DETAILED ACTION
This action is responsive to the amendment filed on 03/27/2026. Claims 1, 3-9, 11-18 and 20-21 are pending in the case. Claims 1, 9, and 17 are independent claims. Claims 1, 3, 9, 11 and 17 are amended with claims 2, 10, 19 and 20 being cancelled.
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 .
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.
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.
Claim 1, 3-9, 11-16 and 21-22 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.
Claims 1 and 9 recites the limitation wherein the radial basis function utilizes a distance between an input value and the target value. There is insufficient antecedent basis for this limitation in the claim. Dependent claims 3-8 and 11-16 are rejected as claims dependent from claims 1 and 9 inherent deficiencies from parent claim.
Claim 21 recites the limitation the set of vectors are principal components of the (PC). There is insufficient antecedent basis for this limitation in the claim. Dependent claim 22 is rejected as claims dependent from claims 21 inherent deficiencies from parent claim.
Claim 21 recites the limitation the set of vectors are principal components of the (PC). There is insufficient antecedent basis for this limitation in the claim.
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, wherein the radial basis function utilizes a distance between an input value and a target 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 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, wherein the radial basis function utilizes a distance between an input value and a target 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 21:
The rejection of claim 17 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recite 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:
The claim 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 22:
The rejection of claim 21 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim 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:
The claim 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, [0190], “As an example, extraction of training data can be performed by taking a sub-image around each point in P (e.g., seismic trace data around each point in P), which can be positive examples. In such an example, amplitude values can be extracted at subsample precision with interpolation” where the values extracted and created used to generate positive/negative examples corresponds to creating a corresponding synthetic target value)… based at least in part on the input value (HELLEM, [0139], “…As an example, a model may be combined with a seismic wavelet (e.g., a pulse) to generate a synthetic seismic trace ” where the generation of synthetic seismic traces being based on a seismic wavelet corresponds to creating a corresponding synthetic target value based at least in part on the input value as it is used in the determination of target positive/negative examples. See also HELLEM, [0150], “…The FDMOD features can generate synthetic shot gathers by using full 3D, two-way wavefield extrapolation modelling, which can utilize wavefield extrapolation logic matches that are used by reverse-time migration (RTM)” where the generation of synthetic shot gathers corresponds creating synthetic target value based at least in part on the input value as it uses input data to generate the extrapolated model))
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 creating a corresponding synthetic target value with a radial basis function based at least in part on the input value, wherein the radial basis function utilizes a distance between an input value and a target value.
Russell discloses the use of creating a corresponding synthetic target value with a radial basis function based at least in part on the input value(Russell, Pages 4-7, Equations, 2, 9 and Equation 13, where the prediction of the value y(xk) corresponds to creating a corresponding synthetic target value with a radial basis function based at least in part on the input value as it is based on the input value xk and using Equation 2’s radial basis function being used by substituting d =xk-sj giving φ(d) = exp(|xk - sj|2/σ2 ) into Equation 13 corresponds to with a radial basis function as the equation used to create a the value y(xk) uses a radial basis function), wherein the radial basis function utilizes a distance between an input value and a target value(Russell, Pages 7, Equation 13, and Russel, Page 3, Paragraph 3, “Recall that two of these vectors are from the training dataset (si and sj) and one is from the application dataset (xk)” where |xk - sj|2 corresponds to a distance between an input and a target value as xk is a new input whose output is being predicted and sj is training input associated with a known training target tj (See also Russel, Page 3, Fig. 1 “A schematic illustration of the differences between the training vectors, si and sj, in which the output samples ti and tj are known and are used in the training process, and the application vector xk, in which the output sample yk is not known”)
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(HELLEM, [0190], “As an example, extraction of training data can be performed by taking a sub-image around each point in P (e.g., seismic trace data around each point in P), which can be positive examples. In such an example, amplitude values can be extracted at subsample precision with interpolation” where the values extracted and created used to generate positive/negative examples corresponds to creating a corresponding synthetic target value)… based at least in part on the input value (HELLEM, [0139], “…As an example, a model may be combined with a seismic wavelet (e.g., a pulse) to generate a synthetic seismic trace ” where the generation of synthetic seismic traces being based on a seismic wavelet corresponds to creating a corresponding synthetic target value based at least in part on the input value as it is used in the determination of target positive/negative examples. See also HELLEM, [0150], “…The FDMOD features can generate synthetic shot gathers by using full 3D, two-way wavefield extrapolation modelling, which can utilize wavefield extrapolation logic matches that are used by reverse-time migration (RTM)” where the generation of synthetic shot gathers corresponds creating synthetic target value based at least in part on the input value as it uses input data to generate the extrapolated model))
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 creating a corresponding synthetic target value …based at least in part on the input value however it doesn’t explicitly disclose creating a corresponding synthetic target value with a radial basis function based at least in part on the input value, wherein the radial basis function utilizes a distance between an input value and a target value.
Russell discloses the use of creating a corresponding synthetic target value with a radial basis function based at least in part on the input value(Russell, Pages 4-7, Equations, 2, 9 and Equation 13, where the prediction of the value y(xk) corresponds to creating a corresponding synthetic target value with a radial basis function based at least in part on the input value as it is based on the input value xk and using Equation 2’s radial basis function being used by substituting d =xk-sj giving φ(d) = exp(|xk - sj|2/σ2 ) into Equation 13 corresponds to with a radial basis function as the equation used to create a the value y(xk) uses a radial basis function), wherein the radial basis function utilizes a distance between an input value and a target value(Russell, Pages 7, Equation 13, and Russel, Page 3, Paragraph 3, “Recall that two of these vectors are from the training dataset (si and sj) and one is from the application dataset (xk)” where |xk - sj|2 corresponds to a distance between an input and a target value as xk is a new input whose output is being predicted and sj is training input associated with a known training target tj (See also Russel, Page 3, Fig. 1 “A schematic illustration of the differences between the training vectors, si and sj, in which the output samples ti and tj are known and are used in the training process, and the application vector xk, in which the output sample yk is not known”)
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 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 claim 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 claim 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 claim 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 claim 7 the claim is rejected as not patent eligible under the same analysis as claim 7.
Regarding claim 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 claim 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 sensor downhole)
HELLEM discloses performing a … analysis … on 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 of core samples to(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) form a synthetic data(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” where generating more data is considered synthetic data and the generation of more data may be based on actual data which is considered a set of data that was taken from measurements)
HELLEM discloses augmenting the one or more measurements of core samples with the synthetic data(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” where augmenting data to generate training data by using more data is considered augmenting one or more measurements with synthetic data)
While HELLEM does disclose performing a … analysis … on one or more measurements of core samples to produce a set of … data HELLEM doesn’t explicitly teach performing a principal component analysis (PCA) on one or more measurements of core sample to form synthetic data, wherein the one or more measurements are acquired from a core laboratory or from a sensor disposed downhole
KRUSPE teaches the use of performing a principal component analysis (PCA) on one or more measurements of core sample to form synthetic data, wherein the one or more measurements are acquired from a core laboratory or from a sensor disposed downhole.
KRUSPE teaches performing a principal component analysis (PCA) (KRUSPE, ABSTRACT, “Principal Component Analysis is used to represent the signals by a weighted combination of the principal components”) on one or more measurements of core samples(KRUSPE, [0014], “… The method includes representing, using a set of basis vectors derived from component analysis, at least one signal obtained by a nuclear magnetic resonance sensing apparatus in a borehole;” where the basis vectors are vectors obtained by a core sample and by Principal Component Analysis) to form synthetic data, (KRUSPE, [0014], “… telemetering a representation of the at least one signal as a combination of the basis vectors to a surface location” where a combination of the basis vector and telemetered signal is considered forming a synthetic data) wherein the one or more measurements are acquired from a core laboratory or from a sensor disposed downhole (KRUSPE, Figure 1 and [0019], “In one embodiment of the disclosure, a drilling sensor module 59 is placed near the drill bit 50. The drilling sensor module contains sensors,” where a sensor attached to a drill bit at the borehole bottom is considered a sensor downhole)
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 HELLEM and KRUSPE before him or her, to modify the analysis algorithm of HELLEM to include the component analysis of KRUSPE because for the lossless data compression and the ability to produce independent, uncorrelated features of the data. The suggestion/motivation for including KRUSPE’s Principal Component Analysis would be, as KRUSPE states, “In fact, if n is large enough to include all the eigenvectors with non-zero eigenvalues, the reprojection is lossless. The goal in PCA is to minimize the reconstruction error from compressed data”(KRUSPE, [0038]) for the lossless data compression and “In ICA, on the other hand the goal is to minimize the statistical dependence between the basis vectors”(KRUSPE, [0039]) for the motivation for production of independent sets of data .
Regarding claim 18, HELLEM-KRUSPE teaches the method of claim 17 (and thus the rejection of claim 17 is incorporated).
KRUSPE teaches further comprising eliminating multiple dominant peaks in a latent space with the PCA (KRUSPE, [0029], “Data compression is accomplished by truncating the matrix V to the first k rows corresponding to the dominant eigenvalues” where the "dominant eigenvalue" is considered a peak, as it represents the eigenvalue with the largest absolute value, appearing as the highest point (peak) on the graph)
Regarding claim 21, HELLEM-KRUSPE teaches the method of claim 17 (and thus the rejection of claim 17 is incorporated).
KRUSPE teaches wherein the set of vectors are principal components of the (PC)(KRUSPE , [0044], “F is a matrix that spans all single components decays, Loads is a matrix of eigenvectors of the corresponding type of acquisition (Created from Principal components decomposition of the F matrix) and …The scores Scores, of T is a linear combination of F defined by Loads” where the linear combination of F is considered a linear combination)
Regarding claim 22, HELLEM-KRUSPE teaches the method of claim 21 (and thus the rejection of claim 21 is incorporated).
KRUSPE teaches performing a linear combination of principal components(KRUSPE, [0044], “F is a matrix that spans all single components decays, Loads is a matrix of eigenvectors of the corresponding type of acquisition (Created from Principal components decomposition of the F matrix) and …The scores Scores, of T is a linear combination of F defined by Loads” where the linear combination of F is considered a linear combination)
Relevant Art:
While not used in the current rejection the following the relevant arts were found:
CN112380769A as it uses a similar radial basis function of input x to training input xi in a fashion that is similar to the claims
Response to Arguments:
Applicant's arguments filed 03/27/2026 have been fully considered but they are not persuasive. A breakdown can be found below:
Regarding 112(b) cited in previous office action
Applicant amendments appears to overcome the 112(b) rejection outlined in the previous office action.
Regarding 112(d) cited in previous office action:
Applicant amendments appears to overcome the 112(d) rejection outlined in the previous office action.
Regarding 101 arguments:
Applicant appears to argue on page 7 that the claims meets the requirement for a core laboratory and a sensor to be particular machines under MPEP 2106.05(b)(I).
Examiner respectfully disagrees as the cited section of the MPEP requires a particular machine requires a degree to which the machine in the claim can be specifically identified (not any and all machines) which is based on the particularity or generality of the elements of the machine or apparatus. Examiner did not find a specific argument that specifically points out why the core laboratory and a sensor are particular machines, rather Applicant’s arguments appear to assert they are particular machines as a general assumption. MPEP 2106.05(b)(I) requires a particular machine the particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified (not any and all machines). The sensors and core laboratory are cited at a high level with no/minimal details or identifying features as no specificity of the core laboratory or sensor disposed downhole are given. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine . As no specific details or identifying features were given of the core laboratory or sensor that can be considered specifically identifiable the limitations does not meet MPEP 2106.5(b)(I) threshold.
Applicant appears to argue on page 7 that, under MPEP 2106.05(b)(II), the obtaining measurements using a core laboratory and sensors is an integral to the performance of the invention which should integrate the recited judicial exception into a practical application or provide significantly more .
Examiner respectfully disagrees as the cited MPEP 2106.05(b)(II) requires integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. As the focus of the claims are not focused on using the core laboratory or sensor to obtain data the limitation Examiner finds it is not used as an integral part to achieve performance or recited as performing the abstract idea and does not meet MPEP 2106.5(b)(II) threshold as the core lab/sensor disposed downhole merely describes where the data is originating from.
Applicant appears to argue on page 7 that the claimed acquiring measurements are not extra solution activity because the later steps relies on particular machine to acquire measurement under MPEP 2106.05(b)(III).
Examiner respectfully disagrees as discussed above Examiner does not find the core lab or sensors to be particular machines and MPEP 2106.05(b)(III) checks if the limitation involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. As cited, the limitations merely describe the locations data(measurements) are received from rather than any particular data gathering steps, which does not meet MPEP 2106.5(b)(III) threshold.
Regarding 103 arguments:
Applicant appears to argue on page 8 that the claims Russell does not have disclose or teach the amended argument of wherein the radial basis function utilizes a distance between an input value and the target value. Applicant appears to argue that Russell’s function of the distances is different and Russell’s radial basis function does not utilize a distance between an input value and the target value.
Examiner respectfully disagrees as Russell’s Equation 13 on page 7 shows that the radial basis function used has |xk - sj|2 corresponding to a distance between an input and a target value as xk is a new input whose output is being predicted and sj is training input associated with a known training target tj. Russel outlines this on page 3, Fig. 1 “A schematic illustration of the differences between the training vectors, si and sj, in which the output samples ti and tj are known and are used in the training process, and the application vector xk, in which the output sample yk is not known”.
Applicant appears to argue on page 8-9 that a person of ordinary skill in the art would not consider synthetic data to be the same as a basis vector with telemetered signals and cites to the specification for support on the defining characteristics of the synthetic data of “underlining the relationship between input and target data embedded in the original training data set”.
Examiner respectfully disagrees as, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims do not positively recite synthetic data underlining the relationship between input and target data embedded in the original training data set. As the current claims state it is forming synthetic data, the broadest reasonable interpretation is simply forming new data and the combining of the basis vector and telemetered signal is forming new data.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES JEFFREY JONES JR whose telephone number is (703)756-1414. The examiner can normally be reached Monday - Friday 8:00 - 5:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
Prosecution Insights