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 .
DETAILED ACTION
Art Unit – Location
The Art Unit location of your application in the USPTO may have changed. To aid in correlating any papers for this application, all further correspondence regarding this application should be directed to Art Unit 2682.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/14/2026 has been entered.
Remarks
Re: 101 Rejections
The Applicant argues: MPEP §2106(a)(1) gives an example which would overcome an Abstract Idea. The example in MPEP §2106(a)(1) is similar to the Applicants claim. Therefore, the §101 Abstract Idea rejection should be reversed.
The Examiner responds:
The Applicants argument citing MPEP 2106.04 (a)(1) vii where the training a neural network is considered not an abstract idea: The Examiner interprets the training of neural networks is neither a mathematical concept, nor a method of organizing human activity. Training a neural network requires both hardware and software to implement training. The instant claim is different. A human can determine emissions based on results collected for a particular region. The Examiner suggests amending the claim to include possibly novel hardware and/or to result in a practical application which cannot be considered a mental process; such as displaying and taking action upon a threshold being exceeded or the like.
The Applicant argues: Atmospheric inversion modeling is a specialized process. It is infeasible for humans to meaningfully perform the inverse modeling or posterior emission steps.
The Examiner responds: In this case, a human can perform atmospheric inversion modeling with a physical aid such as a computer: Please refer to MPEP 2106.04(a)(2) III B. “A Claim That Encompasses a Human Performing the Step(s) Mentally With or Without a Physical Aid Recites a Mental Process”.
Please refer to:
Gagliano et al. (US 2022/0316321 A1) “Gagliano”
Griffon (US 2013/0179078 A1) “Griffon”
Re: 103 Rejections
The Applicant argues: A more current time frame of satellite data is obtained and transformed via atmospheric inversion modeling to generate posterior emissions estimation along with hydrocarbon related attributes to update the learning machine for more accurate emission factors for each of the hydrocarbon related attributes.
To which, Applicants claim 1 recites a fundamentally different modeling architecture than the machine learning model disclosed by Gagliano, whereas the claimed system outputs emission factors using posterior inversion estimations and hydrocarbon attribute features as inputs.
The Examiner responds: Gagliano teaches an emission sensor on a satellite [0009]. Gagliano teaches: “the emissions sensor can detect emission data at the wellbore over a subsequent period of time” where the “emission data includes a type of the emissions, an amount of the emissions, and an origination location in the wellbore of the emissions” [0053]. Furthermore, in FIG. 4 of Gagliano, step “402 Detect first emission data using a sensor at a wellsite” [0038] and step “406 Detect second emission data subsequent to performing the plug and abandonment operation” [0042] where in step “408 Determine reduction in emission using the first emission data and the second emission data” is determined by a “computing system” [0043].
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) mathematical concepts, a mental process, or certain methods of organizing human activity. This judicial exception is not integrated into a practical application because the abstract idea is implemented using generic machine learning.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of machine learning is generic and lacks detailed improvements to machine learning technology.
Step 1. The claims are directed to a Process, Machine, and Article of Manufacture.
Step 2A. Prong 1. The invention comprises an Abstract Idea of: Mathematical Concepts, a Mental Process, and/or Certain Methods of Organizing Human Activity.
Mathematical Concepts of: “determining current values of hydrocarbon related attributes that affect emissions” and to “generate an emissions factor for each of the hydrocarbon related attributes”. Numerical values of hydrocarbon related attributes and generation of emission factors for each of the hydrocarbon related attributes involve mathematical concepts.
Atmospheric inversion modeling is a mathematical concept. Updating using posterior emissions estimation is a mathematical concept. Updating a learning machine is a mathematical concept. Determining emissions factors is a mathematical concept. Please refer to MPEP 2106.04(a)(2) I C. e.g. “Mathematical Calculations”.
A Mental Process of: “determining current values of hydrocarbon related attributes that affect emissions” and to “generate an emissions factor for each of the hydrocarbon related attributes”. A person assigns values and generates emission factors for the hydrocarbon related attributes. MPEP 2106.04(a)(2) III B. e.g. “A Claim That Encompasses a Human Performing the Step(s) Mentally With or Without a Physical Aid Recites a Mental Process”.
A Method of Organizing Human Activity of “selecting the hydrocarbon site”; “determining current values of hydrocarbon related attributes”; and “inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine”. A person selects the site, determines attributes related to emissions at the hydrocarbon site, and inputs attributes into a learning machine. E.g. “following rules or instructions” MPEP 2106.04(a)(2) II C.
Step 2A Prong 2. There are no additional elements which integrate into a practical application. There is no technical improvement to the claimed learning-machine 2106.04(d) I and 2106.05(b).
Step 2B. Are there additional elements that amount to significantly more?
The claims cite a process which can be performed by a human using mathematical concepts, mental processes, and following known rules or instructions of human activity; where the inclusion of a learning machine as a substitute for a human is generic and lacks the details for a significant technical improvement or an inventive concept. MPEP 2106.05.
The learning machine is merely a generic machine which is well-understood, routine, and conventional. The additional claim limitation elements in combination with the learning machine do not amount to significantly more because: In combination the well-understood, routine, conventional functions do not improve the function of learning machine. There appears to be no meaningful result from the updated learning machine.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Griffon (US 2013/0179078 A1) “Griffon” in view of Gagliano et al. (US 2022/0316321 A1) “Gagliano”.
1. Griffon teaches: A method for determining an emissions associated with hydrocarbon recovery of a hydrocarbon site within a geographic region ("Method for measuring weekly and annual emissions of a greenhouse gas generated over a determined geographical area and measuring system implementing the method." [ABSTRACT]), the method comprising: selecting the hydrocarbon site for which to determine the emissions ("FIG. 6 represents atmospheric observation sites" [0082]); determining current values of hydrocarbon related attributes that affect emissions at the hydrocarbon site for a current time frame ("In a coal-fired power plant, the more the burning and the production of electricity increase, the higher the emissions of CO2, NOx and CO are released. " [0332]);
Griffon does not explicitly teach: inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site at the current time frame; obtaining, via one or more satellites, satellite data of the geographic region for a subsequent time frame:.
However, Gagliano teaches: inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site at the current time frame; obtaining, via one or more satellites, satellite data of the geographic region for a subsequent time frame:. ("the computing system 200 can include a machine-learning model. The machine- learning model can be trained on historical data about previously executed plug and abandonment operations. The machine- learning model can take, as input, the first emission data that includes the type, the amount, and the origination of emissions from the wellbore" [0026]. Step “402 Detect first emission data using a sensor at a wellsite” [0038] and step “406 Detect second emission data subsequent to performing the plug and abandonment operation” [0042].) .
The attributes associated with a coal fired power plant of Griffon can be modified by Gagliano to train a machine-learning model to identify emissions from hydrocarbon sites.
The motivation for the combination is provided by Gagliano to address hydrocarbon sites from “abandoned or orphaned wellbores may produce gas emissions, which may include emissions that can negatively affect sensitive environments. [0002].
Furthermore, the combination of Griffon and Gagliano teach:
performing atmospheric inversion modeling for the geographic region ("The next step in the method for measuring according to the invention consists in the use of a data inversion and assimilation module" [0048] of Griffon; for "greenhouse gas, by means of a data inversion" [Claim 1] of Griffon); to generate a posterior emissions estimation based on the satellite data of the geographic region for the subsequent time frame ("means for measuring concentrations and fluxes of greenhouse gases” [0068] of Griffon. “means for measuring satellite, meteorological, marine and ecosystem parameters” [0069] of Griffon. The measurement of concentrations is a posterior emissions estimation. Step “408 Determine reduction in emission using the first emission data and the second emission data” in FIG. 4 is determined by a “computing system” [0043] of Gagliano.).
updating the learning machine using the posterior emissions estimation for the subsequent time frame and the current values of the hydrocarbon related attributes for the subsequent time frame ("The system can detect second emission data about emissions with respect to the wellbore over a second period of time” [ABSTRACT] of Gagliano). The machine learning model in [0026] of Gagliano is updated on posterior time data. “At block 408, the computing system 200 determines a reduction in emissions using the first emission data and the second emission data” [0043] of Gagliano.
and determining, via the updated learning machine, an emission factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site for the subsequent time frame ("The system can determine an amount of reduction in emissions from the wellbore using the first emission data and the second emission data. The system can output the amount of reduction in the emissions. " [ABSTRACT] of Gagliano) .
The inversion modeling for greenhouse gas of Griffon can be modified by Gagliano to update a machine-learning model using posterior data to determine an emission amount.
The motivation for the combination is provided by Gagliano “to address hydrocarbon sites from “abandoned or orphaned wellbores may produce gas emissions, which may include emissions that can negatively affect sensitive environments. [0002].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
2. The method of claim 1, wherein the learning machine has been trained on data samples over a past number of time frames, wherein each data sample comprises, identification of the geographic region, emissions samples, the emissions samples including a level of emissions caused by hydrocarbon recovery in the geographic region for the past time frame, and previous values of the hydrocarbon related attributes that affect emissions in the geographic region for the past time frame ("time-series" [0113] of “Atmospheric Observations” [0112] from e.g. “satellites” [0043] of Griffon.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
3. The method of claim 2, further comprising: obtaining a first satellite dataset for the geographic region at a first time frame, wherein the first satellite dataset includes columnar concentrations of one or more greenhouse gas; obtaining the emissions samples for the first time frame for the geographic region ("time-series" [0113] of “Atmospheric Observations” [0112] from e.g. “satellites” [0043] of Griffon.) , wherein the emissions samples include emissions factors samples from an open-source dataset ("Fig. 3" of Griffon); and generating, via the atmospheric inversion modelling, the posterior emissions estimation for the geographic region at the first time frame based on the first satellite dataset and the emissions samples ("a weighting module enables one to determine the final weighted fluxes, using a modeling of production activities and of the emissions market, and to validate the results provided by the data inversion and assimilation module." [0049] of Griffon.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
4. The method of claim 3, further comprising: identifying one or more hydrocarbon sites ("emitting facilities" [0052] of Griffon) within the geographic region ("local regions" [0052]), the one or more hydrocarbon sites comprising hydrocarbon related attribute samples; generating training samples, wherein the training samples include the hydrocarbon related attribute samples within the geographic region and the posterior emissions estimation corresponding to the geographic region("time-series" [0113] of “Atmospheric Observations” [0112] from e.g. “satellites” [0043] of Griffon.); and training, with the training samples, the learning machine to generate emissions factors for hydrocarbon site attributes ("the computing system 200 can include a machine-learning model. The machine- learning model can be trained on historical data” [0026] of Gagliano; where “The method for measuring according to the invention therefore samples inventories with a descending approach in the following order: planet, continents, continental regions, states/countries, local regions, down to emitting facilities.” [0052] of Griffon.)
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
5. The method of claim 3, further comprising: obtaining a second satellite dataset from a second time frame; generating an updated posterior emissions estimation for the geographic region ("the computing system 200 can include a machine-learning model. The machine- learning model can be trained on historical data” [0026] of Gagliano; updating the learning machine based on the updated posterior emissions estimation and the emissions samples for the second time frame ("time-series" [0113] from e.g. “satellites” [0043] of Griffon.) .
and inputting the hydrocarbon related attributes into the learning machine to generate an updated emissions factor for each of the hydrocarbon related attributes ("the computing system 200 can include a machine-learning model. The machine- learning model can be trained on historical data about previously executed plug and abandonment operations. The machine- learning model can take, as input, the first emission data that includes the type, the amount, and the origination of emissions from the wellbore" [0026] of Gagliano.).
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
6. The method of claim 3, wherein the first satellite dataset includes satellite data from the one or more satellites, the satellite data from each of the one or more satellites having different resolutions ("The ascending inventories of emissions by country are allocated on a high spatial resolution grid of 0.1.degree..times.0.1.degree. (.apprxeq.100 km.sup.2) which can be enlarged to lower resolutions from 0.5.degree..times.0.5.degree. to 1.degree..times.1.degree. and are integrated into the different resolutions" [0182] of Griffon.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
7. The method of claim 1, further comprising: determining the emissions associated with hydrocarbon recovery of the hydrocarbon site based on the emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site ("the system precisely identifies and quantifies the types of emitted gases (CO2, CH4, N2O, NOx, HFC, HCFC, CFC, PFC, SF6, O3, H20, CO, H2) " [0332] of Griffon.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
8. Griffon teaches: wherein the hydrocarbon related attributes include metadata of the hydrocarbon site ("greenhouse gas generated over a determined geographical area" [0029] of Griffon. The metadata is data about a determined geographical area.) .
Griffon does not explicitly teach: wherein the hydrocarbon related attributes include inventory data and operational data.
However, Gagliano teaches: wherein the hydrocarbon related attributes include inventory data ("The wellbore 108 can be a vertical wellbore, a horizontal wellbore , a general wellbore , an open-hole wellbore , or other suitable type of wellbore." [0016]) , and operational data (flow of "20 pounds of methane per hour " [0038]).
The attribute of an area which greenhouse gas is generated can be modified by Gagliano to identify the type of a wellbore and the operational data of a wellbore.
The motivation for the combination is provided by Gagliano to address hydrocarbon sites from “abandoned or orphaned wellbores may produce gas emissions, which may include emissions that can negatively affect sensitive environments. [0002].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
9. The method of claim 1, further comprising: performing a hydrocarbon site operation based on the emissions factor for each of the hydrocarbon related attributes ("a weighting module enables one to determine the final weighted fluxes, using a modeling of production activities" [0049] of Griffon as shown in Fig. 17.) .
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
10-15. The non-transitory computer-readable medium of claims 10-15 have been analyzed in view of the non-transitory computer-readable medium of Gagliano “A non-transitory computer-readable medium” in claim 15 and further in view of claims 1-4, 7, and 8 respectively.
The method of Griffon can be implemented by a processor to include instructions recorded on a non-transitory computer-readable medium.
The motivation for the combination is provided by Gagliano “The processor 302 can execute instructions stored in the memory 304 to perform the operations” [0029].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
16-20. The system of claims 16-20 have been analyzed in view of the system of Griffon “geographical area and measuring system implementing the method.”, in view of the processing device and non-transitory computer-readable medium of Gagliano in claim 15, and further in view of claims 1-4 and 7 respectively.
The method of Griffon can be implemented by a processor to include instructions recorded on a non-transitory computer-readable medium.
The motivation for the combination is provided by Gagliano “The processor 302 can execute instructions stored in the memory 304 to perform the operations” [0029].
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Relevant Prior Art
Gomez et al. (US 2024/0161495 A1)
Abstract:
A method can include receiving data that include satellite data of a region of interest where the region of interest includes multiple hydrocarbon production sites that include gas flaring equipment, identifying one or more unlit gas flares at one or more of the multiple hydrocarbon production sites by using a trained machine learning model and at least a portion of the data and, for an unlit gas flare, issuing an instruction related to operation of the gas flaring equipment in the region of interest.
Conclusion
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/TED W. BARNES/ Ph.D. Electrical Engineering
Primary Examiner
Art Unit 2682
/TED W BARNES/Primary Examiner, Art Unit 2682
Prosecution Insights