DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-5, 7-11, and 14-17 are 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.
The terms “larger-scale data”, “medium scale, “smaller-scale data”, and “fine-scale data” in claims 3-5, 7-11, and 14-17 are relative terms which render the claims indefinite. The terms are 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.
As stated in MPEP 2173.05(b)(IV), “Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005).” In this case the public would be unable to determine the scope of the claims, in particular what would constitute larger-scale data, smaller-scale data, medium-scale data, or fine-scale data such that a person would be able to ascertain the metes and bounds of the claims. Accordingly, claims 3-5, 7-11, and 14-17 are indefinite for failing to particularly point out and distinctly claim the subject matter which the inventors regard as the invention.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1 of MPEP §2106’s subject matter eligibility guidelines, claims 1-25 fall within the category of a process.
Under Step 2A, prong 1, the claim(s) recite(s) “Claim 1: for a defined volume of the subsurface: generating template instances for the defined volume; conditioning the template instances based on data indicative of features at a lobe/channel complex set level or a lobe/channel complex level; and separately conditioning the template instances based on data indicative of features at a lobe/channel level…Claim 2: dividing one or more geological zones into a plurality of sub-zones; and for a respective sub-zone of the plurality of sub-zones: generating the template instances for the respective sub-zone; conditioning the template instances based on the data indicative of the features at the lobe/channel complex set level or the lobe/channel complex level; and separately conditioning the template instances based on the data indicative of the features at the lobe/channel level…Claim 3: conditioning the template instances based on the data indicative of the features at the lobe/channel complex set level or the lobe/channel complex level comprises conditioning the templates instances based on at least one of large-scale data or medium-scale data; and wherein conditioning the template instances based on the data indicative of the features at the lobe/channel level comprises conditioning the template instances based on fine-scale data…Claim 4: wherein conditioning the template instances based on larger-scale data comprises: conditioning the template instances based on large-scale data; and conditioning the template instances based on medium-scale data; and wherein conditioning the template instances based on the data indicative of the features at the lobe/channel level comprises conditioning the template instances based on fine-scale data…Claim 5: wherein the template instances comprise multiple stacked lobe/channel complex template instances…Claim 6: wherein the template instances comprise one or more smaller scale template instances…Claim 7: wherein conditioning comprises: conditioning the multiple lobe/channel complex template instances based on the large-scale data; thereafter conditioning the multiple lobe/channel complex template instances based on the medium-scale data; and thereafter conditioning the multiple lobe/channel complex template instances based on fine- scale data…Claim 8: wherein the large-scale data comprises at least one of up-scaled well log or upscaled seismic data in order to model large- scale features…Claim 9: wherein the large-scale data comprises net versus non-net data and trend constraints based on analysis of at least one of the well log or the seismic data…Claim 10: wherein the medium- scale data is generated based on well log and seismic data indicative of sub-EoD (Environment of Deposition) constraints…Claim 11: wherein the fine-scale data comprise at least one of well log data, seismic data, or core data…Claim 12: wherein the one or more geological models comprises one or more reservoir models of a subsurface reservoir…Claim 13: wherein the template instances comprise multiple lobe/channel complex template instances; and wherein generating the multiple lobe/channel complex template instances for the respective sub-zone is based on one or more lobe/channel complex templates generated by machine learning…Claim 14: wherein conditioning the template instances based on large-scale data is at a complex set level; wherein conditioning the template instances based on medium-scale data is at a complex level; and wherein the template instances comprise one or more complex template instances…Claim 15: wherein conditioning the template instances based on large-scale data is at a complex set level; wherein conditioning the template instances based on medium-scale data is at a complex level; and wherein the template instances comprise one or more complex set template instances…Claim 16: wherein conditioning the template instances based on large-scale data is at a complex set level; wherein conditioning the template instances based on medium-scale data is at a complex level; and wherein the template instances comprise at least one complex template instance and at least one complex set template instance. Claim 17: wherein conditioning the template instances based on the large-scale data modifies configuration geometry, location and properties of the template instances; wherein conditioning the template instances based on the medium-scale data modifies at least one of the configuration geometry, the location or the properties of the template instances; and wherein conditioning the template instances based on the fine-scale data modifies the configuration geometry, the location and the properties of the template instances.” These claims fall within the judicial exception of mathematical concepts as articulated in MPEP §2106.04(a)(2)(I).
Similarly, in claims 18-25, the claims recite “Claim 18: accessing one or more geological constraints; parameterizing, using the one or more geological constraints, a template in order to generate the complex template that is geologically feasible; and using the complex template in order to generate a reservoir model…Claim 19: the complex template comprises a lobe/channel complex template; and wherein parameterizing the template includes using a functional form model to parameterize at a lobe/channel complex level…Claim 20: parameterizing is via unsupervised learning for training an encoder…Claim 21: parameterizing the template comprises using training data from the functional form model of channel/lobes; and wherein supervised learning is employed to build geological realism and other geological considerations into the training…Claim 22: wherein parameterizing is based on machine learning…Claim 23: wherein parameterizing is via supervised learning for training a neural network…Claim 24: wherein parameterizing is via deep learning…Claim 25: wherein parameterizing the template is using statistical generative modeling.” These claims fall within the judicial exception of mathematical concepts as articulated in MPEP §2106.04(a)(2)(I).
Under Step 2A, prong 2, the claim has the additional limitations of the methods being “computer-implemented” as stated in claims 1 and 18. Regarding the computer-implemented, "use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more." MPEP §2106.05(f). Accordingly, the claims do not recite additional elements that integrate the judicial exception into a practical application.
Under Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above in regard to Step 2A, prong 2, the claims recite the additional limitations of being “computer-implemented”. Regarding the computer-implemented, "use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more." MPEP §2106.05(f). Accordingly, the claims do not recite additional elements that are significantly more than the judicial exception.
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.
Claim(s) 1, 3-8, 10-15, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hocker (US 2012/0296618) (hereinafter Hocker).
Regarding claim 1, Hocker teaches a computer-implemented method for generating one or more geological models for a subsurface, the method comprising: for a defined volume of the subsurface:
generating template instances for the defined volume (fig. 1, simulation results 113, 123, and 133; ph. [0020]-[0021], “FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale… The right-hand elements 115, 125 and 135 of each row, may refer to an auxiliary variable that is used to condition simulations. The middle elements of each row, 113, 123 and 133 may refer to simulation results obtained using the training image to the left, conditioned to honor the auxiliary variable to the right. Definition of symmetries and geometric opposition in the modeling process at various scales may reduce complexity of the modeling and may enable the use of multi-point statistics in generating realistic models of highly complex depositional environments.”);
conditioning the template instances based on data indicative of features at a lobe/channel complex set level or a lobe/channel complex level (fig. 1, Level 1, simulation result 113 conditioned on auxiliary variable 115; ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels. It should be noted that in FIGS. 2(a)-2(c), the model is essentially a two-dimensional model. Increasing complexity is added in the third dimension in FIGS. 3(a)-3(c) and FIGS. 4(a)-4(c).”); and
separately conditioning the template instances based on data indicative of features at a lobe/channel level (fig. 1, Level 2, simulation 123 conditioned on auxiliary variable 125; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of `. Of interest are the azimuths of point bar deposits on either side of the belt axis.”; ph. [0034], “FIG. 5 shows details of the model in FIG. 4(b). The main view shows a horizontal slice showing facies and, as overlay, also the point bar lobes model at level 2”).
Regarding claim 3, Hocker teaches the method of claim 1, wherein
conditioning the template instances based on the data indicative of the features at the lobe/channel complex set level or the lobe/channel complex level comprises conditioning the templates instances based on at least one of large-scale data or medium-scale data (ph. [0020], “FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale”; ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels.”); and
wherein conditioning the template instances based on the data indicative of the features at the lobe/channel level comprises conditioning the template instances based on fine-scale data (ph. [0020], ““FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale”; ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs.”).
Regarding claim 4, Hocker teaches the method of claim 1, wherein conditioning the template instances based on the data indicative of the features at the lobe/channel complex set level or the lobe/channel complex level comprises:
conditioning the template instances based on large-scale data (fig. 1, Level 1, results 113 conditioned on data 115; ph. [0020], “FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale”; ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels.”); and
conditioning the template instances based on medium-scale data (fig. 1, Level 2, results 123 conditioned on data 125; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis.”); and
wherein conditioning the template instances based on the data indicative of the features at the lobe/channel level comprises conditioning the template instances based on fine-scale data (fig. 1, Level 3, results 133 conditioned on data 135; ph. [0020], “FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale”; ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs. This is discussed with reference to FIGS. 4(a)-4(c).”).
Regarding claim 5, Hockert teaches the method of claim 4, wherein the template instances comprise multiple stacked lobe/channel complex template instances (ph. [0028], “FIG. 3(c) is the result of combining 301 with the image produced in the first level, i.e., FIG. 2(b).”; ph. [0033], “FIG. 4(c) is the result of combining 401 with the model produced in the second level, i.e., FIG. 3(b).”).
Regarding claim 6, Hockert teaches the method of claim 1, wherein the template instances comprise one or more smaller scale template instances (ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs. This is discussed with reference to FIGS. 4(a)-4(c).”).
Regarding claim 7, Hockert teaches the method of claim 5, wherein conditioning comprises:
conditioning the multiple lobe/channel complex template instances based on the large- scale data (fig. 1, Level 1, results 113 conditioned on data 115; ph. [0020], “FIG. 1 illustrates the various components of a hierarchical geological facies modeling process. The levels are indicated: level 1, level 2 and level 3. In the example discussed below, the three levels correspond to a large scale, medium scale and small scale”; ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels.”);
thereafter conditioning the multiple lobe/channel complex template instances based on the medium-scale data (fig. 1, Level 2, results 123 conditioned on data 125; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis.”);; and
thereafter conditioning the multiple lobe/channel complex template instances based on the fine-scale data (ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs. This is discussed with reference to FIGS. 4(a)-4(c).”).
Regarding claim 8, Hockert teaches the method of claim 3, wherein the large-scale data comprises at least one of up-scaled well log or upscaled seismic data in order to model large- scale features (ph. [0005], “The training images provide conceptual descriptions of the subsurface geological formations. These may be derived on outcrop analysis, well log interpretation, seismic data and general experience (otherwise referred to as "ground truth").”;ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to . In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries.”; ph. [0030], “It should also be noted that dipmeter data may be used not only at the medium-scale but also at the large-scale. Depending on the dips observed, a well log with classes "channel belt left", "channel belt right" and "floodplain/overbank" may be created, thereby forcing the division of the belt to honor well data. This is an example of the same auxiliary variable being usable at two different levels.”).
Regarding claim 10, Hockert teaches the method of claim 4, wherein the medium-scale data is generated based on well log and seismic data indicative of sub-EoD (Environment of Deposition) constraints (ph. [0005], “The training images provide conceptual descriptions of the subsurface geological formations. These may be derived on outcrop analysis, well log interpretation, seismic data and general experience”; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis. This is discussed with reference to FIGS. 3(a)-3(c).”).
Regarding claim 11, Hockert teaches the method of claim 3, wherein the fine-scale data comprise at least one of well log data, seismic data, or core data(ph. [0005], “The training images provide conceptual descriptions of the subsurface geological formations. These may be derived on outcrop analysis, well log interpretation, seismic data and general experience”; ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs. This is discussed with reference to FIGS. 4(a)-4(c).”).
Regarding claim 12, Hockert teaches the method of claim 3, wherein the one or more geological models comprises one or more reservoir models of a subsurface reservoir (ph. [0020], “The disclosure herein is directed to different methods of development of a hydrocarbon reservoir.”).
Regarding claim 13, Hockert teaches the method of claim 2, wherein the template instances comprise multiple lobe/channel complex template instances (ph. [0006], “This is not to be construed as a limitation and the method disclosed herein may also be used for other depositional environments with various kinds of symmetry and geometric opposition in depositional patterns, including, but not limited to those affected by channelized flow(s) such as delta complexes crevasse splays, and turbidite deposits. Opposed geometries may also occur in shoals, bars, and dunes.”; ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels.”; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis. This is discussed with reference to FIGS. 3(a)-3(c).”); and wherein generating the multiple lobe/channel complex template instances for the respective sub-zone is based on one or more lobe/channel complex templates generated by machine learning (ph. [0005], [0026], [0028], multi-point statistics (or multiple-point statistics) simulation, or MPS simulation).
Regarding claim 14, Hockert teaches the method of claim 4, wherein conditioning the template instances based on large-scale data is at a complex set level; wherein conditioning the template instances based on medium-scale data is at a complex level; and wherein the template instances comprise one or more complex template instances (ph. [0006], “This is not to be construed as a limitation and the method disclosed herein may also be used for other depositional environments with various kinds of symmetry and geometric opposition in depositional patterns, including, but not limited to those affected by channelized flow(s) such as delta complexes crevasse splays, and turbidite deposits. Opposed geometries may also occur in shoals, bars, and dunes.”; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis. This is discussed with reference to FIGS. 3(a)-3(c).”).
Regarding claim 15, Hockert teaches the method of claim 4, wherein conditioning the template instances based on large-scale data is at a complex set level; wherein conditioning the template instances based on medium-scale data is at a complex level; and wherein the template instances comprise one or more complex set template instances (ph. [0006], “This is not to be construed as a limitation and the method disclosed herein may also be used for other depositional environments with various kinds of symmetry and geometric opposition in depositional patterns, including, but not limited to those affected by channelized flow(s) such as delta complexes crevasse splays, and turbidite deposits. Opposed geometries may also occur in shoals, bars, and dunes.”; ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis. This is discussed with reference to FIGS. 3(a)-3(c).”).
Regarding claim 17, Hockert teaches the method of claim 4,
wherein conditioning the template instances based on the large-scale data modifies configuration geometry, location and properties of the template instances (ph. [0022], “The first level in FIG. 1 relates to large-scale depositional features. This typically corresponds to a scale of kilometers. In the example considered here, the features of importance may include the presence, orientation, and shape of meander belts and also the division of the belt into two sides with opposite depositional geometries. This is discussed with reference to FIGS. 2(a)-2(c). The starting point is the basic concept illustrated in 211 which includes two channels. It should be noted that in FIGS. 2(a)-2(c), the model is essentially a two-dimensional model. Increasing complexity is added in the third dimension in FIGS. 3(a)-3(c) and FIGS. 4(a)-4(c).”);
wherein conditioning the template instances based on the medium-scale data modifies at least one of the configuration geometry, the location or the properties of the template instances (ph. [0025], “The second level in FIG. 1 relates to medium scale modeling. The scale is typically on the order of hundreds of meters and features of importance are point bar deposits preserved after the lateral movement of meandering river channels. Of interest are the azimuths of point bar deposits on either side of the belt axis. This is discussed with reference to FIGS. 3(a)-3(c).”); and
wherein conditioning the template instances based on the fine-scale data modifies the configuration geometry, the location and the properties of the template instances (ph. [0031], “The third level in FIG. 1 relates to small-scale modeling. Included therein may be the effects of lateral accretion and abandonment fills marking the end of the life cycle of point bars. The model may include heterogeneity associated with channel lags, shale drapes and mudstone plugs. This is discussed with reference to FIGS. 4(a)-4(c).”).
Claim(s) 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gentilhomme (US 2017/0140079) (hereinafter Gentilhomme).
Regarding claim 18, Gentilhomme teaches a computer-implemented method for generating and using a complex template, the method comprising:
accessing one or more geological constraints (fig. 6, initial ensemble of seismic derived reservoir models 606);
parameterizing, using the one or more geological constraints, a template in order to generate the complex template that is geologically feasible (fig. 6, wavelet reparameterization 608; ph. [0075], “A multi-scale approach is used herein based on the LMenRML optimization using wavelet parameterization,”; ph. [0099]-[0100], “in step 620 estimated and measured seismic data are compared to each other and if a given criterion Δ1 is met, the method advances to step 626… Then, in step 626, an overall criterion A is used to evaluate all the parameters, both seismic and production.” i.e. determining if the generated complex template is geologically feasible/accurate to the measured seismic data based on the criteria delta/difference); and
using the complex template in order to generate a reservoir model (fig. 6, step 628, generating the final ensemble of reservoir models).
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.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hockert as applied to claim 1 above, and further in view of Landis, JR. et al. (US 2007/0061117) (hereinafter Landis) and Marx et al. (US 2015/0218914) (hereinafter Marx).
Regarding claim 9, Hockert teaches the method of claim 1. Hockert does not explicitly teach the large-scale data comprises net versus non-net data and trend constraints based on analysis of at least one of the well log or the seismic data. However, Landis teaches the large-scale data comprises net versus non-net data based on analysis of at least one of the well log or the seismic data (fig. 1, step 10 and ph. [0089]-[0090], “Another set of source data that the user may specify is well data, from which porosity, permeability and water saturation values may be obtained.”). One of ordinary skill in the art before the effective filing date would have been motivated to modify Hockert in the manner taught by Landis in order to “minimize the number of fine-scale simulations required” (Landis, ph. [0113]).
This Hockert /Landis combination does not explicitly teach the data comprises trend constraints based on analysis of at least one of the well log or the seismic data. However, Marx teaches the data comprises trend constraints based on analysis of at least one of the well log or the seismic data (fig. 3, well log data 303 analyzed by trend fusion engine 308, eg. Mud parameters 315; claim 5, the expert decision engine (328 in fig. 3) applies rules relating to a mud volume parameter.). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Hockert /Landis combination in the manner taught by Marx to permit use of an expert system engine to warn of safety hazards or other issues of concern to the operator while drilling (Marx, ph. [0094]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gentilhomme as applied to claim 18 above, and further in view of Wu et al. (US 2013/0246031) (hereinafter Wu).
Regarding claim 19, Gentilhomme teaches the method of claim 1, wherein parameterizing the template includes using a functional form model to parameterize (ph. [0083], “In this embodiment, a modified localization to the current parameterization used in the optimization is proposed. A localization function, Agb, similar to the one proposed by Furrer and Bengtsson [31] is used to screen the estimate of the Kalman gain in the grid-block space:
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where f(h, r) is a distance function which depends both on the separation distance h and the resolution r of the multi-scale loop.”). Gentilhomme does not explicitly teach the template instances comprise lobe/channel complex template instances. However, Wu teaches the template instances comprise lobe/channel complex template instances (fig. 14 and ph. [0111], “FIG. 14 shows a hierarchical interpretation of a deepwater channel-lobe system.”; ph. [0006], “Recently, a stochastic surface modeling technique was proposed for deepwater depositional systems. Stacking of lobes in turbidite systems are modeled sequentially following a series of stochastic depositional events.”). One of ordinary skill in the art before the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Wu in order for the models to be able to be “to predict hydrocarbon production from a petroleum reservoir over time.” (Wu, ph. [0003]).
Claim(s) 20, 22, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gentilhomme as applied to claim 18 above, and further in view of Liu et al., “Ensemble-based seismic history matching with data re-parameterization using convolutional autoencoder” (hereinafter Liu).
Regarding claim 20, Gentilhomme teaches the method of claim 18. Gentilhomme does not explicitly teach parameterizing is via unsupervised learning for training an encoder. However, Liu teaches parameterizing is via unsupervised learning for training an encoder (pg. 3156, “In our inversion approach, we introduce a deep learning method, the convolutional autoencoder (Masci et al., 2011), for the re-parametrization of seismic data to avoid the phenomenon of ensemble collapse due the high dimensionality of data. The convolutional autoencoder is an unsupervised method that aims to learn a sparse representation for a set of data.). One of ordinary skill in the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Liu in order “To avoid the common underestimation of uncertainty in ensemble based optimization approaches” (Liu, pg. 3156).
Regarding claim 22, Gentilhomme teaches the method of claim 18. Gentilhomme does not explicitly teach parameterizing is based on machine learning. However, Liu teaches parameterizing is based on machine learning (pg. 3156, “In our inversion approach, we introduce a deep learning method, the convolutional autoencoder (Masci et al., 2011), for the re-parametrization of seismic data to avoid the phenomenon of ensemble collapse due the high dimensionality of data. The convolutional autoencoder is an unsupervised method that aims to learn a sparse representation for a set of data.). One of ordinary skill in the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Liu in order “To avoid the common underestimation of uncertainty in ensemble based optimization approaches” (Liu, pg. 3156).
Regarding claim 24, Gentilhomme teaches the method of claim 18. Gentilhomme does not explicitly teach parameterizing is via deep learning. However, Liu teaches parameterizing is via deep learning. (pg. 3156, “In our inversion approach, we introduce a deep learning method, the convolutional autoencoder (Masci et al., 2011), for the re-parametrization of seismic data to avoid the phenomenon of ensemble collapse due the high dimensionality of data. The convolutional autoencoder is an unsupervised method that aims to learn a sparse representation for a set of data.). One of ordinary skill in the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Liu in order “To avoid the common underestimation of uncertainty in ensemble based optimization approaches” (Liu, pg. 3156).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gentilhomme as applied to claim 18 above, and further in view of Zheng et al., “Applications of supervised deep learning for seismic interpretation and inversion” (hereinafter Zheng).
Regarding claim 23, Gentilhomme teaches the method of claim 18. Gentilhomme does not explicitly teach parameterizing is via supervised learning for training a neural network. However, Zheng teaches parameterizing is via supervised learning for training a neural network (pg. 526, “A convolutional neural network (CNN) is trained to pick faults automatically in 3D seismic volumes”). One of ordinary skill in the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Zheng so “that high-quality fault picks can be predicted from migrated seismic images” (Zheng, pg. 526).
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gentilhomme as applied to claim 18 above, and further in view of Chan et al. “Parameterization and generation of geological models with generative adversarial networks” (hereinafter Chan).
Regarding claim 25, Gentilhomme teaches the method of claim 18. Gentilhomme does not explicitly teach parameterizing the template is using statistical generative modeling. However Chan teaches parameterizing the template is using statistical generative modeling (pg. 1, title, “Parameterization and generation of geological models with generative adversarial networks”). One of ordinary skill in the art before the effective filing date would have been motivated to modify Gentilhomme in the manner taught by Chan to more accurately realize channelized patterns of the data (Chan. pg. 8, “It is evident from these images that the GAN models outperform PCA in capturing the channelized patterns of the data.”).
Response to Arguments
§112 Rejection
Applicants argue that the claims have been amended to replace “large scale data” with “data indicative of features at a lobe/channel complex set level or a lobe/channel complex level” and “smaller scale data” with “data indicative of features at a lobe/channel level” (Remarks, pgs. 7-8). However, the arguments don’t address the indefinite scope of “larger-scale data”, “medium scale, “smaller-scale data”, and “fine-scale data”, and these terms are still used in claims 3-5, 7-11, and 14-17. Accordingly, the §112 rejection has been maintained for claims 3-5, 7-11, and 14-17.
§101 Rejection
Applicants argue the following:
“Applicant respectfully submits that Claim 1 is, therefore, directed toward an improvement for generating geological models by "conditioning the template instances based on data indicative of features at a lobe/channel complex set level or a lobe/channel complex level" and "separately conditioning the template instances based on data indicative of features at a lobe/channel level." Accordingly, because of the technical improvement recited in the claims (e.g., "conditioning the template instances based on data indicative of features at a lobe/channel complex set level or a lobe/channel complex level" and "separately conditioning the template instances based on data indicative of features at a lobe/channel level"), the claimed method improves geological model generation. Similarly, independent Claim 18 recites a method for generating and using a complex template by "parameterizing, using the one or more geological constraints, a template in order to generate the complex template that is geologically feasible" and "using the complex template in order to generate a reservoir model," as recited in Claim 18. This reflects the practical application of using machine learning-based parameterization (as further recited in dependent Claims 19-25) to generate geologically feasible templates for reservoir modeling-not merely abstract mathematical manipulation.” (Remarks, pg. 9)
However, Applicants arguments are not consistent with federal case law and the MPEP. The Federal Circuit has made it clear that such an alleged improvement of added an abstract mathematical calculation to another abstract mathematical calculation does not make the invention non-abstract under §101. “Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract.” RecogniCorp, LLC v. Nintendo Co., 122 USPQ2d 1377,1380 (Fed. Cir. 2017); See also, PersonalWeb Technologies LLC v. GOOGLE LLC, 8 F. 4th 1310 (Fed. Cir. 2021).
In this case the quoted conditioning steps generally refers to mathematical calculations, e.g. Specification ph. [0054], “As merely one example, a Bayesian conditioning framework may be used.” For context see also, Wingate et al., “A New Approach for Conditioning Process-Based Geologic Models to Well Data”, pgs. 375-377. Looking at claims 1 and 18, each of the limitations refers to a mathematical calculation. Combining or adding additional mathematical calculation steps does not render the claims less abstract.
As further stated in MPEP §2106.04(II)(A)(2):
“Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible.” (Emphasis Added).
In this case, a mathematical solution to a mathematically modeling problem to generate a mathematical geological model of a subsurface is ineligible under Federal Circuit case law and MPEP §2106.04(II)(A)(2). Accordingly, Applicants’ arguments are found to be unpersuasive, and the §101 rejection has been maintained.
§102/103 Rejections
Regarding claim 1, Applicants’ amendments to claim 1 overcome the §102 rejection in the amendment submitted on 03/18/2026. The rejection has therefore been withdrawn.
Regarding claim 18, Applicants argue:
“Claim 18 requires generating a complex template (such as a "lobe/channel complex template", as recited in dependent Claim 19) that is constrained to be geologically feasible, and then using that template to generate a reservoir model. Gentilhomme's wavelet coefficient optimization does not generate geologically feasible complex templates; rather, it updates mathematical coefficients representing property field variations. As such, not every element of Claim 18 is found in Gentilhomme.” (Remarks, pg. 11).
However, the Examiner respectfully disagrees as the reservoir model/template generated by Gentrilhomme is clearly geologically feasible. Feasible refers to something that is possible or doable (See www.merriam-webster.com/dictionary/feasible). Not only are reservoir models/templates produced by Gentrilhomme geologically possible and doable to create, Gentrilhomme ensures their geological accuracy (ph. [0099]-[0100], “in step 620 estimated and measured seismic data are compared to each other and if a given criterion Δ1 is met, the method advances to step 626… Then, in step 626, an overall criterion A is used to evaluate all the parameters, both seismic and production.”). Since Applicants do not explain or present any evidence as to how the reservoir models produced by Gentrilhomme are geologically impossible, Applicants’ arguments are found to be unpersuasive, the rejection has been maintained.
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.
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BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/Primary Examiner, Art Unit 2151
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