DETAILED ACTION
Claims 1, 4-7, 15, and 18-20 are presented for examination.
Claims 2-3, 8-14, and 16-17 have been cancelled.
Claims 1 and 15 have been amended.
This office action is in response to the request for continued examination submitted on 03-NOV-2025.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/12/2025 has been entered.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to 35 U.S.C. 101 have been fully considered and are persuasive. The rejection of 35 U.S.C. 101 has been withdrawn.
Response to Arguments - 35 USC § 103
On pgs. 10-13 of the Applicant’s Arguments/Remarks dated 11/03/2025 (hereinafter ‘Remarks’), Applicant argues the claims prior art of Bahr et al., “Exponential approximations to compacted sediment porosity profiles” [2001] (hereinafter ‘Bahr’) in view of Bootstrapping (statistics) Wikipedia entry Wayback machine 03 Oct 2020 (hereinafter ‘Wikipedia’) further in view of Renaudeau et al., U.S. Patent Application Publication 2018/0347320 A1 (hereinafter ‘Renaudeau’) does not teach the claims as presented. Examiner respectfully disagrees.
On pg. 11 of the Remarks, the following limitations are argued.
(b) for each sequence among the first sequence and the plurality of alternate sequences: fitting a porosity compaction model to each sequence, wherein the porosity compaction model comprises a porosity compaction model parameter comprising a compaction coefficient
(c) fitting a probability density function to a histogram of the plurality of values of the compaction coefficient
Applicant argues the above limitations were cited in the Final Rejection as not taught by Bahr. Examiner notes, Bahr is in combination with Wikipedia. Some of the elements may have been taught by Bahr but presented using methods in Wikipedia, notably the compaction coefficient.
Applicant argues the following on pg. 11 of the Remarks,
“Wikipedia fails to supply that which Bahr lacks. Wikipedia fails to disclose or suggest at least limitations (b) and (c). Wikipedia fails to disclose or suggest a porosity compaction model, let alone fitting the porosity compaction model to each sequence among the first sequence and the plurality of alternate sequences (i.e., the bootstrapped alternate sequences). Wikipedia discloses fitting a parametric model to an original data set such that "samples of random numbers [can be] drawn from this fitted [parametric] model" to thereby determine a bootstrapped data set. Wikipedia at 6, para. 2. Wikipedia thus discloses a method of bootstrapping the original data set using the fitted parametric model. In contrast to Wikipedia, the recited porosity compaction model is fit to each sequence among the already bootstrapped alternate sequences. Wikipedia fails to disclose or suggest fitting any type of model to Wikipedia's bootstrapped data set as may be needed for Wikipedia to read on limitation (b).”
Examiner interprets the claims to be performing bootstrapping. The “alternate sequences” are used in performing this function. This alternate sequence can also be seen in Fig. 3, elements 310-316. Further, dependent claim 4 recites specifically using bootstrapping as a method for performing this function. The combination of Bahr and Wikipedia is applying the method of bootstrapping to Bahr which Bahr determines the compaction coefficient.
Applicant argues the following on pg. 12 of the Remarks,
“It then follows that Wikipedia fails to disclose or suggest a histogram of the plurality of values of the compaction coefficient of the porosity compaction model as limitation (c) requires. Wikipedia discloses creating "a histogram of bootstrap means." Wikipedia at 3, para. 1. In Wikipedia's example, a height of each individual among a sample size of N individuals is measured. The set of N heights are then bootstrapped, meaning that N samples are randomly selected from the N heights to generate a bootstrapped set of N heights. This process continues to generate many bootstrapped sets. The bootstrap mean or average of each bootstrapped set is determined. A histogram of the bootstrap mean for all bootstrapped sets is determined. By definition, the histogram provides a frequency or count of bootstrap means among the bootstrapped sets. In view of this, Wikipedia's histogram of bootstrap means would not have taught the recited histogram of the plurality of values of the compaction coefficient. Wikipedia's bootstrap means and the recited plurality of values of the compaction coefficient are completely different measures. For Wikipedia's histogram to read on the recited histogram, a porosity compaction model may need to be fit to each of Wikipedia's bootstrapped sets, a value of a compaction coefficient determined for each of Wikipedia's bootstrapped sets, and a histogram of the plurality of values of the compaction coefficient determined. The recited histogram is thus not derived directly from each sequence as Wikipedia's histogram is but is derived from fitting a porosity compaction model to each sequence.”
Examiner is interpreting the claim in view of Applicant’s drawing Fig. 3. In Fig. 3, there appears to be three alternate sequences and produce the distributions as 322 and 332. The method of bootstrapping is generating the different sets leading to a pdf and histogram. The method described in Wikipedia also produces the histogram on pg. 13. The histogram is a discrete approximation of the continuous probability density function.
Applicant argues the following on pg. 12 of the Remarks,
“It then further follows that Wikipedia fails to disclose or suggest fitting a probability density function to the histogram of the plurality of values of the compaction coefficient as limitation (c) also requires. By definition, a histogram provides a frequency or count of some variable. A probability density function can be fit to a histogram but a histogram, in and of itself, is not a probability density function. Wikipedia is silent regarding a probability density function and fitting thereof.”
The rejection has been updated to further provide clarification of performing this function.
Applicant’s arguments with respect to the rejection(s) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of
Bahr et al., “Exponential approximations to compacted sediment porosity profiles” [2001] (hereinafter ‘Bahr’) in view of
Bootstrapping (statistics) Wikipedia entry Wayback machine 03 Oct 2020 (hereinafter ‘Wikipedia’) further in view of
Venkatakrishnan et al., U.S. Patent Application Publication 2021/0285316 A1 (hereinafter ‘Venkatakrishnan’) further in view of
Intro to Statistics: Part 6: Probability Density Functions wayback machine 20 May 2018 (hereinafter ‘Intro to Statistics’).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-7, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Bahr et al., “Exponential approximations to compacted sediment porosity profiles” [2001] (hereinafter ‘Bahr’) in view of
Bootstrapping (statistics) Wikipedia entry Wayback machine 03 Oct 2020 (hereinafter ‘Wikipedia’) further in view of
Venkatakrishnan et al., U.S. Patent Application Publication 2021/0285316 A1 (hereinafter ‘Venkatakrishnan’) further in view of
Intro to Statistics: Part 6: Probability Density Functions wayback machine 22 May 2018 (hereinafter ‘Intro to Statistics’).
Regarding Claim 1: A method comprising:
Bahr teaches determining a first sequence of depth-porosity duplets… (Pg. 692 left col 2nd paragraph Bahr teaches a collection of sediment cores with depth and porosity in a large collection “…In this paper we describe the differential equation’s underlying exponential trend between porosity and depth of sediment burial and then demonstrate agreement with a large collection of porosity data from marine sediment cores…”
Bahr teaches …a porosity compaction … (Pg. 694 left col 3rd paragraph Bahr teaches the compaction based on porosity values “…We have devised a numerical method to solve the more general compaction equation (2). This eliminates some of the assumptions that we used to obtain the above analytical solution. Namely, different sediments are now able to both compact to different minimum porosities (have different values for Φmin), and compact at different rates (have different values for c)…”)
Bahr teaches wherein the porosity compaction model comprises a porosity compaction model parameter comprising a compaction coefficient, and generating, by fitting, a value of the compaction coefficient, wherein the value is among a plurality of values of the compaction coefficient;
(Interpreted in view of [0025] of the specification as published equation 1
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Pg. 694 left col last paragraph eqn. 7 Bahr teaches
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where the variables line up nearly identical with the notable different being a z is now σ. The equation is the porosity compaction model. The compaction coefficient is represented a c.
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Pg. 696 right col 3rd paragraph Bahr teaches a fit among the plurality of values “…As with the real data (presented previously), sediments were separated into lithologies and then porosity as a function of depth was plotted for an individual sediment type. Figs. 7 and 8 show the porosity trends with burial depth when the compaction routine was not and was used, respectively. Fig. 8 displays the obvious exponential trend for modeled silts, and shales when our compaction routine is used (R ¼ 0:907 and R ¼ 0:955, respectively). In contrast, Fig. 7 which very low correlation of porosity with depth (R ¼ 0:105 for shale, and R ¼ 0:456 for silt). We have left the sands out of this experiment as this lithology was only found at shallow burial depths and so experienced minimal compaction…”)
Pg. 695 left col 2nd paragraph Bahr teaches using an exponential fit “…On a log-linear plot (Fig. 4), however, the data appears convincingly linear, demonstrating that as predicted from the analytical solutions the observed porosities show an exponential trend with depth. For the exponential fits, the correlation coefficients are improved to R ¼ 0:73 for shale, R ¼ 0:88 for silt, and R ¼ 0:63 for sandstone…”)
Bahr does not appear to explicitly disclose
generating a plurality of alternate sequences of depth-porosity duplets based on resampling the first sequence;
for each sequence among the first sequence and the plurality of alternate sequences: fitting a porosity compaction model to each sequence,
fitting a probability density function to a histogram of the plurality of values of compaction coefficient;
quantifying, by fitting, an uncertainty in the compaction coefficient based on the probability density function;
However, Wikipedia teaches generating a plurality of alternate sequences of depth-porosity duplets based on resampling the first sequence; (Pg. 3 1st paragraph Wikipedia teaches resampling to produced resampled data to achieve alternate sequence “…sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N. The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5]and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample…”)
Wikipedia teaches for each sequence among the first sequence and the plurality of alternate sequences: fitting a porosity compaction model to each sequence, (Pg. 6 2nd paragraph Wikipedia teaches fitting the model for each random sample, i.e. alternate sequence for multiple times “…Based on the assumption that original data set is realization of a random sample from a distribution of a specific parametric type, in this case a parametric model is fitted by parameter θ, often by maximum likelihood, and samples of random numbers are drawn from this fitted model. Usually the sample drawn has the same sample size as the original data. Then the estimate of original function F can be written as F^=F^0. This sampling process is repeated many times as for other bootstrap methods…)
Wikipedia teaches … a histogram of the plurality of values of compaction coefficient; (Pg. 3 1st paragraph Wikipedia teaches using a histogram to estimate the shape of the distribution, i.e. probability density function where the plurality of values is shown “…We now can create a histogram of bootstrap means. This histogram provides an estimate of the shape of the distribution of the sample mean from which we can answer questions about how much the mean varies across samples…”)
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Bahr and Wikipedia are analogous art because they are from the same field of endeavor, computer aided modeling and statistics.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the wherein the porosity compaction model comprises a porosity compaction model parameter comprising a compaction coefficient, and generating, by fitting, a value of the compaction coefficient, wherein the value is among a plurality of values of the compaction coefficient as disclosed by Bahr by generating a plurality of alternate sequences of depth-porosity duplets based on resampling the first sequence and for each sequence among the first sequence and the plurality of alternate sequences: fitting a porosity compaction model to each sequence and fitting a probability density function to a histogram of the plurality of values of compaction coefficient and quantifying, by fitting, an uncertainty in the compaction coefficient based on the probability density function as disclosed by Wikipedia.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the statistical modeling by deriving confidence intervals and to check the stability as the results as discussed on pg. 3 2nd paragraph section Advantages in Wikipedia “…A great advantage of bootstrap is its simplicity. It is a straightforward way to derive estimates of standard errors and confidence intervals for complex estimators of complex parameters of the distribution, such as percentile points, proportions, odds ratio, and correlation coefficients. Bootstrap is also an appropriate way to control and check the stability of the results. Although for most problems it is impossible to know the true confidence interval, bootstrap is asymptotically more accurate than the standard intervals obtained using sample variance and assumptions of normality.[16] The bootstrapping also is a convenient method that avoids the cost of repeating the experiment to get other groups of sample data…”
Bahr and Wikipedia do not appear to explicitly disclose
Iteratively: drilling, using a drill system, a portion of a borehole through sedimentary layers;
obtaining, while drilling, a wellbore log associated with the portion of the borehole;
using the wellbore log;
defining a sedimentary basin model based on the uncertainty,
wherein the sedimentary basin model models an evolution of a basin over a geological time;
determining a probability of a location of a hydrocarbon resource based on the sedimentary basin model; and
updating the drill system by directing a drill bit of the drill system towards the location of the hydrocarbon resource.
However, Venkatakrishnan teaches Iteratively: drilling, using a drill system, a portion of a borehole through sedimentary layers; ([0150] Venkatakrishnan “…As an example, the BHA 814 may include sensors 808, a rotary steerable system 809, and a bit 810 to direct the drilling toward the target guided by a pre-determined survey program for measuring location details in the well. Furthermore, the subterranean formation through which the directional well 817 is drilled may include multiple layers (not shown) with varying compositions, geophysical characteristics, and geological conditions. Both the drilling planning during the well design stage and the actual drilling according to the drilling plan in the drilling stage may be performed in multiple sections (e.g., sections 801, 802, 803 and 804) corresponding to the multiple layers in the subterranean formation. For example, certain sections (e.g., sections 801 and 802) may use cement 807 reinforced casing 806 due to the particular formation compositions, geophysical characteristics, and geological conditions…”)
Venkatakrishnan teaches obtaining, while drilling, a wellbore log associated with the portion of the borehole; ([0066] Venkatakrishnan “…The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254, a measuring-while-drilling (MWD) module 256, an optional module 258, a roto-steerable system and motor 260, and the drill bit 226…”)
Venkatakrishnan teaches using the wellbore log; ([0153] Venkatakrishnan “…The static and dynamic data collected via a bore, a formation, equipment, etc. may be used to create and/or update a three dimensional model of one or more subsurface formations. As an example, static and dynamic data from one or more other bores, fields, etc. may be used to create and/or update a three dimensional model. As an example, hardware sensors, core sampling, and well logging techniques may be used to collect data. As an example, static measurements may be gathered using downhole measurements, such as core sampling and well logging techniques. Well logging involves deployment of a downhole tool into the wellbore to collect various downhole measurements, such as density, resistivity, etc., at various depths. Such well logging may be performed using, for example, a drilling tool and/or a wireline tool, or sensors located on downhole production equipment…”)
Venkatakrishnan teaches defining a sedimentary basin model based on the uncertainty, ([0273] Venkatakrishnan “…As explained, a well construction process automation system can provide for tracking of the state or states of equipment and/or operations with a degree of confidence as to safe and efficient operations. Robust state detection can be performed in a manner to handle uncertain models and data from imperfect sensors. As explained, a system such as the system 1500 of FIG. 15 can provide for practical implementation of well construction state inference, for instance bit interacting with rock and slips status…”)
Venkatakrishnan teaches wherein the sedimentary basin model models an evolution of a basin over a geological time; ([0099] Venkatakrishnan “…As an example, a framework may provide for modeling petroleum systems. For example, the modeling framework marketed as the PETROMOD® framework (Schlumberger Limited, Houston, Tex.) includes features for input of various types of information ( e.g., seismic, well, geological, etc.) to model evolution of a sedimentary basin. The PETRO MOD® framework provides for petroleum systems modeling via input of various data such as seismic data, well data and other geological data, for example, to model evolution of a sedimentary basin. The PETROMOD® framework may predict if, and how, a reservoir has been charged with hydrocarbons, including, for example, the source and timing of hydrocarbon generation, migration routes, quantities, pore pressure and hydrocarbon type in the subsurface or at surface conditions. In combination with a framework such as the PETREL® framework, workflows may be constructed to provide basin-to-prospect scale exploration solutions. Data exchange between frameworks can facilitate construction of models, analysis of data ( e.g., PETROMOD® framework data analyzed using PETREL® framework capabilities), and coupling of workflows…”)
Venkatakrishnan teaches determining a probability of a location of a hydrocarbon resource based on the sedimentary basin model; and ([0154] Venkatakrishnan “…To facilitate the processing and analysis of data, simulators may be used to process data. Data fed into the simulator(s) may be historical data, real time data or combinations thereof. Simulation through one or more of the simulators may be repeated or adjusted based on the data received. As an example, oilfield operations can be provided with wellsite and non-wellsite simulators. The wellsite simulators may include a reservoir simulator, a wellbore simulator, and a surface network simulator. The reservoir simulator may solve for hydrocarbon flowrate through the reservoir and into the wellbores. The wellbore simulator and surface network simulator may solve for hydrocarbon flowrate through the wellbore and the surface gathering network of pipelines…”
[0196] Venkatakrishnan “…As an example, a system can utilize a Bayesian network backed by a mixture model to provide fast, adaptive and robust detection of one or more states from drilling operation time series data with complex temporally correlated patterns. By learning from data and using priors from domain experts, inference features of such a system can optionally be operated without user tuned thresholds or parameters. A system, as being or including a computational framework with appropriate interfaces, can include features for implementing a probabilistic Bayesian approach to characterize and act on uncertainty in drilling systems ( e.g., rigsite systems, etc.). Such a system can be extensible in that, for example, additional observations drawn from new types of measurements can be integrated to detect additional states and/or reduce uncertainty in a core set of states…”)
Venkatakrishnan teaches updating the drill system by directing a drill bit of the drill system towards the location of the hydrocarbon resource. ([0074] Venkatakrishnan “…The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, can allow for implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets…”)
Bahr, Wikipedia, and Venkatakrishnan are analogous art because they are from the same field of endeavor, computer aided modeling and statistics.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the wherein the porosity compaction model comprises a porosity compaction model parameter comprising a compaction coefficient, and generating, by fitting, a value of the compaction coefficient, wherein the value is among a plurality of values of the compaction coefficient as disclosed by Bahr and Wikipedia by drilling, using a drill system, a portion of a borehole through sedimentary layers and obtaining, while drilling, a wellbore log associated with the portion of the borehole and defining a sedimentary basin model based on the uncertainty, wherein the sedimentary basin model models an evolution of a basin over a geological time and determining a probability of a location of a hydrocarbon resource based on the sedimentary basin model and updating the drill system by directing a drill bit of the drill system towards the location of the hydrocarbon resource as disclosed by Venkatakrishnan.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the exploration phase of drilling as discussed in [0004] “…Field planning can occur over one or more phases, which can include an exploration phase that aims to identify and assess an environment (e.g., a prospect, a play, etc.), which may include drilling of one or more bores ( e.g., one or more exploratory wells, etc.). Other phases can include appraisal, development and production phases…”
Bahr, Wikipedia, and Venkatakrishnan do not appear to explicitly disclose
fitting a probability density function to a histogram of the plurality of values of compaction coefficient;
quantifying, by fitting, an uncertainty in the compaction coefficient based on the probability density function;
However, Intro to Statistics teaches fitting a probability density function to a histogram of the plurality of values of compaction coefficient; (Pg. 1 Intro to Statistics “…In the previous article we used a density histogram to chart the distribution of a random variable representing people's heights. We also discussed how to interpret probability densities and how to convert them to probabilities. In the process we caught a glimpse of a probability density function when we overlaid the sample data with a normal distribution curve…”)
Intro to Statistics teaches quantifying, by fitting, an uncertainty in the compaction coefficient based on the probability density function; (Pg. 5 Intro to Statistics “We can use pnorm to determine the area of the region shaded in red above,which gives us the probability of observing a height between 70 - 71in(according to the normal distribution curve that we fitted to the data). Firstwe compute the pnorm result for 71in, which gives us the probability of 71 orless, then subtract away the pnorm result for 70in. This leaves us with theprobability of observing an outcome in the range 70 – 71in”)
Bahr, Wikipedia, Venkatakrishnan, and Intro to Statistics are analogous art because they are from the same field of endeavor, computer aided modeling and statistics.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the wherein the porosity compaction model comprises a porosity compaction model parameter comprising a compaction coefficient, and generating, by fitting, a value of the compaction coefficient, wherein the value is among a plurality of values of the compaction coefficient as disclosed by Bahr, Wikipedia, and Venkatakrishnan by fitting a probability density function to a histogram of the plurality of values of compaction coefficient and quantifying, by fitting, an uncertainty in the compaction coefficient based on the probability density function as disclosed by Intro to Statistics.
One of ordinary skill in the art would have been motivated to make this modification in order to perform basic statistical functions.
Regarding Claim 4: Bahr, Wikipedia, Venkatakrishnan, and Intro to Statistics teach The method of claim 1,
Wikipedia teaches wherein resampling the first sequence of depth-porosity duplets comprises using at least one of a bootstrapping method; a jackknifing method; or a Monte Carlo method. (Pg. 1 2nd paragraph Wikipedia teaches bootstrapping “…Bootstrapping estimates the properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution…”)
Regarding Claim 5: Bahr, Wikipedia, Venkatakrishnan, and Intro to Statistics teach The method of claim 1,
Bahr teaches wherein the porosity compaction model parameter comprises an archaic porosity parameter. (Pg. 694 left col last paragraph eqn. 7 Bahr teaches
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(Pg. 694 right col 1st paragraph Bahr teaches solving for the different variables “…In our model, the loading s is strictly the weight of the overlying sediment. If a value for excess pore pressure were known it would be subtracted from this load. Since both c and fmin are grain type dependent, Eq. (7) must be solved separately for each of these types…”)
In solving for Φ0, the following can be accomplished by simple rearrangement of the equation.
Φ - Φmin = (Φ0 - Φmin)e-cσ
(Φ - Φmin)/(e-cσ) = (Φ0 - Φmin)
(Φ - Φmin)ecσ = (Φ0 - Φmin)
Φmin + (Φ - Φmin)ecσ = Φ0
Φ0 = Φmin + (Φ - Φmin)ecσ
This is equivalent to eqn 2 in [0025] of the specification as published.
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Regarding Claim 6: Bahr, Wikipedia, Venkatakrishnan, and Intro to Statistics teach The method of claim 1,
Bahr teaches wherein the probability density function comprises at least one of a uniform distribution; a normal distribution; a skew distribution; an exponential distribution; a power-law distribution; or a binomial distribution. (Pg. 695 right col 1st paragraph Bahr teaches the power law and the exponential distributions “…A power-law curve can always be reasonably fit to an exponential over some limited range, but regressions on the data show that the exponential fits for porosity are consistantly better than the power-law fits (see regression coefficients above)…”)
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Regarding Claim 7: Bahr, Wikipedia, Venkatakrishnan, and Intro to Statistics teach The method of claim 1, wherein the histogram displays a frequency for each value within the plurality of values. (Pg. 12 last paragraph Wikipedia teaches a histogram where each value is show in the plot displaying the frequency “…Histograms of the bootstrap distribution and the smooth bootstrap distribution appear below. The bootstrap distribution of the sample-median has only a small number of values. The smoothed bootstrap distribution has a richer support…”)
Claims 15 and 18-20 are system claims, containing substantially the same elements as method Claims 1 and 4-6, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1 and 4-6, respectively, Mutatis mutandis.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yen, “An Introduction to the Bootstrap Method” [26 Jan 2109] teaches several fundamental elements of Bootstrapping and how to implement the bootstrap method.
Efron, “Bootstrap Methods: Another Look at the Jackknife” [1979] is an early academic paper on implementing Bootstrap methods and Jackknife.
Mathworks document on Bootstrp Wayback Machine [05 Aug 2020] teaches the implementation of bootstrapping using the Bootstrp command. Syntax and requires for the process along with examples are included.
Conclusion
Claims 1, 4-7, 15, and 18-20 are rejected.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187