DETAILED ACTION
This office action is in response to communication filed on January 27, 2026.
Response to Amendment
Amendments filed on January 27, 2026 have been entered.
The specification has been amended.
Claims 1-6, 9, 12, 15 and 17-18 have been amended.
Claims 7-8, 10-11, 13-14 and 19-20 have been canceled.
Claims 21-22 have been added.
Claims 1-6, 9, 12, 15-18 and 21-22 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 10), filed on 01/27/2026, with respect to the objections to the drawings have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the drawings have been withdrawn.
Applicant’s arguments, see Remarks (p. 10-11), filed on 01/27/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 11), filed on 01/27/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below in order to address additional informalities.
Applicant’s arguments, see Remarks (p. 11-12), filed on 01/27/2026, with respect to the rejection of claims 1-6 and 9 under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the rejection of the claims under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter has been withdrawn.
Applicant’s arguments, see Remarks (p. 11-17), filed on 01/27/2026, with respect to the rejection of claims 12 and 15-18 under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more have been fully considered but are moot in view of new grounds of rejection.
Applicant argues (p. 13-14) that the interpretation of claim 12 as directed to a mental process is an overbroad characterization of claim 12 that fails to consider the claim as a whole. Indeed, Applicant respectfully submits that the claimed invention cannot be performed by a set of human operators or in the human mind … Instead, amended claim 12 recites a method including acts performed by machine learning models in a way that human minds are not equipped to perform. The recited method includes operations that have real world and measurable impacts on the process of adjusting drilling parameters.
These arguments are not persuasive.
The examiner submits that according to the MPEP, under Step 2A – Prong One of the test: “Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above” (see MPEP 2106.04(a)).
Based on this, the examiner submits that regarding claim 12, “identify a trigger event, the trigger event including a deviation from a threshold range of at least a portion of the drilling information” falls under the mental processes and/or mathematical concepts groupings because, under the broadest reasonable interpretation in light of the specification, the human mind is capable of identifying a deviation from a range based on evaluations, observations, judgment, as well as using mathematical calculations (e.g., mathematical comparisons) to identify this deviation (see below for detailed analysis regarding claim 1).
Regarding the argument about the use of machine learning models, the examiner submits that this feature adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as explained in example 47 - claim 2 of the guidance (see p. 8: “The limitations in (d) and (e) reciting “using the trained ANN” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f)”; see also specification at [0093] and [0095]).
Regarding the argument about the recited method including operations that have real world and measurable impacts on the process of adjusting drilling parameters, the examiner submits that, as indicated in the rejection below, “adjusting drilling parameters” appends a transformation at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (see specification at [0089]-[0090], [0117]) and according to the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
Applicant also argues (p. 14) that similar to Example 39, the recited machine learning training methods are not mental processes because the human mind is not equipped to train itself like a machine learning model.
This argument is not persuasive.
The examiner submits that, as indicated above, using machine learning for training models based on any type of data (see specification at [0084]-[0085]) in a broad field (see specification at [0028], [0081], [0116]) refers to mere computer implementation (the examiner notes that the specification does not even provide details regarding the machine learning model but instead generically describes the training a machine learning model), and as explained in the MPEP: “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 MPEP 2106.05(f)).
Applicant further argues (p. 15-16) that claim 1 has been amended to recite, in part, “adding the drilling information, the event data, the manual information, and the recommendation to the knowledge database; and training the machine learning model on the drilling information, the event data, and the manual information added to the knowledge database.” Applicant respectfully submits that such claim elements are integrated into a practical application … Thus, training and operation of the machine learning model are directed to improvements in the functioning of the machine learning model itself, which is an integration into a practical application.
These arguments are not persuasive.
The examiner submits that adding data to a database and training a machine learning model based on data refer to additional elements that add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea while also appending extra-solution activities (e.g., source/type of data to be manipulated) as indicated in the MPEP:
“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 MPEP 2106.05(f)); and
“Below 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” (see MPEP 2106.05(g)).
Furthermore, applicant argues (p. 16) that Claim 12 is further integrated into a practical application by “using the knowledge type, preparing a recommendation to adjust drilling parameters based on the trigger event; adjusting the drilling parameters to return the drilling information to within the threshold range.” Recommending changes to and adjusting drilling parameters according to the recommendation is significant post-solution activity because it facilitates automatic intervention in the drilling process to “return the drilling information to within the threshold range.”
These arguments are not persuasive.
The examiner submits that, under the broadest reasonable interpretation in light of the specification:
“preparing a recommendation to adjust drilling parameters based on the trigger event” covers performance of the limitation using mental processes (e.g., evaluations) for updating information (e.g., data/activity adjustments, see specification at [0089]) while adding extra-solution activities recited at a high level of generality (e.g., knowledge type; see specification at [0092], [0096]), and
“adjusting the drilling parameters to return the drilling information to within the threshold range” appends a transformation at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (see specification at [0085], [0089]-[0090], [0117]; see also MPEP 2106.05(c)).
Moreover, applicant argues (p. 17) that Independent claim 12 includes limitations, or combinations of limitations, which are missing from the prior art and are not otherwise well-understood, routine, or conventional activity in the field, thus making independent claim 1 novel and non-obvious in view of the prior art. For example, “prompting a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data,” as recited in amended claim 12, is not described in the prior art, as explained below.
This argument is not persuasive.
The examiner submits that new grounds of rejection under 35 U.S.C. 103 are presented below to address the amended/new features incorporated in the current response. Moreover, the examiner submits that as described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence: “Even if the judicial exception is narrow ( e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”).
Additionally, the examiner submits that according to the current Office’s guidance: “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were “‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection.” Myriad, 569 U.S. 576, 589, 106 USPQ2d at 1976, 1978 (noting that Myriad discovered the BRCA1 and BRCA1 genes and quoting Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 (“the novelty of the mathematical algorithm is not a determining factor at all”); Mayo, 566 U.S. 73-74, 78, 101 USPQ2d 1966, 1968 (noting that the claims embody the researcher’s discoveries of laws of nature). The Supreme Court’s cited rationale for considering even “just discovered” judicial exceptions as exceptions stems from the concern that “without this exception, there would be considerable danger that the grant of patents would ‘tie up’ the use of such tools and thereby ‘inhibit future innovation premised upon them.’” Myriad, 569 U.S. at 589, 106 USPQ2d at 1978-79 (quoting Mayo, 566 U.S. at 86, 101 USPQ2d at 1971). See also Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 (“Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry.”). The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was “new”. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a new abstract idea is still an abstract idea”) (emphasis in original)” (see MPEP 2106.04).
Furthermore, the examiner submits that according to the disclosure: “In this document, approaches to capturing information and data are described to help preserve knowledge and make it more accessible” (see specification at [0006]; see also [0004]). Based on this, the examiner submits that applicant seeks patent protection for a series of mental/mathematical steps used for manipulating data, and according to the MPEP: “For data, mere “manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’” has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994))” (see MPEP 2106.05(c)).
Applicant’s arguments, see Remarks (p. 17-18), filed on 01/27/2026, with respect to the rejection of claims 1-6, 9, 12, 15-18 under 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Applicant agues (p. 18) that Johnston appears to only identify “deltas” caused by human action, and not “prompt a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data,” as recited in amended claim 1 …Chahine does not correct this deficiency. Chahine appears to disclose that “Knowledge, such as measurement logs, maintenance records, and expert input, may be used to eliminate and/or supplement data in the signals. The initial pattern(s) may be used as the defined pattern 448 during an initial use of the pattern detector 130.” Chahine [0043]. But Chahine appears to be silent regarding “prompt[ing] a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data,” as recited in amended claim 1.
This argument is not persuasive.
The examiner submits that Johnston describes a human operator deviating from the digital well plan information based on experience and providing feedback (new tacit knowledge) regarding where/how the operator deviated from the well plan (see [0213]; see also Chahine at [0037], [0080] regarding building a library of defined patterns based on knowledge input).
Therefore, the examiner submits that, under the broadest reasonable interpretation in light of the specification (see [0092]), the prior art of record discloses/teaches the claimed invention.
Claim Objections
Claim 3 is objected to because of the following informalities:
Claim language should read “The non-transitory computer readable medium of claim 1,wherein prompting the user to enter the manual information includes .
Appropriate correction is required.
Claim 12 is objected to because of the following informalities:
Claim language “A method for monitoring a drilling system, comprising:” should read “A method for monitoring a drilling system, the method comprising:” in order to provide appropriate antecedence basis.
Appropriate correction is required.
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 1-6, 9, 12, 15-18 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a product (machine/manufacture), which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text):
the limitation “monitor a sensor suite, the sensor suite collecting drilling information about a drilling system during a wellsite operation, the drilling system performing drilling operations according to a drill plan” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes to evaluate/observe data (e.g., evaluate/observe sensor information; see specification at [0009], [0084]-[0085], [0087], [0112], [0129]; see also Figs. 5-6). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation/observation of data.
the limitation “detect, via a machine learning model trained on knowledge in a knowledge database, a trigger event during the wellsite operation by identifying that at least a portion of the drilling information is outside of a threshold range of the drilling information, wherein the knowledge in the knowledge database includes explicit knowledge and tacit knowledge” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data for identifying abnormalities/outliers in the data (e.g., identify a trigger event based on a threshold comparison; see specification at [0007], [0087]-[0088], [0093], [0100], [0103]-[0109], [0112]-[0113], [0116], [0122]; see also Figs. 5-6). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated), the computer implementation (e.g., machine learning) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data for identifying abnormal information (i.e., trigger event).
the limitation “in response to detecting the trigger event: identify event data from the sensor suite at a time of the trigger event, the event data including unrelated data not relevant to the trigger event; and identify plan information from the drill plan that describes an activity being performed during the wellsite operation when the trigger event was detected” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to identify additional data based on the detected abnormal data (e.g., identify other information at the time of the trigger event; see specification at [0006], [0008], [0091], [0101], [0113]-[0114], [0123], [0125]-[0126]; see also [0114] regarding this identification corresponding to data gathering). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to identify additional information (i.e., event data and plan information) based on identifying the abnormal data (i.e., trigger event).
the limitation “preparing a recommendation to adjust drilling parameters based on the trigger event” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes for updating information (e.g., data/activity adjustments, see specification at [0089]). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of the claim mainly refers to performing a mental evaluation/judgment to suggest changes of information or activities.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application. In particular, the additional elements recited in the claim (see non-italic text):
“A non-transitory computer readable medium having stored therein instructions executable by a computer system” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see specification at [0006], [0068]-[0070], [0110]-[0111], [0115], [0121]; see also MPEP 2106.05(f));
“monitor a sensor suite, the sensor suite collecting drilling information about a drilling system during a wellsite operation, the drilling system performing drilling operations according to a drill plan” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements recited at a high level of generality (i.e., a sensor suite; see specification at [0060], [0078], [0084]-[0085], [0107], [0112]; see also MPEP 2106.05(g)) while generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., drilling operations; see specification at [0003]-[0004], [0006]; see also MPEP 2106.05(h));
“detect, via a machine learning model trained on knowledge in a knowledge database, a trigger event during the wellsite operation by identifying that at least a portion of the drilling information is outside of a threshold range of the drilling information, wherein the knowledge in the knowledge database includes explicit knowledge and tacit knowledge” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (e.g., machine learning, knowledge database; see specification at [0009], [0075], [0091]-[0095], [0101], [0130]; see also MPEP 2106.05(f)) while also appending extra-solution activities (e.g., source/type of data to be manipulated; see specification at [0091]-[0094]; see also MPEP 2106.05(g));
“in response to detecting the trigger event: identify event data from the sensor suite at a time of the trigger event, the event data including unrelated data not relevant to the trigger event; and identify plan information from the drill plan that describes an activity being performed during the wellsite operation when the trigger event was detected” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements recited at a high level of generality (i.e., the sensor suite; see specification at [0078], [0084]-[0085], [0113]; see also MPEP 2106.05(g));
“prompt a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data” adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated; specification at [0092]-[0093], [0097], [0114]; see also MPEP 2106.05(g));
“implementing, via a drilling integrator, the recommendation in the drilling system by automatically adjusting the drilling parameters at the drilling system” appends a transformation at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered and/or contributing only nominally or insignificantly to the execution of the claimed method (e.g., transformation used for data gathering or as a field-of-use limitation; see specification at [0089]-[0090], [0117]; see also MPEP 2106.05(c));
“adding the drilling information, the event data, the plan information, the manual information, and the recommendation to the knowledge database” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (e.g., storing data in a database, see specification at [0006], [0009], [0130]; see also MPEP 2106.05(f)); and
“training the machine learning model on the drilling information, the event data, the plan information, and the manual information added to the knowledge database” adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (e.g., training a machine learning model using data, see specification at [0093], [0095]; see also MPEP 2106.05(f)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., monitoring drilling operations for capturing information and data; see specification at [0006]), which as indicated in the MPEP: “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 amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h));
recite extra-solution activities (e.g., mere data gathering by selecting a particular data source/type to be manipulated) using elements (i.e., a sensor suite) specified at a high level of generality, which as indicated in the MPEP: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process” (see MPEP 2106.05(g)) and “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b), section III);
append generic computer components (i.e., a non-transitory computer readable medium having stored therein instructions executable by a computer system, a knowledge database) used to facilitate the application of the abstract idea (i.e., mere computer implementation such as generic machine learning), which as indicated in the MPEP: “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 provide significantly more” (see MPEP 2106.05(f), item 2); and
append transformations at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered and/or contributing only nominally or insignificantly to the execution of the claimed method (e.g., transformation used for data gathering or as a field-of-use limitation), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” and “A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application)” (see MPEP 2106.05(c)).
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claim 12 and 18 is directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 2, 6, 9 and 16-17), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering using a data type or source, training machine learning models), transformations recited at a high level of generality (e.g., adjust a procedure), generic computer components and/or a field of use (Claims 3-6, 15 and 22), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Examiner’s Note
Claim 21 was evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
The examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a product (machine/manufacture), which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test, the examiner submits that the claim recites a judicial exception (i.e., abstract idea) as indicated regarding claim 1 (see above) and:
the limitation “detecting the trigger event includes detecting that the pressure measurements are outside of a pressure threshold range” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data for identifying abnormalities/outliers in the data (e.g., identify a trigger event based on a threshold comparison; see specification at [0103]-[0105], [0119]). Except for the recitation of the extra-solution activities (i.e., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of this claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data for identifying abnormal information (i.e., trigger event).
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the claim recites the additional elements as indicated regarding claim 1 (see above) and:
“the drilling information includes pressure measurements” which adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated; see MPEP 2106.05(g)); and
“the recommendation includes adjusting a drilling fluid flow rate to return the pressure measurements to within the pressure threshold range, and wherein implementing the recommendation includes providing pump instructions to a pump to adjust the drilling fluid flow rate and return the pressure measurements to within the pressure threshold range” which, when considering the claim as a whole, integrates the judicial exception into a practical application by effecting a transformation or reduction of a particular article to a different state or thing (e.g., providing pump instructions to a pump to adjust the drilling fluid flow rate; see MPEP 2106.05(a)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 9, 12, 15-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Johnston (US 20200277848 A1), hereinafter ‘Johnston’, in view of Chahine (US 20200291764 A1), hereinafter ‘Chahine’.
Regarding claim 1.
Johnston discloses
A non-transitory computer readable medium (Fig. 20, item 2006 – “storage media”) having stored therein instructions executable by a computer system (Fig. 20, item 2001-1 – “computing system”; [0305]-[0309]: a computing system, comprising one or more processors and memory with executable instructions, is used to receive a well plan and monitor well operations by a wellsite system (Figs. 2 and 7) according to the well plan (see also [0003], [0127], [0317])) to cause the computer system to:
monitor a sensor suite (Fig. 2, item 264 – ‘sensor(s)’; [0071], [0149]-[0151]: sensors in the wellsite system are used to monitor drilling activities (see also [0037], [0163])), the sensor suite collecting drilling information about a drilling system (Fig. 2, item 200 – “wellsite system”) during a wellsite operation (Fig. 14, item 1420; [0151]-[0152], [0247]: data corresponding to various operations at a wellsite system is captured for monitoring the operations (see also [0159], [0164], [0181], [0263])), the drilling system performing drilling operations according to a drill plan (Fig. 14, item 1410; [0148], [0247]: drilling operations are performed based on a well plan (see also [0031]-[0033], [0090]-[0091], [0178], [0194], [0260]));
detect, via a machine learning model (Fig. 10, item 1036; [0178], [0182]: comparison between actual drilling and well plan based on machine learning is used to determine unfavorable conditions (see also [0035], [0229]-[0230])) trained on knowledge in a knowledge database (Fig. 11, item 1118; [0187], [0204], [0238]: machine learning model is trained using information in a database (see also [0224], [0244])), a trigger event during the wellsite operation by identifying that at least a portion of the drilling information is outside of a threshold of the drilling information (Fig. 14, items 1430 and 1440; [0247], [0252]: during a drilling operation, well information is analyzed in order to detect a deviation or delta (trigger event) from the well plan (see also [0195], [0206], [0211]-[0214], [0217], [0250])), wherein the knowledge in the knowledge database includes explicit knowledge and tacit knowledge ([0224]: deltas, either resulting in positive or negative outcomes, are stored in a database with the associated factors, including operator feedback based on experience (tacit knowledge) and equipment deltas (explicit knowledge) (see [0212]-[0214]; see also [0234]));
in response to detecting the trigger event:
identify event data from the sensor suite at a time of the trigger event (Fig. 14, item 1450; [0247], [0250], [0256]: deviation (trigger event) is analyzed in order to determine additional factors from sensors (event data) (see also [0223]-[0224], [0229], [0234], [0266])), the event data including unrelated data not relevant to the trigger event ([0214], [0229]: deviations, when produced by humans, can be associated to operator or team of operators (see also [0155], [0181])); and
identify plan information from the drill plan that describes an activity being performed during the wellsite operation when the trigger event was detected (Fig. 14, item 1450; [0247], [0251]: deviation (trigger event) is analyzed in order to determine activity being performed during operation (plan information) (see also [0034], [0160], [0164], [0181], [0211], [0271], [0287], [0300])); and
prompt a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data ([0212]-[0214]: when a deviation is caused by a human action based on experience, an operator interacts with a GUI in order to provide reasons/feedback (new tacit knowledge) about the generated deviation (e.g., where/how the operator deviated from the drill plan; see also [0090]-[0091], [0155], [0276]));
preparing a recommendation to adjust drilling parameters based on the trigger event (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation, with notices regarding adjustments being generated (see also [0143], [0197], [0205]-[0206], [0242], [0292]));
implementing, via a drilling integrator ([0275], [0317]: a computer system implements the method), the recommendation in the drilling system by automatically adjusting the drilling parameters at the drilling system (Fig. 14, item 1460; [0205]-[0206], [0247], [0288]: well plan is revised/adjusted based on analyzing the deviation (see also [0090], [0242], [0249], [0292]; see also [0033], [0133], [0142], [0174] regarding automation of operations at a wellsite));
adding the drilling information, the event data, the plan information and the manual information to the knowledge database ([0224], [0247], [0292]-[0293]: information related to deviations, associated factors and outcomes are stored in a database (see also [0092], [0155], [0163], [0166], [0180], [0192], [0195], [0227], [0229], [0300]-[0301])); and
training the machine learning model on the drilling information, the event data, the plan information, and the manual information added to the knowledge database ([0182], [0187], [0224]: machine learning model is updated based on analysis (see also [0190], [0237], [0244])).
Johnston does not explicitly disclose:
the threshold is a threshold range.
adding the recommendation to the knowledge database.
Regarding “the threshold is a threshold range”, Chahine teaches:
“FIG. 3 shows a graph 300 depicting an example BOP signal 332 that can be collected by the sensors S. Graph 332 plots pressure P (y-axis) versus time t (x-axis) for one of the regulators 123 of the BOP 222. As shown by this graph, the pressure may vary due to events, such as scheduled maintenance that occur during operation . As also shown by this graph, the pressure may fluctuate even during normal operation. Graphs of one or more of the regulators 123 may be collected over time to define patterns for the regulators 123 when operating according to a pre-designed specification SP. Specifications for operation of the regulators 123 may be defined by, for example, equipment manufacturers, operators, oilfield service companies, government regulations, etc. As shown, the pressure fluctuates within the specification SP, except during maintenance at events E1, E2. The pattern detection unit 101 may be coupled to the BOP 222 to collect information, such as the graph 300 of FIG. 3. This information may be gathered, analyzed and/or fed back to the BOP 222 via the pattern detection system 101 and/or the control units (e.g., 110, 124 of FIG. 1). This information may be used to determine, for example, if a deviation from normal operation has occurred which may require maintenance or operational adjustments to the regulators 123, BOP 222, and/or other portions of the wellsite 100” ([0032]-[0034]: a pre-designed specification SP (threshold range) is employed to check whether signals are normal, or a deviation (Fig. 3, items E1 and E2 - trigger event) from normal operation has occurred in order to adjust wellsite operations (see also [0002], [0017]-[0018], [0049])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to implement a threshold range for detecting trigger events, in order to provide flexibility during the analysis for deviation identification (e.g., using a range instead of a value to define normal operations) and improve accuracy regarding corresponding adjustments based on the direction of the deviation (e.g., apply higher or lower values as part of the adjustment).
Regarding “adding the recommendation to the knowledge database”, Johnston teaches:
“The OPTIDRILLTM technology can help to manage downhole conditions and BHA dynamics as a real-time drilling intelligence service. The service can incorporate a rigsite display (e.g., a wellsite display) of integrated downhole and surface data that provides actionable information to mitigate risk and increase efficiency. As an example, such data may be stored, for example, to a database system (e.g., consider a database system associated with the STUDIOTM framework)” ([0166]: data used for mitigating risk and increase efficiency (analogous to recommendations) can be stored to a database system for real-time drilling).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to add the recommendation to the knowledge database, in order to quickly provide accurate responses to issues present during drilling operations based on robust analysis.
Regarding claim 2.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
detecting the trigger event includes detecting at least one of a drilling dysfunction, a likelihood of the drilling dysfunction, a near-miss event, or a deviation from the drill plan (Fig. 14, items 1430 and 1440; [0247], [0252]: during a drilling operation, well information is analyzed in order to detect a deviation or delta (trigger event) from the well plan (see also [0195], [0206], [0211]-[0214], [0217], [0250])).
Regarding claim 3.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston does not explicitly disclose:
prompting the user to enter the manual information includes wherein prompting the user to enter at least one of a label of the trigger event, a categorization of the trigger event, or free text related to the trigger event.
However, Johnston teaches:
“As another example, consider a digital well plan that includes information as to how to mix mud (a recipe for drilling fluid) where a human operator deviates from the information in the digital well plan. In such an example, the human operator may proceed to follow how to pump the mud as set forth in the digital well plan or may adjust how to pump the mud based on experience. As to the latter, a delta generated by the lack of adherence to mud recipe may be flagged as a possible reason (or outcome) as to why the human operator deviated from the digital well plan on how to pump the mud. In such a scenario, a system can include generating output from input delta (s) and rendering one or more GUI notices to a display at a rigsite where the notices may indicate that (i) the how to mix mud was not followed and (ii) the how to pump the mud was not followed. In such an example, the human operator may interact with the GUI to confirm the notices, which may provide feedback that where a human operator deviates from how to mix mud, that human operator may deviate from how to pump the mud” ([0213]: when human operator deviates from well plan instructions, a GUI may allow the operator to provide feedback (analogous to label or categorize the trigger event) regarding the deviation (see also [0143], [0155], [0214])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to prompt the user to enter the manual information by prompting the user to enter at least one of a label of the trigger event, a categorization of the trigger event, or free text related to the trigger event, in order to obtain real-time feedback regarding trigger events based on experience/knowledge from operators to further improve the analysis of an event occurring during drilling processes.
Regarding claim 4.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
the event data comprises at least one of a time stamp, a current depth, drilling parameters, surface sensor readings, or an actual procedure being executed (Fig. 14, item 1450; [0223]-[0224], [0247], [0256], [0266]: deviation (trigger event) is analyzed in order to determine additional factors from sensors (see [0250]), using time as a basis for identification (see [0229]), drilling parameters (see [0234], [0250]) (see also [0163] regarding aligning collected data by time and depth)).
Regarding claim 5.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
the plan information comprises at least one of a planned activity, standard operating procedures, or planned drilling parameters (Fig. 14, item 1450; [0247], [0251]: deviation (trigger event) is analyzed in order to determine activity being performed during operation (standard operating procedures) (see also [0034], [0160], [0164], [0181], [0211], [0271], [0287], [0300])).
Regarding claim 6.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
monitoring the sensor suite includes monitoring at least one of a multi-sensor system, manual entries by personnel in reporting systems, surface sensor information, downhole sensor information, recorded video, or the plan information ([0071], [0149]-[0150]: surface and downhole sensors in a wellsite system are used to monitor drilling activities (see also [0037], [0143], [0155], [0163], [0213])).
Regarding claim 9.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
generating a likelihood of one or more trigger events occurring for a planned wellbore ([0240]: deviation is evaluated in order to determine likelihood of occurrence of outcomes during drilling of a well based on the well plan (see also [0224], [0229], [0288], [0292])).
Regarding claim 10.
Johnston in view of Chahine discloses all the features of claim 8 as described above.
Johnston further discloses:
preparing a recommendation to adjust drilling parameters based on the trigger event (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation, with notices regarding adjustments being generated (see also [0205]-[0206], [0242], [0292])).
Regarding claim 11.
Johnston in view of Chahine discloses all the features of claim 10 as described above.
Johnston further discloses:
implementing the recommendation in the drilling system (Fig. 14, item 1460; [0205]-[0206], [0247], [0288]: well plan is revised/adjusted based on analyzing the deviation (see also, [0242], [0292])).
Regarding claim 12.
Johnston discloses
A method (Fig. 14) for monitoring a drilling system (Fig. 2, item 200 – “wellsite system”; [0247]: a method for drilling a well according to a well plan and monitor well operations is presented (see also [0003]), comprising:
performing a drilling operation (Fig. 14, item 1410; [0148], [0247]: a well is drilled according to the well plan (see also [0031]-[0033], [0090]-[0091], [0178], [0194], [0260]));
collecting drilling information for the drilling operation (Fig. 14, item 1420; [0151]-[0152], [0247]: data corresponding to various operations at a wellsite system is captured for monitoring the operations (see also [0159], [0164], [0181], [0263])) from one or more sensors (Fig. 2, item 264 – ‘sensor(s)’; [0071], [0149]-[0150]: sensors in the wellsite system are used to monitor drilling activities (see also [0037], [0163]));
applying the drilling information as input to a machine learning model (Fig. 10, item 1036) trained on knowledge in a knowledge database (Fig. 11, item 1118; [0187], [0204], [0238]: machine learning model is trained using information in a database (see also [0224], [0244])) to identify a trigger event ([0178], [0182]: comparison between actual drilling and well plan based on machine learning is used to determine unfavorable conditions (see also [0035], [0229]-[0230])), the trigger event including a deviation from a threshold of at least a portion of the drilling information (Fig. 14, items 1430 and 1440; [0247], [0252]: during a drilling operation, well information is analyzed in order to detect a deviation or delta (trigger event) from the well plan (see also [0195], [0206], [0211]-[0214], [0217], [0250])), wherein the knowledge in the knowledge database includes explicit knowledge and tacit knowledge ([0224]: deltas, either resulting in positive or negative outcomes, are stored in a database with the associated factors, including operator feedback based on experience (tacit knowledge) and equipment deltas (explicit knowledge) (see [0212]-[0214]; see also [0234]));
associating the trigger event with a knowledge type (Fig. 14, item 1450; [0247], [0250]-[0251], [0256]: deviation (trigger event) is analyzed in order to determine activity/factors during operation (knowledge type) (see also [0034], [0155], [0160], [0164], [0181], [0211]-[0214], [0223]-[0224], [0229], [0234], [0266], [0271], [0287], [0300])); and
using the knowledge type, identifying event data from the drilling information at a time of the trigger event (Fig. 14, item 1450; [0247], [0250], [0256]: deviation (trigger event) is analyzed in order to determine additional factors from sensors (event data) (see also [0223]-[0224], [0229], [0234], [0266])), the event data including unrelated data not relevant to the trigger event ([0214], [0229]: deviations, when produced by humans, can be associated to operator or team of operators (see also [0155], [0181]));
prompting a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data ([0212]-[0214]: when a deviation is caused by a human action based on experience, an operator interacts with a GUI in order to provide reasons/feedback (new tacit knowledge) about the generated deviation (e.g., where/how the operator deviated from the drill plan; see also [0090]-[0091], [0155], [0276]));
using the knowledge type, preparing a recommendation to adjust drilling parameters based on the trigger event (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation and activity/factors, with notices regarding adjustments being generated (see also [0143], [0197], [0205]-[0206], [0242], [0292]));
adjusting the drilling parameters to return the drilling information to within the threshold (Fig. 14, item 1460; [0205]-[0206], [0247], [0288]: well plan is revised/adjusted based on analyzing the deviation (see also [0090], [0242], [0249], [0292]); examiner interprets after implementing the adjustment, drilling operations and information return to normal conditions);
adding the drilling information, the event data and the manual information to the knowledge database ([0224], [0247], [0292]-[0293]: information related to deviations, associated factors and outcomes are stored in a database (see also [0092], [0155], [0163], [0166], [0180], [0192], [0195], [0227], [0229], [0300]-[0301])); and
training the machine learning model on the drilling information, the event data, and the manual information added to the knowledge database ([0182], [0187], [0224]: machine learning model is updated based on analysis (see also [0190], [0237], [0244])).
Johnston does not explicitly disclose:
the threshold is a threshold range; and
adding the recommendation to the knowledge database.
Regarding “the threshold is a threshold range’, Chahine teaches:
“FIG. 3 shows a graph 300 depicting an example BOP signal 332 that can be collected by the sensors S. Graph 332 plots pressure P (y-axis) versus time t (x-axis) for one of the regulators 123 of the BOP 222. As shown by this graph, the pressure may vary due to events, such as scheduled maintenance that occur during operation . As also shown by this graph, the pressure may fluctuate even during normal operation. Graphs of one or more of the regulators 123 may be collected over time to define patterns for the regulators 123 when operating according to a pre-designed specification SP. Specifications for operation of the regulators 123 may be defined by, for example, equipment manufacturers, operators, oilfield service companies, government regulations, etc. As shown, the pressure fluctuates within the specification SP, except during maintenance at events E1, E2. The pattern detection unit 101 may be coupled to the BOP 222 to collect information, such as the graph 300 of FIG. 3. This information may be gathered, analyzed and/or fed back to the BOP 222 via the pattern detection system 101 and/or the control units (e.g., 110, 124 of FIG. 1). This information may be used to determine, for example, if a deviation from normal operation has occurred which may require maintenance or operational adjustments to the regulators 123, BOP 222, and/or other portions of the wellsite 100” ([0032]-[0034]: a pre-designed specification SP (threshold range) is employed to check whether signals are normal, or a deviation (Fig. 3, items E1 and E2 - trigger event) from normal operation has occurred in order to adjust wellsite operations (see also [0002], [0017]-[0018], [0049])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to implement a threshold range for detecting trigger events, in order to provide flexibility during the analysis for deviation identification (e.g., using a range instead of a value to define normal operations) and improve accuracy regarding corresponding adjustments based on the direction of the deviation (e.g., apply higher or lower values as part of the adjustment).
Regarding “adding the recommendation to the knowledge database”, Johnston teaches:
“The OPTIDRILLTM technology can help to manage downhole conditions and BHA dynamics as a real-time drilling intelligence service. The service can incorporate a rigsite display (e.g., a wellsite display) of integrated downhole and surface data that provides actionable information to mitigate risk and increase efficiency. As an example, such data may be stored, for example, to a database system (e.g., consider a database system associated with the STUDIOTM framework)” ([0166]: data used for mitigating risk and increase efficiency (analogous to recommendations) can be stored to a database system for real-time drilling).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to add the recommendation to the knowledge database, in order to quickly provide accurate responses to issues present during drilling operations based on robust analysis.
Regarding claim 15.
Johnston in view of Chahine discloses all the features of claim 12 as described above.
Johnston further discloses:
adjusting the drilling parameters includes instructing a drilling operator to adjust a procedure (Fig. 11, item 1142; Fig. 14, item 1460; [0247], [0288]: well plan is revised/adjusted based on analyzing the deviation (see also [0205]-[0206]), with this step incorporating human interaction (see [0133], [0155], [0276])).
Regarding claim 16.
Johnston in view of Chahine discloses all the features of claim 12 as described above.
Johnston further discloses:
based on the knowledge type, generating an alert (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation, with notices/alerts regarding adjustments being generated based on analysis (see also [0155], [0205]-[0206], [0242], [0292])).
Regarding claim 17.
Johnston in view of Chahine discloses all the features of claim 16 as described above.
Johnston further discloses:
the alert includes a severity level based on at least one of the knowledge type or the deviation from the threshold (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation, with notices/alerts regarding adjustments being generated based on analysis and provided as color-coded outcomes (see also [0155], [0205]-[0206], [0242], [0292]; see also Chahine at [0063])).
Johnston does not disclose:
the threshold is a threshold range.
Chahine teaches:
“FIG. 3 shows a graph 300 depicting an example BOP signal 332 that can be collected by the sensors S. Graph 332 plots pressure P (y-axis) versus time t (x-axis) for one of the regulators 123 of the BOP 222. As shown by this graph, the pressure may vary due to events, such as scheduled maintenance that occur during operation . As also shown by this graph, the pressure may fluctuate even during normal operation. Graphs of one or more of the regulators 123 may be collected over time to define patterns for the regulators 123 when operating according to a pre-designed specification SP. Specifications for operation of the regulators 123 may be defined by, for example, equipment manufacturers, operators, oilfield service companies, government regulations, etc. As shown, the pressure fluctuates within the specification SP, except during maintenance at events E1, E2. The pattern detection unit 101 may be coupled to the BOP 222 to collect information, such as the graph 300 of FIG. 3. This information may be gathered, analyzed and/or fed back to the BOP 222 via the pattern detection system 101 and/or the control units (e.g., 110, 124 of FIG. 1). This information may be used to determine, for example, if a deviation from normal operation has occurred which may require maintenance or operational adjustments to the regulators 123, BOP 222, and/or other portions of the wellsite 100” ([0032]-[0034]: a pre-designed specification SP (threshold range) is employed to check whether signals are normal, or a deviation (Fig. 3, items E1 and E2 - trigger event) from normal operation has occurred in order to adjust wellsite operations (see also [0002], [0017]-[0018], [0049])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to implement a threshold range for detecting trigger events, in order to provide flexibility during the analysis for deviation identification (e.g., using a range instead of a value to define normal operations) and improve accuracy regarding corresponding adjustments based on the direction of the deviation (e.g., apply higher or lower values as part of the adjustment).
Regarding claim 18.
Johnston discloses
A system (Figs. 2 and 7; [0046], [0125]: a system is presented (see also [0003])) comprising:
a drilling system (Fig. 2, item 200 – “wellsite system”; Fig. 7, item 700 - “wellsite system”) that performs a drilling operation (Fig. 14, item 1410; [0148], [0247]: a well is drilled according to the well plan (see also [0031]-[0033], [0049], [0090]-[0091], [0178], [0194], [0260]));
one or more sensors configured to sense drilling information about the drilling system (Fig. 2, item 264 – ‘sensor(s)’; [0071], [0149]-[0150]: sensors in the wellsite system are used to monitor drilling activities (see also [0037], [0163]));
a processor (Fig. 7, item 772 – ‘processor(s)’) and memory (Fig. 7, item 774 – ‘memory’), the memory including instructions which cause the processor ([0305]-[0309]: a computing system, comprising one or more processors and memory with executable instructions, is used to receive a well plan and monitor well operations according to the well plan (see also [0003], [0127], [0317])) to:
collect the drilling information for the drilling operation from the one or more sensors (Fig. 14, item 1420; [0151]-[0152], [0247]: data corresponding to various operations at a wellsite system is captured for monitoring the operations (see also [0159], [0164], [0181], [0263]));
apply the drilling information as input to a machine learning model (Fig. 10, item 1036) trained on knowledge in a knowledge database (Fig. 11, item 1118; [0187], [0204], [0238]: machine learning model is trained using information in a database (see also [0224], [0244])) to identify a trigger event ([0178], [0182]: comparison between actual drilling and well plan based on machine learning is used to determine unfavorable conditions (see also [0035], [0229]-[0230])) to identify a trigger event ([0178], [0182]: comparison between actual drilling and well plan based on machine learning is used to determine unfavorable conditions (see also [0035], [0229]-[0230])), the trigger event including a deviation from a threshold of at least a portion of the drilling information (Fig. 14, items 1430 and 1440; [0247], [0252]: during a drilling operation, well information is analyzed in order to detect a deviation or delta (trigger event) from the well plan (see also [0195], [0206], [0211]-[0214], [0217], [0250])), wherein the knowledge in the knowledge database includes explicit knowledge and tacit knowledge ([0224]: deltas, either resulting in positive or negative outcomes, are stored in a database with the associated factors, including operator feedback based on experience (tacit knowledge) and equipment deltas (explicit knowledge) (see [0212]-[0214]; see also [0234]));
associate the trigger event with a knowledge type (Fig. 14, item 1450; [0247], [0250]-[0251], [0256]: deviation (trigger event) is analyzed in order to determine activity/factors during operation (knowledge type) (see also [0034], [0155], [0160], [0164], [0181], [0211]-[0214], [0223]-[0224], [0229], [0234], [0266], [0271], [0287], [0300]));
using the knowledge type, identify event data from the drilling information at a time of the trigger event (Fig. 14, item 1450; [0247], [0250], [0256]: deviation (trigger event) is analyzed in order to determine additional factors from sensors (event data) (see also [0223]-[0224], [0229], [0234], [0266])), the event data including unrelated data not relevant to the trigger event ([0214], [0229]: deviations, when produced by humans, can be associated to operator or team of operators (see also [0155], [0181]));
prompt a user to enter manual information about the trigger event, the manual information including new tacit knowledge not included in the drilling information or the event data ([0212]-[0214]: when a deviation is caused by a human action based on experience, an operator interacts with a GUI in order to provide reasons/feedback (new tacit knowledge) about the generated deviation (e.g., where/how the operator deviated from the drill plan; see also [0090]-[0091], [0155], [0276]));
using the knowledge type, prepare a recommendation to adjust drilling parameters based on the trigger event (Fig. 14, item 1460; [0247], [0288]: well plan is revised based on analyzing the deviation and activity/factors, with notices regarding adjustments being generated (see also [0143], [0197], [0205]-[0206], [0242], [0292]));
based on the knowledge type, adjust the drilling parameters to return the drilling information to within the threshold (Fig. 14, item 1460; [0205]-[0206], [0247], [0288]: well plan is revised/adjusted based on analyzing the deviation (see also [0090], [0242], [0249], [0292]); examiner interprets after implementing the adjustment, drilling operations and information return to normal conditions);
add the drilling information, the event data and the manual information to the knowledge database ([0224], [0247], [0292]-[0293]: information related to deviations, associated factors and outcomes are stored in a database (see also [0092], [0155], [0163], [0166], [0180], [0192], [0195], [0227], [0229], [0300]-[0301])); and
train the machine learning model on the drilling information, the event data, and the manual information added to the knowledge database ([0182], [0187], [0224]: machine learning model is updated based on analysis (see also [0190], [0237], [0244])).
Johnston does not explicitly disclose:
the threshold is a threshold range; and
adding the recommendation to the knowledge database.
Regarding “the threshold is a threshold range”, Chahine teaches:
“FIG. 3 shows a graph 300 depicting an example BOP signal 332 that can be collected by the sensors S. Graph 332 plots pressure P (y-axis) versus time t (x-axis) for one of the regulators 123 of the BOP 222. As shown by this graph, the pressure may vary due to events, such as scheduled maintenance that occur during operation . As also shown by this graph, the pressure may fluctuate even during normal operation. Graphs of one or more of the regulators 123 may be collected over time to define patterns for the regulators 123 when operating according to a pre-designed specification SP. Specifications for operation of the regulators 123 may be defined by, for example, equipment manufacturers, operators, oilfield service companies, government regulations, etc. As shown, the pressure fluctuates within the specification SP, except during maintenance at events E1, E2. The pattern detection unit 101 may be coupled to the BOP 222 to collect information, such as the graph 300 of FIG. 3. This information may be gathered, analyzed and/or fed back to the BOP 222 via the pattern detection system 101 and/or the control units (e.g., 110, 124 of FIG. 1). This information may be used to determine, for example, if a deviation from normal operation has occurred which may require maintenance or operational adjustments to the regulators 123, BOP 222, and/or other portions of the wellsite 100” ([0032]-[0034]: a pre-designed specification SP (threshold range) is employed to check whether signals are normal, or a deviation (Fig. 3, items E1 and E2 - trigger event) from normal operation has occurred in order to adjust wellsite operations (see also [0002], [0017]-[0018], [0049])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to implement a threshold range for detecting trigger events, in order to provide flexibility during the analysis for deviation identification (e.g., using a range instead of a value to define normal operations) and improve accuracy regarding corresponding adjustments based on the direction of the deviation (e.g., apply higher or lower values as part of the adjustment).
Regarding “adding the recommendation to the knowledge database”, Johnston teaches:
“The OPTIDRILLTM technology can help to manage downhole conditions and BHA dynamics as a real-time drilling intelligence service. The service can incorporate a rigsite display (e.g., a wellsite display) of integrated downhole and surface data that provides actionable information to mitigate risk and increase efficiency. As an example, such data may be stored, for example, to a database system (e.g., consider a database system associated with the STUDIOTM framework)” ([0166]: data used for mitigating risk and increase efficiency (analogous to recommendations) can be stored to a database system for real-time drilling).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to add the recommendation to the knowledge database, in order to quickly provide accurate responses to issues present during drilling operations based on robust analysis.
Regarding claim 21.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
the drilling information includes pressure measurements ([0067], [0151]-[0152], [0265]: pressure is monitored (see also [0075]-[0077], [0135])).
Johnston does not explicitly disclose:
wherein detecting the trigger event includes detecting that the pressure measurements are outside of a pressure threshold range,
wherein the recommendation includes adjusting a drilling fluid flow rate to return the pressure measurements to within the pressure threshold range, and
wherein implementing the recommendation includes providing pump instructions to a pump to adjust the drilling fluid flow rate and return the pressure measurements to within the pressure threshold range.
However, Johnston teaches:
“Once a well is formed and completed, depending on the purpose of the well (e.g., injection and/or production), fluid may flow to the surface (e.g., and/or from the surface) using tubing and other completion equipment. As fluid passes, various dynamic measurements, such as fluid flow rates, pressure, and composition may be monitored” ([0152]: fluid information such as flow rates and pressures are monitored during operations); and
“In the example of FIG. 11, the deltas represent deviations from a corresponding digital well plan. Such deviations can be with respect to one or more of equipment, operation of equipment, timing of operations of equipment, etc. As an example, consider a digital well plan for a well that includes information to ramp up a pump rate of pumping drilling fluid where actual information from the rigsite indicates that the ramp up of the pump rate of pumping the drilling fluid deviated as to the ramp up profile. In such an example, consider a deviation as to duration for the ramp up and/or the final pump rate of pumping the drilling fluid. Such a deviation or deviations can be a delta or deltas. As an example, a trained neural network can be trained based on training data as to deltas such as deviations in ramp up with associated outcomes. In the foregoing example, the trained neural network can take the delta of the drilling operation as input and output a likely outcome. Where the outcome is a beneficial (e.g., positive) outcome, the drilling operation may continue with the deviation; however, where the outcome is a detrimental outcome (e.g., negative), the drilling operation can be adjusted such as by controlling one or more pieces of equipment and/or by adjusting the digital well plan for the well” ([0206]: when fluid pump rate (fluid flow rate) deviates from drilling plan in a negative way, adjustments of the drilling operation are performed (e.g., control pump)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine to detect the trigger event by detecting that the pressure measurements are outside of a pressure threshold range, wherein the recommendation includes adjusting a drilling fluid flow rate to return the pressure measurements to within the pressure threshold range, and wherein implementing the recommendation includes providing pump instructions to a pump to adjust the drilling fluid flow rate and return the pressure measurements to within the pressure threshold range, in order to implement appropriate adjustments based on the particular deviation.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Johnston, in view of Chahine, and in further view of Neal (US 20230151727 A1), hereinafter ‘Neal’.
Regarding claim 22.
Johnston in view of Chahine discloses all the features of claim 1 as described above.
Johnston further discloses:
the unrelated data includes a crew on shift ([0214], [0229]: deviations, when produced by humans, can be associated to operator or team of operators (see also [0155], [0181])).
Johnston does not disclose:
the unrelated data includes weather.
Neal teaches:
“Disclosed herein is a method of predicting pumping equipment maintenance based on pumping equipment utilization. A monitoring application can generate a log file of the volumes and rates of the wellbore treatment pumped by the pumping equipment. The unit controller can transmit the log file to a predictive model to predict a future maintenance event. A machine learning process can utilize a historical database as a training data set to develop the model. The method of predicting pumping equipment maintenance can increase the reliability of the pumping unit” ([0026]: monitoring pumping equipment for predicting maintenance is based on machine learning using information from a historical database (see also [0032], [0035], [0042], [0044]-[0046], [0049]) and environmental log including ambient temperature (analogous to weather, see [0055], [0058]-[0059], [0074]); examiner submits maintenance can be determined based on deviation from normal behavior of pump)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Johnston in view of Chahine, and in further view of Neil, to incorporate the unrelated data including weather, in order to analyze the contributions of environmental conditions (i.e., weather) to abnormalities in well operations for providing a more robust analysis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
RANGARAJAN; Keshava et al., US 20200182036 A1, INTEGRATED SURVEILLANCE AND CONTROL
Reference discloses utilizing weather information in machine learning for managing wellbore activities.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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