DETAILED ACTION
This action is regarding the request for continues examination (RCE) for application number 17/471,345 filed 07/29/2025. Claims 1, 9 and 17 have been amended. Claims 1, 3-9, 11-20 have been examined and are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in
37 CFR 1.17(e), was filed in this application after final rejection. Since this application is
eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e)
has been timely paid, the finality of the previous Office action has been withdrawn pursuant to
37 CFR 1.114. Applicant's submission filed on 07/29/2025 has been entered.
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(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 19 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not). Specifically, 37 CFR 1.121(c)(5) “Reinstatement of previously canceled claim. A claim which was previously canceled may be reinstated only by adding the claim as a "new" claim with a new claim number.”.
Misnumbered claim 19 has been renumbered to 21 for the purposes of examination. Further, claim 20 is dependent from 19 and will now be dependent from 21.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claims 3 and 11 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 3 and 11 are dependent from cancelled claims (claim 3 dependent from claim 2 and claim 11 dependent from claim 10).
Improper dependent claims 3 will be dependent from independent claim 1.
Improper dependent claims 11 will be dependent from independent claim 9.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim 1, 3-9, 11-16 and 20 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.
Claim 3 recites the method of claim 2 There is insufficient antecedent basis for this limitation in the claim as claim 2 has been canceled.
Claim 11 recites the medium of claim 10 There is insufficient antecedent basis for this limitation in the claim as claim 10 has been canceled.
Claim 20 recites the method of claim 19 There is insufficient antecedent basis for this limitation in the claim as claim 19 has been canceled.
Claims 1 and 9 recites the limitation with the radial basis function. There is insufficient antecedent basis for this limitation in the claim. Dependent claims inherent deficiencies from their parent claim. For the purposes of examining the limitation will be interpreted as with a radial basis function
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, 3-9, 11-18 and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites forming a data set from one or more measurements of core samples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a keeping track of the size and shape of a sample. See 2106.04.(a)(2).III.C.
The claim recites selecting one or more parameters from the data set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing the size of a sample. See 2106.04.(a)(2).III.C.
The claim recites inputting the one or more parameters into a kernel estimation function which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites selecting an input value based at least in part on the kernel density estimation which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing a variable or output of the kernel density estimation and deciding what was chosen as an input. See 2106.04.(a)(2).III.C.
The claim recites creating a corresponding synthetic target value with a radial basis function based at least in part on the input value(which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user creating a new value between the input that was chosen and the new synthetic target value (Ex. adding +.01 to the chosen value) and calling the value a synthetic data set . See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
training a petrophysical interpretation machine learning model from the data set and the synthetic data set (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
wherein the one or more measurements … are acquired from a core laboratory or from a sensor disposed downhole(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) and (c) in Claim 1 do/does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 3:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites The method of claim 2, wherein the Radial Basis Function utilizes a vector formed from one or more constraints on a training data set which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 3 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 4:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites The method of claim 1, further comprising comparing the kernel density estimation to a threshold which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 4 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 5:
The rejection of claim 4 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites The method of claim 4, further comprising discarding the kernel density estimation if it is less than the threshold which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 5 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 6:
The rejection of claim 5 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites The method of claim 5, wherein the threshold is predefined and adjustable which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 6 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 7:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites The method of claim 1, wherein the kernel density estimation comprises a kernel which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 7 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 8:
The rejection of claim 7 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites The method of claim 7.wherein the kernel is a Gaussian kernel, a linear kernel, or a cosine kernel which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 8 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites forming a data set from one or more measurements of core samples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a creating track of the size and shape of a sample. See 2106.04.(a)(2).III.C.
The claim recites selecting one or more parameters from the data set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing the size of a sample. See 2106.04.(a)(2).III.C.
The claim recites inputting the one or more parameters into a kernel estimation function which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites selecting an input value based at least in part on the kernel density estimation which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing a variable or output of the kernel density estimation and deciding what was chosen as an input. See 2106.04.(a)(2).III.C.
The claim recites creating a corresponding synthetic target value with a radial basis function based at least in part on the input value(which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user creating a new value between the input that was chosen and the new synthetic target value (Ex. adding +.01 to the chosen value) and calling the value a synthetic data set . See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
a non-transitory computer-readable tangible medium comprising executable instructions that cause a computer device to (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
training a petrophysical interpretation machine learning model from the data set and the synthetic data set (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
wherein the one or more measurements … are acquired from a core laboratory or from a sensor disposed downhole(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) and (b) does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (c) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) and (c) and (d) in Claim 9 do/does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claims 11:
The rejection of claim 9 is incorporated in claim 11. Further, Due to the substantially similar limitations and elements of claims 11 found in claims 3 the claim is rejected as not patent eligible under the same 101 analysis as claims 3.
Regarding Claims 12:
The rejection of claim 9 is incorporated in claim 12. Further, Due to the substantially similar limitations and elements of claims 12 found in claims 4 the claim is rejected as not patent eligible under the same 101 analysis as claims 4.
Regarding Claims 13:
The rejection of claim 12 is incorporated in claim 13. Further, Due to the substantially similar limitations and elements of claims 13 found in claims 5 the claim is rejected as not patent eligible under the same 101 analysis as claims 5.
Regarding Claims 14:
The rejection of claim 13 is incorporated in claim 14. Further, Due to the substantially similar limitations and elements of claims 14 found in claims 6 the claim is rejected as not patent eligible under the same 101 analysis as claims 6.
Regarding Claims 15:
The rejection of claim 9 is incorporated in claim 15. Further, Due to the substantially similar limitations and elements of claims 15 found in claims 7 the claim is rejected as not patent eligible under the same 101 analysis as claims 7.
Regarding Claims 16:
The rejection of claim 9 is incorporated in claim 16. Further, Due to the substantially similar limitations and elements of claims 16 found in claims 8 the claim is rejected as not patent eligible under the same 101 analysis as claims 8.
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites forming a data set from one or more measures of core samples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user creating data from a set of data. See 2106.04.(a)(2).III.C
The claim recites performing a principal component analysis (PCA) on one or more measurements of core samples to form synthetic data which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))) as PCA uses linear algebra and statistics to produce principal components to produces data.
The claim recites and augmenting the one or more measurements of core samples with the synthetic data which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user adding a synthetic data to a measurement. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the one or more measurements comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
are acquired from a core laboratory or from a sensor disposed downhole(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) does not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Additional element (b) does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in claim 17 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 18:
The rejection of claim 17 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites further comprising eliminating multiple dominant peaks in a latent space with the PCA which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 18 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 20:
The rejection of claim 21 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites further comprising performing a linear combination of principal components which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 20 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 21:
The rejection of claim 17 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 21 recites wherein the set of vectors are principal components of the (PC) which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 21 does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 1, 3-9 and 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HELLEM et al.(US 20210247534A1, henceforth known as HELLEM) in view of Russell et al. (“Application of the radial basis function neural network to the prediction of log properties from seismic attributes”, henceforth known as Russell) and further in view of GAN et al.(“Scalable Kernel Density Classification via Threshold-Based Pruning”, henceforth known as GAN).
Regarding Claim 1, HELLEM discloses forming a data set from one or more measurements (HELLEM, [0043], “…In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120” where seismic data and other information are considered one or more measurements and each form data sets) one or more measurements comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(HELLEM, [0063], “Some data may be involved in building an initial…may include one or more of the following: depth or thickness maps…Furthermore, data may include depth and thickness maps stemming from facies variations” where the study of sedimentology includes the study of facies variations and Hellem measuring facies variations is considered a measurement comprising sedimentology) of core samples (HELLEM, [0147], “As to types of measurements, these can include, for example, one or more of resistivity, gamma ray, density, neutron porosity, spectroscopy, sigma, magnetic resonance, elastic waves, pressure, and sample data”, where sample data is considered core samples) , wherein the one or more measurements are acquired from a core laboratory or from a sensor disposed downhole(“As an example, the geologic environment 341 may include a bore 343 where one or more sensors (e.g., receivers) 344 may be positioned in the bore 343” where a sensor in a bore set to receive information is considered measuring from a sensor downhole)
HELLEM discloses selecting one or more parameters from the data set and inputting the one or more parameters (where d(xu,x) in the below Gaussian kernel function x represents an arbitrary instance x that can be described by a feature vector X and where (xu,x) denotes the value of the u’th attribute of instance x, the distance between two instances of x and which feature vector X is considered a parameter) into a kernel estimation function (HELLEM, [0217], “discloses An example of a Gaussian kernel function is presented below” where the Gaussian kernel function is a kernel estimation function)
HELLEM discloses determining a kernel … estimation function based at least in part on the one or more parameters (where the Gaussian kernel from HELLEM is created using the features of the Seismic Data/Other Information from HELLEM, FIG 1.)
HELLEM discloses selecting an input value(HELLEM, [0280], “In such an example, the method may include training to generate the trained machine model where the training includes receiving a selected point … extracting training data based on the selected point; and performing machine learning of a machine model based on the training data to generate the trained machine model” where receiving a selected point and training based on the selected point is considered selecting an input value) based at least in part on the kernel … estimation (HELLEM, [0281], “…As an example, a method can include training a kernel based model to generate a trained kernel based model. As an example, a kernel can be a radial basis function kernel or another type of kernel” where the example of including training a kernel based model to generate a trained kernel based model is considered an input value that is based on a kernel estimation)
HELLEM discloses creating a corresponding synthetic target value(HELLEM, [0234], “As an example, a RBF network may be trained in a two-stage process when given a set of training examples” where the training and creating synthetic data of a neural network can be done with the assistance of a Radial Basis Function as training is disclosed as including and generating more data that is based on synthetic target values)… based at least in part on the input value (HELLEM, [0245], “As to training, a method may include augmenting data. For example, one or more approaches may be taken to generate more data for training where the data may be based on a smaller set of actual data and/or synthetic data” where more data is considered synthetic target value as it is based on smaller set of actual data and/or synthetic data)
HELLEM discloses augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set and training a petrophysical interpretation machine learning model from the data set and the synthetic data set (HELLEM, [0245], “As to training, a method may include augmenting data. For example, one or more approaches may be taken to generate more data for training where the data may be based on a smaller set of actual data and/or synthetic data” where using data and generating synthetic data with augmented data during training is considered training from the data set and synthetic data set and where part of training including augmenting data is considered forming a synthetic data)
While HELLEM does disclose creating a corresponding synthetic target value … based at least in part on the input value however it doesn’t explicitly disclose with a radial basis function.
Russell discloses the use of a radial basis function with respect to creating a synthetic target based on an input value to create an augmented dataset(Russell, Page 7, Equation 13, where the application of RBF to each application input xk to produce predicted output y(xk) is considered creating a corresponding synthetic target value with an RBF based on an input value.)
References HELLEM and Russell are analogous art because they are from the same field of endeavor of seismic interpretation/prediction using ML over seismic attributes/data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of HELLEM and Russell before him or her, to modify the synthetic data generation of HELLEM to include the Radial Base Function of Russell. The suggestion/motivation for doing so would have been “We would therefore expect the RBFN method to give a more high resolution result”(Russell, Page 8, Paragraph 4)
While HELLEM-Russell does disclose determining a kernel … estimation function however it doesn’t explicitly disclose a kernel density estimation function.
GAN teaches a kernel density estimation function (GAN, ABSTRACT, “Kernel Density Estimation (KDE) is a powerful technique for computing these densities, offering excellent statistical accuracy … In this paper, we introduce a simple technique for improving the performance of using a KDE to classify points by their density (density classification)” where KDE is considered a kernel density estimation)
References HELLEM-Russell and GAN are analogous art because they are from the same problem solving area of kernel-driving scoring/classification and confidence-based decisions based on thresholds.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of HELLEM-Russell and GAN before him or her, to modify the kernel estimation function of HELLEM-Russell to include the density calculation of GAN. The suggestion/motivation for doing so would have been “One of the primary benefits of using kernel density estimates is that, at scale, they are guaranteed to converge to the true probability distribution” (GAN, Page 10, Col. 1, Paragraph 2)
Regarding Claim 3, HELLEM-Russell-GAN teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated).
Russell further discloses wherein the Radial Basis Function utilizes a vector formed from one or more constraints on a training data set(Russel, Page 7, Equation 12, where t are constraints that enforce interpolation conditions(it defines the training set and requires the model to satisfy for all i y(si) = ti and t=Φw), w is weight vector which is defined in Equation 12 as having Φ (the Radial Basis Function kernel matrix) is considered having the Radial Basis Function(Φ) utilizing a vector (w) from one or more constraints on the training data set (t))
Regarding Claim 4, HELLEM-Russell-GAN teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated).
GAN further teaches further comprising comparing the kernel density estimation to a threshold (GAN, Page 1, Col. 2, Paragraph 1, “Each of these tasks requires density classification, i.e. building a model of the distribution and using it to compare a density estimate against a threshold” where using a model to compare a density estimate against a threshold is considered comparing a kernel density estimation to a threshold)
Regarding Claim 5, HELLEM-Russell-GAN teaches the method of Claim 4 (and thus the rejection of Claim 4 is incorporated).
GAN further teaches further comprising discarding the kernel density estimation if it is less than the threshold (GAN, Page 2, Col. 1, Paragraph 2, “We short-circuit the density computation as soon as these bounds are above or below the target threshold” where the short-circuit means to stop working on the kernel and discard the kernel if it is above or below the target threshold)
Regarding Claim 6, HELLEM-Russell-GAN teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated).
GAN further teaches wherein the threshold is predefined and adjustable (GAN, Page 7, Col. 2, paragraph 1, “Similarly the multiplicative factors hbackoff ,hbuffer which control how quickly we adjust bad threshold bounds” where multiplicative factors hbackoff ,hbuffer is considered to be able to adjust the threshold and, by nature of being adjustable, the threshold is predefined if the variables that change the threshold never change)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of HELLEM and GAN before him or her, to modify the kernel density estimation function of HELLEM-Russell-GAN to include the threshold of GAN as it would allow for a quicker KDE and would avoid wasting resources on a densities that fall out of the target threshold. The suggestion/motivation for doing so would have been “threshold-based pruning to spatial index traversal to achieve asymptotic speedups over naïve KDE” (GAN, Page 1, Col. 1, Abstract) and “We short-circuit the density computation as soon as these bounds are above or below the target threshold. This way, we can quickly distinguish points in dense regions from points in sparse regions, only paying for more precise density estimates on query points close to the threshold. This avoids the overwhelming majority of kernel evaluations required for density estimation while still guaranteeing classification accuracy.” (GAN, Page 2, Col. 1, Paragraph 2)
Regarding Claim 7, HELLEM-Russell-GAN teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated).
GAN further teaches wherein the kernel density estimation comprises a kernel (GAN, Page 4, Col. 1, paragraph 2, KDE constructs an estimate of the probability density by summing contributions from small kernel distributions centered at each point” where the KDE is considered to comprise a kernel as a KDE summing kernels means kernels are part of the KDE)
Regarding Claim 8, HELLEM-Russell-GAN teaches the method of Claim 7 (and thus the rejection of Claim 7 is incorporated).
GAN further teaches wherein the kernel is a Gaussian kernel, a linear kernel, or a cosine kernel (GAN, Page 4, Col. 1, Paragraph 3, “The Gaussian kernel family given in Equation 2 leads to very smooth density estimates and we will use them by default in this paper” where the kernel used in GAN’s KDE is a Gaussian kernel)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of HELLEM and GAN before him or her, to modify the kernel estimation function of HELLEM-Russell-GAN to include the Gaussian classification of kernels GAN as Gaussian kernels are made provide a smooth density. The suggestion/motivation for doing so would have been “The Gaussian kernel family given in Equation 2 leads to very smooth density estimates” (GAN, Page 4, Col. 1, Paragraph 3)
Regarding Claim 9, HELLEM discloses non-transitory computer-readable tangible medium comprising executable instructions (HELLEM, [0003], “One or more computer-readable storage media can include computer-executable instructions executable to instruct a computing system to”)
HELLEM discloses forming a data set from one or more measurements (HELLEM, [0043], “…In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120” where seismic data and other information are considered one or more measurements and each form data sets) one or more measurements comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(HELLEM, [0063], “Some data may be involved in building an initial…may include one or more of the following: depth or thickness maps…Furthermore, data may include depth and thickness maps stemming from facies variations” where the study of sedimentology includes the study of facies variations and Hellem measuring facies variations is considered a measurement comprising sedimentology) of core samples (HELLEM, [0147], “As to types of measurements, these can include, for example, one or more of resistivity, gamma ray, density, neutron porosity, spectroscopy, sigma, magnetic resonance, elastic waves, pressure, and sample data”, where sample data is considered core samples) , wherein the one or more measurements are acquired from a core laboratory or from a sensor disposed downhole(“As an example, the geologic environment 341 may include a bore 343 where one or more sensors (e.g., receivers) 344 may be positioned in the bore 343” where a sensor in a bore set to receive information is considered measuring from a sensor downhole)
HELLEM discloses selecting one or more parameters from the data set and inputting the one or more parameters (where d(xu,x) in the below Gaussian kernel function x represents an arbitrary instance x that can be described by a feature vector X and where (xu,x) denotes the value of the u’th attribute of instance x, the distance between two instances of x and which feature vector X is considered a parameter) into a kernel estimation function (HELLEM, [0217], “discloses An example of a Gaussian kernel function is presented below” where the Gaussian kernel function is a kernel estimation function)
HELLEM discloses determining a kernel … estimation function based at least in part on the one or more parameters (where the Gaussian kernel from HELLEM is created using the features of the Seismic Data/Other Information from HELLEM, FIG 1.)
HELLEM discloses selecting an input value(HELLEM, [0280], “In such an example, the method may include training to generate the trained machine model where the training includes receiving a selected point … extracting training data based on the selected point; and performing machine learning of a machine model based on the training data to generate the trained machine model” where receiving a selected point and training based on the selected point is considered selecting an input value) based at least in part on the kernel … estimation (HELLEM, [0281], “…As an example, a method can include training a kernel based model to generate a trained kernel based model. As an example, a kernel can be a radial basis function kernel or another type of kernel” where the example of including training a kernel based model to generate a trained kernel based model is considered an input value that is based on a kernel estimation)
HELLEM discloses creating a corresponding synthetic target value with a radial basis function(HELLEM, [0234], “As an example, a RBF network may be trained in a two-stage process when given a set of training examples” where the training and creating synthetic data of a neural network can be done with the assistance of a Radial Basis Function as training is disclosed as including and generating more data that is based on synthetic target values) based at least in part on the input value (HELLEM, [0245], “As to training, a method may include augmenting data. For example, one or more approaches may be taken to generate more data for training where the data may be based on a smaller set of actual data and/or synthetic data” where more data is considered synthetic target value as it is based on smaller set of actual data and/or synthetic data)
HELLEM discloses augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set; (HELLEM, [0245], “As to training, a method may include augmenting data. For example, one or more approaches may be taken to generate more data for training where the data may be based on a smaller set of actual data and/or synthetic data” where more data is considered synthetic target value and where actual data and/or synthetic data is considered an input value and where part of training may include augmenting data is considered forming a synthetic data) and training a petrophysical interpretation machine learning model from the data set and the synthetic data set (HELLEM, [0245], “As to training, a method may include augmenting data. For example, one or more approaches may be taken to generate more data for training where the data may be based on a smaller set of actual data and/or synthetic data” where using data, augmenting data with synthetic data during training is considered training from the data set and synthetic data set)
While HELLEM does disclose determining a kernel … estimation function it doesn’t explicitly use a kernel density estimation function.
GAN teaches the use of a kernel density estimation function.
GAN teaches a kernel density estimation function (GAN, ABSTRACT, “Kernel Density Estimation (KDE) is a powerful technique for computing these densities, offering excellent statistical accuracy … In this paper, we introduce a simple technique for improving the performance of using a KDE to classify points by their density (density classification)” where KDE is considered a kernel density estimation)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of HELLEM and GAN before him or her, to modify the kernel estimation function of HELLEM to include the density calculation of GAN y. The suggestion/motivation for doing so would have been “One of the primary benefits of using kernel density estimates is that, at scale, they are guaranteed to converge to the true probability distribution” (GAN, Page 10, Col. 1, Paragraph 2)
Regarding Claims 11:
The rejection of claim 9 is incorporated in claim 11. Further, Due to the substantially similar limitations and elements of claims 11 found in claims 3 the claim is rejected as not patent eligible under the same analysis as claim 3.
Regarding Claims 12:
The rejection of claim 9 is incorporated in claim 12. Further, Due to the substantially similar limitations and elements of claims 12 found in claims 4 the claim is rejected as not patent eligible under the same analysis as claim 4.
Regarding Claims 13:
The rejection of claim 12 is incorporated in claim 13. Further, Due to the substantially similar limitations and elements of claims 13 found in claims 5 the claim is rejected as not patent eligible under the same analysis as claim 5.
Regarding Claims 14:
The rejection of claim 13 is incorporated in claim 14. Further, Due to the substantially similar limitations and elements of claims 14 found in claims 6 the claim is rejected as not patent eligible under the same analysis as claim 6.
Regarding Claims 15:
The rejection of claim 9 is incorporated in claim 15. Further, Due to the substantially similar limitations and elements of claims 15 found in claims 7 the claim is rejected as not patent eligible under the same analysis as claim 7.
Regarding Claims 16:
The rejection of claim 9 is incorporated in claim 16. Further, Due to the substantially similar limitations and elements of claims 16 found in claims 8 the claim is rejected as not patent eligible under the same analysis as claim 8.
Claim(s) 17-18 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over HELLEM (US 20210247534 A1) and further in view of KRUSPE (WO 2009143424 A2).
Regarding Claim 17, HELLEM discloses forming a data set from one or more measurements (HELLEM, [0043], “…In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120” where seismic data and other information are considered one or more measurements and each form data sets) one or more measurements comprise one or more of: sedimentology, mineralogy, formation wettability, fluid saturations and distributions, formation factor, pore structure and pore volume, capillary pressure behavior, sediment grain density, horizontal and vertical permeability and relative permeabilities, porosity, and/or presence of diagenesis(HELLEM, [0063], “Some data may be involved in building an initial…may include one or more of the following: depth or thickness maps…Furthermore, data may include depth and thickness maps stemming from facies variations” where the study of sedimentology includes the study of facies variations and Hellem measuring facies variations is considered a measurement comprising sedimentology) of core samples (HELLEM, [0147], “As to types of measurements, these can include, for example, one or more of resistivity, gamma ray, density, neutron porosity, spectroscopy, sigma, magnetic resonance, elastic waves, pressure, and sample data”, where sample data is considered core samples) and are acquired from a core laboratory or from a sensor disposed downhole(“As an example, the geologic environment 341 may include a bore 343 where one or more sensors (e.g., receivers) 344 may be positioned in the bore 343” where a sensor in a bore set to receive information is considered measuring from a se