DETAILED ACTION
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 Claims
The following is a non-final, first office action in response to the communication filed on 03/21/2023. Claims 1—20 are currently pending.
Information Disclosure Statement
Information Disclosure Statement received 03/21/2023 and 03/02/2026 have been reviewed and 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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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 limitation(s) is/are: “an arrangement for analyzing the rock samples” as recited in claim 10. The instant specification at para. [0030] provides the following insight regarding the limitation “[t]he collected rock samples may then be separated for further processing and for analysis via a suitable arrangement or venue 140 for rock sample analysis. Such an arrangement 140 may include an on-site or remote laboratory, merely by way of illustrative example.” Accordingly, the foregoing claim element (e.g., “an arrangement”) is understood to be a laboratory or location where rock cuttings/samples are analyzed, and combinations thereof.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Step 1 of the USPTO’s eligibility analysis entails 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.
Claims 1, 10, and 19 are directed to a method (process), a system (machine or manufacture), and a system (machine or manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the 2019 Revised Patent SUBJECT Matter Eligibility Guidance is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception
Claim 1 recites the following limitations identified as abstract:
“transforming… the input data into numerical data for a first database via a first protocol” (e.g., a mental process).
Claim 10 recites the following abstract limitations:
“transforming the input data into numerical data for a first database via a first protocol” (e.g., a mental process).
Claim 19 recites the following abstract limitations:
“transforming the input data into numerical data for a first database via a first protocol” (e.g., a mental process).
Under the broadest reasonable interpretation, the above identified limitations cover abstract ideas directed to mental processes. For example, actions such as “transforming” categorical data (e.g., text data) into numerical data using a protocol (e.g., algorithm) constitutes actions which may be performed in a human mind with or without the benefit of a pen and paper. For example, the MPEP states the following regarding mental processes:
“[t]he courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’… Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. A discussion of concepts performed in the human mind, as well as concepts that cannot practically be performed in the human mind and thus are not ‘mental processes’, is provided below with respect to point A.” (MPEP 2106.04(a)(2), Section III).
Additionally, the mere recitation of generic computing elements (e.g., a computer processor) does not take the claim out of the mental process grouping. Accordingly, the above identified limitations are directed to abstract ideas such that claims 1, 19, and 20 recite abstract ideas.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites the following additional elements:
“a computer processor” (e.g., mere recitation of generic computing components invoked to perform the abstract idea are equivalent to reciting “apply it”);
“providing, using a computer processor, input data…” (e.g., extra-solution activity);
“input data relating to one or more hydrocarbon shows” (e.g., directed to extra-solution activity);
“wherein the input data include descriptors characterizing one or more wells at two or more different depths” (e.g., directed to extra-solution activity); and
“wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths” (e.g., extra-solution activity and/or field of use).
Claim 10 recited the following additional elements:
“a sample collector that collects one or more rock samples from a wellbore, each of the one or more rock samples corresponding to one or more hydrocarbon shows” (e.g., indicative of a field of use);
“an arrangement for analyzing the rock samples” (e.g., indicative of a field of use);
“a computer processor” (e.g., mere recitation of generic computing components invoked to perform the abstract idea are equivalent to reciting “apply it”);
“providing, using a computer processor, input data…” (e.g., extra-solution activity);
“input data relating to one or more hydrocarbon shows” (e.g., directed to extra-solution activity);
“wherein the input data include descriptors characterizing one or more wells at two or more different depths” (e.g., directed to extra-solution activity); and
“wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths” (e.g., extra-solution activity and/or field of use).
Claim 19 recites the following additional elements:
“non-transitory computer readable medium” (e.g., mere recitation of generic computing components invoked to perform the abstract idea are equivalent to reciting “apply it”);
“providing input data relating to one or more hydrocarbon shows” (e.g., extra-solution activity);
“wherein the input data include descriptors characterizing one or more wells at two or more different depths” (e.g., extra-solution activity and/or field of use); and
“wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths” (e.g., extra-solution activity and/or field of use).
The above identified limitations of claims 1, 10, and 19 constitute additional elements. However, for the reasons identified above, and discussed further below, the additional elements do not impose any meaningful limits on practicing the abstract idea. Accordingly, the above identified additional elements do not integrate the identified judicial exceptions into a practical application.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
Claims 1, 10, and 19 recite limitations directed to generic computing components including “a computer processor” (e.g., claims 1 and 10) and “non-transitory computer readable medium” (e.g., claim 19). While such limitations constitute additional elements, they do not provide for a practical application of the identified judicial exceptions. For example, the MPEP states “[w]hen determining whether a claim simply recites a judicial exception with the words ‘apply it’ (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following… (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, ‘claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” (MPEP 2106.05(f), Section 2). Accordingly the generically recited computer components of claims 1, 10, and 19 do not provide for a practical application of the judicial exception because the limitations are equivalent to a mere directive to apply the exception.
Claims 1, 10, and 19 recite the limitation “providing, using a computer processor, input data…” (e.g., claims 1 and 10) or “providing input data relating to one or more hydrocarbon shows” (e.g., claim 19) which are limitations directed to additional elements understood to be equivalent to data entry and/or data retrieval. While such a limitations constitute additional elements, the limitation cannot provide for a practical application of the identified judicial exception because the limitation is directed to court-identified insignificant extra-solution activity. For example, the MPEP states “[t]he courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);… OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);… iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.” (MPEP 2106.05(d), Subsection II). Accordingly the above identified limitations of claims 1, 10, and 19 do not provide for a practical application of the judicial exception because the limitations constitute insignificant extra-solution activity.
Claims 1, 10, and 19 recite the limitations of, or substantially similar to “input data relating to one or more hydrocarbon shows”; “wherein the input data include descriptors characterizing one or more wells at two or more different depths”; and “wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths” which are directed to limiting the type of data used to perform the abstract idea of transforming the data from text/categorical data to numerical data. While such limitations constitute additional elements, the limitation cannot provide for a practical application of the identified judicial exceptions because the limitations are directed to court-identified insignificant extra solution activity. For example, the MPEP states “[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity:… Selecting a particular data source or type of data to be manipulated: i. Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937;… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” (MPEP 2106.05(g)). Accordingly the above identified limitations of claims 1, 10, and 19 do not provide for a practical application of the judicial exception because the limitations constitute insignificant extra-solution activity.
Claim 10 recites the limitations “a sample collector that collects one or more rock samples from a wellbore, each of the one or more rock samples corresponding to one or more hydrocarbon shows” and “an arrangement for analyzing the rock samples.” While the limitations are directed to additional elements, the limitations are merely directed to a field of use in which the identified judicial exceptions are applied. Such limitations cannot provide for a practical application of the identified judicial exceptions. For example, the MPEP states “[a]s 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 amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05(h)). Accordingly the above identified limitations of claim 10 do not provide for a practical application of the judicial exception because the limitations merely limit the judicial except to a particular field of use.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
Claims 2, 11, and 20 recite limitations directed to both abstract ideas and additional elements. For example, the limitation stating “transforming via the first protocol comprises converting the text descriptors into numerical data for the first database” (e.g., or equivalents thereof) merely functions to further define the abstract idea identified in claims 1, 10, and 19. The manner in which the abstract idea is further defined is in itself abstract such that the limitation is directed to an abstract idea constituting a mental process (e.g., see the MPEP citation related to mental processes as provided above with respect to claims 1, 10, and 19).
Claims 2, 11, and 20 further recite additional elements which function to further limit the data utilized in association with the abstract idea (e.g., “the descriptors include one or more text descriptors” and “wherein the numerical indices objectively grade the one or more hydrocarbon shows at the two or more different depths”). While these limitations recite additional elements, they cannot provide for a practical application of the identified judicial exceptions of claims 1, 10, and 19 because these limitations are directed to insignificant extra-solution activity for the same reasons as provided above in claims 1, 10, and 19. Regarding such limitations, the MPEP states “[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity:… Selecting a particular data source or type of data to be manipulated: i. Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937;… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” (MPEP 2106.05(g)). Accordingly, the above discussed limitations of claims 2, 11, and 20 cannot provide for a practical application of the identified judicial exceptions of claims 1, 10, and 19 because claims 2, 11, and 20 are directed to a combination of abstract ideas and insignificant extra-solution activity.
Claims 3—5, 7, 12—14, and 16 recite limitations which function to further limit the input data and the associated numerical data according to source or content. As discussed above with respect to claims 1 and 10, while limiting the data used in association with an abstract idea according to source or content constitutes an additional element, it cannot provide for a practical application of the associated abstract idea because the limitation amounts to court-identified insignificant extra-solution activity. For example, the MPEP states “states “[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity:… Selecting a particular data source or type of data to be manipulated: i. Limiting a database index to XML tags, Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937;… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” (MPEP 2106.05(g)). Accordingly, the above discussed limitations of claims 3—5, 7, 12—14, and 16 are directed to insignificant extra-solution activity and cannot provide for a practical application of the identified judicial exceptions of claims 1, 10, and 19.
Claims 6 and 15 recite the abstract ideas of, or substantially similar to “transforming, using the computer processor, the data from the first database into data for a second database via a second protocol” which is directed to either a mental process, a mathematical concept, or a combination thereof depending on what type of transformation is applied to the data. the MPEP states the following regarding mental processes:
“[t]he courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’… Accordingly, the ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. A discussion of concepts performed in the human mind, as well as concepts that cannot practically be performed in the human mind and thus are not ‘mental processes’, is provided below with respect to point A.” (MPEP 2106.04(a)(2), Section III).
For example, the MPEP states the following regarding mathematical calculations:
“[a] claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word ‘calculating’ in order to be considered a mathematical calculation. For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” (MPEP 2106.04(a)(2), Section I, Subsection C). Accordingly, the above identified limitations are directed to abstract ideas such that claims 6 and 15 recite abstract ideas.
Claims 6 and 15 further recite additional elements directed to the type of data used and/or generated using the identified abstract ideas; however, as discussed above, limiting the data set associated with an abstract idea according to source and/or content constitutes insignificant extra-solution activity and cannot provide for a practical application of the judicial exceptions. Accordingly, claims 6 and 15 do not provide for a practical application, or significantly more, than the judicial exceptions identified in claims 1 and 10.
Claims 8 and 17 recite additional elements directed to data entry “providing… one or more input maps,” which constitutes an additional element which cannot provide for a practical application for the same reasons as set forth with respect to claims 1 and 10 (e.g., please see citation to MPEP 2106.05(d), Subsection II). Claims 8 and 17 further recite “appending the summarized data to the one or more input maps to create a first output map,” which constitutes an abstract idea further comprising a mental process insofar as associating specific data which various parts of a map can be performed in a human mind. Moreover, a human mind can both generate and contain the concept of a map and can also associate data with various portions of the map. Please see above cited MPEP citation regarding mental processes. Accordingly, claims 8 and 17 do not provide for a practical application, or significantly more, than the judicial exceptions identified in claims 1 and 10.
Claims 9 and 18 recite the limitation “determining one or more hydrocarbon migration pathways based on the summarized data and the first output map,” which is directed to a mental process insofar as making a determination is a function which is performable within a human mind with or without the benefit of pen and paper. Claims 9 and 18 also recite the limitation “creating a second output map which shows the one or more hydrocarbon migration pathways,” which is categorized as an additional element equivalent to a mere directive to apply the exception (e.g., “apply it”). Such limitation cannot provide for a practical application of the identified judicial exceptions. For example, the MPEP states “[w]hen determining whether a claim simply recites a judicial exception with the words ‘apply it’ (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.” (MPEP 2106.05(f)).
Accordingly, the limitations of claims 9 and 18 are merely directed to the idea of a solution or outcome and do not properly integrate the judicial exception into a practical application. Examples of limitations which do properly integrate the recited judicial exception into a practical application include the limitations of Diehr. For example, the MPEP states “[i]n contrast, the additional elements in Diamond v. Diehr as a whole provided eligibility and did not merely recite calculating a cure time using the Arrhenius equation ‘in a rubber molding process’. Instead, the claim in Diehr recited specific limitations such as monitoring the elapsed time since the mold was closed, constantly measuring the temperature in the mold cavity, repetitively calculating a cure time by inputting the measured temperature into the Arrhenius equation, and opening the press automatically when the calculated cure time and the elapsed time are equivalent. 450 U.S. at 179, 209 USPQ at 5, n. 5. These specific limitations act in concert to transform raw, uncured rubber into cured molded rubber. 450 U.S. at 177-78, 209 USPQ at 4.” (MPEP 2106.05(h)). Accordingly, the limitations of Diehr which integrated the abstract idea (e.g., calculations using the Arrhenius equation) into a practical application (e.g., opening the press automatically once the calculated cure time and elapsed time are equivalent) provided a more specific application which was directly tied to the outcome of the judicial exception than that of the instant claims. For example, Diehr did not merely state “display a plan for controlling the mold based on a temperature.” Accordingly the limitations of claims 9 and 18 do not provide for a practical application of the judicial exception because the limitations are equivalent to a mere directive to apply the exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1—4, 6, 8—13, 15, and 17—20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Published US Patent Application to Grechishnikova et al., hereinafter “Grechishnikova” (US 20220317324 A1) in view of Published US Patent Application to Francois et al., hereinafter “Francois” (US 20210319257 A1) and Published US Patent Application to Mamtimin et al., hereinafter “Mamtimin” (US 20230193731 A1). Examiner notes that while the filing dates of each of the foregoing applications predates the effective filing date of the instant application (e.g., 03/21/2023).
Regarding claim 1, Grechishnikova discloses providing (step 206 of FIG. 2), using a computer processor (processor 134; Grechishnikova states throughout that the method is performed on a physical computer/physical processor), input data relating to one or more hydrocarbon shows (step 206, of FIG. 2; “obtain training subsurface lithology parameter data”);
wherein the input data include descriptors characterizing one or more wells at two or more different depths (para. [0033], “[f]eatures of the present disclosure provide a combined empirical and machine learning-based method for predicting geostructural properties of natural subsurface fracture networks built on a combination of data and approaches that include structural analysis of seismic horizons calibrated to machine learned predictions of natural fracture attributes from other geologic features… The machine learning component of the model can be trained, tuned, and calibrated using data collected from discrete sample observations, such as well data (e.g., core, petrophysical data and wireline logs, image logs, mud logs, completion design, well spacing, wellbore tortuosity, production logs, mud logs), field outcrop observations, digital surveys, measurements, and/or analysis.” Examiner notes horizons are geological features mapped to various depths in a formation where mud logs are geologic data collected and analyzed at a variety of depths within a wellbore during a drilling operation. Accordingly, it is implicitly understood that the method of Grechishnikova utilizes geological data collected from and associated with various depths within a wellbore).
While Grechishnikova discloses utilizing geological data collected from wellbores, including mud log data which is taken at various depths, Grechishnikova may not disclose:
transforming, using the computer processor, the input data into numerical data for a first database via a first protocol; and
wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths.
Before addressing the above deficient limitations it is worth noting that the data included in a mud log comprises categorical classifications of the rock cuttings which are extracted from a wellbore and subsequently analyzed. While Grechishnikova states that mud logs are used in forming the model described in the primary reference, the disclosure does not provide much insight as to what data is included in a mud log. For example, Francois, which is in the same field of endeavor as the instant application insofar as it is directed to identifying subsurface geological features by analyzing rock cuttings, teaches the following: “[s]urface logging is a wellsite service providing early indications about drilled rocks and reservoir potential. For example, a wellsite operator, known as a “mud logger,” may attempt to perform lithology identification from drill cuttings returning from a well in order to reconstruct a geology map of the well. The mud logger creates a manual description based on images and acid tests. For each sample, the mud logger may examine cutting samples (e.g., through binoculars or other magnifying means) and attempt to recognize different rock types in the samples.” (Francois, para. [0002]). Examiner notes that rock type constitutes a categorical variable rather than a numerical variable. Accordingly, the mud logs of Grechishnikova, which are fed into a machine learning algorithm as described by Grechishnikova at para. [0033] is understood to include categorical variables.
Returning to the above identified deficiencies of Grechishnikova, Mamtimin, which is in the same field of endeavor as the instant application insofar as it is directed to generating subsurface geological models, teaches the deficiency. For example, Mamtimin teaches “[t]he term ‘feature’ is used as understood in the field of machine learning to mean a measurable property or characteristic of a phenomenon. A feature can also be described as based on a variable that relates to the phenomenon that has been selected to be one of multiple features that form a feature vector. For this description, a feature is expressed as a numerical value. Thus, a value of a selected variable that is not a numerical type of variable would be transformed into a numerical feature (e.g., with one hot encoding). The features are organized to form an n-dimensional feature vector as appropriate for a consuming model. A feature vector with values can be referred to as a feature vector instance, datapoint, or observation, regardless of source (e.g., synthetic versus field sourced).” (Mamtimin, para. [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have replaced the categorical features of a mud log (e.g., the mud log features of Grechishnikova which are provided to a machine learning model) with equivalent numerical values (e.g., using one-hot encoding as taught by Mamtimin) according to known methods (e.g., one-hot encoding is a well-known computer science method for converting categorical variables to numerical variables) in order to provide a machine learning algorithm (e.g., the machine learning algorithm of Grechishnikova) with an appropriate type of data for a machine learning model to ingest (e.g., see Mamtimin as described above where categorical variables are converted into numerical form for machine learning purposes).
Regarding claim 2, Grechishnikova modified by Francois and Mamtimin teach the descriptors include one or more text descriptors (the mud log data of Grechishnikova includes at least rock type as assessed by a mud logger (e.g., a person) where rock type is a categorical variable); and
transforming via the first protocol comprises converting the text descriptors into the numerical data for the first database (one-hot encoding as provided by Mamtimin converts the categorical variables to numerical variables for ingestion by a machine learning algorithm), wherein the numerical indices objectively grade the one or more hydrocarbon shows at the two or more different depths (one-hot encoding, as described by Mamtimin provides a one-to-one relationship between the numerical value and associated categorical variable. Accordingly, the numerical value is directly and objectively tied exclusively to the categorical variable).
Regarding claim 3, Grechishnikova modified by Francois and Mamtimin teach wherein the descriptors (mud logging descriptions as provided by Grechishnikova and Francois) correspond to samples taken from the one or more wells at the two or more different depths (mud log data describes the rock cuttings received from various depths in a wellbore as described by Francois).
Regarding claim 4, Grechishnikova modified by Francois and Mamtimin wherein the numerical indices are based on one or more of (any categorical data contained in the mud log would be converted to an associated numerical form for ingestion into a machine learning algorithm): rock stain type, hydrocarbon show type, cut type, cut degree, rock stain degree, rock fluorescence degree and odor (Francois teaches the recited features of claim 4 as the features when are considered when assessing/generating a mud log for lithological classification. For example, Francois teaches “cuttings of the same category come in a wide variety of shapes, textures, colors, orientations, size, and illuminations. Moreover, cuttings of two different categories might be very similar in terms of color and texture. FIG. 3 is presented as an example of intra-class variations of illuminations, sizes, and orientations in chunks from two example sandstone (i.e., a first category) samples 301, 302 and three example shale (i.e., a second category) samples 303-305, illustrating that it may be challenging to discriminate the categories from different classes based solely on visual criteria.”).
Regarding claim 6, Grechishnikova modified by Francois and Mamtimin teach transforming (Grechishnikova; operation 210 of FIG. 2, training an initial fracture distribution grid model based on the lithology data), using the computer processor (the training process takes place on processor 134), the data from the first database into data for a second database (Grechishnikova; initial fracture distribution grid model) via a second protocol (Grechishnikova; machine learning algorithm utilized to train the initial fracture distribution grid model);
wherein the second database includes summarized data for each of one or more formations in each of the one or more wells (Grechishnikova ; the generated initial fracture distribution grid model is a summary of the relationships learned from the training data in items 202—208 using the machine learning model), wherein each of the one or more formations correspond to one or more depths (Grechishnikova; the formations are inherently associated with a given depth according to the seismic horizon, see discussion regarding seismic horizons and mud logs (e.g., which are also a function of depth) as provided in claim 1).
Regarding claim 8, Grechishnikova modified by Francois and Mamtimin teach providing, using the computer processor, one or more input maps (Grechishnikova; initial fracture distribution grid model of operation 210 as described in FIG. 2; the grid model of operation 210 incorporates the subsurface lithology parameter data of operations 206); and appending, using the computer processor, the summarized data to the one or more input maps to create a first output map (Grechishnikova; generating the representation of the natural fracture network attributes as described of operation 216 as described in FIG. 2 where the representation of the fracture network includes subsurface lithology parameter data and structural deformation data (e.g., the appended summary data) where the natural fracture network attributes can be displayed as a model as depicted in FIGs. 5 and 6).
Regarding claim 9 Grechishnikova modified by Francois and Mamtimin teach determining, using the computer processor, one or more hydrocarbon migration pathways based on the summarized data and the first output map (Grechishnikova; the natural fracture attributes determined in operation 216 constitute fluid migration pathways which includes hydrocarbon migration pathways; the natural fracture network attributes are generated using the initial fracture distribution grid model which was determined in operation 210); and creating, using the computer processor, a second output map which shows the one or more hydrocarbon migration pathways (Grechishnikova; displayed representation of the natural fracture network attributes as performed in operation 218; the fractures constitute fluid pathways. Moreover, the initial fracture distribution grid model and the natural fracture network attributes are derived from data collected from one or more hydrocarbon producing wells which are located in a hydrocarbon producing field as described in para. [0033] which states “[f]or example, the region of interest may encompass multiple wells, and may be at the scale of a hydrocarbon producing field or basin.” Examiner notes the data used to develop the model of Grechishnikova is obtained from regions of interest which include hydrocarbon production operations such that at least one of the modeled fractures provides for a pathway for a hydrocarbon fluid.).
Regarding claim 10, Grechishnikova discloses a computer processor (processor 134; Grechishnikova states throughout that the method is performed on a physical computer/physical processor) operatively connected to the sample collector and arrangement (processor 134 receives the requisite mud log data as discussed below and is therefore operatively coupled to any devices/databases/laboratories which collect the requisite data) and comprising functionality for:
providing input data relating to the one or more hydrocarbon shows (mud logging data gathered from wells drilled for the purpose of producing hydrocarbons; para. [0033], the region of interest may encompass multiple wells, and may be at the scale of a hydrocarbon producing field or basin. The machine learning component of the model can be trained, tuned, and calibrated using data collected from discrete sample observations, such as well data (e.g., core, petrophysical data and wireline logs, image logs, mud logs, completion design, well spacing, wellbore tortuosity, production logs, mud logs), field outcrop observations, digital surveys, measurements, and/or analysis.” Examiner notes the data used to develop the model of Grechishnikova is obtained from regions of interest which include hydrocarbon production operations such that the mud logs constitute data related to hydrocarbon shows), wherein the input data correspond to analyzed rock samples (mud logs and petrophysical data, which is an input to the machine learning model, constitute analyzed rock samples, see para. [0033] as cited above);
wherein the input data include descriptors characterizing one or more wells at two or more different depths (para. [0033], “[f]eatures of the present disclosure provide a combined empirical and machine learning-based method for predicting geostructural properties of natural subsurface fracture networks built on a combination of data and approaches that include structural analysis of seismic horizons calibrated to machine learned predictions of natural fracture attributes from other geologic features… The machine learning component of the model can be trained, tuned, and calibrated using data collected from discrete sample observations, such as well data (e.g., core, petrophysical data and wireline logs, image logs, mud logs, completion design, well spacing, wellbore tortuosity, production logs, mud logs), field outcrop observations, digital surveys, measurements, and/or analysis.” Examiner notes horizons are geological features mapped to various depths in a formation where mud logs are geologic data collected and analyzed at a variety of depths within a wellbore during a drilling operation. Accordingly, it is implicitly understood that the method of Grechishnikova utilizes geological data collected from and associated with various depths within a wellbore).
Grechishnikova may not explicitly disclose the following limitations:
a sample collector that collects one or more rock samples from a wellbore, each of the one or more rock samples corresponding to one or more hydrocarbon shows;
an arrangement for analyzing the rock samples;
transforming the input data into numerical data for a first database via a first protocol; and
wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths
Regarding items 1 and 2, while Grechishnikova discloses the use of mud log data along with other lithology-based data (e.g., cores, petrophysical data, wireline data, and so forth) which implicitly require devices for sample collection along with devices/locations for analyzing the collected samples and/or data, Grechishnikova may not explicitly disclose the limitations “a sample collector that collects one or more rock samples from a wellbore, each of the one or more rock samples corresponding to one or more hydrocarbon shows” and “an arrangement for analyzing the rock samples.” However, Francois, which is in the same field of endeavor as the instant application insofar as it is directed to identifying subsurface geological features by analyzing rock cuttings, teaches the following: “[s]urface logging is a wellsite service providing early indications about drilled rocks and reservoir potential. For example, a wellsite operator, known as a “mud logger,” may attempt to perform lithology identification from drill cuttings returning from a well in order to reconstruct a geology map of the well. The mud logger creates a manual description based on images and acid tests. For each sample, the mud logger may examine cutting samples (e.g., through binoculars or other magnifying means) and attempt to recognize different rock types in the samples.” (Francois, para. [0002]). Francois further teaches “[t]he shale shaker 156 collects the drilling fluid, charged with drilling cuttings, flowing out from the discharge pipe 128. The shale shaker 156 comprises a sieve 164 allowing the separation of the solid drilling cuttings 168 from the drilling mud. The shale shaker 156 also comprises an outlet 172 for evacuating the drilling cuttings 168.” (Francois, para. [0020]). Accordingly, Francois discloses a sample collector that collects one or more rock samples (e.g., a shale shaker 156 as set forth in Francois) along with an arrangement for analyzing the rock samples (e.g., the area of the wellsite location where the surface logging wellsite services take place).
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to have added the sample collector and the arrangement for analyzing rock samples as described by Francois to the system of Grechishnikova. As described above, Grechishnikova utilizes the data provided by a mud logger as inputs to the model of Grechishnikova. The combination would generate the predictable result of providing for devices and services which gather the data utilized in the model of Grechishnikova.
Regarding items 3 and 4, Mamtimin, which is in the same field of endeavor as the instant application insofar as it is directed to generating subsurface geological models, teaches the deficiency. For example, Mamtimin teaches “[t]he term ‘feature’ is used as understood in the field of machine learning to mean a measurable property or characteristic of a phenomenon. A feature can also be described as based on a variable that relates to the phenomenon that has been selected to be one of multiple features that form a feature vector. For this description, a feature is expressed as a numerical value. Thus, a value of a selected variable that is not a numerical type of variable would be transformed into a numerical feature (e.g., with one hot encoding). The features are organized to form an n-dimensional feature vector as appropriate for a consuming model. A feature vector with values can be referred to as a feature vector instance, datapoint, or observation, regardless of source (e.g., synthetic versus field sourced).” (Mamtimin, para. [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have replaced the categorical features of a mud log (e.g., the mud log features of Grechishnikova which are provided to a machine learning model) with equivalent numerical values (e.g., using one-hot encoding as taught by Mamtimin) according to known methods (e.g., one-hot encoding is a well-known computer science method for converting categorical variables to numerical variables) in order to provide a machine learning algorithm (e.g., the machine learning algorithm of Grechishnikova) with an appropriate type of data for a machine learning model to ingest (e.g., see Mamtimin as described above where categorical variables are converted into numerical form for machine learning purposes).
Regarding claim 11, Grechishnikova modified by Francois and Mamtimin teach the descriptors include one or more text descriptors (the mud log data of Grechishnikova includes at least rock type as assessed by a mud logger (e.g., a person) where rock type is a categorical variable); and
transforming via the first protocol comprises converting the text descriptors into the numerical data for the first database (one-hot encoding as provided by Mamtimin converts the categorical variables to numerical variables for ingestion by a machine learning algorithm), wherein the numerical indices objectively grade the one or more hydrocarbon shows at the two or more different depths (one-hot encoding, as described by Mamtimin provides a one-to-one relationship between the numerical value and associated categorical variable. Accordingly, the numerical value is directly and objectively tied exclusively to the categorical variable).
Regarding claim 12, Grechishnikova modified by Francois and Mamtimin teach wherein the descriptors (mud logging descriptions as provided by Grechishnikova and Francois) correspond to samples taken from the one or more wells at the two or more different depths (mud log data describes the rock cuttings received from various depths in a wellbore as described by Francois).
Regarding claim 13, Grechishnikova modified by Francois and Mamtimin wherein the numerical indices are based on one or more of (any categorical data contained in the mud log would be converted to an associated numerical form for ingestion into a machine learning algorithm): rock stain type, hydrocarbon show type, cut type, cut degree, rock stain degree, rock fluorescence degree and odor (Francois teaches the recited features of claim 13 as the features when are considered when assessing/generating a mud log for lithological classification. For example, Francois teaches “cuttings of the same category come in a wide variety of shapes, textures, colors, orientations, size, and illuminations. Moreover, cuttings of two different categories might be very similar in terms of color and texture. FIG. 3 is presented as an example of intra-class variations of illuminations, sizes, and orientations in chunks from two example sandstone (i.e., a first category) samples 301, 302 and three example shale (i.e., a second category) samples 303-305, illustrating that it may be challenging to discriminate the categories from different classes based solely on visual criteria.”).
Regarding claim 15, Grechishnikova modified by Francois and Mamtimin teach transforming (Grechishnikova; operation 210 of FIG. 2, training an initial fracture distribution grid model based on the lithology data), using the computer processor (the training process takes place on processor 134), the data from the first database into data for a second database (initial fracture distribution grid model) via a second protocol (machine learning algorithm utilized to train the initial fracture distribution grid model);
wherein the second database includes summarized data for each of one or more formations in each of the one or more wells (Grechishnikova ; the generated initial fracture distribution grid model is a summary of the relationships learned from the training data in items 202—208 using the machine learning model), wherein each of the one or more formations correspond to one or more depths (Grechishnikova; the formations are inherently associated with a given depth according to the seismic horizon, see discussion regarding seismic horizons and mud logs (e.g., which are also a function of depth) as provided in claim 1).
Regarding claim 17, Grechishnikova modified by Francois and Mamtimin teach the computer processor (processor 134 of Grechishnikova) further comprising functionality for: providing one or more input maps (Grechishnikova; initial fracture distribution grid model of operation 210 as described in FIG. 2; the grid model of operation 210 incorporates the subsurface lithology parameter data of operations 206); and appending the summarized data to the one or more input maps to create a first output map (Grechishnikova; generating the representation of the natural fracture network attributes as described of operation 216 as described in FIG. 2 where the representation of the fracture network includes subsurface lithology parameter data and structural deformation data (e.g., the appended summary data) where the natural fracture network attributes can be displayed as a model as depicted in FIGs. 5 and 6).
Regarding claim 18, Grechishnikova modified by Francois and Mamtimin teach determining, using the computer processor, one or more hydrocarbon migration pathways based on the summarized data and the first output map (Grechishnikova; the natural fracture attributes determined in operation 216 constitute fluid migration pathways which includes hydrocarbon migration pathways; the natural fracture network attributes are generated using the initial fracture distribution grid model which was determined in operation 210); and
creating, using the computer processor, a second output map which shows the one or more hydrocarbon migration pathways (Grechishnikova; displayed representation of the natural fracture network attributes as performed in operation 218; the fractures constitute fluid pathways. Moreover, the initial fracture distribution grid model and the natural fracture network attributes are derived from data collected from one or more hydrocarbon producing wells which are located in a hydrocarbon producing field as described in para. [0033] which states “[f]or example, the region of interest may encompass multiple wells, and may be at the scale of a hydrocarbon producing field or basin.” Examiner notes the data used to develop the model of Grechishnikova is obtained from regions of interest which include hydrocarbon production operations such that at least one of the modeled fractures provides for a pathway for a hydrocarbon fluid.).
Regarding claim 19, Grechishnikova discloses [a] non-transitory computer readable medium storing instructions executable by a computer processor (para. [0014], “[t]he computer-implemented method may be implemented in a computer system that includes a physical computer processor, non-transient electronic storage, and a graphical user interface.”; processor 134 is the processor on which the operation is performed), the instructions comprising functionality for:
providing input data relating to one or more hydrocarbon shows (step 206, of FIG. 2; “obtain training subsurface lithology parameter data”; Notably, FIG. 2 is performed on processor 134);
wherein the input data include descriptors characterizing one or more wells at two or more different depths (para. [0033], “[f]eatures of the present disclosure provide a combined empirical and machine learning-based method for predicting geostructural properties of natural subsurface fracture networks built on a combination of data and approaches that include structural analysis of seismic horizons calibrated to machine learned predictions of natural fracture attributes from other geologic features… The machine learning component of the model can be trained, tuned, and calibrated using data collected from discrete sample observations, such as well data (e.g., core, petrophysical data and wireline logs, image logs, mud logs, completion design, well spacing, wellbore tortuosity, production logs, mud logs), field outcrop observations, digital surveys, measurements, and/or analysis.” Examiner notes horizons are geological features mapped to various depths in a formation where mud logs are geologic data collected and analyzed at a variety of depths within a wellbore during a drilling operation. Accordingly, it is implicitly understood that the method of Grechishnikova utilizes geological data collected from and associated with various depths within a wellbore).
While Grechishnikova discloses utilizing geological data collected from wellbores, including mud log data which is taken at various depths, Grechishnikova may not disclose:
transforming, using the computer processor, the input data into numerical data for a first database via a first protocol; and
wherein the numerical data include numerical indices characterizing the one or more wells at the two or more different depths.
Before addressing the above deficient limitations it is worth noting that the data included in a mud log comprises categorical classifications of the rock cuttings which are extracted from a wellbore and subsequently analyzed. While Grechishnikova states that mud logs are used in forming the model described in the primary reference, the disclosure does not provide much insight as to what data is included in a mud log. For example, Francois, which is in the same field of endeavor as the instant application insofar as it is directed to identifying subsurface geological features by analyzing rock cuttings, teaches the following: “[s]urface logging is a wellsite service providing early indications about drilled rocks and reservoir potential. For example, a wellsite operator, known as a “mud logger,” may attempt to perform lithology identification from drill cuttings returning from a well in order to reconstruct a geology map of the well. The mud logger creates a manual description based on images and acid tests. For each sample, the mud logger may examine cutting samples (e.g., through binoculars or other magnifying means) and attempt to recognize different rock types in the samples.” (Francois, para. [0002]). Examiner notes that rock type constitutes a categorical variable rather than a numerical variable. Accordingly, the mud logs of Grechishnikova, which are fed into a machine learning algorithm as described by Grechishnikova at para. [0033] is understood to include categorical variables.
Returning to the above identified deficiencies of Grechishnikova, Mamtimin, which is in the same field of endeavor as the instant application insofar as it is directed to generating subsurface geological models, teaches the deficiency. For example, Mamtimin teaches “[t]he term ‘feature’ is used as understood in the field of machine learning to mean a measurable property or characteristic of a phenomenon. A feature can also be described as based on a variable that relates to the phenomenon that has been selected to be one of multiple features that form a feature vector. For this description, a feature is expressed as a numerical value. Thus, a value of a selected variable that is not a numerical type of variable would be transformed into a numerical feature (e.g., with one hot encoding). The features are organized to form an n-dimensional feature vector as appropriate for a consuming model. A feature vector with values can be referred to as a feature vector instance, datapoint, or observation, regardless of source (e.g., synthetic versus field sourced).” (Mamtimin, para. [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have replaced the categorical features of a mud log (e.g., the mud log features of Grechishnikova which are provided to a machine learning model) with equivalent numerical values (e.g., using one-hot encoding as taught by Mamtimin) according to known methods (e.g., one-hot encoding is a well-known computer science method for converting categorical variables to numerical variables) in order to provide a machine learning algorithm (e.g., the machine learning algorithm of Grechishnikova) with an appropriate type of data for a machine learning model to ingest (e.g., see Mamtimin as described above where categorical variables are converted into numerical form for machine learning purposes).
Regarding claim 20, Grechishnikova modified by Francois and Mamtimin teach the descriptors include one or more text descriptors (the mud log data of Grechishnikova includes at least rock type as assessed by a mud logger (e.g., a person) where rock type is a categorical variable); and
transforming via the first protocol comprises converting the text descriptors into the numerical data for the first database (one-hot encoding as provided by Mamtimin converts the categorical variables to numerical variables for ingestion by a machine learning algorithm), wherein the numerical indices objectively grade the one or more hydrocarbon shows at the two or more different depths (one-hot encoding, as described by Mamtimin provides a one-to-one relationship between the numerical value and associated categorical variable. Accordingly, the numerical value is directly and objectively tied exclusively to the categorical variable).
Subject Matter Not Rejected Under the Prior Art Rejection
Claims 5, 7, 14, and 16 are not rejected under the above provided prior art rejection; however, all claims in the application are rejected under 35 U.S.C. 101. For example, claims 5, 7, 14, and 16 are identified as depending from claims which are directed to patent ineligible subject matter for the recitation of a judicial exception. Moreover, while claims 5, 7, 14, and 16 recite additional elements, the limitations do not provide for a practical application of the identified judicial exceptions. Accordingly, merely amending the independent claims with the subject matter of claims 5, 7, 14, and/or 16 will not place the application in condition for allowance.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Published US Patent Application to Yamada et al., (US 20230220770 A1) which teaches an autonomous method of classifying rock cuttings to generate consolidated lithological classifications. See FIG. 7, 8A—B, and FIG. 12;
Published US Patent Application to Biniwale et al., (US 20260099794 A1) which teaches utilizing one-hot encoding for converting text/categorical data into numerical data (see. para. [0049]);
Published US Patent Application to Rowe et al., (US 20250067173 A1) which teaches an automated method for analyzing rock cuttings received from a wellbore;
Published US Patent Application to Molla et al., (US 20210062650 A1) which teaches a method of analyzing hydrocarbon components based on mud logging data;
Published US Patent Application to Agarwal et al. (US 20240144458 A1) which teaches a computer vision method of analyzing drill cuttings received from a wellbore, where the analysis may be done in real-time (e.g., augmenting or replacing traditional mud logging);
Published US Patent Application to Sharma (US 20210248428 A1) which teaches an automated method of labelling rock cuttings utilizing a neural network-based model; and
Published US Patent to Sharma et al. (US 20240420299 A1) which teaches a method of using ultra-violet light to identify hydrocarbons in rock cuttings at the pixel-level.
Conclusion
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/U.L.N./Examiner, Art Unit 3676
/TARA SCHIMPF/Supervisory Patent Examiner, Art Unit 3676
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