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 Final Office Action in response to the communication filed on 01/30/2026. Claims 1—24 and 26—28 are currently pending.
Priority
The Applicant’s claim for benefit of WIPO Patent Application PCT/IB2022/051515 filed on 02/21/2022, has been received and acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 08/14/2024, 06/16/2025, and 12/29/2025 has been reviewed and considered.
Response to Arguments
Applicant's arguments and amendments filed 01/30/2026 with respect to the objection of claims 25—28 have been fully considered and are persuasive. Examiner notes that claim 25 is cancelled thereby rendering the objection moot. The objection to claims 25—28 is withdrawn.
Applicant's arguments and amendments filed 01/30/2026 in response to the rejection of claim 21 under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of claim 21 under both 35 U.S.C. 112(a) and 35 U.S.C. 112(b) is withdrawn in view of the provided amendment.
Applicant's arguments and amendments filed 01/30/2026 in response to the rejection of claim 25 under 35 U.S.C. 101 for being directed to non-statutory subject matter have been fully considered and are rendered moot in view of the cancellation of claim 25.
Applicant's arguments and amendments filed 01/30/2026 in response to the rejection of claims 1—28 under 35 U.S.C. 101 (e.g., for being directed to an abstract idea without significantly more) have been fully considered and are not persuasive. Examiner notes that claim 25 has been cancelled. As such, the rejection of claims 1—24 and 26—28 under 35 U.S.C. 101 is maintained as modified below in view of the provided amendments. The arguments provided on pages 10—13 of the Response are addressed as follows:
To start, Examiner notes that multiple statements under the section labelled “Step 2A, Prong 1: The Claims Do Not ‘Recite’ an Abstract Idea,” are not directed to the abstract ideas as identified on pages 12—13 of the Non-Final Office Action dated 10/02/2025. For example, the statements directed to the alleged recitation of “specific sensors”, “control outputs”, and “claim limitations requiring interaction with physical oilfield infrastructure,” are all statements directed features (e.g., or types of features) which would be considered additional elements. Additional elements are identified in Step 2A, Prong 2 and are subsequently analyzed under Step 2B (e.g., to determine whether they properly integrate the judicial exception into a practical application). The identification and analysis of additional elements does not take place under Step 2A, Prong 1. Moreover, the only analysis performed under Step 2A Prong 1, as stated in the Office Action is, “whether the claim recites a judicial exception.” The Response makes multiple statements regarding how the utilization of LaGrange multipliers, as recited in the claims, should be interpreted in view of the Specification. However, as stated above, the analysis performed at Step 2A, Prong 1 is whether or not the claim recites a judicial exception. To this end, the independent claims were identified as reciting limitations directed to the mental process of making determinations. The fact that the claims recite one or more mathematical concepts, which is the only judicial exception addressed in the Response, is not the primary reason that the claims were identified as reciting an abstract idea.
For example, as stated on page 12 of the Non-Final Rejection, the claims recite the limitations of, or substantially similar to “determining, based on the Lagrange multipliers, as least one hydrocarbon flow rate,” where determining (e.g., making a determination) is an action which is performable in a human mind with or without the benefit of: pen, paper, a computer, and/or a mathematical concept (e.g., LaGrange multipliers). As such, the claim was identified as being directed to, at least, a mental process, which is not addressed in the Response. For the foregoing reasons, the arguments set forth in the Response at page 10 are not persuasive in showing that the claims do not recite an abstract idea.
Under the section labelled “Step 2A, Prong 2…” the Response at page 11 states “[t]he claimed solution explains that the technical purpose of the claimed method is to address a recognized technical limitation in oilfield operations… The claimed solution improves this technical field by producing flow-rate estimates that are consistent with measured pipeline data and historical well behavior…”. As best understood by the Examiner, this statement appears to be asserting that the claims are directed to improvements to a technology or a technical field as described in MPEP 2106.05(a) and therefore integrate the recited judicial exceptions (e.g., claimed abstract ideas) into a practical application. However, in order for this type of argument to be successful in showing integration, the improvement has to be directed to an improvement in the technology and cannot be directed to an improvement to the abstract idea itself. For example, the MPEP states “[n]otably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Examiner asserts that an improvement to the generation of “flow-rate estimates that are consistent with measured pipeline data,” amounts to an improvement to the abstract idea and does not constitute an improvement to the technology or a technological field. For the foregoing reasons, the arguments set forth under the header “Step 2A, Prong 2,” are not persuasive in overcoming the rejection of record.
The arguments set forth under the section labelled “Step 2B…” are not persuasive as addressed in the following. The Response at page 12 states “Applicant’s claims are similar to the examples of Diamond v. Diehr and Thales because they apply mathematical techniques to control or improve physical systems in an oilfield,” to which the Examiner does not agree. As previously stated in both the Non-Final Rejection and above, the alleged application recited in the claims is too generic to constitute a practical application. For example, the following limitations were identified in the instant independent claims as being directed to an application of the judicial exception:
Claim 1: “adapting at least one operating parameter of at least one well of the plurality of well by controlling oilfield equipment associated with the at least one well, wherein adapting is at least partially based on the at least one determined hydrocarbon fluid flow rate of the at least one of the plurality of wells”;
Claim 2: “adjusting a wellhead pressure of at least one of the plurality of wells to reduce stress on the components of the well”; and
Claim 20: “adapting at least one operating parameter of at least one well of the plurality of wells, wherein adapting is at least partially based on the at least one determined hydrocarbon fluid flow rate.”
The foregoing limitations do not provide the level of specificity recited in the limitations of Diehr and are equivalent to merely reciting the limitation “apply it”. The MPEP provides the following guidelines with respect to understanding how and why the limitations of Diehr provided for a practical application of the judicial exception:
“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 2016.05(h)).
In view of the MPEP citation provided above, Examiner asserts that, in consideration as to whether the claims recite a practical application, the following deficiencies exists between the instant claims and the features recited in the claims of Diehr which lead to eligibility:
with regards to claim 1, generically recited “oilfield equipment” is not equivalent to a specifically recited apparatus such as a rubber molding press;
with regards to claims 1 and 2, the limitations directed to generically “controlling” and “adapting” a piece of equipment are not equivalent to specifically stating how a specific piece of equipment is operated (e.g., the specific outcome of the analysis performed using the Arrhenius equation is that the mold is opened when the cure time and elapsed time are equivalent). For example, “controlling” and “adapting” are not equivalent to “opening”;
with regards to claim 1, generally tying, in a non-specific manner, an outcome/determination/assessment of the judicial exception to a generically recited control operation (e.g., “adapting is at least partially based on”) is not equivalent to directly and explicitly tying the determination made from the abstract idea (e.g., calculated cure time) to an exact control outcome (e.g., elapsed time is exactly equivalent to the calculated time so the mold is opened). For example, Diehr does not generically state “control the operation of the mold based at least partially upon the time calculated from the Arrhenius equation”; and
with regards to claim 2, reciting an operational objective which has no apparent or recited relationship to the determination made from the abstract idea does not succeed in showing eligibility (e.g., there is no clear relationship between the determination of the flow rates, the associated stress on the well components, the threshold at which an adjustment would be required, and the specific type of adjustment made).
For the reasons identified above, the assertion that the instant claims integrate the identified abstract idea(s) into a practical application in a manner similar and/or equivalent to Diehr, is not persuasive. With respect to the comparison with Thales, Examiner notes that mere recitation of generic physical objects (e.g, wellbore equipment, wellbores, and pipelines) in a claim directed to an abstract idea is not the same as reciting a specific configuration of a particular object (e.g., inertial sensors in a specific configuration). While it is unclear to the Examiner which limitations of the instant claims the Applicant believes are analogous to Thales, the instant claims do not currently recite any specific/specialized configurations of any specific/specialized objects.
In view of the foregoing, the rejection of claims 1—24 and 26—28 under 35 U.S.C. 101 is maintained as provided below.
EXAMINER NOTE REGARDING THE REJECTION UNDER 35 U.S.C. 101: interviews have often been beneficial when it comes to addressing why the claims are being rejected under 35 U.S.C. 101 and for discussing potential amendments/avenues for integrating the abstract idea into a practical application/overcoming the rejection.
Applicant's arguments and amendments filed 01/30/2026 in response to the rejection of claims 1—28 under 35 U.S.C. 103 have been fully considered and are not persuasive. Examiner notes that claim 25 has been cancelled. As such, the rejection of claims 1—24 and 26—28 under 35 U.S.C. 103 is maintained as modified below in view of the provided amendments.
The Response at page 14 states “Middya refers to Lagrange multipliers only in the mathematical formulation of the SQP algorithm and are ‘a measure of the sensitivity of the objective function to the associated constraints’ so the engineer can decide which constraints to relax… There is no teaching or suggestion that would indicate to the ordinary skilled person that the multipliers themselves should be used as an input to compute/derive the well-specific flow rates,” to which the Examiner does not agree.
For example, Middya states “[t]he invention generally is a method for enhancing allocation of fluid flow rates among a plurality of wellbores coupled to surface facilities… An optimizer adapted to determine an optimal value of an objective function corresponding to the modeled fluid flow characteristics of the wellbores and the surface facilities is then operated. The objective function relates to at least one production system performance parameter. Fluid flow rates are then allocated among the plurality of wellbores as determined by the operating the optimizer.” (Middya, para. [0009]). As such, it is clear that the optimizer of Middya is capable of generating allocated fluid flow rates. Furthermore, the mathematical minimization of Middya recited in para. [0030]—[0033], including equations (4)—(8), expressly show the utilization of the Lagrange function (e.g., which is a function of the Lagrange multipliers) in the minimization process where the Lagrange multipliers are present in the optimum conditions identified in equations (5)—(8). Moreover, the instant claims do not recite any special utilization of the Lagrange multipliers which would require a more specific reading than that provided by the combination of Poonacha and Middya. For example, claim 1 states “determining based on the Lagrange multipliers, at least one hydrocarbon flow rate.” Utilizing the Lagrange function to generate the optimized equation from a cost function (e.g., as discussed in Middya) which is subsequently used to generate the flow values (e.g., as discussed in Middya and Poonacha) fully reads on the foregoing limitation where the claim does not limit or recite the manner in which the Lagrange multipliers are used. As such, the arguments provided with respect to the combination of Poonacha and Middya are not persuasive and the rejection is maintained as modified below.
Claim Objections
Claims 1 and 2 are objected to because of the following informalities:
claim 1 has been amended to recite “measuring a comingled-flow rate…”; however, the claim still recites “using the received comingled-flow measurement data…” which, for consistently, should likely be amended to state “using the acquired comingled-flow measurement data…”; and
claim 2 utilizes a comma to separate the limitations of “determinig…” and “adjusting” where the comma should likely be replaced with a semi-colon (e.g., the comma should likely be replaced with ; and)
Appropriate correction is required.
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.
The following claim elements are impacted by the invocation of 35 U.S.C. 112(f):
“data acquisition unit,” which is equivalent to reciting “unit for data acquisition,” and is recited in at least claim 17. A data acquisition unit is understood to be a generic computer component such as a processor;
“optimizer unit,” which is equivalent to reciting “unit for optimizing,” and is recited in claim 17. As best understood from the Specification, an optimizer unit is a generic computer component such as a processor. For example, para. [0066] states “[i]t is to be understood, that the method described above, can be a computer implemented method. The entire method may be computer implemented or only some of the method steps”; and
“data storing means,” which is equivalent to reciting “means for data storing,” and is recited in claim 17. As best understood from the Specification, a data storing means is a generic computer component such as a processor. For example, para. [0066] states “[i]t is to be understood, that the method described above, can be a computer implemented method. The entire method may be computer implemented or only some of the method steps.” Additionally, para. [0069] states “[t]he system comprises a data storing means, such as a database, configured to store historical well test data”
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—28 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.
Independent claims 1, 2, 17, and 25—28 are directed to methods/processes (e.g., claims 1 and 2) and systems/machines/manufacture (e.g., claims 17 and 25—28). 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(s) 1, 2, and 25—28 recite(s) abstract limitations including, or substantially similar to: “determining, based on Lagrange multipliers, at least one hydrocarbon fluid flow rate…” (e.g., a mental process and/or mathematical concept).
Claim 17 recites the abstract limitation of “determine, based on Lagrange multipliers, at least one hydrocarbon flow rate…” (e.g., a mental process and/or mathematical concept).
Under the broadest reasonable interpretation, limitations directed to “determining,” and making “determinations” cover mental processes in that making determinations is within the scope of the human mind. The limitations may further include mathematical concepts which may be performed in a human mind, performed using pen and paper, or performed on a generic computing device (e.g., a processor). However, nothing in the claim precludes the above identified judicial exceptions from practically being performed in the human mind, with or without the benefit of pen and paper, and with or without the benefit of a mathematical concept. The mere recitation of generic computing elements and/or sensors does not take the claim out of the mental process grouping. Thus the claim recites an abstract idea.
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.
Claims 1 and 25—28 recite the additional element of, or substantially similar to: “measuring a comingled-flow flow rate of the common pipeline to acquire comingled-flow measurement data” (e.g., extra-solution activity); “accessing historical well test data… representative of test hydrocarbon fluid flow rates” (e.g., extra-solution activity); “one of the plurality of wells” (e.g., field of use); and “using the received comingled-flow measurement data and the accessed historical well test data” (e.g., extra-solution activity).
Claims 2 and 17 recite the additional element of, or substantially similar to: “receiving comingled-flow measurement data” (e.g., extra-solution activity); “one sensor” (e.g., field of use); “accessing historical well test data… representative of test hydrocarbon fluid flow rates” (e.g., extra-solution activity); “one of the plurality of wells” (e.g., field of use); and “using received comingled-flow measurement data and the accessed historical well test data” (e.g., extra-solution activity).
In addition to the above identified additional elements, claims 1, 17, and 25—28 recite the additional element of, or substantially similar to: “adapting at least one operating parameter of at least one well… at least partially based on the at least one determined hydrocarbon fluid flow…” (e.g., equivalent to reciting “apply it”).
In addition to the above identified additional elements, claim 17 recites the additional elements of: “a data storing means” (e.g., understood to be a generic computer equivalent to reciting “apply it”); “a data acquisition unit” (e.g., understood to be a generic computer equivalent to reciting “apply it”); and “an optimizer unit” (e.g., understood to be a generic computer equivalent to reciting “apply it”).
In addition to the above identified additional elements, claim 28 recites the additional elements of: “the control unit comprising at least one processor” (e.g., understood to be a generic computer equivalent to reciting “apply it”) and “a memory” (e.g., understood to be a generic computer equivalent to reciting “apply it”).
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).
The additional elements of “measuring a comingled-flow rate of the common pipeline to acquire comingled-flow measurement data” and “receiving comingled-flow measurement data,” constitutes mere data gathering and are therefore insignificant extra-solution activity. Regarding this manner of additional element, the MPEP states:
“[w]hen determining whether an additional element is insignificant extra-solution activity, examiners may consider the following: Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” (MPEP 2106.05(g)).
“[b]elow are examples of activities that the courts have found to be insignificant extra-solution activity: Mere Data Gathering: Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)… Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).” (MPEP 2106.05(g)).
As such, the limitations of “measuring a comingled-flow rate of the common pipeline to acquire comingled-flow measurement data” and “receiving comingled-flow measurement data,” constitutes insignificant extra solution activity because it recites a step which is considered mere data gathering which amounts to necessary data gathering where all uses of the recited judicial exception would require such data. Examiner notes that, under the broadest reasonable interpretation, the limitation directed to “receiving [data] from at least one sensor,” could be interpreted and classified as an abstract idea. For example, a human mind is fully capable of receiving flow data from one or more sensors. However, in view of the Specification, it is understood that “receiving comingle-flow measurement data from at least one sensor,” is likely to mean “receiving, at a processor, [data] from at least one sensor.” As such, the limitation has been granted the benefit of being classified as an additional element rather than an abstract idea.
The additional elements of “one sensor” and “one of the plurality of wells” are recited at a high level of generality and are merely indicative of a field of use in which the judicial exception is applied. The inclusion of such elements does not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use as described in MPEP 2106.05(h) and cannot provide for a practical application of the judicial exception.
The additional element of “accessing historical well test data… representative of test hydrocarbon fluid flow rates,” constitutes insignificant extra-solution activity because it merely amounts to selecting data for analysis according to source or content. With regards to 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: 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)). As such the additional element of “accessing historical well test data… representative of test hydrocarbon fluid flow rates,” cannot provide for a practical application of the judicial exception.
The additional element of “using received comingled-flow measurement data and the accessed historical well test data,” constitutes insignificant extra-solution activity because it merely amounts to selecting data for analysis according to source or content. With regards to 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: 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)). As such the additional element of “using received comingled-flow measurement data and the accessed historical well test data,” cannot provide for a practical application of the judicial exception.
The additional elements of “a data storing means”; “a data acquisition unit”; “an optimizer unit”; “a processor”; and “a memory” are understood to be generic computing components such as a processor. The MPEP states “[u]se 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.” (MPEP 2106.05(f)). As such, the mere recitation of generic computing elements as a means to perform the abstract idea amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible as addressed in MPEP 2106.05(f).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The limitations of claims 3—4, 14—15, 18—19, and 22—24 function to further define the data used to perform the judicial exception (e.g., the determining step of claims 1, 2, and 17). The claims recite additional elements directed to insignificant extra-solution activity where the recited additional elements amount to selecting data for analysis according to source or content. As noted above, the MPEP states “the courts have found to be insignificant extra-solution activity: Selecting a particular data source or type of data to be manipulated: 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)). As such the limitations of claims 3—4, 14—15, 18—19, and 22—23 constitute insignificant extra-solution activity and cannot provide for a practical application of the judicial exception.
Claims 5 and 21 recites the limitation of, or substantially similar to “identifying the ones of the plurality of wells for which the difference between [insert variables], exceeds a predefined threshold value.” Actions such as “identifying,” constitute limitations which may be performed in a human mind (e.g., a mental process) insofar as a human mind is capable of identifying things. The nature of the identifying (e.g., a mental process) as detailed in claim 5 appears to further benefit from the application of mathematical concepts (e.g., taking a difference between values and comparing the difference to a threshold). As such, claim 5 is directed to an abstract idea insofar as it recites both mental processes and mathematical concepts without significantly more. Therefore, claim 5 cannot provide for a practical application of the judicial exception identified in claim 1 because it is also directed to a judicial exception.
The limitations of claim 6 function to further define the determination step by “using at least one uncertainty value for at least one of the plurality of wells.” The limitation is directed to an abstract idea insofar as it merely expands upon the previously recited mental process without providing a practical application. Moreover, the uncertainty value appears to based in a mathematical concept such that the claim recites a mental process (e.g., using a piece of data) which may use mathematically derived data. Therefore, claim 6 cannot provide for a practical application of the judicial exception identified in claim 1 because it is also directed to a judicial exception.
Claim 7 further defines the mathematical concept used in the mental process of “determining,” by reciting limitations which further define the mathematical structure of the mathematical concept. As such, claim 7 is directed to an abstract idea.
Claims 8—13 further limit the abstract idea by providing additional limitations related to the mathematical concepts set forth in claim 1. The recitation of specific data used to perform the mathematical concept does not provide more than the judicial exception insofar as selecting data for analysis by source or content constitutes insignificant extra-solution activity as addressed above.
Claim 16 is directed to an abstract idea in that “determining,” is a mental process, where the specific mental process of determining ratios may further include a mathematical concept. As such claim 16 is directed to a judicial exception and cannot provide for a practical application of the abstract idea.
Claim 20 is directed to the additional element of “adapting at least one operating parameter of the at least one well of the plurality of wells,” which is recited at a high level of generality such that it amounts to mere instruction to apply the judicial exception (e.g., equivalent to reciting “apply it”). The MPEP sets forth guidance as to what types of limitations may constitute a practical application where it states: “[t]he 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)). As claimed the limitation merely recites the idea of a solution or outcome without reciting the details of how the solution is accomplished. For example, what operating parameter is adjusted (e.g., in Diehr the mold is opened), how is the parameter adjusted (e.g., in Diehr the mold is opened), how is this hydrocarbon fluid flow rate used in determining that an adjustment should be made (e.g., in Diehr the elapsed time being equal to the calculated time triggers the specific adjustment of opening the mold) Therefore the limitations fail to integrate the judicial exception into a practical application.
Regarding claims 26—27, and as stated above with respect to claim 1, the inclusion of a generic processor to perform the judicial exception cannot provide for a practical application of claim 1. The same statement would apply to claim 25 if it were amended to positively recite a processor. As it stands, claim 25 is separately rejected under 35 U.S.C. 101 for being directed to patent ineligible subject matter given that it merely recites software per se.
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—20, and 22—28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Published US Patent Application to Poonacha et al., hereinafter “Poonacha” (US 20190093474 A1) in view of Published US Patent Application to Middya (US 20020165671 A1).
Regarding claim 1, Poonacha discloses [a] method for determining at least one hydrocarbon fluid flow rate (Qi[HC]) of at least one of a plurality of wells connected to a common pipeline with a comingled hydrocarbon fluid flow from the plurality of wells (para. [0001], “a system and a method for determining fluid flow estimates of individual wells based on a measurement of commingled flow at a pool-line.”) and to adapt at least one operating parameter of at least one well of the plurality of wells (para. [0005], “[t]he method also includes controlling operation of at least one of the plurality of well-pumps based on the well-flow data to control fluid production from the plurality of wells.”), the method comprising the following steps:
Measuring a comingled-flow rate of the common pipeline to acquire comingled-flow measurement data (para. [0001], “a method for determining fluid flow estimates of individual wells based on a measurement of commingled flow at a pool-line.”; para. [0005], “[t]he method further includes receiving commingled-flow measurement data using a plurality of commingled-flow measurement sensors.”; para. [0028], “[t]he instructions further enable the at least one processor module 122 to receive the commingled-flow measurement data 112, using the commingled-flow measurement sensors 132.”), wherein the comingled-flow measurement data are representative of a comingled hydrocarbon fluid flow rate (Qfield[HC]) (para. [0005], “The commingled-flow measurement data are representative of a combined fluid flow data of the plurality of wells.”; para. [0001] “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir.”) ;
accessing historical well test data (“field measurement data”; para. [0019], “[t]he oil-field data 134 includes field measurement data 146 of the plurality of well-pumps 148. In one embodiment, the field measurement data 146 include speed data 110 and run-time data 136 generated by the well pumps 148. Specifically, the field measurement data 146 are sensed by a plurality of POC sensors 130.”), wherein the well test data are representative of test hydrocarbon fluid flow rates (Qi[test]) (para. [0002], “Produced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.” The field measurement data is representative of the individual wellbore production.), of the ones of the plurality of wells, wherein the well test data are derived from past well tests at the ones of the plurality of wells (the field measurement data is historical data insofar as the associated measurement has to have previously occurred in order to be collected and measured);
adapting at least one operating parameter of at least one well of the plurality of wells by controlling oilfield equipment associated with the at least one well (para. [0005], “[t]he method also includes controlling operation of at least one of the plurality of well-pumps based on the well-flow data to control fluid production from the plurality of wells.”), wherein adapting is at least partially based on the at least one determined hydrocarbon fluid flow of the at least one of the plurality of wells (para. [0005], “[t]he method further includes determining, by an optimizer unit, well-flow data of the plurality of wells based on the commingled-flow measurement data, the field measurement data, and a plurality of conservation constraints generated by a constraint generator. The well-flow data are representative of fluid flow data from each of the plurality of wells.”).
Poonacha discloses an optimizer unit which utilizes a cost function along with constraints and field data (e.g., commingled flow data and well-specific operational data) to determine optimum operating parameters of the associated wellbores. For example, Poonacha discloses:
“[t]he system also includes an optimizer unit communicatively coupled to the data acquisition unit and configured to generate a plurality of conservation constraints using a constraint generator, based on the commingled-flow measurement data and the field measurement data.” (Poonacha, para. [0006]).
“[i]n one embodiment, the plurality of conservation constraints 144 is determined based on frequency domain analysis of the commingled-flow measurement data 112, the speed data 110, and the run-time data 136. In another embodiment, the plurality of conservation constraints 144 is determined based on a mass conservation principle.” (Poonacha, para. [0022]).
“[t]he optimizer unit 118 is further configured to determine well-flow data 138 of the plurality of wells 140 based on the plurality of conservation constraints 144. Specifically, the optimizer unit 118 is configured to determine a cost function based on the plurality of conservation constraints 144. The optimizer unit 118 is further configured to optimize the cost function to determine the well-flow data 138. In one embodiment, the optimization refers to minimization of the cost function. In another embodiment, the optimization refers to maximization of the cost function.” (Poonacha, para. [0023]).
“the optimizer unit 118 is configured to determine the cost function based on a probability distribution function of the well-flow data 138. The probability distribution function of the well-flow data 138 may be determined based on statistics of the field measurement data 146 and the commingled-flow measurement data 112. In one embodiment, the probability distribution function may be determined based on at least one of an apriori distribution function and an aposteriori distribution function of the well-flow data 138. In a further embodiment, the cost function may include one or more conservation equations and statistics corresponding to the oil-field. The statistics of the well-flow data 138 includes, but not limited to, a plurality of variance values. In one embodiment, the plurality of variance values may be determined by a variance model. In some embodiments, a trailer test data or pump card data may be used to determine the statistics of the well-flow data 138.” (Poonacha, para. [0024]).
As such, while Poonacha discloses determining the hydrocarbon flow rate using commingled flow data and historical well test data, Poonacha does not explicitly disclose utilizing Lagrange multipliers as the avenue for solving/optimizing the relationships in the system. However, Middya, which is in the same field of endeavor as the instant application insofar as it is directed to a system/method of allocating commingled production data back to the associated wells from which the fluid was produced (see para. [0009] of Middya) teaches the deficient limitation. For example, Middya teaches “[a]n optimizer adapted to determine an optimal value of an objective function corresponding to the modeled fluid flow characteristics of the wellbores and the surface facilities is then operated. The objective function relates to at least one production system performance parameter. Fluid flow rates are then allocated among the plurality of wellbores as determined by the operating the optimizer.” (Middya, para. [0009]). Middya further teaches “[i]n some embodiments, the optimizer includes successive quadratic programming. A value of a Lagrange multiplier associated with at least one system constraint is determined as a result of the successive quadratic programming. The value of the Lagrange multiplier can be used to determine a sensitivity of the production system to the at least one constraint.” (Middya, para. [0012]). The successive quadratic programming (SQP) utilized to optimize the objective function is performed using a Lagrange function which is a function the Lagrange multipliers u and v as discussed in detail in Middya from para. [0029]—[0033].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the successive quadratic programming including the Lagrange multipliers as taught by Middya, to the system of equations and constraints as identified by Poonacha in order to achieve the predictable result of allocating the commingled production to the wellbores and assessing the sensitivity of various production parameters in determining how to optimize the production of the system. Notably, the data being assessed in both of Middya and Poonacha are substantially similar where both references are solving similar problems and where the cost function of Poonacha is analogous to the objective function of Middya.
Regarding claim 2, Poonacha discloses [a] method for determining at least one hydrocarbon fluid flow rate (Qi[HC]) of at least one of a plurality of wells connected to a common pipeline with a comingled hydrocarbon fluid flow from the plurality of wells (para. [0001], “a system and a method for determining fluid flow estimates of individual wells based on a measurement of commingled flow at a pool-line.”) and to adapt at least one operating parameter of at least one well of the plurality of wells (para. [0005], “[t]he method also includes controlling operation of at least one of the plurality of well-pumps based on the well-flow data to control fluid production from the plurality of wells.”), the method comprising the following steps:
receiving comingled-flow measurement data from at least one sensor (para. [0005], “[t]he method further includes receiving commingled-flow measurement data using a plurality of commingled-flow measurement sensors.”), wherein the comingled-flow measurement data are representative of a comingled hydrocarbon fluid flow rate (Qfield[HC]) (para. [0005], “The commingled-flow measurement data are representative of a combined fluid flow data of the plurality of wells.”; para. [0001] “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir.”);
accessing historical well test data (field measurement data 146 and test device 106; para. [0019], “[t]he oil-field data 134 includes field measurement data 146 of the plurality of well-pumps 148. In one embodiment, the field measurement data 146 include speed data 110 and run-time data 136 generated by the well pumps 148. Specifically, the field measurement data 146 are sensed by a plurality of POC sensors 130.”; para. [0019], “[t]he oil field 100 may further include a test device 106 for generating reference data for managing the fluid production of the oil field 100. The test device 106 may include a trailer test device and may use existing or additional sensors to acquire the reference data.”), wherein the well test data are representative of test hydrocarbon fluid flow rates (Qi[test]) (para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.” The field measurement data is representative of the individual wellbore production.), of the ones of the plurality of wells, wherein the well test data are derived from past well tests at the ones of the plurality of wells (the field measurement data is historical data insofar as the associated measurements must have previously occurred in order to be measured); and adjusting a wellhead pressure of at least one of the plurality of well to reduce stress on components of the well (para. [0021], “[t]he pump-off controller data are representative of the data generated by a pump-off controller used to control a corresponding well-pump 148. Specifically, the pump-off controller data include speed values, pressure values, torque values, and fluid flow values.”; para. [0007], “[t]he instructions also enable the at least one processor module to control operation of at least one of the plurality of well-pumps based on the well-flow data to control fluid production from the plurality of wells.” Examiner notes that controlling the operation of the pumps impacts the wellhead pressure where the purpose of a pump-off controller (POC) is to reduce wear to the pumping equipment. For example, para. [0019] states “[t]he plurality of well-pumps 148 is controlled by a plurality of pump-off controllers (POCs) 102. The pump-off controllers (POC) 102 are microprocessor based devices enabling autonomous operation of the well-pumps 148 such as sucker rod pumps, for example. Each POC 102 monitors conditions of the corresponding well 140 and shuts down the corresponding well-pump 148 when fluid level in the corresponding well 140 is below a certain level.)
Poonacha discloses an optimizer unit which utilizes a cost function along with constraints and field data (e.g., commingled flow data and well-specific operational data) to determine optimum operating parameters of the associated wellbores. For example, Poonacha discloses:
“[t]he system also includes an optimizer unit communicatively coupled to the data acquisition unit and configured to generate a plurality of conservation constraints using a constraint generator, based on the commingled-flow measurement data and the field measurement data.” (Poonacha, para. [0006]).
“[i]n one embodiment, the plurality of conservation constraints 144 is determined based on frequency domain analysis of the commingled-flow measurement data 112, the speed data 110, and the run-time data 136. In another embodiment, the plurality of conservation constraints 144 is determined based on a mass conservation principle.” (Poonacha, para. [0022]).
“[t]he optimizer unit 118 is further configured to determine well-flow data 138 of the plurality of wells 140 based on the plurality of conservation constraints 144. Specifically, the optimizer unit 118 is configured to determine a cost function based on the plurality of conservation constraints 144. The optimizer unit 118 is further configured to optimize the cost function to determine the well-flow data 138. In one embodiment, the optimization refers to minimization of the cost function. In another embodiment, the optimization refers to maximization of the cost function.” (Poonacha, para. [0023]).
“the optimizer unit 118 is configured to determine the cost function based on a probability distribution function of the well-flow data 138. The probability distribution function of the well-flow data 138 may be determined based on statistics of the field measurement data 146 and the commingled-flow measurement data 112. In one embodiment, the probability distribution function may be determined based on at least one of an apriori distribution function and an aposteriori distribution function of the well-flow data 138. In a further embodiment, the cost function may include one or more conservation equations and statistics corresponding to the oil-field. The statistics of the well-flow data 138 includes, but not limited to, a plurality of variance values. In one embodiment, the plurality of variance values may be determined by a variance model. In some embodiments, a trailer test data or pump card data may be used to determine the statistics of the well-flow data 138.” (Poonacha, para. [0024]).
As such, while Poonacha discloses determining the hydrocarbon flow rate using commingled flow data and historical well test data, Poonacha does not explicitly disclose utilizing Lagrange multipliers as the avenue for solving/optimizing the relationships in the system. However, Middya, which is in the same field of endeavor as the instant application insofar is it is directed to a system/method of allocating commingled production data back to the associated wells from which the fluid was produced (see para. [0009] of Middya) teaches the deficient limitation. For example, Middya teaches “[a]n optimizer adapted to determine an optimal value of an objective function corresponding to the modeled fluid flow characteristics of the wellbores and the surface facilities is then operated. The objective function relates to at least one production system performance parameter. Fluid flow rates are then allocated among the plurality of wellbores as determined by the operating the optimizer.” (Middya, para. [0009]). Middya further teaches “[i]n some embodiments, the optimizer includes successive quadratic programming. A value of a Lagrange multiplier associated with at least one system constraint is determined as a result of the successive quadratic programming. The value of the Lagrange multiplier can be used to determine a sensitivity of the production system to the at least one constraint.” (Middya, para. [0012]). The successive quadratic programming (SQP) utilized to optimize the objective function is performed using a Lagrange function which is a function the Lagrange multipliers u and v as discussed in detail in Middya from para. [0029]—[0033].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the successive quadratic programming including the Lagrange multipliers as taught by Middya, to the system of equations and constraints as identified by Poonacha in order to achieve the predictable result of allocating the commingled production to the wellbores and assessing the sensitivity of various production parameters in determining how to optimize the production of the system. Notably, the data being assessed in both of Middya and Poonacha are substantially similar where both references are solving similar problems and where the cost function of Poonacha is analogous to the objective function of Middya.
Regarding claim 3, Poonacha modified by Middya teaches wherein the comingled-flow measurement data are further representative of at least one of the following: a comingled gas flow rate and a comingled water flow rate (Poonacha, para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.”; para. [0021], “[t]he data acquisition unit 116 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 having the field measurement data 146 from the POC sensors 130 and the commingled-flow measurement data 112 from the commingled-flow measurement sensors 132. In one embodiment, the data acquisition unit 116 is configured to acquire the oil-field data 134 in the form of at least one of multiphase meter data, custody transfer data, virtual flow meter data, pressure data, temperature data, pump-off controller data, and trailer test data.” The operation of the pumps/compressors/fluid injection devices is representative of the oil, water, and gas produced out of the well where the fluids from the wells are commingled in a pool-line.).
Regarding claim 4, Poonacha modified by Middya discloses wherein the well test data are further representative of at least one of the following: test gas-to-hydrocarbon-fluid, and test water-to-hydrocarbon-fluid ratios, of the ones of the plurality of wells (Middya, para. [0004], “it is possible to change production rates in other wellbores coupled to the surface facilities to maintain throughput in the surface facilities. As is known in the art, however, such production rate changes may be accompanied by changes in relative quantities of water, oil and gas produced from the affected wellbores.”; para. [0025], “[c]onstraints may include system operating parameters such as gas/oil ratio (GOR), flow rate, pressure, water cut (fractional amount of produced liquid consisting of water), or any similar parameter which is affected by changing the fluid flow rate out of any of the wellbores W, or by changing any operating parameter of any element of the surface facilities, such as separators S or compressors 14, 16.” Examiner notes that Middya directly acknowledges that it is well known that a relative relationship exists between the fluids produced from the wells such that it is beneficial to use the relative fluid production relationships as a constraint. While Poonacha may not directly state this, Poonacha does teach mass balance as a critical component of system of equations for the optimization where the ratios would clearly function as a constraint in the mass balance equation.).
Regarding claim 6, Poonacha modified by Middya teaches wherein determining the at least one hydrocarbon fluid flow rate comprises using at least one uncertainty value for at least one of the plurality of wells, wherein preferably the at least one uncertainty value is at least partially based on a measurement error of the well test data (Poonacha, para. [0035], “[t]he optimizer unit 118 is configured to determine a cost function based on at least one of a plurality of conservation equations representative of the plurality of conservation constraints 234, 236, 238, 240 determined based on the field measurement data 146 and the commingled-flow measurement data 112… In an alternate embodiment, a variance model corresponding to a variance value among the plurality of variance values is determined. The variance model is representative of a polynomial equation. The optimizer unit 118 is further configured to determine an estimate of a mean and a standard deviation corresponding to the variance value based on trailer test data and the variance model. In another embodiment, the plurality of variance values is determined based on pump card data representative of relationship between stroke values and load values of the pump-off controller 102.”).
Regarding claim 7, Poonacha modified by Middya teaches wherein determining the at least one hydrocarbon fluid flow rate of the at least one of the plurality of wells is based on a Lagrangian function that comprises at least one objective function (cost function of Poonacha and the objective function of Middya as detailed in claim 1) and at least one constraint function (conservation constraints 144 of Poonacha and constraints of Middya as detailed in at least para. [0025]—[0027] of Middya.), wherein the Lagrangian function optionally comprises two objective functions and at least two constraint functions, wherein the Lagrangian function further optionally comprises at least three objective functions and at least three constraint functions, wherein the Lagrangian function even further optionally comprises exactly three objective functions and exactly three constraint functions.
Regarding claim 8, Poonacha modified by Middya teaches wherein the Lagrangian function comprises a hydrocarbon fluid flow rate objective function, wherein the hydrocarbon fluid flow rate objective function is configured to minimize the difference between a well specific hydrocarbon fluid flow (Poonacha, para. [0023], “[t]he optimizer unit 118 is further configured to optimize the cost function to determine the well-flow data 138. In one embodiment, the optimization refers to minimization of the cost function.”; see Middya para. [0029]—[0033]), and a respective well specific test hydrocarbon fluid flow wherein the difference may be minimized for each well (the solution is solved for each well in order to generate allocation values for each well under consideration), and wherein optionally the hydrocarbon fluid flow rate objective function comprises the sum of the squared differences of each well's hydrocarbon fluid flow and the respective test hydrocarbon fluid flow rate.
Regarding claim 9, Poonacha modified by Middya teaches wherein the Lagrangian function comprises a gas-to-hydrocarbon-fluid ratio objective function (Middya teaches constraints including fluid ratios which explicitly include gas/oil ratio), wherein the gas-to-hydrocarbon-fluid ratio objective function is configured to minimize the difference between a well specific gas-to-hydrocarbon-fluid and a respective well specific test gas-to-hydrocarbon-fluid (see Middya para. [0030]—[0036]; Middya, para. [0027], “[t]he ones of the constraints {overscore (C)} which represent selected (“target”) values of fluid production rates for the system, such as total water flow rate, GOR, or oil flow rate, for example, are preferably inequality constraints with the target values set as an upper or lower boundary, as is consistent with the particular target.”; Middya, para. [0033], “minimizing L(x, u, v) also minimizes F(x) subject to the above constraints. Here u i and vj represent the Lagrange multiplier for equality constraint i and inequality constraint j, respectively. vj>0 for active constraints, while vj=0 when the constraint is inactive.”; Middya, para. [0039], “the Lagrange multipliers defined in equation (4) can be used to determine a sensitivity of the optimizer to any or all of the optimizer constraints. The values of one or more of the Lagrange multipliers are a measure of the sensitivity of the objective function to the associated constraints. The measure of sensitivity can be used to determine which of the constraints may be relaxed or otherwise adjusted to provide a substantial increase in the value of the system performance parameter that is to be optimized.” Examiner notes that the optimizer constraints can include any metric pertaining to oil, gas, or water, including the relative ratios where Middya at para. [0025] teaches “[c]onstraints may include system operating parameters such as gas/oil ratio (GOR), flow rate, pressure, water cut (fractional amount of produced liquid consisting of water), or any similar parameter which is affected by changing the fluid flow rate out of any of the wellbores W, or by changing any operating parameter of any element of the surface facilities, such as separators S or compressors 14, 16.”), wherein the difference may be minimized for each well, and wherein optionally the gas-to- hydrocarbon-fluid ratio objective function comprises the sum of the squared differences of each well's gas-to-hydrocarbon-fluid ratio, and the respective test gas-to- hydrocarbon-fluid ratio. Claim 10 is rejected for the same reason as it uses the same methodology for a different fluid metric.
Poonacha modified by Middya teaches the limitations of claims 11—13 which each address a different fluid constraint related to the fluids which may be produced from a hydrocarbon wellbore. For example, Middya teaches “Variable ω k in the above objective function represents a set of weighting factors, which can be applied individually to individual contribution variables, ψk, in the objective function. The individual contribution variables may include flow rates of the various fluids from each of the wellbores W, although the individual contribution variables are not limited to flow rates. As previously explained, the flow rates can be calculated using well known mass and momentum balance equations.” (Middya, para. [0026]). Furthermore, Middya teaches “[o]ne aspect of the invention is to determine an allocation of fluid flow rates from each of the wellbores W in the production system so that a particular production performance parameter is optimized. The production performance parameter may be, for example, maximization of oil production, minimization of gas and/or water production, or maximizing an economic value of the entire production system, such as by net present value or similar measure of value, or maximizing an ultimate oil or gas recovery from the one or more subsurface reservoirs (not shown).” (MIddya, para. [0020]). Examiner notes that in order to maximize/minimize oil, water, and/or gas production, the model must be capable of determining a value for each fluid type.
Regarding claim 14, Poonacha modified by Middya teaches wherein the comingled-flow measurement data are representative of the comingled hydrocarbon fluid flow rate, the comingled gas flow rate, and the comingled water flow rate (Poonacha, para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.”; para. [0021], “[t]he data acquisition unit 116 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 having the field measurement data 146 from the POC sensors 130 and the commingled-flow measurement data 112 from the commingled-flow measurement sensors 132. In one embodiment, the data acquisition unit 116 is configured to acquire the oil-field data 134 in the form of at least one of multiphase meter data, custody transfer data, virtual flow meter data, pressure data, temperature data, pump-off controller data, and trailer test data.” The operation of the pumps/compressors/fluid injection devices is representative of the oil, water, and gas produced out of the well where the fluids from the wells are commingled in a pool-line.).
Regarding claim 16, Poonacha modified by Middya teaches wherein the step of determining further comprises determining gas- to-hydrocarbon-fluid and/or water-to-hydrocarbon-fluid ratios, of individual ones of the plurality of wells (Middya, para. [0018], “[i]n a production system, such as the one shown in FIG. 1, as some of the wellbores W are operated to extract particular amounts (at selected rates) of fluid from the one or more subsurface reservoirs (not shown), various quantities of gas, oil and/or water will flow into these wellbores W at rates which may be estimated by solution to reservoir mass and momentum balance equations. Such mass and momentum balance equations are well known in the art for estimating wellbore production… The change over time, as is known in the art, is related to the change in pressure and fluid content distribution in the reservoir as fluids are extracted at known rates. These changes in fluid flow rates may also be calculated using mass and momentum balance equations known in the art. Such changes in fluid flow rates will have an effect on operation of the various components of the surface facilities, including for example, the compressors 14, 16, and the separators S.”; para. [0025], “[c]onstraints may include system operating parameters such as gas/oil ratio (GOR), flow rate, pressure, water cut (fractional amount of produced liquid consisting of water), or any similar parameter which is affected by changing the fluid flow rate out of any of the wellbores W, or by changing any operating parameter of any element of the surface facilities, such as separators S or compressors 14, 16.”; para. [0039], “[i]n a particular embodiment of the invention, the Lagrange multipliers defined in equation (4) can be used to determine a sensitivity of the optimizer to any or all of the optimizer constraints. The values of one or more of the Lagrange multipliers are a measure of the sensitivity of the objective function to the associated constraints.”).
Regarding claim 17, Poonacha discloses [a] system for determining at least one hydrocarbon fluid flow, of at least one of a plurality of wells connected to a common pipeline with a comingled hydrocarbon fluid flow from the plurality of wells (para. [0001], “a system and a method for determining fluid flow estimates of individual wells based on a measurement of commingled flow at a pool-line.”), the system comprising: a data storing means (memory module 124, para. [0026], “[t]he processor module 122 is further configured to store and retrieve contents into and from the memory module 124. In one embodiment, the processor module 122 is configured to initiate and control the functionality of at least one of the data acquisition unit 116, the optimizer unit 118, and the controller 120.”) configured to: store historical well test data, wherein the well test data are representative of test hydrocarbon fluid flow of the ones of the plurality of wells, wherein the well test data are derived from past well tests at the ones of the plurality of wells (para. [0020], “[t]he control system 108 includes a data acquisition unit 116, a controller 120, an optimizer unit 118, a processor module 122, and a memory module 124 interconnected with each other by a communications bus 126.”; para. [0021], “[t]he data acquisition unit 116 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 having the field measurement data 146 from the POC sensors 130 and the commingled-flow measurement data 112 from the commingled-flow measurement sensors 132. In one embodiment, the data acquisition unit 116 is configured to acquire the oil-field data 134 in the form of at least one of multiphase meter data, custody transfer data, virtual flow meter data, pressure data, temperature data, pump-off controller data, and trailer test data.” The memory module 124 of processor module 122 intakes the data from data acquisition unit 116); a data acquisition unit (data acquisition unit 116) configured to: receive comingled-flow measurement data, wherein the comingled-flow measurement data are representative of a comingled hydrocarbon fluid flow rate (see above citation); and access historical well test data from the data storing means (see FIG. 1 where memory module 124 and data acquisition unit communicate through communication bus 126); and an optimizer unit (optimizer unit 118) being able to receive data from the data acquisition unit (see control system 108 portion of FIG. 1), the optimizer unit configured to: determine… at least one hydrocarbon fluid flow rate (“well-flow data”), of at least one of the plurality of wells using the received comingled-flow measurement data (“commingled-flow measurement data”) and the accessed historical well test data (para. [0005], “[t]he method further includes determining, by an optimizer unit, well-flow data of the plurality of wells based on the commingled-flow measurement data, the field measurement data, and a plurality of conservation constraints generated by a constraint generator. The well-flow data are representative of fluid flow data from each of the plurality of wells.”).
Poonacha discloses an optimizer unit which utilizes a cost function along with constraints and field data (e.g., commingled flow data and well-specific operational data) to determine optimum operating parameters of the associated wellbores. For example, Poonacha discloses:
“[t]he system also includes an optimizer unit communicatively coupled to the data acquisition unit and configured to generate a plurality of conservation constraints using a constraint generator, based on the commingled-flow measurement data and the field measurement data.” (Poonacha, para. [0006]).
“[i]n one embodiment, the plurality of conservation constraints 144 is determined based on frequency domain analysis of the commingled-flow measurement data 112, the speed data 110, and the run-time data 136. In another embodiment, the plurality of conservation constraints 144 is determined based on a mass conservation principle.” (Poonacha, para. [0022]).
“[t]he optimizer unit 118 is further configured to determine well-flow data 138 of the plurality of wells 140 based on the plurality of conservation constraints 144. Specifically, the optimizer unit 118 is configured to determine a cost function based on the plurality of conservation constraints 144. The optimizer unit 118 is further configured to optimize the cost function to determine the well-flow data 138. In one embodiment, the optimization refers to minimization of the cost function. In another embodiment, the optimization refers to maximization of the cost function.” (Poonacha, para. [0023]).
“the optimizer unit 118 is configured to determine the cost function based on a probability distribution function of the well-flow data 138. The probability distribution function of the well-flow data 138 may be determined based on statistics of the field measurement data 146 and the commingled-flow measurement data 112. In one embodiment, the probability distribution function may be determined based on at least one of an apriori distribution function and an aposteriori distribution function of the well-flow data 138. In a further embodiment, the cost function may include one or more conservation equations and statistics corresponding to the oil-field. The statistics of the well-flow data 138 includes, but not limited to, a plurality of variance values. In one embodiment, the plurality of variance values may be determined by a variance model. In some embodiments, a trailer test data or pump card data may be used to determine the statistics of the well-flow data 138.” (Poonacha, para. [0024]).
As such, while Poonacha discloses determining the hydrocarbon flow rate using commingled flow data and historical well test data, Poonacha does not explicitly disclose utilizing Lagrange multipliers as the avenue for solving/optimizing the relationships in the system. However, Middya, which is in the same field of endeavor as the instant application insofar it is directed to a system/method of allocating commingled production data back to the associated wells from which the fluid was produced (see para. [0009] of Middya) teaches the deficient limitation. For example, Middya teaches “[a]n optimizer adapted to determine an optimal value of an objective function corresponding to the modeled fluid flow characteristics of the wellbores and the surface facilities is then operated. The objective function relates to at least one production system performance parameter. Fluid flow rates are then allocated among the plurality of wellbores as determined by the operating the optimizer.” (Middya, para. [0009]). Middya further teaches “[i]n some embodiments, the optimizer includes successive quadratic programming. A value of a Lagrange multiplier associated with at least one system constraint is determined as a result of the successive quadratic programming. The value of the Lagrange multiplier can be used to determine a sensitivity of the production system to the at least one constraint.” (Middya, para. [0012]). The successive quadratic programming (SQP) utilized to optimize the objective function is performed using a Lagrange function which is a function the Lagrange multipliers u and v as discussed in detail in Middya from para. [0029]—[0033].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the successive quadratic programming including the Lagrange multipliers as taught by Middya, to the system of equations and constraints as identified by Poonacha in order to achieve the predictable result of allocating the commingled production to the wellbores and assessing the sensitivity of various production parameters in determining how to optimize the production of the system. Notably, the data being assessed in both of Middya and Poonacha are substantially similar where both references are solving similar problems and where the cost function of Poonacha is analogous to the objective function of Middya.
Regarding claim 18, Poonacha modified by Middya discloses wherein the well test data are further representative of at least one of the following: test gas-to-hydrocarbon-fluid, and test water-to-hydrocarbon-fluid ratios, of the ones of the plurality of wells (Middya, para. [0004], “it is possible to change production rates in other wellbores coupled to the surface facilities to maintain throughput in the surface facilities. As is known in the art, however, such production rate changes may be accompanied by changes in relative quantities of water, oil and gas produced from the affected wellbores.”; para. [0025], “[c]onstraints may include system operating parameters such as gas/oil ratio (GOR), flow rate, pressure, water cut (fractional amount of produced liquid consisting of water), or any similar parameter which is affected by changing the fluid flow rate out of any of the wellbores W, or by changing any operating parameter of any element of the surface facilities, such as separators S or compressors 14, 16.” Examiner notes that Middya directly acknowledges that it is well known that a relative relationship exists between the fluids produced from the wells such that it is beneficial to use the relative fluid production relationships as a constraint. While Poonacha may not directly state this, Poonacha does teach mass balance as a critical component of system of equations for the optimization where the ratios would clearly function as a constraint in the mass balance equation.).
Regarding claim 19, Poonacha modified by Middya teaches wherein the comingled-flow measurement data are representative of the comingled hydrocarbon fluid flow rate, the comingled gas flow rate, and the comingled water flow rate (Poonacha, para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.”; para. [0021], “[t]he data acquisition unit 116 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 having the field measurement data 146 from the POC sensors 130 and the commingled-flow measurement data 112 from the commingled-flow measurement sensors 132. In one embodiment, the data acquisition unit 116 is configured to acquire the oil-field data 134 in the form of at least one of multiphase meter data, custody transfer data, virtual flow meter data, pressure data, temperature data, pump-off controller data, and trailer test data.” The operation of the pumps/compressors/fluid injection devices is representative of the oil, water, and gas produced out of the well where the fluids from the wells are commingled in a pool-line.).
Regarding claim 20, Poonacha modified by Middya teaches wherein the system further comprises a signal transmission unit (Poonacha, controller 120) that transmits a control signal to oilfield equipment associated with at least one of the plurality of wells (para. [0020], “[t]he control system 108 is further configured to control operation of at least one of the plurality of well-pumps by a control signal 114 generated based on the well-flow data 138 to control fluid production from the plurality of wells 140. The control system 108 includes a data acquisition unit 116, a controller 120, an optimizer unit 118, a processor module 122, and a memory module 124 interconnected with each other by a communications bus 126.”; para. [0025], “[t]he controller 120 is communicatively coupled to the data acquisition unit 116 and the optimizer unit 118 and configured to control operation of at least one of the plurality of well-pumps based on the well-flow data 138 to control fluid production from the plurality of wells 140.”) for adapting at least one operating parameter of at least one well of the plurality of wells, wherein adapting is at least partially based on the at least one determined hydrocarbon fluid flow rate of the at least one of the plurality of wells (Poonacha, para. [0025], “[t]he controller 120 is communicatively coupled to the data acquisition unit 116 and the optimizer unit 118 and configured to control operation of at least one of the plurality of well-pumps based on the well-flow data 138 to control fluid production from the plurality of wells 140.”).
Regarding claim 22, Poonacha modified by Middya teaches wherein determining the at least one hydrocarbon fluid flow rate comprises using at least one uncertainty value for at least one of the plurality of wells, wherein preferably the at least one uncertainty value is at least partially based on a measurement error of the well test data (Poonacha, para. [0035], “[t]he optimizer unit 118 is configured to determine a cost function based on at least one of a plurality of conservation equations representative of the plurality of conservation constraints 234, 236, 238, 240 determined based on the field measurement data 146 and the commingled-flow measurement data 112… In an alternate embodiment, a variance model corresponding to a variance value among the plurality of variance values is determined. The variance model is representative of a polynomial equation. The optimizer unit 118 is further configured to determine an estimate of a mean and a standard deviation corresponding to the variance value based on trailer test data and the variance model. In another embodiment, the plurality of variance values is determined based on pump card data representative of relationship between stroke values and load values of the pump-off controller 102.”).
Regarding claim 23, Poonacha modified by Middya teaches wherein the comingled-flow measurement data are representative of the comingled hydrocarbon fluid flow rate, the comingled gas flow rate, and the comingled water flow rate (Poonacha, para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.”; para. [0021], “[t]he data acquisition unit 116 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 having the field measurement data 146 from the POC sensors 130 and the commingled-flow measurement data 112 from the commingled-flow measurement sensors 132. In one embodiment, the data acquisition unit 116 is configured to acquire the oil-field data 134 in the form of at least one of multiphase meter data, custody transfer data, virtual flow meter data, pressure data, temperature data, pump-off controller data, and trailer test data.” The operation of the pumps/compressors/fluid injection devices is representative of the oil, water, and gas produced out of the well where the fluids from the wells are commingled in a pool-line.).
Regarding claim 24, Poonacha modified by Middya teaches wherein the well test data are representative of test hydrocarbon fluid flow (para. [0002], “[p]roduced fluid from wells may include various quantities of crude oil, natural gas and/or water, depending on the specific conditions of reservoir. The amount and rate at which fluid is extracted from a well depends on condition of the reservoir such as a pressure difference between the reservoir and a wellbore. The wellbore pressure may be adjusted by various devices such as pumps, compressors, fluid injection devices the like.” As noted with respect to claim 1, the operational parameters for the wells are representative of the fluid production. Additionally, in para. [0035] of Poonacha, the well test data is directly tied to the field measurement data 146 where Poonacha teaches “In another embodiment, the cost function may include a distribution function characterized by one or more of a plurality of variance values of the well-flow data 138 based on the field measurement data 146… The optimizer unit 118 is further configured to determine an estimate of a mean and a standard deviation corresponding to the variance value based on trailer test data and the variance model.”), test gas-to-hydrocarbon-fluid ratios, and test water-to- hydrocarbon-fluid ratios (Middya, para. [0004], “it is possible to change production rates in other wellbores coupled to the surface facilities to maintain throughput in the surface facilities. As is known in the art, however, such production rate changes may be accompanied by changes in relative quantities of water, oil and gas produced from the affected wellbores.”; Middya, para. [0025], “[c]onstraints may include system operating parameters such as gas/oil ratio (GOR), flow rate, pressure, water cut (fractional amount of produced liquid consisting of water), or any similar parameter which is affected by changing the fluid flow rate out of any of the wellbores W, or by changing any operating parameter of any element of the surface facilities, such as separators S or compressors 14, 16.”), of the ones of the plurality of wells.
Regarding claim 26, Poonacha modified by Middya, as provided with respect to claim 1, teaches [a] non-transitory computer readable medium having stored thereon software instructions that, when carried out by at least one processor, cause the processor to perform a method according to claim 1 (Poonacha, para. [0027], “the memory module 124 may be a non-transitory computer readable medium encoded with a program having a plurality of instructions to instruct at least one of the data acquisition unit 116, optimizer unit 118, and the controller 120 to perform a sequence of steps to generate the well-flow data 138 of the plurality of wells 140.”).
Regarding claim 27, Poonacha modified by Middya, as provided with respect to claim 1, teaches [a] control unit (Poonacha, control system 108) for determining at least one hydrocarbon fluid flow of at least one of a plurality of wells connected to a common pipeline with a comingled hydrocarbon fluid flow from the plurality of wells, the control unit comprising at least one processor and a memory coupled with the at least one processor (para. [0020], “[t]he control system 108 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 including the field measurement data 146 and the commingled-flow measurement data 112. The control system 108 is further configured to process the oil-field data 134 and generate well-flow data 138, representative of fluid flow data from each of the plurality of wells 140. The control system 108 is further configured to control operation of at least one of the plurality of well-pumps by a control signal 114 generated based on the well-flow data 138 to control fluid production from the plurality of wells 140. The control system 108 includes a data acquisition unit 116, a controller 120, an optimizer unit 118, a processor module 122, and a memory module 124 interconnected with each other by a communications bus 126.”); the at least one processor and memory configured to perform a method according to claim 1 (see rejection of claim 1).
Regarding claim 28, Poonacha modified by Middya, as provided with respect to claim 1, teaches [a]n oilfield (Poonacha, oilfield 100) comprising a plurality of wells (Poonacha, wells 140) connected to a common pipeline (Poonacha, lines 128) and a control unit (Poonacha, control system 108) for determining at least one hydrocarbon fluid flow rate, of at least one of the plurality of wells connected to the common pipeline with a comingled hydrocarbon fluid flow from the plurality of wells, the control unit comprising at least one processor and a memory coupled with the at least one processor (Poonacha, FIG. 1; para. [0020], “[t]he control system 108 is communicatively coupled to the plurality of sensors 130, 132 and configured to receive the oil-field data 134 including the field measurement data 146 and the commingled-flow measurement data 112. The control system 108 is further configured to process the oil-field data 134 and generate well-flow data 138, representative of fluid flow data from each of the plurality of wells 140. The control system 108 is further configured to control operation of at least one of the plurality of well-pumps by a control signal 114 generated based on the well-flow data 138 to control fluid production from the plurality of wells 140. The control system 108 includes a data acquisition unit 116, a controller 120, an optimizer unit 118, a processor module 122, and a memory module 124 interconnected with each other by a communications bus 126.”); the at least one processor and memory configured to perform a method according to claim 1, such that the control unit is configured to perform the method according to claim 1 (see rejection of claim 1).
Allowable Subject Matter
Claims 5 and 21 recite subject matter which is not rejected under the prior art rejection of record; however, claims 5 and 21 stand rejected for other reasons as provided above.
The primary reference to Poonacha at para. [0024] states “the optimizer unit 118 is configured to determine the cost function based on a probability distribution function of the well-flow data 138. The probability distribution function of the well-flow data 138 may be determined based on statistics of the field measurement data 146 and the commingled-flow measurement data 112. In one embodiment, the probability distribution function may be determined based on at least one of an apriori distribution function and an aposteriori distribution function of the well-flow data 138. In a further embodiment, the cost function may include one or more conservation equations and statistics corresponding to the oil-field. The statistics of the well-flow data 138 includes, but not limited to, a plurality of variance values. In one embodiment, the plurality of variance values may be determined by a variance model. In some embodiments, a trailer test data or pump card data may be used to determine the statistics of the well-flow data 138.” However, Poonacha does not disclose identifying wells where the difference between the determined hydrocarbon fluid flow rate and the test hydrocarbon fluid flow rate exceeds a predefined threshold. Additionally, while Middya teaches “infeasible paths,” which include determinations which exceed a maximum threshold as detailed in para. [0040] of Middya, the reference also does not go as far as to disclose the specifics of the limitations as recited in claims 5 and 21.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/U.L.N./Examiner, Art Unit 3676
/TARA SCHIMPF/Supervisory Patent Examiner, Art Unit 3676