Detailed Action
Status of Claims
This is the first office action on the merits. Claims 1-14 are currently pending and addressed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/05/2023 has being considered by the examiner.
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 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea.
Step 1 of the USPTO’s eligibility analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claim 12 is directed to a method (process). As such, the claim is directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the 2019 Revised Patent SUBJECT Matter Eligibility Guidance is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception
The claim(s) recite(s) abstract limitations including:
Claim 12: predicting the value of the fluid at the sample location
Claim 13: predicting the value of the fluid at the sample location
These limitations, as drafted, are abstract mental processes that, under the broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing elements and/or sensors does not take the claim out of the mental process grouping. Thus the claim recites an abstract idea.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 12:
receiving measured mud-gas data and measured petrophysical data for the sample location is considered an insignificant extra-solution activity.
Computer-based model according to claim 10 is considered an additional element and is recited at a high level of generality and amount to no more than mere instructions to apply the exception.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
Claim 12
As discussed above, regarding the “receiving measured mud-gas data and measured petrophysical data” step is considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity.
With respect a computer based model according to claim 10, these elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than generic computing elements and are even shown as black boxes on the figures. (see MPEP2106.05(f)).
Furthermore, a computer based model according to claim 10 merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. changing a parameter) to an identified problem (i.e. high frequency torsional oscillation) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. what aspects are changed or how the change is affected by the abstract idea) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
Therefore, the claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The various metrics of claim 14 further merely the recitation of the specific variables and data limitations are insufficient as “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from §101 undergirds the information-based category of abstract ideas," (See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claim 12, this recitation does not provide a practical application of the abstract idea, and is not significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1
The metes and bounds of claim 1 are unclear as the claim language establishes the “generating a model” antecedent basis in line one. However, later limitations introduce “generating a model” in line 11 of claim 1. As antecedent basis was established in the preamble the claim language is ambiguous if this is a new model being generated or if it is referring to the original model being generated in line 1. Therefore, the metes and bounds of claim 1 are unclear and claim 1 is rejected on this basis.
Claims 2-14 are rejected for depending on a rejected claim.
Claim 12
The metes and bounds of claim 12 are ambiguous as the claim language does not make clear how “predicting the value of the property of the fluid” is achieved by “supplying”. Predictions occur by analyzing, calculating, comparing etc. Therefore, the metes and bounds of claim 12 are ambiguous and claim 12 is rejected on this basis.
Examiners note: Suggested claim language such as “Supplying the measured mud gas data and the measured petrophysical data to the computer based model according to claim 10 to generate a predicted value of the property of the fluid at the sample location” would further clarify the claim language.
Claims 13-14 are rejected for depending on a rejected claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Molla (US Pub No 20210062650)
Molla discloses in claim 1. A method of generating a model for predicting at least one property of a fluid (Molla abstract) at a sample location within a hydrocarbon reservoir, comprising:
providing a training data set comprising input data (Molla [0039]-[0040] sample database) and target data (Molla [0039]-[0040] sample database comprising different fluid properties), the input data comprising mud-gas data and petrophysical data for each of a plurality of sample locations (Molla [0039]-[0040] sample database filled with 1000+ samples [0071] and includes advanced mud-gas data for providing a continuous fluid property log during drilling), and the target data comprising the at least one property of the fluid for each of the plurality of sample locations (Molla [0039]-[0040] sample database comprising 1000+ samples comprising different fluid properties such as gas composition [0066]);
and generating a model (Molla Fig 11; 445 [0067] training data used to train and optimize cluster classification model) using the training data set such that the model can be used to predict the at least one property of the fluid at the sample location based on measured mud-gas data and measured petrophysical data for the sample location (Molla [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+),
wherein a drilling fluid recycling correction has not been applied to the mud-gas data (Molla Fig 11 does not show a drilling fluid recycling correction has been applied to collected data).
Molla discloses in claim 2. The method according to claim 1, wherein generating the model comprises instructing a machine learning algorithm to generate the model using the training data set (Molla Fig 3 & Fig 7 [0040] & [0059] model is developed utilizing sample data base to build and train a machine learning model).
Molla discloses in claim 3. The method according to claim 1, wherein the at least one property comprises a property influenced by the oil-related components of the fluid (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density).
Molla discloses in claim 4. The method according to claim1, wherein the at least one property comprises one or more of:
a density of the fluid at the sample location (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density);
a gas-oil ratio of the fluid at the sample location(Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density);
and a concentration of C7+ hydrocarbons within the fluid at the sample location (Molla [005] [0038] properties influenced are black oil, gas condensate, dry gas, molar gas composition [0058] % GOR, STO density).
Molla discloses in claim 5. The method according to claim1, wherein the mud-gas data of the training data set comprises measured standard mud-gas data for the sample location (Molla [0058] data includes standard mud-gas data C1-C5 Mol % ).
Molla discloses in claim 6. The method according to claim 5, wherein an extraction efficiency correction has been applied to the mud-gas data of the training data set (Molla [0058] data includes standard mud-gas data C1-C5 Mol % [0071] advanced mud gas data is applied as it is considered optional).
Molla discloses in claim 7. The method according to claim 5, wherein an extraction efficiency correction has not been applied to the mud-gas data of the training data set (Molla [0058] data includes standard mud-gas data C1-C5 Mol % [0071] advanced mud gas data is not applied as it is considered optional), and wherein the training data comprise drilling mud compositional data (Molla [0022] [0037] drilling mud composition used in developing the training data).
Molla discloses in claim 8. The method according to claim 5, wherein the measured mud-gas data was collected without the use of heating (Molla [0029] mud-gas data can be collected without the use of heating as Molla describes an optional ability to heat drilling mud).
Molla discloses in claim 9. The method according to claim1, wherein the petrophysical data comprise one or more of:
bulk density (Molla [0041] density);
neutron porosity (Molla [0071] neutron density-porosity);
resistivity data (Molla [0041] resistivity data);
acoustic data (Molla [0071] sonic);
natural gamma ray (Molla [0041] gamma ray);
nuclear magnetic resonance data (Molla [0041] NMR);
Molla discloses in claim 10. A computer-based model for predicting at least one property of a fluid at a sample location within a hydrocarbon reservoir based on measured mud-gas data and measured petrophysical data for that sample location (Molla Fig 13 processing system [0072] [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+), the computer-based model having been generated by the method according to claim 1 (See claim 1 above and Molla [0072] processing system utilizing computing devices).
Molla discloses in claim 11. A tangible computer-readable medium storing the computer-based model according to claim 10 (Molla Fig 13; 930).
Molla discloses in claim 12. A method of predicting a value of a property of a fluid at a sample location within a hydrocarbon reservoir, the method comprising:
receiving measured mud-gas data and measured petrophysical data for the sample location (Molla [0039]-[0040] sample database comprising 1000+ samples comprising different fluid properties such as gas composition [0066] disclosing the work flow in Fig 11 utilizing various measured data samples and measured petrophysical data points further emphasized in [0071] disclosing various properties to be collected);
and predicting the value of the property of the fluid at the sample location by supplying the measured mud-gas data and the measured petrophysical data to the computer-based model according to claim 10 (Molla [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+).
Molla discloses in claim 13. A method of predicting a value of a fluid property of a fluid along a length of a well through a hydrocarbon reservoir (Molla [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+), the method comprising: predicting a value of a fluid property of a fluid at a plurality of sample locations along a length of a well using the method according to claim 12 for each sample location (Molla [0039]-[0040] sample database filled with 1000+ samples [0071] and includes advanced mud-gas data for providing a continuous fluid property log during drilling [0068] Fig 11; 455 & 456 are statistical prediction models used to validate prediction models 465 & 466 to predict C6+).
Molla discloses in claim 14. The method according to claim 13, further comprising: displaying, using an electronic display screen (Molla [0077] display device), a graph plotting the predicting values of the fluid property against a location of the respective sample location for each of the plurality of sample locations along the length of the well (Molla [0023] inter and intra well fluid facies mapping, reservoir complexities, [0071] selection for well landing points, geo-steering using fluid information).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang US Pat No 12210129: discloses that extraction efficiency is usually referred to as “advanced” mud-gas data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas D Wlodarski whose telephone number is (571)272-3970. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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, Nicole Coy can be reached at (571) 272-5405. 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.
/NICHOLAS D WLODARSKI/Examiner, Art Unit 3672
/Nicole Coy/Supervisory Patent Examiner, Art Unit 3672