DETAILED ACTION
Notice of 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 .
Information Disclosure Statement
The information disclosure statements submitted on 1/5/2024 and 10/25/2024 have been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations and associated disclosure are:
Limitation
Applicable Claims
Disclosure
measurement assembly
Claims 14-20
Fig. 1, measurement assembly 134, para. 0017
information handling system
Claims 14-20
Fig. 1, information handling system 131, para. 0021
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claims 7-11 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 recites “wherein a question is an input to the tuned ML model.” It’s unclear if this “a question” is meant to refer to the same “a question” recited in the last line of claim 1, or is meant to require an additional question. For purposes of compact prosecution, this will be interpreted as “wherein the question is an input to the tuned ML model”
Claims 8-11 depend from claim 7, do not remedy the deficiencies of claim 7, and are therefore rejected for the same reasons explained with respect to claim 7.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-13 are directed to a method (a process), and Claims 14-20 are directed to a system (a machine), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper.
extracting relevant information from the historical data (under the broadest reasonable interpretation, a human can review historical data and extract relevant information, such as reviewing historical drilling data and only selecting data from a particular window of time)
providing an answer to a question ... (under the broadest reasonable interpretation, a human can write an answer to a question on paper; this is also an abstract idea under the “managing personal behavior or relationships or interactions between people” category)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application.
Regarding the “disposing a bottom hole assembly (BHA) into a wellbore, wherein the BHA comprises a measurement assembly” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (disposing a BHA into a wellbore with respect to drilling). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “acquiring one or more measurements with the measurement assembly” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “acquiring historical data from the wellbore” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “training a machine learning (ML) model with the relevant information to form a trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic training of a machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic training of a machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “... utilizing the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding the “disposing a bottom hole assembly (BHA) into a wellbore, wherein the BHA comprises a measurement assembly” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding the “acquiring one or more measurements with the measurement assembly” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “acquiring historical data from the wellbore” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “training a machine learning (ML) model with the relevant information to form a trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “... utilizing the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Regarding Claim 2
Step 2A, Prong 2
Regarding the “further comprising tuning the trained ML model with a geosteering context to form a tuned ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning model tuning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning model tuning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “further comprising tuning the trained ML model with a geosteering context to form a tuned ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 3
Step 2A, Prong 2
Regarding the “wherein the tuning the geosteering context comprises rating a quality of an output of the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning model tuning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning model tuning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the tuning the geosteering context comprises rating a quality of an output of the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 4
Step 2A, Prong 2
Regarding the “further comprising tuning parameter, hyper parameter, function, and/or architecture of the trained ML model based at least on the quality of an output of the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning model tuning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning model tuning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “further comprising tuning parameter, hyper parameter, function, and/or architecture of the trained ML model based at least on the quality of an output of the trained ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 5
Step 2A, Prong 2
Regarding the “wherein historical data comprises previous logging data from the wellbore, a different wellbore within the same formation as the wellbore, information from the same formation, or information about an adjacent formation” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “wherein historical data comprises previous logging data from the wellbore, a different wellbore within the same formation as the wellbore, information from the same formation, or information about an adjacent formation” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 6
Step 2A, Prong 2
Regarding the “wherein historical data comprises internal geosteering reports” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “wherein historical data comprises internal geosteering reports” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 7
Step 2A, Prong 2
Regarding the “wherein a question is an input to the tuned ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a tuned machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a tuned machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein a question is an input to the tuned ML model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 8
Step 2A, Prong 1
wherein the question asks any number of drilling parameters, drilling operation suggestions, tool orientation, formation evaluation, or current and modifications to a well plan of the wellbore. (under the broadest reasonable interpretation, a human can ask a question on paper with respect to the parameters of this limitation; this is also an abstract idea under the “managing personal behavior or relationships or interactions between people” category)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 9
Step 2A, Prong 1
wherein the answer is one or more solutions to the question. (under the broadest reasonable interpretation, a human can write an answer to a question on paper that is a solution to the question; this is also an abstract idea under the “managing personal behavior or relationships or interactions between people” category)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 10
Step 2A, Prong 1
wherein the answer comprises suggested drilling parameters, suggested drilling operation, current tool orientation, or answers about the formation, or current and modifications to the well plan. (under the broadest reasonable interpretation, a human can write an answer to a question on paper, where the answer provides suggestions as set forth in this limitation; this is also an abstract idea under the “managing personal behavior or relationships or interactions between people” category)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 11
Step 2A, Prong 2
Regarding the “further comprising providing a geosteerer an interface to provide a question and receive an answer, wherein the interface comprises screens with keyboards, audio interfaces” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic user interface with screens, keyboards, and audio interfaces. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic user interface with screens, keyboards, and audio interfaces). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “further comprising providing a geosteerer an interface to provide a question and receive an answer, wherein the interface comprises screens with keyboards, audio interfaces” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 12
Step 2A, Prong 2
Regarding the “wherein the one or more measurements are performed in real time and comprise resistivity, drilling parameter, and sensor data measurements” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “wherein the one or more measurements are performed in real time and comprise resistivity, drilling parameter, and sensor data measurements” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 13
Step 2A, Prong 1
further comprising processing a customer question and customer answer. (under the broadest reasonable interpretation, a human such as a customer can write a question, and then a human can provide an answer to the question on paper; this is also an abstract idea under the “managing personal behavior or relationships or interactions between people” category)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 14
Step 2A, Prong 1
Claim 14 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 14. While claim 14 recites additional generic computing components (“information handling system”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 14 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 14. While claim 14 recites additional generic computing components (“information handling system”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 14 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 14. While claim 14 recites additional generic computing components (“information handling system”), such additional generic computing components do not change the analysis under Step 2B.
Claim 15 depends from claim 14 and recites a system that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 14.
Claim 16 depends from claim 15 and recites a system that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 15.
Claim 17 depends from claim 16 and recites a system that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 16.
Claim 18 depends from claim 14 and recites a system that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 14.
Claim 19 depends from claim 14 and recites a system that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 14.
Claim 20 depends from claim 14 and recites a system that corresponds to the method of claim 12, and is therefore rejected for the same reasons explained above with respect to claims 12 and 14.
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, 5, 12-14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240202407 A1, hereinafter referenced as BOUALLEG, in view of US 20250111336 A1, hereinafter referenced as MICHALOPULOS.
Regarding Claim 1
BOUALLEG teaches:
A method comprising: (BOUALLEG, para. 0005: “Methods and systems for optimizing a drill bit are disclosed.”)
disposing a bottom hole assembly (BHA) into a wellbore, wherein the BHA comprises a measurement assembly; (BOUALLEG, para. 0017: “FIG. 1 depicts an example drilling rig 20 including a system 60 for optimizing a drill bit. The drilling rig 20 may be positioned over a subterranean formation (not shown) and may be configured for drilling a geothermal well or a hydrocarbon exploration and/or production well. The rig 20 may include, for example, a derrick and a hoisting apparatus (also not shown) for raising and lowering a drill string 30, which, as shown, extends into wellbore 40 and includes, for example, a drill bit 32 (such as a drill bit, optimized using system 60), a steering tool 34 (such as a rotary steerable tool), and optional logging while drilling (LWD) 36 and measurement while drilling (MWD) 38 tools.”;
BOUALLEG, para. 0020: “Various sensors may be located about the wellsite to collect data (or drilling parameters) related to the drilling operation, such as standpipe pressure, pump pressure, hook load, wellbore depth, surface torque, rotary rpm, among others. The bottom hole assembly (BHA) 50 may also include downhole sensors disposed in the drill bit, the steering tool 34, the LWD tool 36, or the MWD tool 38 to provide information about downhole conditions, such as wellbore pressure, weight on bit, torque on bit, or attitude (inclination and azimuth), collar rpm, tool temperature, annular temperature, and toolface, among others. These sensors (e.g., both uphole and downhole sensors) may be configured to provide data to the system 60 for optimizing the drill bit 32 (e.g., for a subsequent drilling operation).”
Examiner’s Note: the sensors correspond to the recited “measurement assembly”)
acquiring one or more measurements with the measurement assembly; (BOUALLEG, para. 0020: “Various sensors may be located about the wellsite to collect data (or drilling parameters) related to the drilling operation, such as standpipe pressure, pump pressure, hook load, wellbore depth, surface torque, rotary rpm, among others. The bottom hole assembly (BHA) 50 may also include downhole sensors disposed in the drill bit, the steering tool 34, the LWD tool 36, or the MWD tool 38 to provide information about downhole conditions, such as wellbore pressure, weight on bit, torque on bit, or attitude (inclination and azimuth), collar rpm, tool temperature, annular temperature, and toolface, among others. These sensors (e.g., both uphole and downhole sensors) may be configured to provide data to the system 60 for optimizing the drill bit 32 (e.g., for a subsequent drilling operation).”
Examiner’s Note: the sensors correspond to the recited “measurement assembly” and measurements include wellbore pressure, weight on bit, torque on bit, etc.)
acquiring historical data from the wellbore; (BOUALLEG, para. 0027: “Historical data 115 from a large number of wells is input into the ML model 110 at 115. The historical data includes drilling operation parameters that may include drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, formation parameters, and a measured drill bit performance metric (PM) such as ROP, DLS, or ACC. ... Example wellbore parameters may include, for example, wellbore diameter, measured depth, inclination, azimuth, and curvature. Example BHA and/or RSS parameters may include, for example, drill string diameter, flex or stiffness, RSS type, RSS pad pressure, and the axial distance between the drill bit cutting surface and the RSS actuators.”)
extracting relevant information from the historical data; (BOUALLEG, para. 0027: “Historical data 115 from a large number of wells is input into the ML model 110 at 115. The historical data includes drilling operation parameters that may include drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, formation parameters, and a measured drill bit performance metric (PM) such as ROP, DLS, or ACC. ... Example wellbore parameters may include, for example, wellbore diameter, measured depth, inclination, azimuth, and curvature. Example BHA and/or RSS parameters may include, for example, drill string diameter, flex or stiffness, RSS type, RSS pad pressure, and the axial distance between the drill bit cutting surface and the RSS actuators.”;
Examiner’s Note: extracted relevant information includes wellbore diameter, measured depth, inclination, azimuth, and curvature)
training a machine learning (ML) model with the relevant information to form a trained ML model; and (BOUALLEG, para. 0028: “With continued reference to FIG. 4, the machine learning model 110 processes the historical drilling data 115 to generate a trained model 120 including relationships and/or correlations between a drill bit performance metric (PM) and the drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, and/or formation parameters in the historical drilling data 115.”)
However, BOUALLEG fails to explicitly teach:
providing an answer to a question utilizing the trained ML model.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
providing an answer to a question utilizing the trained ML model. (MICHALOPULOS, para. 0165: “The system would be able to use large language models (like ChatGPT) or other AI/ML tools to be interactive, such as by accepting users' input questions and then outputting the short video clips with answers to the questions, such as by providing additional information, showing backup information, or providing a more detailed explanation. An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?” The large language models can take that input, output the correct language response, and then the video reporting tool would generate a video report including the audio narration of the answer, together with the 3D representation of the well, with the display of the current well provided with displays of the earlier three reference wells so that the video report visually shows the similarities and differences of the current well and the three earlier reference wells that were the subject of the question. The engineer could then refine the request after reviewing the generated video report and say “now take wells MB14 and X well and also compare the dip angles on each well as well as direction the wells were drilled”. The large language model would then provide a response, which the video reporting engine can use to provide an updated report with updated text and/or audio narration including the response to the second input query.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS combination now modifies the trained machine learning model of BOUALLEG to add the interactive question/answering functionality of MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Regarding Claim 5
BOUALLEG and MICHALOPULOS teach the method of claim 1 as explained above. However, BOUALLEG fails to explicitly teach:
wherein historical data comprises previous logging data from the wellbore, a different wellbore within the same formation as the wellbore, information from the same formation, or information about an adjacent formation.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
wherein historical data comprises previous logging data from the wellbore, a different wellbore within the same formation as the wellbore, information from the same formation, or information about an adjacent formation. (MICHALOPULOS, para. 0058: “The input information may also include a drill plan, a regional formation history, drilling engineer parameters, downhole toolface/inclination information, downhole tool gamma/resistivity information, economic parameters, and reliability parameters, among various other parameters. Some of the input information, such as the regional formation history, may be available from a drilling hub 410, which may have respective access to a regional drilling database (DB) 412 (see FIG. 4).”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS combination now trains the machine learning model of BOUALLEG using the regional formation history from the regional drilling database of MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164). One of ordinary skill would further be motivated to do so in order to take advantage of related information relevant to the training of the model, including information readily available in a database.
Regarding Claim 12
BOUALLEG and MICHALOPULOS teach the method of claim 1 as explained above. BOUALLEG further teaches:
wherein the one or more measurements ... comprise ... drilling parameters, and sensor data measurements. (BOUALLEG, para. 0020: “Various sensors may be located about the wellsite to collect data (or drilling parameters) related to the drilling operation, such as standpipe pressure, pump pressure, hook load, wellbore depth, surface torque, rotary rpm, among others. The bottom hole assembly (BHA) 50 may also include downhole sensors disposed in the drill bit, the steering tool 34, the LWD tool 36, or the MWD tool 38 to provide information about downhole conditions, such as wellbore pressure, weight on bit, torque on bit, or attitude (inclination and azimuth), collar rpm, tool temperature, annular temperature, and toolface, among others. These sensors (e.g., both uphole and downhole sensors) may be configured to provide data to the system 60 for optimizing the drill bit 32 (e.g., for a subsequent drilling operation).;
Examiner’s Note: sensor measurements, such as drilling parameters, include wellbore pressure, weight on bit, torque on bit, etc.)
However, BOUALLEG fails to explicitly teach:
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
wherein the one or more measurements are performed in real time and comprise resistivity, drilling parameter, and sensor data measurements. (MICHALOPULOS, para. 0168: “Downhole sensors 1106 at a drilling rig can provide various types of real-time data and information about the drilling process, the formation being drilled, and the condition of the wellbore. These downhole sensors 1106 can help drilling operators make informed decisions to optimize drilling performance, ensure safety, and maximize well productivity.”;
MICHALOPULOS, para. 0169: “Downhole sensors 1106 can monitor and transmit information about drilling parameters such as toolface, weight on bit (WOB), rate of penetration (ROP), rotary speed, torque, mud flow rate, vibration, accelerations, other drilling dysfuctions, and differential pressure. This data helps operators optimize drilling efficiency and prevent issues like bit wear or hole instability.”
MICHALOPULOS, para. 0170: “Downhole sensors 1106 can provide information about the properties of the formation being drilled, including resistivity, porosity, permeability, and fluid saturation. This data aids in understanding the potential for hydrocarbon reservoirs and in determining the best drilling strategy.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS combination now trains the machine learning model of BOUALLEG using the real-time sensor data of MICHALOPULOS, including data relating to resistivity)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to “help drilling operators make informed decisions to optimize drilling performance, ensure safety, and maximize well productivity.” (para. 0168).
Regarding Claim 13
BOUALLEG and MICHALOPULOS teach the method of claim 1 as explained above. However, BOUALLEG fails to explicitly teach:
further comprising processing a customer question and customer answer.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
further comprising processing a customer question and customer answer. (MICHALOPULOS, para. 0142: “The application can be customizable to be able to deliver the pertinent information through the video to the appropriate internal and external customer levels and personas.”;
MICHALOPULOS, para. 0164: “The video reports system disclosed herein may use Large Language Models (like ChatGPT) or other artificial intelligence and/or machine learning systems. For example, the video reports system may include one or more AI systems designed to receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.”;
MICHALOPULOS, para. 0165: “The system would be able to use large language models (like ChatGPT) or other AI/ML tools to be interactive, such as by accepting users' input questions and then outputting the short video clips with answers to the questions, such as by providing additional information, showing backup information, or providing a more detailed explanation. An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?” The large language models can take that input, output the correct language response, and then the video reporting tool would generate a video report including the audio narration of the answer”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS combination now modifies the trained machine learning model of BOUALLEG to add the interactive question/answering functionality of MICHALOPULOS, so that stakeholders, including customers, can query the machine learning model as taught by MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Regarding Claim 14
BOUALLEG teaches:
A system comprising: (BOUALLEG, para. 0005: “Methods and systems for optimizing a drill bit are disclosed.”)
a bottom hole assembly (BHA) disposed in a wellbore, wherein the BHA comprises a measurement assembly configured to acquire one or more measurements; and (BOUALLEG, para. 0017: “FIG. 1 depicts an example drilling rig 20 including a system 60 for optimizing a drill bit. The drilling rig 20 may be positioned over a subterranean formation (not shown) and may be configured for drilling a geothermal well or a hydrocarbon exploration and/or production well. The rig 20 may include, for example, a derrick and a hoisting apparatus (also not shown) for raising and lowering a drill string 30, which, as shown, extends into wellbore 40 and includes, for example, a drill bit 32 (such as a drill bit, optimized using system 60), a steering tool 34 (such as a rotary steerable tool), and optional logging while drilling (LWD) 36 and measurement while drilling (MWD) 38 tools.”;
BOUALLEG, para. 0020: “Various sensors may be located about the wellsite to collect data (or drilling parameters) related to the drilling operation, such as standpipe pressure, pump pressure, hook load, wellbore depth, surface torque, rotary rpm, among others. The bottom hole assembly (BHA) 50 may also include downhole sensors disposed in the drill bit, the steering tool 34, the LWD tool 36, or the MWD tool 38 to provide information about downhole conditions, such as wellbore pressure, weight on bit, torque on bit, or attitude (inclination and azimuth), collar rpm, tool temperature, annular temperature, and toolface, among others. These sensors (e.g., both uphole and downhole sensors) may be configured to provide data to the system 60 for optimizing the drill bit 32 (e.g., for a subsequent drilling operation).”
Examiner’s Note: the sensors correspond to the recited “measurement assembly” and measurements include wellbore pressure, weight on bit, torque on bit, etc.)
an information handling system configured to: (BOUALLEG, para. 0021: “The system 60 may be further configured to receive the trained machine learning model(s). It will be further understood that the disclosed embodiments may include processor executable instructions stored in the data storage device. The executable instructions may be configured, for example, to execute method 100 to optimize the drill bit characteristics. It will, of course, be understood that the disclosed embodiments are not limited to the use of or the configuration of any particular computer hardware and/or software.”)
acquire historical data from the wellbore; (BOUALLEG, para. 0027: “Historical data 115 from a large number of wells is input into the ML model 110 at 115. The historical data includes drilling operation parameters that may include drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, formation parameters, and a measured drill bit performance metric (PM) such as ROP, DLS, or ACC. ... Example wellbore parameters may include, for example, wellbore diameter, measured depth, inclination, azimuth, and curvature. Example BHA and/or RSS parameters may include, for example, drill string diameter, flex or stiffness, RSS type, RSS pad pressure, and the axial distance between the drill bit cutting surface and the RSS actuators.”)
extract relevant information from the historical data; (BOUALLEG, para. 0027: “Historical data 115 from a large number of wells is input into the ML model 110 at 115. The historical data includes drilling operation parameters that may include drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, formation parameters, and a measured drill bit performance metric (PM) such as ROP, DLS, or ACC. ... Example wellbore parameters may include, for example, wellbore diameter, measured depth, inclination, azimuth, and curvature. Example BHA and/or RSS parameters may include, for example, drill string diameter, flex or stiffness, RSS type, RSS pad pressure, and the axial distance between the drill bit cutting surface and the RSS actuators.”;
Examiner’s Note: extracted relevant information includes wellbore diameter, measured depth, inclination, azimuth, and curvature)
train a machine learning (ML) model with the relevant information to form a trained ML model; and (BOUALLEG, para. 0028: “With continued reference to FIG. 4, the machine learning model 110 processes the historical drilling data 115 to generate a trained model 120 including relationships and/or correlations between a drill bit performance metric (PM) and the drill bit parameters, drilling parameters, BHA and/or RSS parameters, wellbore parameters, and/or formation parameters in the historical drilling data 115.”)
However, BOUALLEG fails to explicitly teach:
provide an answer to a question utilizing the trained ML model.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
provide an answer to a question utilizing the trained ML model. (MICHALOPULOS, para. 0165: “The system would be able to use large language models (like ChatGPT) or other AI/ML tools to be interactive, such as by accepting users' input questions and then outputting the short video clips with answers to the questions, such as by providing additional information, showing backup information, or providing a more detailed explanation. An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?” The large language models can take that input, output the correct language response, and then the video reporting tool would generate a video report including the audio narration of the answer, together with the 3D representation of the well, with the display of the current well provided with displays of the earlier three reference wells so that the video report visually shows the similarities and differences of the current well and the three earlier reference wells that were the subject of the question. The engineer could then refine the request after reviewing the generated video report and say “now take wells MB14 and X well and also compare the dip angles on each well as well as direction the wells were drilled”. The large language model would then provide a response, which the video reporting engine can use to provide an updated report with updated text and/or audio narration including the response to the second input query.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS combination now modifies the trained machine learning model of BOUALLEG to add the interactive question/answering functionality of MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Claim 18 depends from claim 14 and recites a system that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 14.
Claim 20 depends from claim 14 and recites a system that corresponds to the method of claim 12, and is therefore rejected for the same reasons explained above with respect to claims 12 and 14.
Claims 2, 7-11, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over BOUALLEG in view of MICHALOPULOS and further in view of US 20240212384 A1, hereinafter referenced as GUNE.
Regarding Claim 2
BOUALLEG and MICHALOPULOS teach the method of claim 1 as explained above. However, BOUALLEG and MICHALOPULOS fail to explicitly teach:
further comprising tuning the trained ML model with a geosteering context to form a tuned ML model.
However, in a related field of endeavor (geologic drilling, see para. 0027, 0030), GUNE teaches and makes obvious:
further comprising tuning the trained ML model with a geosteering context to form a tuned ML model. (GUNE, para. 0079: “As explained, a system may be a steerable system and may include equipment to perform a method such as geosteering. A steerable system can include equipment on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. Above directional drilling equipment, a drillstring can include MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment. As to the latter, LWD equipment can make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).”;
GUNE, para. 0082: “Geosteering can include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. Geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.”;
GUNE, para. 0144: “FIG. 13 shows an example of a method 1300 for ML model fine-tuning and/or retraining. As shown, the method 1300 can include a training block 1310 for training of a base ML model, and a test block 1320 for testing the trained base ML model using test data for evaluation and feedback.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now fine-tunes the machine learning model of BOUALLEG using the teachings of GUNE, where such fine-tuning uses geosteering-related data as provided by GUNE)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by GUNE, one of ordinary skill would have been motivated to do so in order to use machine learning techniques for denoising raster images of the wellbore. (para. 0100). One of ordinary skill would have further been motivated to do so in order to fine-tune a machine learning model using additional data, which one of skill in the art would understand to be less computationally intensive than fully re-training the model.
Regarding Claim 7
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 2 as explained above. However, BOUALLEG and MICHALOPULOS fail to explicitly teach:
wherein a question is an input to the tuned ML model.
However, in a related field of endeavor (geologic drilling, see para. 0027, 0030), GUNE teaches and makes obvious:
wherein a question is an input to the tuned ML model. (GUNE, para. 0144: “FIG. 13 shows an example of a method 1300 for ML model fine-tuning and/or retraining. As shown, the method 1300 can include a training block 1310 for training of a base ML model, and a test block 1320 for testing the trained base ML model using test data for evaluation and feedback.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now fine-tunes the machine learning model of BOUALLEG using the teachings of GUNE, so that a user can now input a question to the tuned model as in MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by GUNE, one of ordinary skill would have been motivated to do so in order to use machine learning techniques for denoising raster images of the wellbore. (para. 0100). One of ordinary skill would have further been motivated to do so in order to fine-tune a machine learning model using additional data, which one of skill in the art would understand to be less computationally intensive than fully re-training the model.
Regarding Claim 8
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 7 as explained above. However, BOUALLEG fails to explicitly teach:
wherein the question asks any number of drilling parameters, drilling operation suggestions, tool orientation, formation evaluation, or current and modifications to a well plan of the wellbore.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
wherein the question asks any number of drilling parameters, drilling operation suggestions, tool orientation, formation evaluation, or current and modifications to a well plan of the wellbore. (MICHALOPULOS, para. 0165: “An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?””;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now inputs questions to the model as in MICHALOPULOS, where questions can include drilling parameters such as trajectories, and also information about geological formulations and faults)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Regarding Claim 9
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 8 as explained above. However, BOUALLEG fails to explicitly teach:
wherein the answer is one or more solutions to the question.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
wherein the answer is one or more solutions to the question. (MICHALOPULOS, para. 0165: “The system would be able to use large language models (like ChatGPT) or other AI/ML tools to be interactive, such as by accepting users' input questions and then outputting the short video clips with answers to the questions, such as by providing additional information, showing backup information, or providing a more detailed explanation. An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?” The large language models can take that input, output the correct language response, and then the video reporting tool would generate a video report including the audio narration of the answer, together with the 3D representation of the well, with the display of the current well provided with displays of the earlier three reference wells so that the video report visually shows the similarities and differences of the current well and the three earlier reference wells that were the subject of the question. The engineer could then refine the request after reviewing the generated video report and say “now take wells MB14 and X well and also compare the dip angles on each well as well as direction the wells were drilled”. The large language model would then provide a response, which the video reporting engine can use to provide an updated report with updated text and/or audio narration including the response to the second input query.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now inputs questions to the model as in MICHALOPULOS, where questions can include drilling parameters such as trajectories, and also information about geological formulations and faults, and the model outputs an answer as in MICHALOPULOS)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Regarding Claim 10
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 9 as explained above. However, BOUALLEG fails to explicitly teach:
wherein the answer comprises suggested drilling parameters, suggested drilling operation, current tool orientation, or answers about the formation, or current and modifications to the well plan.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
wherein the answer comprises suggested drilling parameters, suggested drilling operation, current tool orientation, or answers about the formation, or current and modifications to the well plan. (MICHALOPULOS, para. 0165: “The system would be able to use large language models (like ChatGPT) or other AI/ML tools to be interactive, such as by accepting users' input questions and then outputting the short video clips with answers to the questions, such as by providing additional information, showing backup information, or providing a more detailed explanation. An example would be the drilling engineer could ask “how does this current H2-MB14 well intermediate section and curve compared to the last three MB14 wells we drilled, include trajectories, bit and BHA's, hours on each of those during that time, geological formations and faults, mud type and mud additives added at this stage, mud properties?” The large language models can take that input, output the correct language response, and then the video reporting tool would generate a video report including the audio narration of the answer, together with the 3D representation of the well, with the display of the current well provided with displays of the earlier three reference wells so that the video report visually shows the similarities and differences of the current well and the three earlier reference wells that were the subject of the question. The engineer could then refine the request after reviewing the generated video report and say “now take wells MB14 and X well and also compare the dip angles on each well as well as direction the wells were drilled”. The large language model would then provide a response, which the video reporting engine can use to provide an updated report with updated text and/or audio narration including the response to the second input query.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now inputs questions to the model as in MICHALOPULOS, where questions can include drilling parameters such as trajectories, and also information about geological formulations and faults, and the model outputs an answer as in MICHALOPULOS that includes responses with respect to drilling parameters and geological formulations and faults)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Regarding Claim 11
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 10 as explained above. However, BOUALLEG fails to explicitly teach:
further comprising providing a geosteerer an interface to provide a question and receive an answer, wherein the interface comprises screens with keyboards, audio interfaces.
However, in a related field of endeavor (drilling of wells for oil and gas production, see para. 0001), MICHALOPULOS teaches and makes obvious:
further comprising providing a geosteerer an interface to provide a question and receive an answer, wherein the interface comprises screens with keyboards, audio interfaces. (MICHALOPULOS, para. 0115: “ In embodiments suitable for use with user interfaces, controller 1000, as depicted in FIG. 10, may include peripheral adapter 1006, which provides connectivity for the use of input device 1008 and output device 1009. Input device 1008 may represent a device for user input, such as a keyboard or a mouse, or even a video camera. Output device 1009 may represent a device for providing signals or indications to a user, such as loudspeakers for generating audio signals.”;
MICHALOPULOS, para. 0163: “The system can connect via application programming interface (API) to other client applications to gather the necessary data to create the video report, like an electronic drilling recorder (EDR), geosteering software (e.g., Starsteer), customer well data recording (e.g. WellView), well planning software (e.g. Compass), enterprise resource planning (ERP), optical character recognition (OCR) to text applications, etc.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE combination now inputs questions to the model as in MICHALOPULOS, where a user such as a person using geosteering software (corresponding to recited “geosteerer”) can use an interface comprising keyboards and audio interfaces)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and GUNE as explained above. As disclosed by MICHALOPULOS, one of ordinary skill would have been motivated to do so in order to use a LLM or other AI to “ receive the drilling operations information, the well plan, and existing information from systems and reports, and consolidate that information and use it to generate a new report. The AI system may be used to accept additional requests or feedback from stakeholders and use that information to generate future video reports that include information like that requested for that stakeholder.” (para. 0164).
Claim 15 depends from claim 14 and recites a system that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 14.
Claims 3-4 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over BOUALLEG, in view of MICHALOPULOS and GUNE, and further in view of US 20240362421 A1, hereinafter referenced as MARKOV.
Regarding Claim 3
BOUALLEG, MICHALOPULOS, and GUNE teach the method of claim 2 as explained above. However, BOUALLEG, MICHALOPULOS, and GUNE fail to explicitly teach:
wherein the tuning the geosteering context comprises rating a quality of an output of the trained ML model.
However, in a related field of endeavor (fine-tuning a language model, see para. 0041), MARKOV teaches and makes obvious:
wherein the tuning the geosteering context comprises rating a quality of an output of the trained ML model. (MARKOV, para. 0041: “System 100 can further include LM refinement engine 116. In some embodiments, LM refinement engine 116 may be configured to execute optimization operations, such as by aligning or fine-tuning a language model from language model access engine 108. In some embodiments, aligning or fine-tuning may be based on one or more desired output behaviors or user intent derived from a set of user instructions, or one or more content policies as exemplified by content policy 101b. ... In some embodiments, LM refinement engine 116 may use refined data as validation data to determine quality scores or other metrics of model output, to train a language model to generate improved digital text outputs or content classification.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE-MARKOV combination now fine-tunes the trained model of BOUALLEG using the fine-tuning teachings of GUNE and MARKOV using the quality scores of model output of MARKOV)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS, GUNE, and MARKOV as explained above. As disclosed by MARKOV, one of ordinary skill would have been motivated to do so in order to improve the efficiency and resource usage of model training. (para. 0033). One of ordinary skill would have further been motivated to do so in order to fine-tune a machine learning model using additional data, which one of skill in the art would understand to be less computationally intensive than fully re-training the model.
Regarding Claim 4
BOUALLEG, MICHALOPULOS, GUNE, and MARKOV teach the method of claim 3 as explained above. However, BOUALLEG, MICHALOPULOS, and GUNE fail to explicitly teach:
further comprising tuning parameter, hyper parameter, function, and/or architecture of the trained ML model based at least on the quality of an output of the trained ML model.
However, in a related field of endeavor (fine-tuning a language model, see para. 0041), MARKOV teaches and makes obvious:
further comprising tuning parameter, hyper parameter, function, and/or architecture of the trained ML model based at least on the quality of an output of the trained ML model. (MARKOV, para. 0041: “System 100 can further include LM refinement engine 116. In some embodiments, LM refinement engine 116 may be configured to execute optimization operations, such as by aligning or fine-tuning a language model from language model access engine 108. In some embodiments, aligning or fine-tuning may be based on one or more desired output behaviors or user intent derived from a set of user instructions, or one or more content policies as exemplified by content policy 101b. ... In some embodiments, LM refinement engine 116 may use refined data as validation data to determine quality scores or other metrics of model output, to train a language model to generate improved digital text outputs or content classification.”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-GUNE-MARKOV combination now fine-tunes at least the parameters of the trained model of BOUALLEG using the fine-tuning teachings of GUNE and MARKOV using the quality scores of model output of MARKOV)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS, GUNE, and MARKOV as explained above. As disclosed by MARKOV, one of ordinary skill would have been motivated to do so in order to improve the efficiency and resource usage of model training. (para. 0033). One of ordinary skill would have further been motivated to do so in order to fine-tune a machine learning model using additional data, which one of skill in the art would understand to be less computationally intensive than fully re-training the model.
Claim 16 depends from claim 15 and recites a system that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 15.
Claim 17 depends from claim 16 and recites a system that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 16.
Claims 6 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over BOUALLEG in view of MICHALOPULUS and further in view of US 20200248545 A1, hereinafter referenced as MAUS.
Regarding Claim 6
BOUALLEG and MICHALOPULOS teach the method of claim 1 as explained above. However, BOUALLEG and MICHALOPULOS fail to explicitly teach:
wherein historical data comprises internal geosteering reports.
However, in a related field of endeavor (drilling wells for oil and gas production, see para. 0002), MAUS teaches and makes obvious:
wherein historical data comprises internal geosteering reports. (MAUS, para. 0189: “In addition, geosteering control system 168 may have the ability to switch on/off different interpretations, the ability to load a 3rd party interpretation, and the ability to select a definitive interpretation, among others. For every subject well MD, geosteering control system 168 may be enabled to display the likelihood of being at a given TVD on the type log, to generate a highly customizable geosteering report, and to generate a geosteering report without delay with minimal user input. Further, geosteering control system 168 may have the ability to compute and display KPI of how much of the well is “in zone”, the ability to export an interpretation in various different formats, such as MS-Excel (Microsoft Corp.), or a format supported by another application program, and the ability to export a complete geosteering data set (e.g., all input data, trajectories, interpretations, etc.)”;
Examiner’s Note: the BOUALLEG-MICHALOPULOS-MAUS combination now modifies the training of the model of BOULLEG to also include generated geosteering reports by the user (so they are therefor internal reports to the user’s organization) in the historical data for training the model)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of BOUALLEG with MICHALOPULOS and MAUS as explained above. As disclosed by MAUS, one of ordinary skill would have been motivated to do so in order to utilize machine learning techniques to aid humans in recognizing patterns, such as for predictive analytics. (para. 0200).
Claim 19 depends from claim 14 and recites a system that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240229632 A1 (Samuel). Discloses a wellbore system, including a bottom hole assembly, where machine learning algorithms are used to improve the operational parameters of the wellbore operation. (paras. 0011-0013).
US 12158070 B1 (Al-Qubaisi). “In this way, newly acquired data may also be used to train, re-train, or fine tune the AI model (264). Training of the AI model (264) is described in greater detail later in the instant disclosure.” (col. 11, line 67 – col. 12, line 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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