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 .
Status of the Claims
Claims 1, 2, 4, 6-12 and 20-28 are rejected under 35 USC §101.
Claims 3, 5 and 13-19 are canceled claims.
Claims 21-28 are new claims.
35 USC § 112 Rejections
Based on the changes introduced by amendment of (11 November 2025), the 35 U.S.C. 112 (pre-AIA ), second paragraph Rejection of Claim 5 is withdrawn.
Remarks
Applicant’s arguments, filed (11 November 2025), with respect to pending claims 1, 2, 4, 6-12 and 20-28 have been fully considered and are directed to claims as amended. The arguments addressed to the 101, rejection is not persuasive, but persuasive with respect to 103 Rejection.
Arguments
Regarding 35 USC §101
1. The Applicant argues (Pages 8, line 20 through 9, line 10):
“Each of independent claims 1, 20 and 23 recites patent-eligible subject matter, which is not merely an abstract idea. Rather, the subject matter, as claimed, provides a practical application in the field of well logging and reservoir evaluation, specifically in environments with low resistivity where conventional measurement tools fail. Each claim recites a series of concrete steps. These steps include: (1) obtaining specific types of well log data from a target well and from existing wells; (2) obtaining known RSS logs of existing wells, where such logs are only available through specialized measurements; (3) using the well log data and known RSS logs as training data for an artificial intelligence (AI) model; (4) training the AI model to generate a trained model; and (5)
using the trained model to predict an RSS log for the target well, which otherwise cannot be measured directly due to the reservoir's low resistivity.
This process is not an abstract idea because it is rooted in a specific technological context-namely, the generation of true sand resistivity (RSS) logs in reservoirs where conventional resistivity measurements are inadequate. The claim does not merely recite generic data processing or mathematical algorithms; instead, it applies a series of well-defined, technical steps to solve a real-world, technological problem.
The use of AI to predict RSS logs based on specific well log types constitutes a practical application, enabling improved reservoir characterization and more accurate resource estimation where direct measurements are not possible.”
The Examiner respectfully disagree with assertion above.
The Claims just directed to the general field of use (well reservoir).
The examiner respectfully submits that the scope of the claim includes performing a assigning known RSS logs/assigning logic data to the AI model; predicting a RSS log…, …generate a trained AI model; which is related to the mathematical steps on the recited obtained logic data and known RSS logs.
The claims limitations are not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself;
a particular machine;
effecting a transformation or reduction of a particular article to a different state or thing.
Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field.
The obtaining steps without any description of any means to perform the making of any measurements other than the use of a generic processor, thus these steps include insignificant extra solution data gathering.
There is no particular machine recited, no real-world transformation recited in claims 1, 20 and 23.
2. The Applicant argues (Page 9 , lines 11-21):
“The claimed subject matter also provides "significantly more" than an abstract idea by
specifying a particular machine (the processor) and a particular set of data inputs (well logs of multiple types and known RSS logs), and by requiring the training and use of an AI model tailored to the unique challenges of low-resistivity reservoirs. This is not a case of simply organizing human activity or mental steps; rather, the claimed subject matter provides a technical solution to a technical problem, as confirmed by the requirement that the RSS log cannot be measured by conventional tools due to physical limitations in the reservoir.
Accordingly, under the Alice/Mayo framework, the independent claims are not directed to an abstract idea. Instead, the claims recite an inventive concept that transforms the nature of the claims into patent-eligible applications. The recited steps are not routine or conventional; they represent a specific and novel approach for generating critical reservoir data where direct measurement is not feasible.”
These arguments not persuasive to the Examiner.
The present claims do not recite an improvement in a particular computer system, or in computer components/the processor, or in any computer-related technology. Instead, the claims recite performing a mathematical calculation and the claims would tend to monopolize all uses of the mathematical calculation. Any computer being used in these claims merely acts as a tool to perform routine calculations, which could easily be performed by a generic computer structure. In order to amount to significantly more than the abstract idea, the claim must have additional elements which make the claim, taken as a whole, significantly more than the abstract idea. For claims 1, 20 and 23 for instance, the only additional element is making the obtaining basic log data and RSS logs, which are just obtaining data which is insignificant additional steps.
Regarding 35 USC §103
3.The Applicant argues (Page 11, lines 3-20):
“…the claim recites "training the AI model using the assigned training inputs and
training outputs to generate a trained AI model." The "assigned inputs" include "the basic log data of the existing wells." The "assigned outputs" include "the known RSS logs of the existing wells."
Thus, the claimed "AI model" is trained to correlate "basic log data of the existing wells" and "known RSS logs of the existing wells." Once trained, the claimed "AI model" "predict[s] a RSS target well using the basic log data of the target well" because the "[RSS] log [of the of [a] Log target well] cannot be measured by a multicomponent or tri-axial induction resistivity logging tool due to the low resistivity of the reservoir."
Neither Kim nor Clegg have been shown to describe or suggest training an Al model by providing basic logs and RSS logs of existing wells as inputs, and using the trained model to predict an RSS log of a training well using a basic log of that training well. Kim states that "considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the [deep neural network]." Abstract. The Office Action notes that Kim does not address RSS logs. Office Action, page 7.
The Office attempts to rely on Clegg to remedy this deficiency by stating that "Clegg disclose a resistivity (RSS) log (Fig. 7, para [0061]"). Id. Clegg's Fig. 7 is "an example graph 700 showing deflection of two components of resistivity using a simulated set of inputs." Clegg,[0061]. Clegg's Fig. 7 does not show "true sand resistivity (RSS) logs," as claimed.”
Examiner agree with assertion above. Therefore, the 35 USC §103 Rejection is withdrawn, based on the new amendments and arguments.
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, 2, 4, 6-12 and 20-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more as addressed below.
The new 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (Vol. 84 No. 4, Jan 7, 2019 pp 50-57) has been applied and the claims are deemed as being patent ineligible.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a
statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C.
101: Process, machine, manufacture, or composition of matter. The below claims is considered to be in a statutory category (process).
Under Step 2A Prong 1, the independent claim 1 all include abstract ideas as highlighted (using a bold font) shown below.
“Claim 1. A computer-implemented method, comprising:
obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well formed in a reservoir having a low resistivity and for which a true sand resistivity (RSS) log cannot be measured by a multicomponent or tri-axial induction resistivity logging tool due to the low resistivity of the reservoir, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray ;and
obtaining known RSS logs of existing wells, wherein each existing well is within a defined proximity of the target well, wherein the known RSS logs of the existing wells were obtained based on at least one measurement of the multicomponent or tri-axial induction resistivity logging tool;
obtaining basic log data of the existing wells, the basic log data of the existing wells comprising well logs of multiple types of the existing wells, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray;
assigning the basic log data of the existing wells as training inputs of an artificial
intelligence (AI) model;
assigning the known RSS logs of the existing wells as training outputs of the AI model:
training the AI model using the assigned training inputs and training outputs to generate a trained AI model; and
predicting a RSS log of the target well using the trained artificial intelligence (AI) model using the basic log data of the target well as a test input to the trained AI model.”
“Claim 20. A computing system comprising:
at least one processor; and
at least one non-transitory machine readable storage medium coupled to the at least one processor having machine-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well, multiple types of the target well formed in a reservoir having a low resistivity and for which a true sand resistivity (RSS) log cannot be measured by a multicomponent or tri-axial induction resistivity logging tool due to the low resistivity of the reservoir, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray;
obtaining known RSS logs of existing wells, wherein each existing well is within
a defined proximity of the target well, wherein the known RSS logs of the existing wells were obtained based on at least one measurement of the multicomponent or tri-axial induction resistivity logging tool;
obtaining basic log data of the existing wells, the basic log data of the existing
wells comprising well logs of multiple types of the existing wells, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray;
assigning the basic log data of the existing wells as training inputs of an artificial intelligence (AI) model;
assigning the known RSS logs of the existing wells as training outputs of the AI
model:
training the AI model using the assigned training inputs and training outputs to
generate a trained AI model; and
predicting a RSS log of the target well using the trained artificial intelligence (AI) model using the basic log data of the target well as a test input to the trained AI model.”
“Claim 23. A non-transitory machine readable storage medium storing machine-executable
instructions stored thereon and coupled to at least one processor to cause the at least one processor to perform operations comprising:
obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well formed in a reservoir having a low resistivity and for which a true sand resistivity (RSS) log cannot be measured by a multicomponent or tri-axial induction resistivity logging tool due to the low resistivity of the reservoir, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray;
obtaining known RSS logs of existing wells, wherein each existing well is within a defined proximity of the target well, wherein the known RSS logs of the existing wells were obtained based on at least one measurement of the multicomponent or tri-axial induction resistivity logging tool;
obtaining basic log data of the existing wells, the basic log data of the existing wells comprising well logs of multiple types of the existing wells, wherein the multiple types comprise two or more of resistivity, density, neutron and gamma ray;
assigning the basic log data of the existing wells as training inputs of an artificial intelligence (AI) model;
assigning the known RSS logs of the existing wells as training outputs of the AI model;
training the AI model using the assigned training inputs and training outputs to generate a trained AI model; and
predicting a RSS log of the target well using the trained artificial intelligence (AI) model using the basic log data of the target well as a test input to the trained Al model.”
The steps of indicated as Abstract idea is considered to be equivalent of a mathematical calculations or directed to mental process concepts performed in the human mind (including observation, evaluation and opinion).
Under step 2A prong 2,
The claims do not comprises any particular field of use and claims do not direct to any practical application.
Under step 2B:
The Claims 1, and 20 does not comprise any additional elements into which the Abstract idea can be integrated to create a practical application.
The steps of “obtaining well data of a target well, the well data comprising well logs of multiple types of the target well” in claims 1 and “obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well, the multiple types comprising two or more of resistivity, density, neutron, and gamma ray” in claim 20 and 23 just obtaining data steps, which is insignificant extra solution activity.
Claim 20 is comprises a “processor” and " the computer and software running on the computer" is the computer and software running on the computer.
The computer is the general computer, which is not significantly more.
Claim 23 is comprises a “a non-transitory machine readable storage medium storing machine-executable instructions stored thereon and coupled to at least one processor to cause the at least one processor" is the parts of the computer and software running on the computer. The computer is the general computer, which is not significantly more.
The claims 2, and 3 just additionally describes the data.
Claim 5 just additionally obtaining data, which is insignificant additional steps.
The depended claims 4, 6-10, 21, 22, 25, 26, 27 and 28 are merely extend the details of the abstract idea of mathematical concepts, more particularly mathematical calculations or mental steps as accrued.
Claim 11 comprises the type of drilled well, which is routing and common steps in the art. Additionally, claims 11 and 12 just describes more details of obtained data through well logs, which is insignificant extra solution activity.
Claim 24 comprises the target well is formed in a laminated shale or silty sand reservoir, which is routing and common installation/steps in reservoir formation in the art. Additionally, claim 24 just describes more details of formation of reservoir, which is insignificant extra solution activity.
Therefore claims 2, 4, 6-12, 21, 22, and 24-28 are similarly rejected under 35 U.S.C. 101.
1) Examiner note regarding the prior art of the record:
Regarding Claim 1, Kim (S. Kim and et al, “Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada”, disclose a computer-implemented method, comprising:
obtaining basic log data of a target well ([Fig. 1a 1g]), the basic log data comprising well logs of multiple types of the target well (sonic & density logs with the location information of the well [pg. 3 right col par 1]); and
predicting log of the target well using a trained artificial intelligence (AI) model with inputs comprising the well logs of the multiple types of the target well (Abstract, [Fig.1(c)], [pg. 2 left col par. 4]).
Clegg (US Pub.20210381365A1), disclose a resistivity (RSS) log (Fig. 7, para [0061]) as training inputs and known RSS logs of the [existing wells] active boreholes as a training output (para [0065]; Fig. 8B, para [0067]).
Hagiwara (EU Pat.0247669), disclose predicting a true sand resistivity (Col. 6, lines 15-20, where the true sand resistivity is estimated as 5.25 ohm-m in the upper zone and 1.21 ohm-m in the lower zone).
The prior art of record does not teach or fairly suggest a method of having the steps of “training the AI model using the assigned training inputs and training outputs to generate a trained AI model; and
predicting a RSS log of the target well using the trained artificial intelligence (AI) model using the basic log data of the target well as a test input to the trained AI model.”, e.g., training an Al model by providing basic logs and RSS logs of existing wells as inputs, and using the trained model to predict an RSS log of a training well using a basic log of that training well.
Claims 2, 4, and 6-12 are allowed as being dependent from an allowed base claim 1.
Regarding Claims 20 and 23, Kim and Clegg and Hagiwara disclose obtaining basic log data of a target well, the basic log data comprising well logs of multiple types of the target well, the multiple types comprising two or more of resistivity, density, neutron, and gamma ray; and predicting a true sand resistivity (RSS) log of the target well using a trained artificial intelligence (AI) model with inputs comprising the well logs of the multiple types of the target well, the AI model being trained with well logs of the multiple types of existing wells as training inputs and known RSS logs of the existing wells as a training output, as recited in claim 1.
Additionally, in claim 20, Clegg discloses a computing system comprising: at least one processor; and at least one non-transitory machine readable storage medium coupled to the at least one processor having machine-executable instructions (para [0037], and [0067].) stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations (para [0067]).
Additionally, in claim 23, Clegg discloses a non-transitory machine readable storage medium storing machine-executable instructions stored thereon and coupled to at least one processor to cause the at least one processor to perform operations (para [0037], and [0067].) stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations (para [0067]).
The prior art of record does not teach or fairly suggest a method of having the steps of “training the AI model using the assigned training inputs and training outputs to generate a trained AI model; and
predicting a RSS log of the target well using the trained artificial intelligence (AI) model using the basic log data of the target well as a test input to the trained AI model.” e.g., training an Al model by providing basic logs and RSS logs of existing wells as inputs, and using the trained model to predict an RSS log of a training well using a basic log of that training well.
Claims 21-22 and 24-28 are allowed as being dependent from an allowed base claim 20 and 23.
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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/KALERIA KNOX/
Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857