DETAILED ACTION
Claims 1, 3, 5 – 21, 23 and 25 - 40 have been presented for examination. Claims 1 and 21 currently amended. Claims 2, 4, 22 and 24 are cancelled.
This office action is in response to submission of the amendments on 01/30/2026.
Response to Priority
Examiner acknowledges that receipt of the International Application by the Office was provided on 04/26/2022. Therefore, the priority date is 07/19/2019.
Response to 35 U.S.C. § 112 Rejection
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “Paragraph [0083] and also Paragraphs [0023] and [0043] of the published application US 2022/0245300 provide a detailed description of "difficult to measure or determine" - demonstrating that this term is not vague but supported by the disclosure”
Applicant points to Paragraph 23, 43, and 83 of the published application. Examiner notes that those paragraph merely repeat the phrase “difficult to measure or determine” and similarly “difficult to measure or cannot be measured determined” which does in any way further clarify the recited “difficult to measure or determine”. Therefore, the 112(b) rejection is maintained.
Response to 35 U.S.C. § 101 Rejection
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “Applicant submits that claims 1 and 21 do not include a math concept itself, and thus is not sufficient to fall into the category of grouping mathematical relationships.”
Examiner notes that the instant claims are not alleged to recite mathematical relationships. Therefore, Applicant’s arguments are not persuasive.
Applicant argues: “Applicant submits that the claims recite additional elements that reflect "[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field," … In particular, the Applicant identifies certain limitations of independent claim 1 and asserts that "the claimed subject matter provides technical improvements over conventional systems by addressing challenges in statistical emulations for simulation models and the need for improving efficiency methods that allow for optimization of reservoir models under uncertainty … Here, Applicant's specification notes several technological improvements. For example, Paragraph [0064] states: "The techniques proposed here may provide methods to build a statistical emulator for the production controls incorporating the uncertainty from the geological parameters. This may allow optimization of the controls under the geological uncertainty … This results in far fewer runs being required to produce an accurate emulator. This allows the acceleration of optimization workflows for reservoir simulation models with uncertain inputs … Thus, it is clear that there are several technological improvements provided by Applicant's claims 1 and 21 and thus, Applicant's invention is integrated into a practical application.” (emphasis added)
Applicant appears to argue that one or more recited elements integrate the claimed invention into a practical application. Examiner notes that merely optimizing the use of a computer for efficiency does not amount to the improvement of the computer. Further, the claimed invention is in the field of oil and gas exploration, and any purported improved use of a computer to perform modeling does not amount to an improvement to other technology or technical field, at least since the computer is merely used as a tool in the same field of oil and gas exploration. Examiner further notes that the use of the model in a control application (i.e., beyond the recited “control design point” which is part of the model itself) is not explicitly recited.
Response to 35 U.S.C. § 103 Rejection
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “In contrast, Leahy only teaches guide points and control points. Leahy fails to teach all of the features, including aggregating of the outputs, the amount of outputs (i.e., all outputs), single control design point, and the amount of geologic random points (i.e., all points). Therefore, Leahy fails to teach all of the features of independent claim 1 as amended. Similarly, Leahy fails to teach all of the features of independent claim 21 as amended” (italicized emphasis in original) (bolded emphasis added)
Applicant acknowledges that Leahy teaches “guide points and control points”, however Applicant appears to argue that these teachings do not cover the amount of the outputs and random points, and do not cover the recited “singe” control design design point. Leahy explicitly teaches that the initial model is inputted based on measured data and any other external user input comprising interpretation points of the geology (inputs of geologic random points of all geologic variables), and is merely an estimate (random) (see Paragraph 49 – 50 “The method consists of two fundamental steps (FIG. 1 ): first, a global estimate of the geologic feature's position is estimated (called "smooth geologic model", or "smooth model") … The smooth model may be generated based on any pre-existing external input. For example well markers, previous interpretation points, or other user input”). Further, the adjustments to the model (i.e., partial outputs) are based on guide points in the model meeting a similarity threshold, which are not a priori limited and potentially include all the model points (i.e., aggregating all outputs). Further, the Leahy explicitly teaches that only one control point is required (see Paragraph 27 “Preferably, step a) can comprise positioning the initial model estimate across one or more model control points.”). Therefore, Applicant’s arguments are not persuasive.
Claim Interpretation
Examiner notes that the numbers enclosed in the parenthesis are not interpreted as limiting the scope of the claim (see MPEP 608.1(m) “Reference characters corresponding to elements recited in the detailed description and the drawings may be used in conjunction with the recitation of the same element or group of elements in the claims … Generally, the presence or absence of such reference characters does not affect the scope of a claim.”)
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claim 13, it recites “difficult to measure or determine” which is a relative term which renders the claim indefinite. The term “difficult” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation is interpreted for examination purposes as related to any difficulty in measuring or determining whatsoever.
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, 3, 5 – 21, 23 and 25 - 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites at Step 1 a statutory category (i.e. a process) method for generating a stochastic emulation model, comprising: determining a partial output (gamma_i) by aggregating all outputs of a deterministic emulation model (n) from inputs of a single control design point (xi) and all geologic random points of all geologic random variables (Y); and using the partial output (gamma) to generate the stochastic emulation model (beta) from the output of the deterministic emulation model (11). At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “generate” amounts to modeling actions recited at a high-level of generality. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: using a computer (100) having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124); that the generating is by the emulation module. The “processor” and “non-transitory memory element” (and the “emulation module” contained therein) are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “processor” and “non-transitory memory element” (and the “emulation module” contained therein) amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons, the claim is not patent eligible.
Dependent claim 3 and 5 – 20 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 3 the generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta) further comprising, determining, by the emulation module (124), at least one building function (gamma) based on one or more of sample outputs of the deterministic emulation model (eta) and the partial output (gamma_i); Claim 5 the generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta), further comprising, determining, by the emulation module (124), a trend of the partial output (gamma_i),wherein the generating, by the emulation module (124), a stochastic emulation model (beta) from the output of a deterministic emulation module (eta) comprises using, by the emulation module (124), the trend of the partial output (gamma_i) to generate the stochastic emulation model (beta); Claim 7 determining, by the emulation module (124), at least one building function (gamma), further comprising, accounting for the trend of the partial output (gamma_i); Claim 8 generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta) further comprising accounting for a trend in building function (gamma) when determining, by the stochastic emulation module (124), the stochastic emulation model (beta) based on the building function (gamma); Claim 9 generating, by a simulation module (122), a deterministic simulation model (f(x,y)) from a stochastic simulation model (f(x,Y)) by providing fixed samples, (y_sample), for the random variables (Y) to the stochastic simulation module (f(x,Y)); Claim 10 generating, by the emulation module (124), the deterministic emulation model (eta) from the deterministic simulation model (f(x,y)); Claim 14 updating, by the emulation module (124), the stochastic emulation model (beta), using emulation update techniques, to generate an updated stochastic emulation model (beta_new), wherein the emulation update techniques account for the stochastic emulation model(beta); Claim 15 further teaches the updating, by the emulation module (124), the stochastic emulation model (beta) further comprising: determining, by the emulation module (124), a new building function (gamma_new) based on a new deterministic emulation model (eta_new);
determining, by the emulation module (124), new partial outputs (eta_new,i) from the new building function (eta_new); wherein the stochastic emulation model (beta) is updated based on the partial outputs (eta_new,i); Claim 18 determining, by the emulation module (124), a new deterministic emulation model (eta_new), by determining, by the simulation module (122), a new deterministic simulation model (f(x,y,)_new) from the stochastic simulation model (f(x,Y)), and determining, using the emulation module (124) a new deterministic emulation model (eta_new) from the new deterministic simulation model (f(x,y,)_new); Claim 19 generating, by the emulation module (124) an updated stochastic emulation model (beta_new), based on the new deterministic emulation model (eta_new) and a prior stochastic emulation model (beta).; Claim 20 wherein the generating, by the emulation module (124) an updated stochastic emulation model (beta_new), based on the new deterministic emulation model (eta_new), further comprises, determining one or more of a new or updated building function (eta_new) and a new or updated partial output (eta_new,i).
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “using” and “accounting” and “generating” and “generate” and “updating” and “updated” amounts to modeling and predicting actions recited at a high-level of generality which are not preclude from being performed mentally in combination with a piece of paper. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that the determining or generating is “by the emulation module”; that the generating is “by a simulation module”. The module” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “module” amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons, the claim is not patent eligible.
Dependent claims 6, 11 – 13 and 16 – 17 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 6 wherein the trend of the partial output (gamma_i) is one or more of an average of the partial output (gamma_bar_i), a variance of the partial output (var(gamma_i), a standard deviation of the partial output (SD(gamma_i)), a maximum of the partial output (max(gamma_i), and a minimum of the partial output (min(gamma_i); Claim 11 determining or receiving, by the emulation module (124), data representing at least one control design point (x) and data representing at least one geologic random point (y); and selecting, by the emulation module (124), from the at least one geologic random point (y), at least one sample geologic random point (y_sample), wherein the output of the deterministic emulation model (eta) is at least one output from inputs of the at least one control design point (x) and the at least one sample geologic random point (y_sample); Claim 12 wherein the at least one geologic random point (y) is a set of points representing a random distribution; Claim 13 wherein the at least one geologic random point (y) represents a parameter in a model that is difficult to measure or determine, the random distribution representing known, measured or estimated variation in the parameter; Claim 16 determining, by the emulation module (124), another partial output (gamma_s) of the building function (gamma); Claim 17 wherein the stochastic emulation model (beta) and the deterministic emulation model (eta) are models of a simulation model of a reservoir.
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “selecting” amount to modeling and predicting actions recited at a high-level of generality which are not preclude from being performed mentally in combination with a piece of paper. The “trend of the partial output (gamma_i) is one or more of” further limits the parent claim(s) abstract idea “determining”. The “output of the deterministic emulation model (eta) is at least one output from” and “the at least one geologic random point (y) is” and “the at least one geologic random point (y) represents” further limits the parent claim(s) abstract idea “generating”. The “stochastic emulation model (beta) and the deterministic emulation model (eta) are models” further limits the parent claim “generating”. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that one or more steps are “by the emulation module”; claim 17 wherein the determining, by the emulation module (124), a partial output (gamma_i) of the building function (gamma), further comprises transmitting, by the emulation module (124), data representing the building function (gamma) and data representing input variables for generating the partial output (gamma_i) to a processing element of a parallel processing system, and wherein the determining, by the emulation module (124), another partial output (gamma_s) of the building function (gamma) further comprises transmitting, by the emulation module (124), data representing the building function (gamma) and data representing different input variables for generating the another partial output (gamma_s) to another processing element of a parallel processing system. The module” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “transmitting” amounts to insignificant data outputting (see MPEP 2106.05(g)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “module” amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The ”transmitting” amounts to well-understood, routine, conventional activity since it covers any electronic means (see MPEP 2106.05(d)(II)(i) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”). For at least these reasons, the claim is not patent eligible.
Independent claim 21 recites at Step 1 a statutory category (i.e. a machine) computer system (100) for generating a stochastic emulation model, configured to: determining a partial output (y;) by aggregating all outputs of a deterministic emulation model (n) from inputs of a single control design point (xi) and all geologic random points of all geologic random variables (Y); and using the partial output (gamma) to generate the stochastic emulation model (beta) from the output of the deterministic emulation model (11); use the partial output (gamma_i) to generate the stochastic emulation model (beta) from the output of the deterministic emulation model (eta). At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “generate” amounts to modeling actions recited at a high-level of generality. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124), the emulation module. The “processor” and “non-transitory memory element” (and the “emulation module” contained therein) are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “processor” and “non-transitory memory element” (and the “emulation module” contained therein) amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons, the claim is not patent eligible.
With regard to claims 23 and 25 – 40, they may be compared to claim 1, 4 and 5 – 20 as reciting substantially similar limitations, but for a different statutory category. They are directed to an abstract idea without significantly more for the same reasons.
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.
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 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 3, 5 – 10 and 12 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baker et al. “Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available” (henceforth “Baker”) in view of Formentin et al. “Systematic Uncertainty Reduction for Petroleum Reservoirs Combining Reservoir Simulation and Bayesian Emulation Techniques” (henceforth “Formentin”), and further in view of Leahy et al. (US 2015/0081259) (henceforth “Leahy”). Baker and Formentin and Leahy are analogous art because they solve the same problem of simulating a reservoir, and because they are from the same field of endeavor of oil and gas exploration.
With regard to claim 1, Baker teaches a method for generating a stochastic emulation model (beta), comprising:
using, by the emulation module (124), a partial output (gamma_i) to generate the stochastic emulation model (beta) from the output of a deterministic emulation model (eta). (Baker Abstract outputs from a readily available approximation is used to efficiently learn about a stochastic model “We alleviate this problem by exploiting readily available deterministic approximations to efficiently learn about the respective stochastic computer models. This is done via the summation of two Gaussian processes; one responsible for modeling the deterministic approximation, the other responsible for using such approximation to better statistically model the stochastic computer model”, and Figure 1 emulation model is determined from various evaluations (using a partial output)
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
Baker does not appear to explicitly disclose: that the method is using a computer (100) having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124); and that the generating is by an emulation module.
However, Formentin teaches:
a method using a computer (100) having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124); and generating is by an emulation module. (Formentin Page 22, Middle “We defined m=n=100 scenarios because we can run 100 simulations in parallel in the cluster available.”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker with the implementing on a cluster disclosed by Formentin. One of ordinary skill in the art would have been motivated to make this modification in order to desirably implement a simulation (see Formentin Page 22, Middle)
Baker in view of Formentin does not appear to explicitly disclose: determining, by the emulation module (124), a partial output (gamma_i) by aggregating all outputs of a deterministic emulation model (eta) from inputs of a single control design point (xi) and all geologic random points of all geologic random variables (Y); and
However, Leahy teaches:
determining a partial output (gamma_i) by aggregating all outputs of a deterministic emulation model (eta) from inputs of a single control design point (x_i) and all geologic random points of all geologic random variables (Y). (Leahy Abstract the model is generated based on seismic measurement data (a deterministic emulation model) “Method of providing a geologic model (1) representing a geologic feature based on geologic measurement data, such as seismic or electromagnetic data.”, and Paragraph 29 inputted control design points are used to adjust the model (determining a partial output) based on a plurality of model outputs (adjusted by interpolation) “In such an embodiment, at locations where the metric function returns a similarity value below the similarity metric threshold, the geologic model can be adjusted by means of interpolation between model guide points and/or model control points. This is one manner of adjusting the geologic model also in regions where geologic measurements are not applicable for such adjustment”, and Paragraph 49 – 50 initial geological model estimate based on user inputted points (all geologic random points and of geological random variables) “The method consists of two fundamental steps (FIG. 1 ): first, a global estimate of the geologic feature's position is estimated (called "smooth geologic model", or "smooth model") … The smooth model may be generated based on any pre-existing external input. For example well markers, previous interpretation points, or other user input”, and Paragraph 27 could be only a single control point “Preferably, step a) can comprise positioning the initial model estimate across one or more model control points.”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker in view of Formentin with the use of control points disclosed by Leahy. One of ordinary skill in the art would have been motivated to make this modification in order to desirably control the simulation (see Leahy Paragraph 29)
With regard to claim 21, it recites the same step(s) as claim 1, which is taught by Baker in view of Formentin, and further in view of Leahy. Claim 21 further recites: a computer system (100) for generating a stochastic emulation model, having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124), the emulation module configured to.
Formentin teaches: a computer system (100) for generating a stochastic emulation model, having a processor (110) configured to execute commands stored on a non-transitory memory element (120), the non-transitory memory element (120) having an emulation module (124), the emulation module configured to (Formentin Page 22, Middle “We defined m=n=100 scenarios because we can run 100 simulations in parallel in the cluster available.”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker with the implementing on a cluster disclosed by Formentin. One of ordinary skill in the art would have been motivated to make this modification in order to desirably implement a simulation (see Formentin Page 22, Middle)
With regard to claim 3, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 2, and further teaches: the generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta) further comprising, determining, by the emulation module (124), at least one building function (gamma) based on one or more of sample outputs of the deterministic emulation model (eta) and the partial output (gamma_i). (Baker Page 6, Top
PNG
media_image2.png
146
583
media_image2.png
Greyscale
)
With regard to claim 5, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 3, and further teaches: the generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta), further comprising, determining, by the emulation module (124), a trend of the partial output (gamma_i), wherein the generating, by the emulation module (124), a stochastic emulation model (beta) from the output of a deterministic emulation module (eta) comprises (Baker Figure 1 trend in emulation model is shown
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
using, by the emulation module (124), the trend of the partial output (gamma_i) to generate the stochastic emulation model (beta). (Baker Figure 2 emulator matches the periodic trend
PNG
media_image3.png
434
711
media_image3.png
Greyscale
)
With regard to claim 6, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 5, and further teaches: wherein the trend of the partial output (gamma_i) is one or more of an average of the partial output (gamma_bar_i), a variance of the partial output (var(gamma_i), a standard deviation of the partial output (SD(gamma_i)), a maximum of the partial output (max(gamma_i), and a minimum of the partial output (min(gamma_i). (Baker Figure 1 trend in emulation model is shown
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
With regard to claim 7, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 5, and further teaches: determining, by the emulation module (124), at least one building function (gamma), further comprising, accounting for the trend of the partial output (gamma_i). (Baker Figure 1 trend in emulation model is shown
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
With regard to claim 8, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 3, and further teaches: generating a stochastic emulation model (beta) from the output of a deterministic emulation model (eta) further comprising accounting for a trend in building function (gamma) when determining, by the stochastic emulation module (124), the stochastic emulation model (beta) based on the building function (gamma). (Baker Page 6, Top
PNG
media_image2.png
146
583
media_image2.png
Greyscale
)
With regard to claim 9, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 1, and further teaches: generating, by a simulation module (122), a deterministic simulation model (f(x,y)) from a stochastic simulation model (f(x,Y)) by providing fixed samples, (y_sample), for the random variables (Y) to the stochastic simulation module (f(x,Y)). (Baker Figure 1 trend in emulation model is shown
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
With regard to claim 10, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 9, and further teaches: generating, by the emulation module (124), the deterministic emulation model (eta) from the deterministic simulation model (f(x,y)). (Baker Abstract “We alleviate this problem by exploiting readily available deterministic approximations to efficiently learn about the respective stochastic computer models. This is done via the summation of two Gaussian processes; one responsible for modeling the deterministic approximation, the other responsible for using such approximation to better statistically model the stochastic computer model”)
With regard to claim 12, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 11, and further teaches: wherein the at least one geologic random point (y) is a set of points representing a random distribution. (Baker Page 2, Top stochastic simulators have randomness)
With regard to claim 13, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 12, and further teaches: wherein the at least one geologic random point (y) represents a parameter in a model that is difficult to measure or determine, the random distribution representing known, measured or estimated variation in the parameter (see Claim Rejections - 35 USC § 112) (Formentin Figure 1
PNG
media_image4.png
394
709
media_image4.png
Greyscale
, and Page 4, Middle “The main challenges for the application of BHM for reservoir models remain in (a) high dimensionality of inputs (e.g., spatial uncertain attributes linked to porosity, permeability and facies maps) and outputs (several observed measures to be used in the calibration process);”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker with the implementing on a cluster disclosed by Formentin. One of ordinary skill in the art would have been motivated to make this modification in order to desirably implement a simulation (see Formentin Page 22, Middle)
With regard to claim 14, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 1, and further teaches: updating, by the emulation module (124), the stochastic emulation model (beta), using emulation update techniques, to generate an updated stochastic emulation model (beta_new), wherein the emulation update techniques account for the stochastic emulation model(beta). (Baker Figure 4 variable number of points (emulation update techniques) can be used to generate the emulation
PNG
media_image5.png
447
804
media_image5.png
Greyscale
)
With regard to claim 15, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 14, and further teaches the updating, by the emulation module (124), the stochastic emulation model (beta) further comprising: determining, by the emulation module (124), a new building function (gamma_new) based on a new deterministic emulation model (eta_new); determining, by the emulation module (124), new partial outputs (eta_new,i) from the new building function (eta_new); wherein the stochastic emulation model (beta) is updated based on the partial outputs (eta_new,i). (Baker Abstract previously applied steps can be repeated with predictable results)
With regard to claim 16, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 3, and further teaches: determining, by the emulation module (124), another partial output (gamma_s) of the building function (gamma), wherein the determining, by the emulation module (124), a partial output (gamma_i) of the building function (gamma), further comprises (Baker Abstract previously applied steps can be repeated with predictable results)
transmitting, by the emulation module (124), data representing the building function (gamma) and data representing input variables for generating the partial output (gamma_i) to a processing element of a parallel processing system, and (Formentin Page 22 “We defined m=n=100 scenarios because we can run 100 simulations in parallel in the cluster available.”)
wherein the determining, by the emulation module (124), another partial output (gamma_s) of the building function (gamma) further comprises transmitting, by the emulation module (124), data representing the building function (gamma) and data representing different input variables for generating the another partial output (gamma_s) to another processing element of a parallel processing system. (Formentin Page 22 “We defined m=n=100 scenarios because we can run 100 simulations in parallel in the cluster available.”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker with the implementing on a cluster disclosed by Formentin. One of ordinary skill in the art would have been motivated to make this modification in order to desirably implement a simulation (see Formentin Page 22, Middle)
With regard to claim 17, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 1, and further teaches: wherein the stochastic emulation model (beta) and the deterministic emulation model (eta) are models of a simulation model of a reservoir. (Formentin Abstract)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker with the implementing on a cluster disclosed by Formentin. One of ordinary skill in the art would have been motivated to make this modification in order to desirably implement a simulation (see Formentin Page 22, Middle)
With regard to claim 18, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 9, and further teaches: determining, by the emulation module (124), a new deterministic emulation model (eta_new), by determining, by the simulation module (122), a new deterministic simulation model (f(x,y,)_new) from the stochastic simulation model (f(x,Y)), and determining, using the emulation module (124) a new deterministic emulation model (eta_new) from the new deterministic simulation model (f(x,y,)_new).
With regard to claim 19, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 18, and further teaches: generating, by the emulation module (124) an updated stochastic emulation model (beta_new), based on the new deterministic emulation model (eta_new) and a prior stochastic emulation model (beta). (Baker Figure 2 another model can be generated after a previous model
PNG
media_image3.png
434
711
media_image3.png
Greyscale
)
With regard to claim 20, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 19, and further teaches: wherein the generating, by the emulation module (124) an updated stochastic emulation model (beta_new), based on the new deterministic emulation model (eta_new), further comprises, determining one or more of a new or updated building function (eta_new) and a new or updated partial output (eta_new,i). (Baker Abstract previously applied steps can be repeated with predictable results)
With regard to claim 11, Baker in view of Formentin, and further in view of Leahy teaches all the elements of the parent claim 2, and further teaches:
selecting, by the emulation module (124), from the at least one geologic random point (y), at least one sample geologic random point (y_sample), wherein the output of the deterministic emulation model (eta) is at least one output from inputs of the at least one control design point (x) and the at least one sample geologic random point (y_sample). (Baker Figure 1 emulation model is determined from various evaluations (partial output)
PNG
media_image1.png
432
803
media_image1.png
Greyscale
)
determining or receiving, by the emulation module (124), data representing at least one control design point (x) and data representing at least one geologic random point (y); and (Leahy Paragraph 29 “In such an embodiment, at locations where the metric function returns a similarity value below the similarity metric threshold, the geologic model can be adjusted by means of interpolation between model guide points and/or model control points. This is one manner of adjusting the geologic model also in regions where geologic measurements are not applicable for such adjustment”)
It would have been obvious to one of ordinary skill in the art to combine the method of simulating a reservoir disclosed by Baker in view of Formentin with the use of control points disclosed by Leahy. One of ordinary skill in the art would have been motivated to make this modification in order to desirably control the simulation (see Leahy Paragraph 29)
With regard to claim 23, 25 – 40, they may be compared to claim 1, 3 and 5 – 20 which recite substantially similar limitations. Therefore, they are taught by: Baker in view of Formentin, and further in view of Leahy.
Examiner General Comments
With regard to the prior art rejection(s), any cited portion of the relied upon reference(s), either by pointing to specific sections or as quotations, is intended to be interpreted in the context of the reference(s) as a whole as would be understood by one of ordinary skill in the art. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention since the entire reference is considered to provide disclosure relating to the cited portions. Further, the claims and only the claims form the metes and bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALFRED H. WECHSELBERGER whose telephone number is (571)272-8988. The examiner can normally be reached M - F, 10am to 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
Prosecution Insights