Detailed Action
Status of Claims
This is the first office action on the merits. Claims 1-20 are currently pending and addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/26/2023 and 10/17/2024 has being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea.
Step 1 of the USPTO’s eligibility analysis entails 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.
Claims 1-2, 6, 9-10, 14, 17-18 are directed to a method (Process) and a system (machine or manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the 2019 Revised Patent SUBJECT Matter Eligibility Guidance is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception
The claim(s) recite(s) abstract limitations including:
Claim 1: predicting a value for the target function…; simulating the subterranean volume…
Claim 2: predicting a second value for the target function…; determining not to simulate the subterranean volume…
Claim 6: determining one or more statistical characteristics for values…
Claim 9: predicting a value for the target function…; simulating the subterranean volume…
Claim 10: predicting a second value for the target function…; determining not to simulate the subterranean volume…
Claim 14: determining one or more statistical characteristics for values…
Claim 17: predicting a value for the target function…; simulating the subterranean volume…
Claim 18: predicting a second value for the target function…; determining not to simulate the subterranean volume…
These limitations, as drafted, are abstract mental processes that, under the broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing elements and/or sensors does not take the claim out of the mental process grouping. Thus the claim recites an abstract idea.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claims 1, 13, 18 recites the additional element of:
Claim 1:
receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function are considered an insignificant extra solution activity;
based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
Claim 2:
based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
Claim 9:
receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function are considered an insignificant extra solution activity;
one or more processors, a memory system, based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
Claim 10:
based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
Claim 17:
receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function are considered an insignificant extra solution activity;
one or more processors, based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
Claim 18:
based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function are recited at a high level of generality and amount to no more than mere instructions to apply the exception.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
Claim 1:
As discussed above, receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function steps are considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition, and the type of data collected, these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
As discussed above, based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicting and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
Claim 2:
As discussed above, based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicating and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
Claim 9:
As discussed above, receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function steps are considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition, and the type of data collected, these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
As discussed above, based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicting and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
With respect to one or more processors, a memory system these elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than generic computing elements and are even shown as black boxes on the figures. (see MPEP2106.05(f)).
Claim 10:
As discussed above, based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicating and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
Claim 17:
As discussed above, receiving one or more input parameters and one or more simulation realizations representing the subterranean volume; modeling the one or more simulation realizations as a target function of the one or more input parameters; training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations; selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function steps are considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition, and the type of data collected, these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
As discussed above, based on a first candidate simulation or a first candidate output parameter of a simulation; using the first candidate simulation, the first candidate output parameter, or both which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicting and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
With respect to one or more processors, non-transitory computer readable medium these elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than generic computing elements and are even shown as black boxes on the figures. (see MPEP2106.05(f)).
Claim 18:
As discussed above, based on at least one of a second candidate simulation or a second candidate output parameter; using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function which are considered an insignificant extra solution activity is recited at a high level of generality and provides no reasonable assertation what constitutes “based on… and using…” and can conceivably cover every and any part of predicating and simulating and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
Therefore, the claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The various metrics of claims 3, 5, 7, 11, 13, 15, 19 further merely the recitation of the specific variables and data limitations are insufficient as “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from §101 undergirds the information-based category of abstract ideas," (See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claim 1, 9, 17 this recitation does not provide a practical application of the abstract idea, and is not significantly more.
The various metrics of claims 4, 8, 12, 16, 20 are considered an insignificant extra solution activity and recited at a high level of generality and provides no reasonable assertation what constitutes “using… and adjusting… based on…” and can conceivably cover every and any part of simulating and adjusting various parameters and as such, the foregoing additional element does not amount to more than a recitation of the words “apply it”.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang (US Pub No 20210326721).
Zhang discloses in claim 1. A method for simulating a subterranean volume, comprising:
receiving one or more input parameters (Zhang Fig 1; 110 Fig 2a; 202) and one or more simulation realizations (Zhang Fig 1; “synthetic logs” [0036] synthetically derived logs furthermore, Zhang Fig 2a; 204b prior models used for model training) representing the subterranean volume (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume);
modeling the one or more simulation realizations as a target function of the one or more input parameters (Zhang Fig 2a; 204a, 204b, 204c [0041] training data parameters are based on a variety of training inputs such as well logs and [0046] synthetic logs);
training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations (Zhang Fig 2a; 212 [0038] utilizing a machine learning trained on previous simulations and models);
predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation (Zhang [0042] machine learning model helps predict multiple log parameters);
selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function (Zhang Fig 4; 406 selecting a preferred model output [0074]); and
simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume).
Zhang discloses in claim 2. The method of claim 1, further comprising: predicting a second value for the target function based on at least one of a second candidate simulation or a second candidate output parameter (Zhang 0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model based on multiple simulations and or output parameters); and
determining not to simulate the subterranean volume using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function (Zhang [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, not the second, based on multiple simulations and or output parameters).
Zhang discloses in claim 3. The method of claim 1, wherein selecting the first candidate simulation, the first candidate output parameter, or both is based on the first candidate simulation or the first candidate output parameter minimizing the first value of the target function (Zhang Fig 2b 212 Fig 4; 406 selecting a preferred model output [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, based on multiple simulations and or output parameters).
Zhang discloses in claim 4. The method of claim 1, wherein simulating the subterranean volume comprises simulating the subterranean volume using an ensemble of different realizations including the selected first candidate simulation (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model. Furthermore Fig 5; 510 generating an earth model based on the simulation output from the first ensemble).
Zhang discloses in claim 5. The method of claim 1, wherein the first candidate simulation, the first candidate output parameter, or both are selected for simulating prior to simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model. Furthermore Fig 5; 510 generating an earth model based on the simulation output from the first ensemble).
Zhang discloses in claim 6. The method of claim 1, wherein predicting the first candidate output parameter comprises determining one or more statistical characteristics for values of the first candidate output parameter (Zhang [0026] model training utilizing “least mean square error” methodology).
Zhang discloses in claim 7. The method of claim 1, further comprising generating a visualization of the subterranean volume based on simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model and [0002] generating a visual model of the system).
Zhang discloses in claim 8. The method of claim 1, further comprising adjusting a weight of a mud in a well based at least in part on the simulating, wherein the simulating is configured to predict a pore pressure, a fracture gradient, or both in a rock formation (Zhang [0028] mud weight is used as an input for training the machine learning model [0033] modification of mud weights as an adjusted input parameter to the model [0030] the model output parameters include pore pressure attributes and fracture gradients).
Zhang discloses in claim 9. A computing system, comprising:
one or more processors (Zhang [0096] processor); and
a memory system (Zhang [0099] memory system for storing information and instructions) comprising one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors (Zhang [0099] memory system for storing information and instructions to be executed by the processor), cause the computing system to perform operations, the operations comprising:
receiving one or more input parameters (Zhang Fig 1; 110 Fig 2a; 202) and one or more simulation realizations (Zhang Fig 1; “synthetic logs” [0036] synthetically derived logs furthermore, Zhang Fig 2a; 204b prior models used for model training) representing the subterranean volume (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume);
modeling the one or more simulation realizations as a target function of the one or more input parameters (Zhang Fig 2a; 204a, 204b, 204c [0041] training data parameters are based on a variety of training inputs such as well logs and [0046] synthetic logs);
training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations (Zhang Fig 2a; 212 [0038] utilizing a machine learning trained on previous simulations and models);
predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation (Zhang [0042] machine learning model helps predict multiple log parameters);
selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function (Zhang Fig 4; 406 selecting a preferred model output [0074]); and
simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume).
Zhang discloses in claim 10. The computing system of claim 9, wherein the operations further comprise: predicting a second value for the target function based on at least one of a second candidate simulation or a second candidate output parameter (Zhang 0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model based on multiple simulations and or output parameters); and
determining not to simulate the subterranean volume using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function (Zhang [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, not the second, based on multiple simulations and or output parameters).
Zhang discloses in claim 11. The computing system of claim 9, wherein selecting the first candidate simulation, the first candidate output parameter, or both is based on the first candidate simulation or the first candidate output parameter minimizing the first value of the target function (Zhang Fig 2b 212 Fig 4; 406 selecting a preferred model output [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, based on multiple simulations and or output parameters).
Zhang discloses in claim 12. The computing system of claim 9, wherein simulating the subterranean volume simulating the subterranean volume using an ensemble of different realizations including the selected first candidate simulation (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model. Furthermore Fig 5; 510 generating an earth model based on the simulation output from the first ensemble).
Zhang discloses in claim 13. The computing system of claim 9, wherein the first candidate simulation, the first candidate output parameter, or both are selected for simulating prior to simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model. Furthermore Fig 5; 510 generating an earth model based on the simulation output from the first ensemble).
Zhang discloses in claim 14. The computing system of claim 9, wherein predicting the first candidate output parameter comprises determining one or more statistical characteristics for values of the first candidate output parameter (Zhang [0026] model training utilizing “least mean square error” methodology).
Zhang discloses in claim 15. The computing system of claim 9, wherein the operations further comprise generating a visualization of the subterranean volume based on simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model and [0002] generating a visual model of the system).
Zhang discloses in claim 16. The computing system of claim 9, wherein the operations further comprise adjusting a weight of a mud in a well based at least in part on the simulating, wherein the simulating is configured to predict a pore pressure, a fracture gradient, or both in a rock formation (Zhang [0028] mud weight is used as an input for training the machine learning model [0033] modification of mud weights as an adjusted input parameter to the model [0030] the model output parameters include pore pressure attributes and fracture gradients).
Zhang discloses in claim 17. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:
receiving one or more input parameters (Zhang Fig 1; 110 Fig 2a; 202) and one or more simulation realizations (Zhang Fig 1; “synthetic logs” [0036] synthetically derived logs furthermore, Zhang Fig 2a; 204b prior models used for model training) representing the subterranean volume (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume);
modeling the one or more simulation realizations as a target function of the one or more input parameters (Zhang Fig 2a; 204a, 204b, 204c [0041] training data parameters are based on a variety of training inputs such as well logs and [0046] synthetic logs);
training a machine-learning model to predict values for the target function using the one or more input parameters and the one or more simulation realizations (Zhang Fig 2a; 212 [0038] utilizing a machine learning trained on previous simulations and models);
predicting a value for the target function based on a first candidate simulation or a first candidate output parameter of a simulation (Zhang [0042] machine learning model helps predict multiple log parameters);
selecting the first candidate simulation, the first candidate output parameter, or both based on the predicted value of the target function (Zhang Fig 4; 406 selecting a preferred model output [0074]); and
simulating the subterranean volume using the first candidate simulation, the first candidate output parameter, or both (Zhang [0043] machine learning model produces an earth model [0047] additional data for representing the subterranean volume).
Zhang discloses in claim 18. The medium of claim 17, wherein the operations further comprise: predicting a second value for the target function based on at least one of a second candidate simulation or a second candidate output parameter (Zhang 0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model based on multiple simulations and or output parameters); and
determining not to simulate the subterranean volume using the second candidate simulation, the second candidate output parameter, or both based on the second value of the target function (Zhang [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, not the second, based on multiple simulations and or output parameters).
Zhang discloses in claim 19. The medium of claim 17, wherein selecting the first candidate simulation, the first candidate output parameter, or both is based on the first candidate simulation or the first candidate output parameter minimizing the first value of the target function (Zhang Fig 2b 212 Fig 4; 406 selecting a preferred model output [0042] machine learning model helps predict multiple log parameters [0074] selection of the preferred model, based on multiple simulations and or output parameters).
Zhang discloses in claim 20. The medium of claim 17, wherein simulation the subterranean volume comprises simulating the subterranean volume using an ensemble of different realizations including the selected first candidate simulation (Zhang Fig 2b; 212 simulating an earth model based on output values including the first instance of the machine learning model. Furthermore Fig 5; 510 generating an earth model based on the simulation output from the first ensemble).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas D Wlodarski whose telephone number is (571)272-3970. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicole Coy can be reached at (571) 272-5405. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS D WLODARSKI/Examiner, Art Unit 3672
/Nicole Coy/Supervisory Patent Examiner, Art Unit 3672
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