Notice of Pre-AIA or AIA Status
Claims 1-20 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered.
Response to Amendment
The amendment filed on 04/21/2026 has been entered and considered by the examiner. By the
amendment, claims 1, 5, 7, 13, 16 and 19 are amended. Following Applicants arguments and amendments made on, the 103 rejection of the claims is modified and the 101 rejection is still maintained. See office action for detail.
Response to Arguments
Applicant 101 arguments
First, considering Step 2A, Prong 1, the pending claims do not recite any abstract ideas. The Office Action alleges that the various claim steps of "collecting a first set of equipment data ... collecting emissions data ... receiving a second set of equipment data ... identifying, using the second set of equipment data at least one equipment component ... and generating a total emissions estimate . . ." cover a "mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper." Office Action, pp. 7-8. However, these steps, particularly the steps involving "collecting a first set of equipment data" and "collecting emissions data . . ." are performed by physical sensors and are not mental processes (see e.g., paragraph [0010] describing "[a]n emission factor prediction can be accomplished in two stages: a data collection/integration stage; and a model building stage. Data can be collected from diverse sources starting with remote data sensors such as Light Detection and Ranging (LiDAR). LiDAR can be a remote sensing method that uses light in the form of a pulsed laser to examine the surface of the Earth;" see also paragraph [0027] describing that "[e]xamples of remote data sensors can include drones, aircrafts, satellites, etc. Types of remote emissions data can include emissions rates, emissions plume heights, maximum emissions concentrations, emissions persistence, locations of at least one tank, etc.").
Examiner response
Examiner respectfully disagrees. Claim limitation only includes "collecting a first set of equipment data" and "collecting emissions data . . ." and do not recite sensor as Applicant argues. A human could, in principle, go to different locations, observe the equipment and emissions, and note the data on a piece of paper or in their mind. Thus, claims recite a mental process since it contains limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: See MPEP 2106.05(a)(2)(III)(A) • a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); Also, Examiner rejected this claim limitation in the previous office action as well under Step 2A prong 2: “collecting..” and “receiving…” “emissions data comprises historical leak data extracted from leak detection and repair (LDAR) reports for the at least one equipment component of the first hydrocarbon operation location” that recites at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014). Similar to the provided mapping above, Examiner asserts that the collection of data for the present claims is simply pre-solution activity for use in the claimed process. Applicant’s arguments are not persuasive.
Applicant arguments
Here, amended claim 1 integrates any alleged judicial exception into a practical application at least because claim 1 includes additional elements that result in an improvement to the technical field. In particular, claim 1, as amended, includes additional elements that demonstrate that claim 1, as a whole, clearly improves upon wellsite operations with respect to reducing environmental emissions and provide a technical solution to a technical problem of predicting emissions factors across disparate locations. Specifically, the claimed techniques leverage "high resolution data, real-time emission data and highly granular equipment level data" to inform machine-learning training and inference to predict "emission factors with reduced uncertainty [as] compared to other methods." Specification, para. [0009]. As a result of these machine-learning generated predictions, the "machine-learning model can provide insights into factors that contribute to emissions" at a granular level that was not possible before. Id. In addition, such insights "can help asset managers make fast, smart decisions related to the emissions and achieve yearly emissions commitments." Id. That is, the insights and predictions provided by the claimed techniques enable operators of wellsite operations to track equipment functionality accordingly to maintain emissions totals within commitment amounts over time. These specific, data-driven and equipment-targeted improvements demonstrate that the claims do not merely recite an abstract idea but instead integrate such outputs into the practical application of predicting emissions factors across disparate locations and it is well established that claims directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field, are patent eligible. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (Enfish recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes").
Examiner response
Examiner respectfully disagrees and begins by asserting that the estimation of predicting emissions factors and total emissions for specific equipment is not a technical problem as the estimation of predicting emissions factors and total emission is a mental process performed as an observation, judgement, and evaluation of the human mind. While the Applicant argues that the use of "high-resolution data" and "granular equipment data" improves wellsite operations, the core of the claim is a mental process that processes data to predict a numerical output (emission factor and total emission). According to Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), applying conventional machine learning to new data environment is an abstract idea because it describes a fundamental economic practice or mathematical process. The claims merely instruct to "apply" a machine-learning model to equipment and emission data. The recited "machine-learning" is a generic tool, not a specific, improved algorithm that changes how the computer operates. Finally, the training of equipment and emission data is recited so generically (no details are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05(f)). The improvement in emissions (environmental benefit) is a result of the prediction, not a technical improvement in how the data is processed or how the well operates. An abstract idea does not become patent-eligible simply because it provides improvement to abstract idea itself. The Applicant argues based on Enfish, but unlike Enfish, where the software improvement allowed the computer to function better (e.g., faster, less memory), the claimed machine learning model here is a "black box" that merely predicts an output, without providing a specific technical improvement to the functioning of the computer or the specific physical mechanism of the wellsite equipment. Applicant’s arguments are not persuasive.
Applicant arguments
Further, the Office Action alleges that the additional elements of "training at least one machine-learning model to estimate an emissions factor of at least one equipment component of the first hydrocarbon operation location . . . and applying the at least one machine-learning to the second set of equipment data to estimate an emissions factor" are no more than mere instructions to apply the exception using generic computer components. Office Action, pp. 9- 10. The Office Action then concludes that because the claim does not describe an improvement to the underlying technology of machine learning itself, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Id. at 10. The conclusion on page 10 of the Office Action is incorrect because the analysis is incomplete. First, claims that improve the underlying technology of machine learning are not the only types of claims that are subject matter eligible (see the discussion above in Step 2A, Prong Two, regarding improvement to other technology or technical field). Second, under USPTO procedure, the Office is supposed to determine "whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry." Here, the claims recite "training a plurality of machine-learning models, wherein a first machine-learning model of the plurality of machine-learning models is trained to estimate an emissions factor of a specific equipment component of the first hydrocarbon operation location, wherein the plurality of machine-learning models are trained using the first set of equipment data and the emissions data of the first hydrocarbon operation location; receiving a second set of equipment data from a second hydrocarbon operation location different from the first hydrocarbon operation location; identifying, using the second set of equipment data, at least one equipment component of the second hydrocarbon operation location that is a same component as the at least one equipment component of the first hydrocarbon operation location," and "executing the first machine-learning model on the second set of equipment data to estimate an emissions factor of at least one equipment component of the second hydrocarbon operation location."
These features expressly tie the machine-learning outputs to equipment-specific, time- bounded operational control in a hydrocarbon operation location, thereby producing concrete effects in the physical world. Further, such techniques utilized ML techniques in ways that are not well-understood, routine, or conventional (see e.g., [0008]-[0010], conventional emissions estimation methodologies rely on static, generic emissions factors and do not leverage machine-learning models tailored to specific equipment components, nor do they dynamically adapt to high-resolution operational data from disparate locations) and in ways that confine the claims to a particular useful application. The Office Action appears to conclude that because the claims recite use of a machine learning model, the claim is abstract without further explanation as to how or why the use of machine learning techniques as claimed is conventional, well-understood, and routine. As a result, the pending claims are subject matter eligible at least because the Office Action has failed to demonstrate how the additional elements of the claims noted above "are well-understood, routine, conventional activities previously known to the industry" and therefore the pending claims amount to significantly more than the alleged judicial exception.
Examiner response
Examiner respectfully disagrees. Under MPEP 2106.04(d)(1), applying conventional machine learning methods to a new data environment—even "high resolution" or "granular" data—does not constitute a technological improvement unless the claim recites a specific improvement to the ML model itself (e.g., a novel architecture or training algorithm). Simply achieving a more accurate "outcome" through a generic ML application is an abstract idea, not a technical solution. The claim's recitation that the estimate is "usable by an operator to control" is a field-of-use restriction or a general instruction to "apply" the result of an abstract calculation. When “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Therefore, the 35 U.S.C. 101 rejection is maintained.
Applicant 103 arguments
The present application was, not later than the effective filing date of the claimed invention, assigned to LANDMARK GRAPHICS CORPORATION, a wholly owned subsidiary of HALLIBURTON ENERGY SERVICES, INC., and is thus owned by HALLIBURTON ENERGY SERVICES, INC. Accordingly, both Cousins and the present application were, not later than the effective filing date of the claimed invention, owned by or subject to an obligation of assignment to HALLIBURTON ENERGY SERVICES, INC. Therefore, although the effective filing date of Cousins is prior to the effective filing date of the present application, Cousins is nonetheless exempted from qualifying as prior art under 35 U.S.C. § 102(a)(2) due to the exception established by 35 U.S.C. § 102(b)(2)(C). Accordingly, Cousins is not a valid prior art reference for claim rejections under 35 U.S.C. § 102 or 35 U.S.C. § 103.
Examiner response
Examiner found the applicant arguments persuasive. Thus, Examiner withdraw the Cousins reference and added new references to reject the claims. See office action for detail.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-9 are directed method or process, which falls on the one of the statutory category.
Claims:10-15 is directed system or machine, which falls on the one of the statutory category.
Claim: 16-20 is directed to non-transitory computer readable storage medium storing one or more program, which falls on the one of the statutory category that is manufacture.
Regarding claims 1, 10 and 16
Step 1 Prong one
collecting a first set of equipment data from a first hydrocarbon operation location; collecting emissions data relating to the first hydrocarbon operation location; and receiving a second set of equipment data from a second hydrocarbon operation location different from the first hydrocarbon operation location; (A human could, in principle, go to different locations, observe the equipment and emissions, and note the data on a piece of paper or in their mind. This makes the steps akin to an unpatentable mental process. Claims recite a mental process since it contains limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: See MPEP 2106.05(a)(2)(III)(A)• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
identifying, using the second set of equipment data, at least one equipment component of the second hydrocarbon operation location that is a same component as the at least one equipment component of the first hydrocarbon operation location; (A claim describing "identifying a common component between two hydrocarbon locations by comparing their equipment lists" is a mental process because a human can: read a list of equipment for Location A (e.g., a "Model X Pressure Valve"), read a list for Location B and look for the same "Model X Pressure Valve." and mentally conclude that both locations use the same component)
generating a total emissions estimate of the second hydrocarbon operation location for a predetermined operation time using the emissions factor of the at least one equipment component of the second hydrocarbon operation location, A technician at a natural gas site (the second location) notes that a specific compressor (equipment component) has an emission factor of 2.5 kg CO2e per hour. To estimate total emissions for a 24-hour shift (predetermined time), they multiply 2.5 by 24 to get 60 kg CO2e. Because this can be done mentally, it is an abstract mental process. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. Also, the process relies on a basic mathematical relationship (Emissions = Activity Data × Emission Factor). So, it also falls under the mathematical concepts of abstract ideas)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. Also claim limitation such as collecting a first set of equipment data from a first hydrocarbon operation location; collecting emissions data relating to the first hydrocarbon operation location; and receiving a second set of equipment data from a second hydrocarbon operation location different from the first hydrocarbon operation location; which also can be recited at a high level of generality (i.e., as a general means of gathering data), and falls under the insignificant extra solution activity. (See MPEP 2106.05(g) The additional elements of training a plurality of machine-learning models, wherein a first machine- learning model of the plurality of machine-learning models is trained to estimate an emissions factor of a specific equipment component of the first hydrocarbon operation location, wherein the plurality of machine-learning models are trained using the first set of equipment data and the emissions data of the first hydrocarbon operation location and executing the first machine-learning model on the second set of equipment data to estimate an emissions factor of at least one equipment component of the second hydrocarbon operation location, wherein the emissions factor comprises a predicted value encoding a relationship between a quantity of emissions to a release of emissions by the at least one equipment component of the second hydrocarbon operation location are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05(f)).The additional elements of “wherein the total emissions estimate of the second hydrocarbon operation location is usable by an operator to track the at least one equipment component of the second hydrocarbon operation location over time” is further limiting it to a particular field of use (hydrocarbon process) and describing its intended use by an operator (usability to control) were considered "incidental or token additions" that did not provide a practical, non-generic application. The additional elements of a system comprising: a processor; and a memory in claim 10 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) The additional elements of a non-transitory computer-readable medium comprising instructions that are executable by a processor in claim 16 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) Therefore, claims 1, 10 and 16 are directed to abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of collecting a first set of equipment data from a first hydrocarbon operation location; collecting emissions data relating to the first hydrocarbon operation location; and receiving a second set of equipment data from a second hydrocarbon operation location different from the first hydrocarbon operation location which is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) The additional elements of training a plurality of machine-learning models, wherein a first machine- learning model of the plurality of machine-learning models is trained to estimate an emissions factor of a specific equipment component of the first hydrocarbon operation location, wherein the plurality of machine-learning models are trained using the first set of equipment data and the emissions data of the first hydrocarbon operation location and executing the first machine-learning model on the second set of equipment data to estimate an emissions factor of at least one equipment component of the second hydrocarbon operation location, wherein the emissions factor comprises a predicted value encoding a relationship between a quantity of emissions to a release of emissions by the at least one equipment component of the second hydrocarbon operation location are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05(f)). When “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim. The additional elements of “wherein the total emissions estimate of the second hydrocarbon operation location is usable by an operator to trackthe at least one equipment component of the second hydrocarbon operation location over time” is further limiting it to a particular field of use (hydrocarbon process) and describing its intended use by an operator (usability to control) were considered "incidental or token additions" that did not provide a practical, non-generic application. The claims do not describe an improvement to the underlying technology of the machine-learning model itself, the computer's functionality. The additional elements of a system comprising: a processor; and a memory in claim 10 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) The additional elements of a non-transitory computer-readable medium comprising instructions that are executable by a processor in claim 16 are amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) Therefore, claims 1, 10 and 16 are directed to abstract idea.
Claim 2 further recites wherein the total emissions estimate of the second hydrocarbon operation location is usable by the operator to control the at least one equipment component of the second hydrocarbon operation location to achieve a yearly emissions commitment. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The underlying abstract idea is the mere calculation of emissions data and the general concept of using data to monitor compliance with a goal (a yearly emissions commitment). Data manipulation and goal setting are fundamental economic/business practices. While the estimate is 'usable by the operator to control the at least one equipment component,' this limitation is merely a field-of-use restriction or a merely instructs an operator to apply an exception using generic components to achieve a business or regulatory goal (the 'yearly emissions commitment'). and does not impose a meaningful limit on the abstract idea. The claim does not improve the functioning of the equipment itself but rather describes an administrative or regulatory goal (achieving a "yearly emissions commitment"). The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 3, 11 and 17 further recites wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations, emissions persistence, and a location of the at least one equipment component of the first hydrocarbon operation location. It is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 4 and 12 further recites preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data. A person can determine which metrics need to be standardized (e.g., equipment power and CO2 emissions), what the target units will be (e.g., converting everything to kilowatts and kilograms of CO2e), and what conversion factors are needed. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper therefore falls within the “Mental Process” grouping of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 5 and 13 further recites wherein each machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location. It amounts to no more than mere instructions to apply the exception using generic computer components. (MPEP 2106.05(f) This type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) Also see claim rejection 1. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 6, 14 and 18 further recites wherein the emissions factor comprises a methane emissions factor. It amounts to no more than mere instructions to apply the exception using generic computer components for estimating methane emission factor. (MPEP 2106.05(f) The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 7, 15 and 19 further recites wherein the first machine-learning model comprises a deep convolutional neural network (DCNN). This type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Also see claim rejection 1. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 8 further recites wherein the emissions data comprises historical leak data extracted from leak detection and repair (LDAR) reports for the at least one equipment component of the first hydrocarbon operation location. It is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 9 and 20 further recites wherein the emissions data comprises weather characteristics extracted from local weather reports for the first hydrocarbon operation location. It is recited at a high level of generality (i.e., as a general means of gathering), and falls under the insignificant extra solution activity and is well-understood, routine or conventional. ((See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
6. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
8. Claim 1-2, 4, 6-7, 9-10, 12, 14-16 and 18-20 is rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. (PUB NO: US20220372860A1), in view of Pickles et al., (PUB NO: US20240086430A1).
Regarding claim 1
Allen teaches a method, (fig1) comprising:
collecting a first set of equipment data from a first hydrocarbon operation location; (see para 005-A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The operations include receiving historical data from one or more first previously-drilled wellbores at a field and one or more second previously-drilled wellbores at one or more other fields. The historical data includes geological properties at the field, the one or more other fields, or both. The historical data also includes rig characteristics of drilling rigs used to drill the one or more first previously-drilled wellbores and the one or more second previously-drilled wellbores. The rig characteristics include specifications of equipment on the drilling rigs, operating performance of the equipment, and an amount of emissions generated by the equipment. The equipment includes one or more generators.)
collecting emissions data relating to the first hydrocarbon operation location; see para 005-A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The operations include receiving historical data from one or more first previously-drilled wellbores at a field and one or more second previously-drilled wellbores at one or more other fields. The historical data includes geological properties at the field, the one or more other fields, or both. The historical data also includes rig characteristics of drilling rigs used to drill the one or more first previously-drilled wellbores and the one or more second previously-drilled wellbores. The rig characteristics include specifications of equipment on the drilling rigs, operating performance of the equipment, and an amount of emissions generated by the equipment. The equipment includes one or more generators.)
receiving a second set of equipment data from a second hydrocarbon operation location different from the first hydrocarbon operation location; (see para 270- The historical data may also or instead be from one or more second previously-drilled wellbores at one or more other fields. The field may include one or more wellbores. The other fields may be greater than a predetermined distance from any of the wellbores in the (first) field. The predetermined distance may be, for example, one mile, five miles, ten miles, or one hundred miles. See para 274- The historical data may also or instead include rig characteristics of drilling rigs used to drill the one or more first previously-drilled wellbores, the one or more second previously-drilled wellbores, or both. The rig characteristics may include specifications of equipment on the drilling rigs, operating performance of the equipment, an amount of emissions generated by the equipment, or a combination thereof. The equipment may include one or more generators. The equipment may also or instead include vehicles (e.g., trucks) that transport materials to and/or from the field.)
training a plurality of machine-learning models, (see para 218- For example, consider a machine learning (ML) approach where one or more models can be revised, further trained, etc.)
wherein a first learning model of the plurality of machine-learning models is trained to estimate an emissions factor of a specific equipment component of the first hydrocarbon operation location, wherein the plurality of machine learning model is trained using the first set of equipment data and the emissions data of the first hydrocarbon operation location. (See para 217- 218-Example Workflow C—GHG Footprint Calibration Using Offset Data. Such a workflow can provide for calculation of one or more GHG footprints that can include one or more assumptions that could potentially make the output different from actual measurements. For example, after actual collection of the real emissions data during execution phase (as per Workflow B), Workflow C may calibrate and/or validate one or more existing calculation models to have higher forecast accuracy for further planned wells. For example, consider a machine learning (ML) approach where one or more models can be revised, further trained, etc. Workflow C may provide a solution to calibrate footprint(s) calculated forecast using offset data and/or real-time data to improve calculation accuracy. See para 261- The fuel used to generate rig power for the equipment may be determined based at least partially upon the calculated power consumed by the rig equipment and/or the input power used by the rig-powered wellsite equipment, as at 2662. Emissions from fuel may be calculated based at least partially upon the fuel used to generate the rig power, as at 2672.Emissions from other wellsite equipment may be calculated based at least partially upon the input fuel used by the other wellsite equipment, as at 2674. Emissions from the transportation units may be calculated based at least partially upon the input emission activity data, as at 2676.)
executing the first machine-learning model on the second set of equipment data to estimate an emissions factor of at least one equipment component of the second hydrocarbon operation location, (see para 199- A model may be a machine learning model that can be trained to generate a trained machine learning model. The model may utilize sensor data, which may be from one or more types of sensors. see see para 218- Such a workflow can provide for calculation of one or more GHG footprints that can include one or more assumptions that could potentially make the output different from actual measurements. For example, after actual collection of the real emissions data during execution phase (as per Workflow B), Workflow C may calibrate and/or validate one or more existing calculation models to have higher forecast accuracy for further planned wells. For example, consider a machine learning (ML) approach where one or more models can be revised, further trained, etc. Workflow C may provide a solution to calibrate footprint(s) calculated forecast using offset data and/or real-time data to improve calculation accuracy. para 261- In addition, input fuel used by other wellsite and/or rig equipment may be determined based at least partially upon the assigned wellsite and/or rig equipment, as at 2656. Input emission activity data for each source (e.g., fuel, distance, etc.) may be determined based at least partially upon the assigned transportation units and/or schedule, as at 2658. The fuel used to generate rig power for the equipment may be determined based at least partially upon the calculated power consumed by the rig equipment and/or the input power used by the rig-powered wellsite equipment, as at 2662. Emissions from other wellsite equipment may be calculated based at least partially upon the input fuel used by the other wellsite equipment, as at 2674.)
wherein the emissions factor comprises a predicted value encoding a relationship between a quantity of emissions to a release of emissions by the at least one equipment component of the second hydrocarbon operation location; (see para 206- The block 1020 can be utilized for calculating and/or estimating energy consumption for each activity from sources, estimating emissions by activity and GHG type, and/or calculating combined emissions (CO2-e), by activity and total (e.g., using GWP, etc.). The block 1030 can be utilized for defining output structure and/or metrics, for example, according to agreed data model(s) (e.g., to exchange data between planning and operations), consolidating GHG emissions output by defined metrics (e.g., per activity, per day, per activity type, per drilled length, etc.) and/or generating report(s) of total GHG emissions, metrics, and associated data (e.g. source of parameters used for estimation) according to selected standard(s). see para 214-For a well construction activity, the EF can generate a GHG footprint estimation during the well planning phase to establish a base line PI(s). During an execution phase, such a workflow can evaluate the simulated value(s) with real values to calculate performance. see para 261-Emissions from the transportation units may be calculated based at least partially upon the input emission activity data, as at 2676. See para 274- The equipment may include one or more generators. The equipment may also or instead include vehicles (e.g., trucks) that transport materials to and/or from the field. For example, this data may include the travel distance and/or travel frequency of each truck.)
generating a total emissions estimate of the second hydrocarbon operation location for a predetermined operation time using the emissions factor of the at least one equipment component of the second hydrocarbon operation location. (see para 205-206- The block 1010 can be utilized for identifying emission sources (e.g., by scope, etc.) and defining parameters (e.g., emission factors, GWP, etc.). The block 1020 can be utilized for calculating and/or estimating energy consumption for each activity from sources, estimating emissions by activity and GHG type, and/or calculating combined emissions (CO2-e), by activity and total (e.g., using GWP, etc.). The block 1030 can be utilized for defining output structure and/or metrics, for example, according to agreed data model(s) (e.g., to exchange data between planning and operations), consolidating GHG emissions output by defined metrics (e.g., per activity, per day, per activity type, per drilled length, etc.) and/or generating report(s) of total GHG emissions, metrics, and associated data (e.g. source of parameters used for estimation) according to selected standard(s).see para 257-FIG. 26 illustrates a schematic view of a workflow that may be used to calculate GHG emissions, according to an embodiment. The emission sources 2610 at a wellsite may be or include global warming potential (GWP) sources 2612, wellsite equipment sources 2614, transportation sources 2616, or a combination thereof. And see para 261- The total emissions and/or emissions by activity may then be calculated based at least partially upon the emissions from fuel, the emissions from other wellsite equipment, the emissions from transportation units, or a combination thereof, as at 2680.)
wherein the total emissions estimate of the second hydrocarbon operation location is usable by an operator to track the at least one equipment component of the second hydrocarbon operation location over time. (see para 206-The block 1030 can be utilized for defining output structure and/or metrics, for example, according to agreed data model(s) (e.g., to exchange data between planning and operations), consolidating GHG emissions output by defined metrics (e.g., per activity, per day, per activity type, per drilled length, etc.) and/or generating report(s) of total GHG emissions, metrics, and associated data (e.g. source of parameters used for estimation) according to selected standard(s).see para 248-251-As an example, an emissions framework may output control decisions as to one or more artificial lift schedules, rates, energy sources, etc., which may aim to optimize production and emissions (e.g., minimize emissions, etc.).As an example, a method can include outputting an energy utilization schedule and associated emissions associated with energy utilization. In such an example, a real time graphical user interface may be rendered to track, trend, control, etc., energy utilization and/or emissions. See para 280-The method 3000 may also include performing a wellsite action using the selected drilling plan, as at 3012. The wellsite action may be or include selecting a location at a wellsite to drill a wellbore into a subterranean formation, (e.g., initiating and/or controlling) drilling the wellbore, varying a trajectory of the wellbore, varying a rate of penetration of a bottom hole assembly (BHA) that is drilling the wellbore, varying a weight on the drill bit (WOB) in the BHA, varying a flow rate and/or composition of a fluid pumped into the wellbore, or a combination thereof. )
Allen does not teach identifying, using the second set of equipment data, at least one equipment component of the second hydrocarbon operation location that is a same component as the at least one equipment component of the first hydrocarbon operation location;
In the related field of invention, Pickles teaches identifying, using the second set of equipment data, at least one equipment component of the second hydrocarbon operation location that is a same component as the at least one equipment component of the first hydrocarbon operation location (see para 95-As an example, an AI component can utilize variables selected by one or more development managers and/or a machine from a database where the AI component may utilize a ML model and, for example, a similarity ranking algorithm to rank fields as available via the database based on similarity to a field of interest using selected variables. In such an example, an analysis and visualization component can facilitate performance of one or more types of analyses on the ranked similar fields (e.g., analogues, etc.), which may involve assessment of one or more economic indicators, equipment availability indicators, drilling related concerns, timing indicators, geographic location indicators, etc., where, for example, decisions (e.g., control and/or other) may be derived from one or more of the ranked fields (e.g., ranked analogues, etc.).see para 154- As an example, a workflow can be used to find similar objects such as wells from a historical database. For example, consider training an autoencoder or other ML model to search or identify wells in a historical database. see para 188-As an example, a database can include various types of data as may be generated during one or more stages of development of a field. For example, consider FIG. 4 where various types of wells, subsurface equipment, surface equipment, etc., may have associated data that can be stored in a database. In such an example, data may include type of bottom hole assembly (BHA), types of logs acquired, wellbore trajectory (e.g., dogleg severity, etc.), types of surface equipment)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allens to include identifying, using the second set of equipment data, at least one equipment component of the second hydrocarbon operation location that is a same component as the at least one equipment component of the first hydrocarbon operation location as taught by Pickles in the system of Allens for construction of a more accurate model of a subsurface region, which, in turn, improve characterization of the subsurface region for purposes of resource extraction, thus make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc. (see para [0004], Pickles)
Regarding claim 10
Allen teaches a system comprising: a processor; and a memory that includes instructions executable by the processor for causing the processor to: (See Allen -fig 31-fig 32)
The rest of the claim limitation of claim 10 is very similar to claim 1 and is rejected for the same reason as claim 1.
Regarding claim 16
Allen teaches a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to perform operations comprising (See Allen -fig 31-fig 32)
The rest of the claim limitation of claim 16 is very similar to claim 1 and is rejected for the same reason as claim 1.
Regarding claim 2
Allen in view of Pickles teaches the method of claim 1. Allen further teaches wherein the total emissions estimate of the second hydrocarbon operation location is usable by an operator to control the at least one equipment component of the second hydrocarbon operation location to achieve a yearly emission commitment. (See para 196- 197- In such an example, the emissions framework may generate a time-based schedule. The emissions framework can be operated to account for particular localities, times of year, weather, etc. For example, depending on weather, emissions may change or behave differently. see para 206-The block 1030 can be utilized for defining output structure and/or metrics, for example, according to agreed data model(s) (e.g., to exchange data between planning and operations), consolidating GHG emissions output by defined metrics (e.g., per activity, per day, per activity type, per drilled length, etc.) and/or generating report(s) of total GHG emissions, metrics, and associated data (e.g. source of parameters used for estimation) according to selected standard(s). see para 221- emissions target for AEA limits. see para 248-251-As an example, an emissions framework may output control decisions as to one or more artificial lift schedules, rates, energy sources, etc., which may aim to optimize production and emissions (e.g., minimize emissions, etc.).As an example, a method can include outputting an energy utilization schedule and associated emissions associated with energy utilization. In such an example, a real time graphical user interface may be rendered to track, trend, control, etc., energy utilization and/or emissions. See para 280-The method 3000 may also include performing a wellsite action using the selected drilling plan, as at 3012. The wellsite action may be or include selecting a location at a wellsite to drill a wellbore into a subterranean formation, (e.g., initiating and/or controlling) drilling the wellbore, varying a trajectory of the wellbore, varying a rate of penetration of a bottom hole assembly (BHA) that is drilling the wellbore, varying a weight on the drill bit (WOB) in the BHA, varying a flow rate and/or composition of a fluid pumped into the wellbore, or a combination thereof. )
Regarding claim 4
Allen in view of Pickles teaches the method of claim 1. Allen does not teach preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data.
However, Pickles further teaches preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data. (see para 166- As an example, a user component can preprocesses variables of a field of interest, for example, to a common scale as it is used to train a machine learning model. Such preprocessed variables may be converted into embeddings using one or more machine learning models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allens to include preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data as taught by Pickles in the system of Allens for construction of a more accurate model of a subsurface region, which, in turn, improve characterization of the subsurface region for purposes of resource extraction, thus make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc. (see para [0004], Pickles)
Regarding claim 20
Claim 12 is similar to claim 4, thus rejected for the same reason as claim 4.
Regarding claim 6
Allen in view of Pickles teaches the method of claim 1. Allen further teaches wherein the emissions factor comprises a methane emissions factor. (see para 86 and fig 18-As to some examples of greenhouse gas (GHG), consider one or more of water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3). See para 257-The models 2620 may also describe the emissions factors (e.g., emissions versus fuel consumption).)
Regarding claim 14 and 18
Claims 14 and 18 are similar to claim 6, thus rejected for the same reason as claim 6.
Regarding claim 7
Allen in view of Pickles teaches the method of claim 1. Allen does not teach wherein the first machine-learning model comprises the first deep convolutional neural network (DCNN).
In the related field of invention, Pickles teaches wherein the at least one machine-learning model comprises a deep convolutional neural network (DCNN). (see para 199-200- As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allens to include wherein the at least one machine-learning model comprises a deep convolutional neural network (DCNN) as taught by Pickles in the system of Allens for construction of a more accurate model of a subsurface region, which, in turn, improve characterization of the subsurface region for purposes of resource extraction, thus make a drilling operation more accurate as to a borehole's trajectory where the borehole is to have a trajectory that penetrates a reservoir, etc. (see para [0004], Pickles)
Regarding claim 15 and 19
Claims 14 and 18 are similar to claim 7, thus rejected for the same reason as claim 7.
Regarding claim 9
Allen in view of Pickles teaches the method of claim 1. Allen further teaches wherein the emissions data comprises weather characteristics extracted from local weather reports for the first hydrocarbon operation location. (see para 196-197-The emissions framework can be operated to account for particular localities, times of year, weather, etc. For example, depending on weather, emissions may change or behave differently. The presence of humidity (e.g., rain, snow, etc.) can have an impact on emissions and how such emissions may impact an environment (e.g., travel, dissipate, react, etc.). see para 228- FIG. 23 shows an example of a GUI 2300 that includes various types of regions, formations, basins, etc. The EF may be tailored to a particular region, which may provide for access to local regulations, local weather, etc.)
Regarding claim 20
Claim 20 is similar to claim 9, thus rejected for the same reason as claim 9.
9. Claims 3, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. (PUB NO: US20220372860A1), in view of Pickles et al., (PUB NO: US20240086430A1) and further in view of Brandt et al., (PUB NO: US 20220357230 A1).
Regarding claim 3, 11 and 17
Allen in view of Pickles teaches the method of claim 1. Allen further teaches wherein the emissions data comprises emissions persistence, and a location for the at least one equipment component of the first hydrocarbon operation location. (See para 201-Site emissions such as emissions from flaring gas, venting gas, etc., which may be generated during one or more well operations may be considered. See para 228- A map such as that of the GUI 2300 may be utilized to render visualizations of trends, which may depend on operations, weather, activities, etc. For example, a GHG emissions visualization map can be utilized to visualize GHG emissions and/or trends at a plurality of sites shown on the map. Such a visualization can provide an indication of activities, increase in activities, decrease in activities, etc. An operator may select a particular site and, for example, execute the EF to modify (e.g., optimize) activities at the selected site to reduce emissions, tailor emissions, schedule emissions, etc.)
The combination of Allen and Pickles does not teach wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations.
In the related field of invention, Brandt teaches wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations. (see para 60- In some embodiments, a sensor unit 120 may include two intake tubes 123, enabling the sensor unit 120 to collect air samples from two different heights (for example, at the top of the mast 126 and at the middle of the mast 126). In addition to increasing the likelihood that at least one of the intake tubes 123 will intercept the emissions plume of a gas leak, collecting spaced-vertically air samples helps to define the spatial distribution of gas within the plume, which can be used within a plume model (as described below with reference to FIGS. 6-17) to better define what the likely emissions rate is at the leak source. see para 166-117-FIG. 5B is a graph of the emission rates measured by all six sensor units 120 when METEC did not release gas. FIG. 5C is a graph of the emission rates measured by all six sensor units 120 when METEC released gas. METEC released methane at rates varying between 0.2 kg/hour and 2.3 kg/hour. FIGS. 5B and 5C show a clear difference in the measured emission rates when METEC released methane relative to when METEC did not release gas. FIG. 5D is a graph of the methane concentration measured by the gas leak detection system 100 when METEC did not release gas. FIG. 5E is a graph of the methane concentration measured by the gas leak detection system 100 when METEC released gas.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allen to include wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations as taught by Brandt in the system of Allen and Pickles in order to monitor gas concentrations at multiple locations across a site (e.g., a well pad or other oil or natural gas facility), enabling the gas leak detection system to model gas leak emission rates in two- or three-dimensional space to reveal the most likely origin of the gas leak. (See Abstract, Brandt)
10. Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. (PUB NO: US20220372860A1), in view of Pickles et al., (PUB NO: US20240086430A1) and further in view of Zhang et al., (PUB NO: US20220178235A1).
Regarding claim 5 and 13
Allen in view of Pickles teaches the method of claim 1. The combination of Allen and Pickles does not teach wherein each machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location.
In the related field of invention, Zhang teaches wherein each
PNG
media_image1.png
9
4
media_image1.png
Greyscale
machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location. (See para 20- The trailers 120, also referred to as fracking rigs, fracking systems, or the like, may have an engine, a transmission, and a pump mounted thereon for pressurizing and injecting the fracking fluid. See para 39-41-For example, in the context of modeling engine 208 performance, trailer model 316 may model engine efficiency of a particular engine 208 as a function of power output and engine age. In the context of modeling a transmission 210, trailer model 316 may model cost of operation of a particular transmission 210 as a function of selected gear and target output torque. Furthermore, in the context of modeling a pump 212, the trailer model 316 may model a mechanical efficiency or parasitic loss of a particular pump as a function of flow rate and output pressure. The trailer models 314 may be of any suitable type, such as any variety of look-up table, fitting function, machine learning, and/or artificial intelligence models, such as neural network models.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allens to include wherein each machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location as taught by Zhang in the system of Allens and Pickles in order to uses the trailer models in conjunction with operating parameters to generate control signals that automatically control the various equipment at the fracking site to optimize various desired outcomes, such as reduced operating costs, reduced emissions, reduced idle time, increased efficiency, etc. The control signals are sent to controllers of the individual equipment to control the operations of those equipment and achieve an overall optimized fracking operation. (see Abstract, Zhang)
11. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. (PUB NO: US20220372860A1), in view of Pickles et al., (PUB NO: US20240086430A1) and further in view of LIN et al., (PUB NO: US20230273705A1).
Regarding claim 8
Allen in view of Pickles teaches the method of claim 1. The combination of Allen and Pickles does not teach wherein the emissions data comprises historical leak data extracted from leak detection and repair (LDAR) reports for the at least one equipment component of the first hydrocarbon operation location.
In the related field of invention, LIN teaches wherein the emissions data comprises historical leak data extracted from leak detection and repair (LDAR) reports for the at least one equipment component of the first hydrocarbon operation location. (see para 004- Volatile organic compounds (“VOCs”) and hazardous air pollutants (“HAPs”) can be emitted from a variety of sources in industrial facilities such as stacks, tanks, vents, and other sources as part of normal operations, and valve packing, pump seals, compressor seals, and flange gaskets as potential leak interfaces on process equipment and components. There are numerous federal, state, and local regulations designed to control fugitive emissions from industrial sources through leak detection and repair (“LDAR”) work practices, which are designed to identify leaking equipment so that emissions of VOCs and HAPs can be reduced through effective repairs. Although the detailed compliance requirements can be quite complex, each LDAR regulation is essentially a variation on the theme of monitoring components to find fugitive leaks, repairing and re-monitoring those leaks in a specified time frame, and maintaining the records necessary to demonstrate compliance)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of generating a drilling plan for drilling a wellbore at a field based on emissions as disclosed by Allens to include preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data as taught by LIN in the system of Allens and Pickles in order to provide a cost-effective detection and management of fugitive emissions (leaks). In addition to reducing emissions, the disclosure contemplates safer working environments, reduced resource waste through more efficient work practices and by minimizing/reducing product loss, and improved emissions inventory knowledge and communications with regulators and communities. (see para 28, LIN)
Conclusion
12. Claims 1-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20170103325 A1 Meinrenken et al.
Discussing the systems for automating carbon footprinting are disclosed. In some embodiments, the methods include a plurality of steps. In some embodiments, related to predetermined resources associated with an item from predetermined data sources is obtained. Then, estimated emission factors are calculated for each of the resources. Next, a contributory uncertainty of the data and of the emission factors is determined. Then, a user is guided based on a comparison of the respective contributory uncertainty of data related to the resources or emission factors. Next, both data related to the resources and the estimated emission factors of the resources are utilized to determine a carbon footprint of the item.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RENEE CHAVEZ can be reached at 5712701104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PURSOTTAM GIRI/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
Prosecution Insights