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 .
DETAILED ACTION
1. This communication is a first office action, non-final rejection on the merits. Claims 1-20, as originally filed, are currently pending and have been considered below.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 05/31/2024 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Form PTO-1449 is signed and attached hereto.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1:
Step Analysis 1: Statutory Category? Yes. The claim is a method claim.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim recites the limitation of determining predictive model of an interior of a pipe based on legacy data observations. This limitation, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “determining predictive model of an interior of a pipe based on legacy data observations,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “determining predictive model of an interior of a pipe based on legacy data observations” language, the claim encompasses a user simply determining predictive model of an interior of a pipe based on legacy data observations in his/her mind. The mere nominal recitation of a generic predictive model of an interior of a pip determining data observations does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental.
2A - Prong 2: Integrated into a Practical Application?
No. The claim recites additional elements: indicating a flow profile within the pipe; analyzing the flowline data using the predictive model; outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and rendering a representation of the data representing the change in the flow profile performs the additional determination step and representation of flow profile steps. The additional determination step and representation of flow profile steps are recited at a high level of generality (i.e., as a general means of gathering data for use in the representing step), and amounts to mere data gathering or manipulations, which is a form of insignificant extra-solution activity. The analyzing the flowline data using the predictive model; outputting, from the predictive model that performs the determination step is also recited at a high level of generality, and merely automates the determination step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (analyzing and representing flowline data using predictive model; outputting, from the predictive model).
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the analyzing predictive model and outputting, from the predictive model). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to the abstract idea.
2B: Claim provides an Inventive Concept?
No. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the determination step was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication that the driver circuit is anything other than a generic, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or determination of data over a driver circuit is a well understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the determining step is well-understood, routine, conventional activity is supported under Berkheimer.
For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
Similar analysis applied to claims 2-20.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
5. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
7. Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ghorayeb (US 20240126959 A1) (hereinafter Ghorayeb) in view of Hauge (US 20190169982 A1) (hereinafter Hauge).
Regarding claim 1, Ghorayeb discloses a method (para 38, FIG. 1 shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well) comprising:
building a predictive model of an interior of a pipe based on legacy data observations (para 48, framework provides a reservoir simulator for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 183, method 1160, prediction block 1172 implement one or more trained machine learning (ML) models (e.g., for prediction , classification, etc.));
receiving flowline data from a sensor indicating a flow profile within the pipe (para 172, automatically generating a list of potential compositional variation versus depth profiles from available sample, para 172, one or more potential profiles (e.g., for initial condition(s)) to generate simulation results);
analyzing the flowline data using the predictive model (para 87, nodal analysis to analyze pressure and temperature profiles, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows);
outputting, from the predictive model, data representing a change in the flow profile (para 172, clustering block 928 for clustering and implementing a trained ML model by identifying apparent compartments and assessing composition variability, profiles (e.g., for initial condition(s)) to generate simulation results; and an output block 960 for outputting at least a portion of the simulation results),
wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data (para 217, ECLIPSE reservoir simulator implement central differences for spatial approximation and forward differences, para 241, compositional variation classification and/or prediction, such a differences indicate different compartments).
Ghorayeb specifically fails to disclose rendering a representation of the data representing the change in the flow profile.
In analogous art, Hauge discloses rendering a representation of the data representing the change in the flow profile (para 105, render information based on an analysis, para 124, FIG. 5, model provide objects 582, act as a data source 584, provide for rendering 586 and provide for various user interfaces 588. rendering 586 provide a graphical environment in which applications can display their data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching automatically generating conditions based on sample information where such conditions utilized in simulating physical phenomena using reservoir model to generate simulation results disclosed by Ghorayeb to use simulated information from a model-based framework for fluid production network that includes boundary conditions based on measurement information as taught by Hauge to utilize nodal analysis to analyze a wellsite or junction of branches, pressure and temperature profile, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows [Hauge, para 068].
Regarding claim 2, Ghorayeb discloses the method of claim 1, wherein the flowline data is collected using a pressure transducer (para 79, sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production, para 112, flowlines to modify flowing characteristics like flow rate, pressure, composition and temperature).
Regarding claim 3, Ghorayeb discloses the method of claim 1, wherein the legacy data observations include data previously captured using at least one pressure transducer, a flowline geometry, or at least one fluid property and storing the legacy data observations in a database as a captured data set (para 82, data repository 352 may be a storage unit (e.g., database, collection of tables, or any other storage mechanism) for storing data, such as sensor data, aggregated oilfield data, or any other such type of data, para 178, building a calibrated EoS for each sample in a PVT samples database for a target reservoir or target reservoirs).
Regarding claim 4, Ghorayeb discloses the method of claim 3, wherein the predictive model is segmented based on the at least one pressure transducer, the flowline geometry, or the at least one fluid property (para 56, data processed and interpreted, to understand better composition, fluid content, extent and geometry of subsurface rocks, para 60, entity characterized by properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property, etc.)).
Regarding claim 5, Ghorayeb discloses the method of claim 1, further comprising: collecting at least one of an initial pressure, an MFR, and incremental MFR, a start time, an end time, a Halland's factor, a friction factor, a pipe diameter, an incremental distance, an incremental diameter, an incremental acoustic velocity, an incremental density, an incremental viscosity, an incremental simulated pressure, an incremental observed pressure, and an incremental calculated deposition (para 95, equation for pressure differential account for factors such as fluid potential energy (e.g., hydrostatic pressure), friction (e.g., shear stress between conduit wall and fluid), and acceleration (e.g., change in fluid velocity), para 97, equation for pressure differential account for factors such as fluid potential energy (e.g., hydrostatic pressure), friction (e.g., shear stress between conduit wall and fluid), and acceleration, para 98, pressure differential (e.g., ΔP) rearranged to solve for flow rate (e.g., Q), where equation may include the Reynolds number (e.g., Re, a dimensionless ratio of inertial to viscous forces), one or more friction factors (e.g., depend on flow regime), etc).
Regarding claim 6, Ghorayeb discloses the method of claim 1, further comprising: initiating a pressure pulse within a flowline of the pipe; and measuring the pressure pulse with the sensor (para 50, provide simulation results such as multiphase flow results (e.g., from reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc., para 89, calculate flow and pressure drop in a wellbore or a single flowline branch given various inputs).
Regarding claim 7, Ghorayeb discloses the method of claim 6, wherein the pressure pulse is created through injecting mass, removing mass, or actuating a valve (para 37, geologic environment 150 outfitted with variety of sensors, detectors, actuators, etc., para 150, reception block 674 for receiving well mass flow information, which involve, reconstituting mass flow rate or range of mass flow rates for wells, which give mass flow rate of fluid components such that a result be ranges of mass flow rate).
Regarding claim 8, Ghorayeb discloses the method of claim 1, wherein the predictive model includes a linear or non-linear regression model (para 174, block 1030 for tuning (e.g., via regression, etc.) based on tuning parameters, para 207, regression utilized to fit an EoS using a fluid model, para 246, ML model for regression (prediction) and classification for determining age of abalone from physical details).
Regarding claim 9, Ghorayeb discloses The method of claim 8, wherein the predictive model is based at least on the following equations: y=β0+β1x1.Math.βrxr+ε.β0,β1,.Math.βr where x=x.sub.1, . . . , x.sub.r have a linear relationship, ε are regression coefficients, and & is random error (para 247, (ML) models, consider one or more of a support vector machine (SVM) model, regularization model (e.g., ridge regression, angle regression), a rule system model, a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, logistic regression, etc.), decision tree model (e.g., classification and regression tree, squares regression, component regression, least squares discriminant analysis, clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.).
Regarding claim 10, Ghorayeb discloses the method of claim 1 wherein the predictive model is run on a programmable logical controller in communication with the sensor (para 48, accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 171, trained ML model or models can be utilized to make predictions, para 55, application programming interface for rendering 2D and 3D vector graphics where API used to interact with a graphics processing unit, para 249, software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications).
Regarding claim 11, Ghorayeb discloses a system (para 38, FIG. 1 shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well) comprising:
a storage configured to store instructions; a processor configured to execute the instructions and cause the processor to: build a predictive model of an interior of a pipe based on legacy data observations (para 08, One or more computer-readable storage media can include processor-executable instructions where the processor-executable instructions include instructions to instruct a computing system, para 48, framework provides a reservoir simulator for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 183, method 1160, prediction block 1172 implement one or more trained machine learning (ML) models (e.g., for prediction , classification, etc.));
receive flowline data from a sensor indicating a flow profile within the pipe (para 172, automatically generating a list of potential compositional variation versus depth profiles from available sample, para 172, one or more potential profiles (e.g., for initial condition(s)) to generate simulation results);
analyze the flowline data using the predictive model (para 87, nodal analysis to analyze pressure and temperature profiles, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows);
outputting, from the predictive model, data representing a change in the flow profile (para 172, clustering block 928 for clustering and implementing a trained ML model by identifying apparent compartments and assessing composition variability, profiles (e.g., for initial condition(s)) to generate simulation results; and an output block 960 for outputting at least a portion of the simulation results),
wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data (para 217, reservoir simulator implement central differences for spatial approximation and forward differences, para 241, compositional variation classification or prediction, such a differences indicate different compartments).
Ghorayeb specifically fails to disclose render a representation of the data representing the change in the flow profile.
In analogous art, Hauge discloses render a representation of the data representing the change in the flow profile (para 105, render information based on an analysis, para 124, FIG. 5, model provide objects 582, act as a data source 584, provide for rendering 586 and provide for various user interfaces 588. rendering 586 provide a graphical environment in which applications can display their data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching automatically generating conditions based on sample information where such conditions utilized in simulating physical phenomena using reservoir model to generate simulation results disclosed by Ghorayeb to use simulated information from a model-based framework for fluid production network that includes boundary conditions based on measurement information as taught by Hauge to utilize nodal analysis to analyze a wellsite or junction of branches, pressure and temperature profile, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows [Hauge, para 068].
Regarding claim 12, Ghorayeb discloses the system of claim 11, wherein the flowline data is collected using a pressure transducer (para 79, sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production, para 112, flowlines to modify flowing characteristics like flow rate, pressure, composition and temperature).
Regarding claim 13, Ghorayeb discloses the system of claim 11, wherein the legacy data observations include data previously captured using at least one pressure transducer, a flowline geometry, or at least one fluid property and storing the legacy data observations in a database as a captured data set (para 82, data repository 352 may be a storage unit (e.g., database, collection of tables, or any other storage mechanism) for storing data, such as sensor data, aggregated oilfield data, or any other such type of data, para 178, building a calibrated EoS for each sample in a PVT samples database for a target reservoir or target reservoirs).
Regarding claim 14, Ghorayeb discloses the system of claim 11, wherein the processor is configured to execute the instructions and cause the processor to: initiate a pressure pulse within a flowline; and measure the pressure pulse with the sensor (para 50, provide simulation results such as multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc., para 89, calculate flow and pressure drop in a wellbore or flowline branch given various inputs).
Regarding claim 15, Ghorayeb discloses the system of claim 11, wherein the predictive model includes a linear or non-linear regression model (para 174, block 1030 for tuning (e.g., via regression, etc.) based on tuning parameters, para 207, regression utilized to fit an EoS using a fluid model, para 246, ML model for regression (prediction) and classification for determining age of abalone from physical details).
Regarding claim 16, Ghorayeb discloses the system of claim 11, wherein the predictive model is run on a programmable logical controller in communication with the sensor (para 48, accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 171, trained ML model or models can be utilized to make prediction, para 55, application programming interface for rendering 2D and 3D vector graphics where API used to interact with a graphics processing unit, para 249, software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications).
Regarding claim 17, Ghorayeb discloses a non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system (para 08, One or more computer-readable storage media can include processor-executable instructions where the processor-executable instructions include instructions to instruct a computing system, para 48, framework provides a reservoir simulator for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 183, method 1160, prediction block 1172 implement one or more trained machine learning (ML) models (e.g., for prediction , classification, etc.)), cause the computing system to:
build a predictive model of an interior of a pipe based on legacy data observations (para 48, framework provides a reservoir simulator for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes, para 183, method 1160, prediction block 1172 implement one or more trained machine learning (ML) models (e.g., for prediction , classification, etc.));
receive flowline data from a sensor indicating a flow profile within the pipe (para 172, automatically generating a list of potential compositional variation versus depth profiles from available sample, para 172, one or more potential profiles (e.g., for initial condition(s)) to generate simulation results);
analyze the flowline data using the predictive model (para 87, nodal analysis to analyze pressure and temperature profiles, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows);
outputting, from the predictive model, data representing a change in the flow profile (para 172, clustering block 928 for clustering and implementing a trained ML model by identifying apparent compartments and assessing composition variability, profiles (e.g., for initial condition(s)) to generate simulation results; and an output block 960 for outputting at least a portion of the simulation results),
wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data (para 217, ECLIPSE reservoir simulator implement central differences for spatial approximation and forward differences, para 241, compositional variation classification and/or prediction, such a differences indicate different compartments).
Ghorayeb specifically fails to disclose render a representation of the data representing the change in the flow profile.
In analogous art, Hauge discloses render a representation of the data representing the change in the flow profile (para 105, render information based on an analysis, para 124, FIG. 5, model provide objects 582, act as a data source 584, provide for rendering 586 and provide for various user interfaces 588. rendering 586 provide a graphical environment in which applications can display their data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching automatically generating conditions based on sample information where such conditions utilized in simulating physical phenomena using reservoir model to generate simulation results disclosed by Ghorayeb to use simulated information from a model-based framework for fluid production network that includes boundary conditions based on measurement information as taught by Hauge to utilize nodal analysis to analyze a wellsite or junction of branches, pressure and temperature profile, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows [Hauge, para 068].
Regarding claim 18, Ghorayeb discloses the computer readable medium of claim 17, the flowline data is collected using a pressure transducer (para 79, sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production, para 112, flowlines to modify flowing characteristics like flow rate, pressure, composition and temperature).
Regarding claim 19, Ghorayeb discloses the computer readable medium of claim 17, wherein the computer readable medium further comprises instructions that, when executed by the computing system, cause the computing system to: initiate a pressure pulse within a flowline; and measure the pressure pulse with the sensor (para 50, provide simulation results such as multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc., para 89, calculate flow and pressure drop in a wellbore flowline branch given various inputs).
Regarding claim 20, Ghorayeb discloses the computer readable medium of claim 17, the predictive model includes a linear or non-linear regression model (para 174, block 1030 for tuning (e.g., via regression, etc.) based on tuning parameters, para 207, regression utilized to fit EoS using fluid model, para 246, ML model for regression (prediction) and classification for determining age of abalone from physical details).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mirza Alam whose telephone number is (469) 295-9286. The examiner can be reached on Monday-Thursday 7:30AM-6:00PM (EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached on 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRZA F ALAM/Primary Examiner, Art Unit 2688
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