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 .
Response to Arguments
Applicant arguments filed on 7/14/26 have been considered. See Velcin which teaches the gran density calibrating against actual grain density in figures 1 and 7.
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.
Claims 1, 3-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tusa Drill Core Mineral Abundance Estimation Using Hyperspectral and High Resolution Mineralogical Data, in view of Craddock (20210231826), in further view of Velcin (Integration of Core Scale Logging, Dual Energy Computed Tomographic Imaging and Geochemical and Mineralogical Analysis of a Composite Core), in further view of Mezghani (20210255353).
Regarding claim 1, Tusa teaches using hyperspectral images of drill cores to determine distributions of mineral abundances. Tusa obtains laboratory mineral abundance ground truth using SEM MLA, links the laboratory abundances to corresponding hyperspectral spectra through multivariate regression models, applies the learned models to estimate mineral abundances across the entire drill core, and validates the image derived estimates against the laboratory ground truth (figures 4 5 and 8, abstract; sections 4.1 4.4 and section 5.1 5.2).
Tusa maps the predicted property across the drill core (section 5.1-5.2; Figs. 5 and 8),
Craddock teaches estimating mineral concentrations at successive depths of a drill core from infrared spectroscopy and determining an aggregate mineral property by summing individual mineral abundances multiplied by their respective properties (par. 58 and 69).
that mineral densities are known, that volume fractions can be obtained using the known absolute densities, and that mineral properties may be property weighted according to the mineral mass or volume fractions (par. 69 and 84-87)
identifying correlations between spectroscopy derived values and laboratory measured reference properties, selecting a fitting law such as linear or nonlinear regression, and using coefficients that optimize the fitted function (par. 71-78 and 82; Figs. 5-10).
Craddock determines properties at successive depths (par. 58 and 69).
It would have been obvious to one of ordinary skill in the art prior to effective filing date to modify Tusa to determine grain density from its image derived mineral abundance distribution as taught by Craddock. The reason is to allow accurate porosity determination.
Tusa teaches generating and applying a multivariate regression model that links laboratory ground truth to image derived estimates across the drill core (section 4.1 and Fig. 4),
Tusa and Craddock do not expressly teach calibrating the resulting image derived grain density distribution by correlating laboratory derived and image derived grain density values to generate a correlation function and then adjusting the image derived distribution with that function.
Velcin teaches a core scale workflow that uses hyperspectral mineralogical logging and image based measurements to calculate a grain density profile and porosity along a core, and then calibrates and validates the profile data against laboratory measurements made on companion core samples (pp. 2-3; Fig. 1). Comparing the calculated grain density profile with laboratory grain density measurements and plots calculated values against laboratory values to evaluate their correlation (pp. 8-9; Fig. 7).
Velcin calculates grain density along the core as a profile (pp. 2, 8 9; Fig. 7) and teaches determining total porosity using the grain density profile and a bulk density profile (pp. 3, 8-9; Fig. 7).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to include in Tusa and Craddock the ability to calibrate the image derived grain density profile as taught by Velcin. The reason is to correct discrepancies and improve the accuracy of the density profile.
Tusa, Craddock, and Velcin do not teach updating, using a well planning system, a portion of the planned well based on the total porosity.
Mezghani teaches determining core properties including density, grain density, and porosity at successive depths, assembling the properties into a core log, and using the core log to determine or control well drilling parameters, including well location and trajectory (pars. 28, 51-52).
It would have been obvious to one of ordinary skill in the art prior the effective filing date of the invention to include in Tusa, Craddock, and Velcin the ability to use a computer based well control or planning process as taught by Mezghani. The reason would have been to provide a feedback.
Regarding claim 3, see see par. 58 and 69 and claim 9 of Craddock.
Regarding claim 4, see sections 2.2 and 4.1 and figure 3 of Tusa and par. 69 of Craddock.
Regarding claim 5, see 45 of Craddock and table 1 of Tusa (scale).
Regarding claim 6, see pars. 47 and 61-63, eqn 6-10 of Craddock.
Regarding claim 7, see pars. 52 of Mezghani.
Regarding claims 8-14 and 16-20, see the rejection of claims 1 and 3-7 above.
Claim(s) 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tusa (Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data) in view of Craddock (20210231826) in further view of Mezghani (20210255353) in view of Velcin and in further view of Laakso (Applying self-organizing maps to characterize hyperspectral drill core data from three ore prospects in Northern Finland).
Tusa, Craddock, and Mezghani does not teach determining distribution of mineral abundances.
Regarding claim 2, Laakso teaches wherein determining the distribution of mineral abundances comprises classifying a spectral response from the hyperspectral image using a self-organizing map (see the abstract and section 4.2).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Velcin, Tusa, Craddock and Mezghani the ability to use SOM based classification in order to provide another means for classification and Tusa already teaches multiple kinds.
Regarding claim 15, see the rejection of claim 2.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
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