on measurement data during the run (FIG. 4 blocks 402, 404, 410 and 412; [0022] performance curves generated to correspond to analysis models; [0023] analysis models updated by on measured conditions (ESP data collected via data acquisition subsystem 310)).”
As to claim 10, Moricca teaches “[t]he method of claim 1, further comprising:
estimating a recommended flow rate for the material (FIG. 2E performance curves 244 including a Best Efficiency Curve including a liquid rate (bb/day) corresponding to the head value and table 246 providing user-selectable configuration including flow rates)to minimize deterioration of the equipment (Best Efficiency Curve in FIG. 2E conveys an optimal operating efficiency that would inherently minimize deterioration) based on the downhole environment (FIG. 2E performance curves including Best Efficiency Curve associate pump head (downhole operation parameter is part of downhole environment) with flow rate. Examiner notes that the correspondence/intersection of the flow rate with pump head constitutes interdependence of head/flow rate in terms of operation efficiency).”
As to claim 11, Moricca teaches “[a] system for providing performance feedback of an extraction facility (Abstract disclosing system for monitoring to optimize operations of ESP; FIG. 4A and 4B; FIG. 3 system 300), comprising:
a storage configured to store instructions (claim 13 system includes memory for storing ESP monitoring, diagnosing and optimizing software; FIG. 1 computer system 45 (inherently includes memory for storing executable instructions));
a processor configured to execute the instructions (claim 13 system includes a processor for executing instructions; FIG. 1 computer system 45 (inherently includes a processor for executing instructions)) and cause the processor to:
receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment (Abstract disclosing collecting measured data representative of state of ESP within a well; [0012] and [0014] monitoring ESP operations including collecting downhole measurement data; [0023] data acquisition system 310 acquired the measurement data);
determine a performance curve based on the first measurement data (Abstract performance curve for ESP generated using updated model that is based on the measured data; [0023] performance curve(s) generated based on nodal model, which is based on the measured data), the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity (FIG. 2E performance curve (as interpreted in view of Applicant’s specification to entail a collection of curves) relates liquid rate (extraction rate of ESP) to head in feet (capacity) and efficiency (ops range including best efficiency)); and
provide the performance curve to an operator of the run (FIG. 3 depicting graphical display that may be viewed by a user; [0023] data generated by matched model) for real-time feedback associated with performance of equipment in the downhole environment ([0023] ESP data may include real-time monitoring data such that the feedback provided by the displayed curved effectively constitutes real-time feedback).
As to claim 18, Moricca teaches “[t]he system of claim 11, wherein an initial performance curve is generated (FIG. 4 blocks 410 (model is updated), 412 (corresponding performance curves generated that per block 414 constitute updated performance curves), and the performance curve changes based on measurement data during the run (FIG. 4 blocks 402, 404, 410 and 412; [0022] performance curves generated to correspond to analysis models; [0023] analysis models updated by on measured conditions (ESP data collected via data acquisition subsystem 310)).”
The Examiner notes that while Moricca teaches “the performance curve changes based on measurement data during the run,” this feature conveys an intended result of unspecified activity that does not further limit the function of the recited method and is therefore not given patentable weight.
As to claim 20, Moricca teaches “[t]he system of claim 11, wherein the processor is configured to execute the instructions and cause the processor to:
estimate a recommended flow rate for the material (FIG. 2E performance curves 244 including a Best Efficiency Curve including a liquid rate (bb/day) corresponding to the head value and table 246 providing user-selectable configuration including flow rates)” “based on the downhole environment (FIG. 2E performance curves including Best Efficiency Curve associate pump head (downhole operation parameter is part of downhole environment) with flow rate. Examiner notes that the correspondence/intersection of the flow rate with pump head constitutes interdependence of head/flow rate in terms of operation efficiency).”
The Examiner notes that “to minimize deterioration of the equipment” conveys an intended result/purpose that does not further limit the function of the recited method and is therefore not given patentable weight.
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 2-4, 6, 12-14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Moricca in view of Jaaskelainen (US 2021/0123431 A1).
As to claim 2, Moricca teaches “[t]he method of claim 1, further comprising:”
[providing] “a second measurement from a second equipment submersed into the downhole environment during the run ([0014] measurement data may be collected using multiple different downhole instruments)” “wherein the performance curve is further based on the second measurement ([0023] performance curve(s) generated based on nodal model, which is based on the measured data that per [0014] may include data from multiple downhole instruments).”
Moricca discloses providing measurement data from multiple sensor instruments that are also used for generating the performance curve but does not disclose using machine learning for predicting (e.g., estimating what a measurement is expected to be) the second measurement data and therefore does not expressly teach “predicting” a second measurement value “by using a first machine learning model.”
Jaaskelainen discloses a method for monitoring and managing well operations including pump operations (Abstract) that includes monitoring by combining actual sensor measurement values and “synthetic” values that are values predicted (in terms of what an actual sensor value is expected to be) using a machine learning model ([0016] and [0033] AI synthetic data model generates unavailable sensor data with respect to pump operations; [0015] and [0030] AI model is trained (machine learning); claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of using a machine learning model to predict pump operation related measurements to the method taught by Moricca in which multiple sensor measurements are used for generating the performance curve such that in combination the method includes “predicting” a second measurement from a second equipment submersed into the downhole environment during the run “by using a first machine learning model.”
A motivation would have been to enable use of potentially useful sensor data that may not be otherwise available by actual sensor measurement as disclosed by Jaaskelainen ([0016]). Furthermore, such a combination would amount to applying a known design option for providing additional sensor data for pump monitoring to achieve predictable results.
As to claim 3, the combination of Moricca and Jaaskelainen teaches “[t]he method of claim 2,” and Jaaskelainen further teaches “wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells; [0028]-[0030] downhole sensor data used in training process).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of training the machine learning modeling using measurement data from the downhole environment to the method taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the method includes use of a machine learning model that has been trained based on measurement data from the downhole environment.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy of modeling output as suggested by Jaaskelainen.
As to claim 4, the combination of Moricca and Jaaskelainen teaches “[t]he method of claim 2,” and Jaaskelainen further teaches “wherein the first machine learning model is further trained based on downhole environments having different characteristics ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of training the machine learning modeling using measurement data different downhole environments (downhole environments having different characteristics) to the method taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the method includes use of a machine learning model that has been trained based on downhole environments having different characteristics.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy over a broader range of operational/environmental conditions, resulting a more robust modeling for complex and dynamic downhole operations and conditions as suggested by Jaaskelainen.
As to claim 6, Moricca teaches “[t]he method of claim 1,” but does not appear to teach
“determining that a third equipment becomes unavailable during the run; and
predicting third measurement data associated with the third equipment during a time that the third equipment is unavailable.”
Jaaskelainen discloses a method for monitoring and managing well operations including pump operations (Abstract) for circumstance in which it is determined that sensing data is unavailable during the monitoring operations ([0002] well may not be tooled with particular sensors that would be useful for AI processing; [0016] synthetic model used for circumstances in which certain sensors may be unavailable (Examiner note the unavailability of the sensor(s) is inherently entailed in the determination to configure a synthetic model to obtain corresponding “predicted” data). Jaaskelainen further teaches predicting additional measurement data associated with other (third) equipment during a time that the other equipment is unavailable by combining actual sensor measurement values and “synthetic” values that are values predicted (in terms of what an actual sensor value is expected to be) using a machine learning model ([0016] and [0033] AI synthetic data model generates unavailable sensor data with respect to pump operations; [0015] and [0030] AI model is trained (machine learning); claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of using a machine learning model to predict, in association with a determination that sensor equipment is/will be unavailable, pump operation related measurements to the method taught by Moricca in which multiple sensor measurements are used for generating the performance curve such that in combination the method includes determining that a third equipment becomes unavailable during the run and predicting an additional measurement from additional equipment submersed into the downhole environment during the run by using a first machine learning model.
A motivation would have been to enable use of potentially useful sensor data that may not be otherwise available by actual sensor measurement as disclosed by Jaaskelainen ([0016]). Furthermore, such a combination would amount to applying a known design option for providing additional sensor data for pump monitoring to achieve predictable results.
As to claim 12, Moricca teaches “[t]he system of claim 11, wherein the processor is configured to execute the instructions and cause the processor to:”
[provide] “a second measurement from a second equipment submersed into the downhole environment during the run ([0014] measurement data may be collected using multiple different downhole instruments)” “wherein the performance curve is further based on the second measurement ([0023] performance curve(s) generated based on nodal model, which is based on the measured data that per [0014] may include data from multiple downhole instruments).”
Moricca discloses providing measurement data from multiple sensor instruments that are also used for generating the performance curve but does not disclose using machine learning for predicting (e.g., estimating what a measurement is expected to be) the second measurement data and therefore does not expressly teach “predicting” a second measurement value “by using a first machine learning model.”
Jaaskelainen discloses a system/method for monitoring and managing well operations including pump operations (Abstract) that includes monitoring by combining actual sensor measurement values and “synthetic” values that are values predicted (in terms of what an actual sensor value is expected to be) using a machine learning model ([0016] and [0033] AI synthetic data model generates unavailable sensor data with respect to pump operations; [0015] and [0030] AI model is trained (machine learning); claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of using a machine learning model to predict pump operation related measurements to the system taught by Moricca in which multiple sensor measurements are used for generating the performance curve such that in combination the system is configured to “predict” a second measurement from a second equipment submersed into the downhole environment during the run “by using a first machine learning model.”
A motivation would have been to enable use of potentially useful sensor data that may not be otherwise available by actual sensor measurement as disclosed by Jaaskelainen ([0016]). Furthermore, such a combination would amount to applying a known design option for providing additional sensor data for pump monitoring to achieve predictable results.
As to claim 13, the combination of Moricca and Jaaskelainen teaches “[t]he system of claim 12,” and Jaaskelainen further teaches “wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells; [0028]-[0030] downhole sensor data used in training process).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of training the machine learning modeling using measurement data from the downhole environment to the system taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the system uses a machine learning model that has been trained based on measurement data from the downhole environment.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy of modeling output as suggested by Jaaskelainen.
As to claim 14, the combination of Moricca and Jaaskelainen teaches “[t]he system of claim 12,” and Jaaskelainen further teaches “wherein the first machine learning model is further trained based on downhole environments having different characteristics ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of training the machine learning modeling using measurement data different downhole environments (downhole environments having different characteristics) to the system taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the system includes uses a machine learning model that has been trained based on downhole environments having different characteristics.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy over a broader range of operational/environmental conditions, resulting a more robust modeling for complex and dynamic downhole operations and conditions as suggested by Jaaskelainen.
As to claim 16, Moricca teaches “[t]he system of claim 11,” but does not appear to teach that the processor is configured to execute instructions to
“determine that a third equipment becomes unavailable during the run; and
predict third measurement data associated with the third equipment during a time that the third equipment is unavailable.”
Jaaskelainen discloses a method for monitoring and managing well operations including pump operations (Abstract) for circumstance in which it is determined that sensing data is unavailable during the monitoring operations ([0002] well may not be tooled with particular sensors that would be useful for AI processing; [0016] synthetic model used for circumstances in which certain sensors may be unavailable (Examiner note the unavailability of the sensor(s) is inherently entailed in the determination to configure a synthetic model to obtain corresponding “predicted” data). Jaaskelainen further teaches predicting additional measurement data associated with other (third) equipment during a time that the other equipment is unavailable by combining actual sensor measurement values and “synthetic” values that are values predicted (in terms of what an actual sensor value is expected to be) using a machine learning model ([0016] and [0033] AI synthetic data model generates unavailable sensor data with respect to pump operations; [0015] and [0030] AI model is trained (machine learning); claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching of using a machine learning model to predict, in association with a determination that sensor equipment is/will be unavailable, pump operation related measurements to the system taught by Moricca in which multiple sensor measurements are used for generating the performance curve such that in combination the system is configured to determine that a third equipment becomes unavailable during the run and predict an additional measurement from additional equipment submersed into the downhole environment during the run by using a first machine learning model.
A motivation would have been to enable use of potentially useful sensor data that may not be otherwise available by actual sensor measurement as disclosed by Jaaskelainen ([0016]). Furthermore, such a combination would amount to applying a known design option for providing additional sensor data for pump monitoring to achieve predictable results.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Moricca in view of Jaaskelainen as applied to claims 2 and 12 above, and further in view of Zhou (US 2022/0188775 A1).
As to claim 5, the combination of Moricca and Jaaskelainen teaches “[t]he method of claim 2,” and Jaaskelainen further teaches that the machine learning model may be a label-classifier type model ([0052] modeled trained via supervised learning; claim 5) “configured to identify classifications of the downhole environment ([0015] synthesis model trained using downhole environment features such as downhole pressure, microseismic data, etc. that per [0052] are used in supervised learning and therefore are labelled for classification) and predict the second measurement based on training data similar to the downhole environment ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells; [0028]-[0030] downhole sensor data used in training process).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching that the machine learning model may be a label-classifier type model configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment to the method taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the method includes using a label-classifier type model configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy of modeling output as suggested by Jaaskelainen. The use of a supervised learning model (a label-classifier type model configured to identify classifications) as the particular type of model would amount to selecting a known design option for implementing machine learning modeling to ascertain predictive outputs based on input features to achieve predictable results.
Neither Moricca nor Jaaskelainen expressly teaches that the classifier model is a “multi-label classifier.”
Prior to the effective filing date, multi-label classifiers were a well-known type of learning classifier option applicable for a variety of applications including pump operational conditions predictions. For example, Zhou discloses a method/system for applying multi-label classification for pump management ([0001]) that includes using a multi-label classifier for determining downhole pump operational conditions (FIG. 2 output of model aggregator 206 used for multi-label classification; FIG. 5 model template 500 including dynamic inputs 108 (per [0038] may include sensor monitoring data) processed by aggregation model to provide multi-label outputs, [0046]; [0034]-[0035] model applied for predicting pump operating conditions (e.g., failure conditions)) for submersible pumps ([0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Zhou’s teaching of using a multi-label classifier for determining downhole pump operational conditions for predicting pump operating conditions to the method taught by Moricca as modified by Jaaskelainen to including using machine learning to provide additional performance curve information such that in combination the method includes implementing the classifier as a multi-label classifier.
The motivation would have been to leverage the more comprehensive labeling capability of a multi-label classifier to enable more accurate and precise feature labeling to ultimately enable more accurate and precise machine learning output as suggested by Zhou.
As to claim 15, the combination of Moricca and Jaaskelainen teaches “[t]he system of claim 12,” and Jaaskelainen further teaches that the machine learning model may be a label-classifier type model ([0052] modeled trained via supervised learning; claim 5) “configured to identify classifications of the downhole environment ([0015] synthesis model trained using downhole environment features such as downhole pressure, microseismic data, etc. that per [0052] are used in supervised learning and therefore are labelled for classification) and predict the second measurement based on training data similar to the downhole environment ([0015] synthesis model trained using historical downhole sensing data or from offset (other) wells; [0028]-[0030] downhole sensor data used in training process).”
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Jaaskelainen’s teaching that the machine learning model may be a label-classifier type model configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment to the system taught by Moricca as modified by Jaaskelainen that uses machine learning modeling to provide additional sensor data for generating the performance curve, such that in combination the system uses a label-classifier type model configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment.
The motivation would have been to select/utilize a model that is trained in a manner that accounts for downhole operations/conditions to optimize accuracy of modeling output as suggested by Jaaskelainen. The use of a supervised learning model (a label-classifier type model configured to identify classifications) as the particular type of model would amount to selecting a known design option for implementing machine learning modeling to ascertain predictive outputs based on input features to achieve predictable results.
Neither Moricca nor Jaaskelainen expressly teaches that the classifier model is a “multi-label classifier.”
Prior to the effective filing date, multi-label classifiers were a known type of learning classifier option applicable for a variety of applications including pump operational conditions predictions. For example, Zhou discloses a method/system for applying multi-label classification for pump management ([0001]) that includes using a multi-label classifier for determining downhole pump operational conditions (FIG. 2 output of model aggregator 206 used for multi-label classification; FIG. 5 model template 500 including dynamic inputs 108 (per [0038] may include sensor monitoring data) processed by aggregation model to provide multi-label outputs, [0046]; [0034]-[0035] model applied for predicting pump operating conditions (e.g., failure conditions)) for submersible pumps ([0036]).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Zhou’s teaching of using a multi-label classifier for determining downhole pump operational conditions for predicting pump operating conditions to the system taught by Moricca as modified by Jaaskelainen to including using machine learning to provide additional performance curve information such that in combination the system implements the classifier as a multi-label classifier.
The motivation would have been to leverage the more comprehensive labeling capability of a multi-label classifier to enable more accurate and precise feature labeling to ultimately enable more accurate and precise machine learning output as suggested by Zhou.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Moricca in view of Van Der Spek (EP 2440784 B1).
As to claim 7, Moricca teaches “[t]he method of claim 1,” but does not expressly teach “wherein calculating the performance curve based on the first measurement data comprises:
predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material,
wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.”
Van Der Spek discloses a method for monitoring pump performance for purposes of predicting needed maintenance ([0011]) in which calculation of performance curves for a particular pump includes predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment (pump) based on a flow rate of material (FIG. 15(b) depicting graphically displayed efficiency curve data in which minimum values (efficiency values on the lower edge of the contour of data points corresponding to respective values of flow coefficient), maximum values (efficiency values on the upper edge of the contour of data points corresponding to respective values of flow coefficient), and actual values (all values predicted/estimated via efficiency computations based on equation 2 (paragraph [0052]) and equation 5 (paragraph [0054] for “pump 1” are determined based on flow rate (flow coefficient in FIG. 15(b) as predicted/estimated using equation 2.). Van Der Spek further teaches “wherein the minimum value, the maximum value, and the actual value of the efficiency are provided as some form of displayed/displayable data output (graphical output in FIG. 15(b)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Van Der Spek’s teaching of generating pump performance curves to include predicting (via calculation of efficiencies based on flow parameters) maximum, minimum, and actual pump efficiencies based on pump flow rate to the method taught by Moricca such that in combination the method includes predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material.
The motivation would have been to provide efficiency data for a pump spanning a range accounting for potential variations operational conditions and outcomes to provide more comprehensive pump operational data to optimize performance analysis as suggested by Van Der Spek.
Regarding wherein the minimum value, the maximum value, and the actual value of the efficiency are provided “to the operator,” Moricca teaches providing various pump performance metrics including performance curve data to an operator (FIG. 2E depicting displayed performance curves including operational range in a user display, [0022] curves displayed to user; [0015] and [0019] user interfaces used by operators to monitor production equipment).
It would have been obvious to one of ordinary skill in the art before the effective filing date, in view of Moricca’s teaching of displaying various pump performance data including pump efficiency data to an operator and Van Der Spek’s teaching of a graphical presentation of the efficiency curve data including minimum, maximum, and actual values to have provided the minimum value, the maximum value, and the actual value of the efficiency are provided “to the operator.”
The motivation would have been to present additional pump performance data that as suggested by Van Der Spek is useful for evaluating pump performance to an operator such as to enable the operator to more effectively monitor and control pump operations.
As to claim 17, Moricca teaches “[t]he system of claim 11,” but does not expressly teach that the processor is configured to execute the instructions to
“predict a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material,
wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.”
Van Der Spek discloses a method for monitoring pump performance for purposes of predicting needed maintenance ([0011]) in which calculation of performance curves for a particular pump includes predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment (pump) based on a flow rate of material (FIG. 15(b) depicting graphically displayed efficiency curve data in which minimum values (efficiency values on the lower edge of the contour of data points corresponding to respective values of flow coefficient), maximum values (efficiency values on the upper edge of the contour of data points corresponding to respective values of flow coefficient), and actual values (all values predicted/estimated via efficiency computations based on equation 2 (paragraph [0052]) and equation 5 (paragraph [0054] for “pump 1” are determined based on flow rate (flow coefficient in FIG. 15(b) as predicted/estimated using equation 2.). Van Der Spek further teaches “wherein the minimum value, the maximum value, and the actual value of the efficiency are provided as some form of displayed/displayable data output (graphical output in FIG. 15(b)).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Van Der Spek’s teaching of generating pump performance curves to include predicting (via calculation of efficiencies based on flow parameters) maximum, minimum, and actual pump efficiencies based on pump flow rate to the system taught by Moricca such that in combination the system is configured to predict a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material.
The motivation would have been to provide efficiency data for a pump spanning a range accounting for potential variations operational conditions and outcomes to provide more comprehensive pump operational data to optimize performance analysis as suggested by Van Der Spek.
Regarding wherein the minimum value, the maximum value, and the actual value of the efficiency are provided “to the operator,” Moricca teaches providing various pump performance metrics including performance curve data to an operator (FIG. 2E depicting displayed performance curves including operational range in a user display, [0022] curves displayed to user; [0015] and [0019] user interfaces used by operators to monitor production equipment).
It would have been obvious to one of ordinary skill in the art before the effective filing date, in view of Moricca’s teaching of displaying various pump performance data including pump efficiency data to an operator and Van Der Spek’s teaching of a graphical presentation of the efficiency curve data including minimum, maximum, and actual values to have provided the minimum value, the maximum value, and the actual value of the efficiency are provided “to the operator.”
The motivation would have been to present additional pump performance data that as suggested by Van Der Spek is useful for evaluating pump performance to an operator such as to enable the operator to more effectively monitor and control pump operations.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Moricca in view of Fowler (US 2021/0017999 A1).
As to claim 9, Moricca teaches “[t]he method of claim 1,” but does not appear to teach initializing modeling using standardized performance testing and therefore does not teach “wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material.”
Fowler discloses a method for controlling/configuring pump operations (Abstract) by that includes using initial pump performance curves (FIG. 3 depicting manufacturer’s pump curves; FIG. 4 block 402, [0059]; FIG. 4 block 418 depicting generated curves based on processing of initial curve data (manufacturer specification curves per [0059]) input at block 402; FIG. 8 block 802 (receive specification curve), block 808 (receive sensor data using pump ops), block 810 (update model including, per [0093], adjusting corresponding performance curve according to the sensor data ) that is based on standardized testing/evaluation of the pump ([0055] manufacturer’s pump curves from a pump manufacturer’s specifications), and further that manufacturer’s specification curves may be based on performance testing using a calibration material ([0023] manufacturer specification curves may describe performance for just water).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Fowler’s teaching of using a manufacturer’s specification curve(s) as an initial curve for pump performance monitoring in which the specification curve is based on performance testing using a calibration material to the method taught by Moricca such that in combination the method uses an initial performance curve of the first equipment that is based on a standardized performance test of the first equipment using a calibration material.
Such a combination would amount to selecting a known design option for initializing performance curve monitoring of pump operations to achieve predictable results.
As to claim 19, Moricca teaches “[t]he system of claim 11,” but does not appear to teach initializing modeling using standardized performance testing and therefore does not teach “wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material.”
Fowler discloses a method for controlling/configuring pump operations (Abstract) by that includes using initial pump performance curves (FIG. 3 depicting manufacturer’s pump curves; FIG. 4 block 402, [0059]; FIG. 4 block 418 depicting generated curves based on processing of initial curve data (manufacturer specification curves per [0059]) input at block 402; FIG. 8 block 802 (receive specification curve), block 808 (receive sensor data using pump ops), block 810 (update model including, per [0093], adjusting corresponding performance curve according to the sensor data ) that is based on standardized testing/evaluation of the pump ([0055] manufacturer’s pump curves from a pump manufacturer’s specifications), and further that manufacturer’s specification curves may be based on performance testing using a calibration material ([0023] manufacturer specification curves may describe performance for just water).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Fowler’s teaching of using a manufacturer’s specification curve(s) as an initial curve for pump performance monitoring in which the specification curve is based on performance testing using a calibration material to the system taught by Moricca such that in combination the system uses an initial performance curve of the first equipment that is based on a standardized performance test of the first equipment using a calibration material.
Such a combination would amount to selecting a known design option for initializing performance curve monitoring of pump operations to achieve predictable results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857