DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-27 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim recites “selecting at least one wellbore variable of a wellbore operation” and “training each respective machine learning model”, however, the specification fails to set forth a sufficient written description. First, regarding “selecting at least one wellbore variable of a wellbore operation”, the specification does not set forth the method, technique or parameters for how or why wellbore variable is selected. At best, the specification discloses “the processor 152 may perform this selection…wellbore variables (Yn) that are selected may be based on the type of wellbore operation, various statistics of which wellbore variables (Yn) are critical in determining change, etc. For example, that wellbore variables (Yn) that are selected may be based on the type of equipment, how long the wellbore operation has currently been running, the type of subsurface formation into which the wellbore is formed, etc.” ([0023]). The specification does not disclose the method, technique, algorithm, etc. that is utilized by the processor that results in a selected wellbore variable. There is no way to ascertain which of the wellbore variables will be selected from the listed or unlisted categories. As such, the scope of the claim covers all known and unknown ways for performing a selection.
Second, regarding “training each respective machine learning model”. The specification sets forth that a first and a second machine learning model are trained (see e.g. [0030]-[0031]), however, the specification fails to set forth or describe a method, technique, algorithm, etc. for how the second machine learning model of the ‘each respective machine learning model’ is trained. At best, the specification recites “train[ing of] the second machine learning model” (e.g. [0036]-[0037]), without more. There is no way to ascertain which method, technique, algorithm, etc. for training the second machine learning model. As such, the scope of the claim covers all known and unknown ways for training the second machine learning model.
Consequently, the claim contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 2, the claim recites “training a second machine learning model” however, the specification fails to set forth or describe a method, technique, algorithm, etc. for how the second machine learning model of the is trained. At best, the specification recites “train[ing of] the second machine learning model” (e.g. [0036]-[0037]), without more. There is no way to ascertain which method, technique, algorithm, etc. for training the second machine learning model. As such, the scope of the claim covers all known and unknown ways for training the second machine learning model.
Consequently, the claim contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 3-13 fails to remedy the deficiencies of the claims from which they depend.
Regarding claims 14 and 22, the claims each recite “select at least one wellbore variable of a wellbore operation” and “train each respective machine learning model”, however, the specification fails to set forth a sufficient written description. First, regarding “select at least one wellbore variable of a wellbore operation”, the specification does not set forth the method, technique or parameters for how or why wellbore variable is selected. At best, the specification discloses “the processor 152 may perform this selection…wellbore variables (Yn) that are selected may be based on the type of wellbore operation, various statistics of which wellbore variables (Yn) are critical in determining change, etc. For example, that wellbore variables (Yn) that are selected may be based on the type of equipment, how long the wellbore operation has currently been running, the type of subsurface formation into which the wellbore is formed, etc.” ([0023]). The specification does not disclose the method, technique, algorithm, etc. that is utilized by the processor that results in a selected wellbore variable. There is no way to ascertain which of the wellbore variables will be selected from the listed or unlisted categories. As such, the scope of the claim covers all known and unknown ways for performing a selection.
Second, regarding “train each respective machine learning model”. The specification sets forth that a first and a second machine learning model are trained (see e.g. [0030]-[0031]), however, the specification fails to set forth or describe a method, technique, algorithm, etc. for how the second machine learning model of the ‘each respective machine learning model’ is trained. At best, the specification recites “train[ing of] the second machine learning model” (e.g. [0036]-[0037]), without more. There is no way to ascertain which method, technique, algorithm, etc. for training the second machine learning model. As such, the scope of the claim covers all known and unknown ways for training the second machine learning model.
Consequently, the claim contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 15 and 23, the claim recites “train a second machine learning model” however, the specification fails to set forth or describe a method, technique, algorithm, etc. for how the second machine learning model of the is trained. At best, the specification recites “train[ing of] the second machine learning model” (e.g. [0036]-[0037]), without more. There is no way to ascertain which method, technique, algorithm, etc. for training the second machine learning model. As such, the scope of the claim covers all known and unknown ways for training the second machine learning model.
Consequently, the claim contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 16-21 and 24-28 fail to remedy the deficiencies of the claims from which they depend.
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 1-11 and 14-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is a method which recite(s) for each of two or more machine learning models, training each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, processing, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples.
In the context of the claim, training machine learning models involves nothing more than mathematical calculations (e.g. linear regression, etc., [0025]-[0026]) and therefore encompasses mathematical concepts. See MPEP 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim element of selecting at least one wellbore variable of a wellbore operation. The limitation is recited with a high level of generality without imposing any meaningful limits on the claim such that it is nothing more than mere data gathering and output. Consequently, the limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional claim limitation is insignificant extra-solution activity. Further, the background of the disclosure explains that individual parameters, i.e. wellbore variables, are selected by visual inspection ([0001]), thus selecting at least one wellbore variable is well-understood, routine, conventional activity. Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claims 2-11 depend from claim 1 and recite the same abstract idea as claim 1. The additional claims limitations recited in claims 2-11 serve merely to add additional limitations to the abstract idea. That is, the additional claim limitations either use mathematical calculations, equations, relationships or formulas and therefore encompasses mathematical concepts (See MPEP 2106.04(a)(2)I.), or are accomplished using by observation, evaluation, judgment, and opinion, and therefore encompasses mental process (See MPEP 2106.04(a)(2)III.) The claims do not recite any additional limitations that integrate into a practical application or amount to significantly more. The claims are not patent eligible.
Claim 14 is an article of manufacture which recites for each of two or more machine learning models, instructions to train each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, instructions to process, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples.
In the context of the claim, training machine learning models involves nothing more than mathematical calculations (e.g. linear regression, etc., [0025]-[0026]) and therefore encompasses mathematical concepts. See MPEP 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim elements of A non-transitory, computer-readable medium having instructions stored thereon that are executable by a processor and select at least one wellbore variable of a wellbore operation. The limitation of a non-transitory, computer-readable medium having instructions stored thereon that are executable by a processor is recited with a high level of generality without imposing any meaningful limits on the claim such that it is no more than mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The limitation of select at least one wellbore variable is recited with a high level of generality without imposing any meaningful limits on the claim such that it is nothing more than mere data gathering and output. Consequently, the limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional claim limitations are mere instructions to implement an abstract idea on a computer, merely uses a computer as a tool to perform an abstract idea or insignificant extra-solution activity. Further, the background of the disclosure explains that individual parameters, i.e. wellbore variables, are selected by visual inspection ([0001]), thus selecting at least one wellbore variable is well-understood, routine, conventional activity. Therefore, this limitation remains insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claims 15-21 depend from claim 14 and recite the same abstract idea as claim 14. The additional claims limitations recited in claims 15-21 serve merely to add additional limitations to the abstract idea. That is, the additional claim limitations either use mathematical calculations, equations, relationships or formulas and therefore encompasses mathematical concepts (See MPEP 2106.04(a)(2)I.), or are accomplished using by observation, evaluation, judgment, and opinion, and therefore encompasses mental process (See MPEP 2106.04(a)(2)III.) The claims do not recite any additional limitations that integrate into a practical application or amount to significantly more. The claims are not patent eligible.
Claim 22 is an article of manufacture which recites for each of two or more machine learning models, instructions to train each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, instructions to process, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples.
In the context of the claim, training machine learning models involves nothing more than mathematical calculations (e.g. linear regression, etc., [0025]-[0026]) and therefore encompasses mathematical concepts. See MPEP 2106.04(a)(2) I. The claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional claim elements of at least one sensor to be positioned at the surface of a wellbore and downhole in the wellbore; a processor; and a computer-readable medium having instructions stored thereon that are executable by the processor to cause the processor to, select at least one wellbore variable of a wellbore operation. The limitation of a processor; and a computer-readable medium having instructions is recited with a high level of generality without imposing any meaningful limits on the claim such that it is no more than mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The limitation of at least one sensor to be positioned at the surface of a wellbore and downhole in the wellbore and select at least one wellbore variable is recited with a high level of generality without imposing any meaningful limits on the claim such that it is nothing more than mere data gathering and output. Consequently, the limitation is insignificant extra-solution activity. See MPEP 2106.05(g). The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional claim limitations are mere instructions to implement an abstract idea on a computer, merely uses a computer as a tool to perform an abstract idea or insignificant extra-solution activity. Further, the background of the disclosure explains that individual parameters, i.e. wellbore variables, are selected by visual inspection ([0001]) and the cited prior art to Bassett (6937923) discloses that at least one sensor to be positioned at the surface of a wellbore and downhole in the wellbore is known in the art, thus the limitations are well-understood, routine, conventional activity. Therefore, the limitations remain insignificant extra-solution activity even upon reconsideration and does not amount to significantly more. The claim is not patent eligible.
Claims 23-27 depend from claim 22 and recite the same abstract idea as claim 22. The additional claims limitations recited in claims 23-27 serve merely to add additional limitations to the abstract idea. That is, the additional claim limitations either use mathematical calculations, equations, relationships or formulas and therefore encompasses mathematical concepts (See MPEP 2106.04(a)(2)I.), or are accomplished using by observation, evaluation, judgment, and opinion, and therefore encompasses mental process (See MPEP 2106.04(a)(2)III.) The claims do not recite any additional limitations that integrate into a practical application or amount to significantly more. The claims are not patent eligible.
Allowable Subject Matter
Claims 1-28 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(a) and 35 U.S.C. 101, set forth in this Office action.
Regarding claim 1, the prior art fails to anticipate or render obvious for each of two or more machine learning models, training each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, processing, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples, in combination with all other limitations as claimed by Applicant.
Regarding claim 14, the prior art fails to anticipate or render obvious for each of two or more machine learning models, instructions to train each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, instructions to process, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples, in combination with all other limitations as claimed by Applicant.
Regarding claim 22 the prior art fails to anticipate or render obvious for each of two or more machine learning models, train each respective machine learning model with a respective set of training data values of the at least one wellbore variable detected in a respective different time interval; and for each of the two or more machine learning models, process, for each of the at least one wellbore variable, a set of data samples of the respective at least one wellbore variable using each of the two or more trained machine learning models to output a respective model output response for each data sample of a set of data samples, in combination with all other limitations as claimed by Applicant.
Claims 2-13, 15-21 and 23-28 would be allowed by virtue of the claims from which they depend.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jandhyala et al. in U.S. Patent Publication 20250259114 teaches “receiving drilling performance data associated with a downhole tool; modeling, using at least one first machine learning model, steering performance of the downhole tool based in part on the drilling performance data; modeling, using at least one second machine learning model, dog leg severity (DLS); determining, based in part on the steering performance and the DLS, operating parameters for drilling a well having a trajectory that includes a dogleg portion; and using the operating parameters to control the downhole tool during drilling of the well” (Claim 1, emphasis added).
Tang et al. in U.S. Patent 11,947,069 teaches “A method can include accessing a measurement model in memory of a downhole tool; determining an optimal parameter set using a processor of the downhole tool and the measurement model; and performing at least one measurement using at least one sensor of the downhole tool operated according to the optimal parameter set” (Abstract).
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/MI'SCHITA' HENSON/ Primary Examiner, Art Unit 2857