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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/17/2025 has been entered.
Response to Amendment
The amendment filed 12/17/2025 has been entered. As directed, claims 1, 8, 10, 17 and 19
have been amended, no claim is added and canceled. Thus claims 1-6, 8-15 and 17-19 are remain pending in the application. The applicant’s amendments to the claims partially overcome rejection under 35 U.S.C 112(a) and 112(b) previously set forth in the Final Office Action mailed 09/15/2025. However, new claim objection and rejections under 35 U.S.C 112(b) has been made based on the newly amendment.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant
Arguments/Remarks Made in an Amendment,”
Applicant argues:
III. Rejection of Claims 1-6, 8-15 and 17-19 under 35 U.S.C. § 101
…
Here, the Office Action recognizes that, in order to provide a proper practical application of an abstract idea, the claims must "reflect the disclosed improvement" over "the background invention or existing technology" by "specifying how the claimed improvement perform(s) the additional element different from existing [oil drilling] technology."
As described at 1. 16 on p. 4 through 1. 11 on p. 5 of the original as-filed application, "manual monitoring and adjustments have been needed to correctly detect the drilling mode." Applicant has, as noted above, amended previously presented Claims 1, 10, and 19 to more clearly point out that the claimed trained at least one initial model is utilized to automatically determine a mud motor drilling mode (for a mud motor) in real time without manual monitoring and adjustment. Thus, pending Claims 1, 10, and 19 recite a disclosed improvement over existing oil drilling technology since pending Claims 1, 10, and 19 reflect the disclosed improvement (automatic determination of a drilling mode in real time) and specifies how the claimed improvement performs the additional element different from existing oil drilling technology (without monitoring and adjustment). As such, pending Claims 1, 10, and 19, per the Office Action's interpretation of proper practical application of an abstract idea noted above, provide a proper practical application of the claimed improvement.
Since pending Claims 1, 10, and 19 properly integrate a practical application of an abstract idea, as established above, pending Claims 1, 10, and 19 comply with the requirements of 35 U.S.C. § 101. For at least this reason, the § 101 rejection of previously presented Claims 1-6, 8- 15 and 17-19 should be overturned and pending Claims 1-6, 8-15 and 17-19 set to issue. Accordingly, Applicant respectfully requests the Office to withdraw the § 101 rejection of previously presented Claims 1-6, 8-15 and 17-19 and allow issuance of pending Claims 1-6, 8-15 and 17-19.
(see Response filed 12/17/2025 [pages 10-12]).
In response to applicant's argument, the examiner disagrees that “pending Claims 1, 10, and 19 recite a disclosed improvement over existing oil drilling technology since pending Claims 1, 10, and 19 reflect the disclosed improvement (automatic determination of a drilling mode in real time) and specifies how the claimed improvement performs the additional element different from existing oil drilling technology (without monitoring and adjustment).”
As explained in MPEP2106.05(a)(I): “… the courts have indicated may not be sufficient to show an improvement in computer-functionality: Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).”
The additional limitations, “utilizing the trained at least one initial model …; actuating the mud motor … steering a drill bit …” are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The limitation is recited at a high level of generality and does not provide detail of how the trained model is utilized to automatically determine the mud motor drilling mode, nor how the actuation of the mud motor or rotary table is technically performed based on the determination, nor how the steering of the drill bit is achieved. Instead, the limitation broadly recites that the result of the abstract idea is used to cause determination, actuation, and steering, without specifying any particular control technique, algorithm, or technical implementation. Although the limitation recites automatically determination of mud motor drilling mode, actuation of drilling components and steering of a drill bit, these actions are functionally described as outcomes of applying the abstract idea (i.e., mental process and mathematical concepts), and are not tied to any specific improvement in the functioning of a computer, drilling device or control system. The limitation does not recite any particular machine configuration or specialized control technique, but instead relies on generic computing components operating in the ordinary capacity to apply the abstract idea. Therefore, this additional limitation merely applies the generic computer components with judicial exception, and does not integrate judicial exception into practical application. See MPEP 2106.05(f).
Regarding Step 2B, for similar reasons, the additional elements do not amount to significantly more than the judicial exception. They are recited at a high level of generality and merely apply the result of the abstract idea to a drilling environment. The claim does not recite any specific control technique, algorithm, or technological mechanism for how the determination is used to control actuation or steering, but instead broadly recites that those actions are performed based on the recited abstract idea. Therefore, the additional elements, when considered individually and in combination, merely apply the judicial exception using conventional computing components and do not provide significantly more than the judicial exception.
For the reasons discussed above, applicant’s arguments have been considered but are not
persuasive. The claims are directed to abstract ideas (mental process and/or mathematical concepts), do not integrate judicial exception into a practical application, and do not recite additional elements that amount to significantly more than the judicial exception. Accordingly, the rejection of claims 1-6, 8-15 and 17-19 under 35 U.S.C. § 101 is maintained.
With respect to the Applicant’s argued rejection under 35 U.S.C 103 in “Applicant
Arguments/Remarks Made in an Amendment,”
Applicant argues:
…
At [0441], relied upon by the Office Action, Yu states:
As an example, a method can include receiving sensor data during drilling of a portion of a borehole in a geologic environment; determining a drilling mode from a plurality of drilling modes using a trained neural network and at least a portion of the sensor data; and issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode. In such an example, the plurality of drilling modes can include a rotary drilling mode and a sliding drilling mode. As an example, a plurality of drilling modes can include a sliding up drilling mode and a sliding down drilling mode.
Thus, Yu's principle of operation is to use sensor data receiving during drilling of a portion of a borehole, determining a drilling mode using a trained neural network and at least a portion of the sensor data received during drilling of the portion of the borehole, and then issuing a control instruction for drilling an additional portion of the borehole. That is, Yu's principle of operation is to utilize real time sensor data, not historical data, to determine how to complete drilling of a borehole.
The Office Action, at Item 3 on p. 10 and at Item 12 on p. 28, recognizes Yu's principle of operation is to utilize real time sensor data to determine how to complete drilling a borehole but then modifies Yu with Kristjansson's teaching of the use of historical data to determine how to complete drilling of the borehole. However, if Yu is modified, as suggested by the Office Action, to utilize the historical run information of Kristjansson (or any other art), such modification would clearly change Yu's principle of operation utilizing real time sensor data to determine how to complete drilling the borehole. Thus, the suggested modification of Yu with the cited portions of Kristjansson changes the principle of operation of Yu. Further, Applicant has, as noted above, amended previously presented Claims 1, 10, and 19 to more clearly point out that the claimed trained at least one initial model is utilized to automatically determine a mud motor drilling mode for a mud motor based on an actual drilling mode of historical data.
The Manual of Patent Examining Procedure (MPEP) states:
If the proposed modification or combination of the prior art would change the principle of operation of the prior art invention being modified, then the teachings of the references are not sufficient to render the claims prima facie obvious. In re Ratti, 270 F.2d 810, 813, 123 USPQ 349, 352 (CCPA 1959)2
Since, as established above, the proposed modification of Yu with Kristjansson changes the
principle of operation of Yu (the art being modified), the references, per the above-cited portion of the MPEP, are not sufficient to render pending Claims 1, 10, and 19 prima facie obvious.
Thus, the applied combination of the cited portions of Yu and Kristjansson does not provide a prima facie case of obviousness for pending Claims 1, 10, and 19. Dursun has not been cited to cure the above-noted deficiencies of the applied combination of the cited portions of Yu and Kristjansson. Instead, Dursun has been cited to teach other features of pending Claims 1, 10, and 19. As such, the applied combination of the cited portions of Yu, Kristjansson, and Dursun does not provide a prima facie case of obviousness for pending Claims 1, 10, and 19 and claims that depend thereon.
For at least this reason, the § 103 rejection of Claims 1-2, 4, 8-11, 13, and 17-19 should be overturned and the claims set to issue. Accordingly, Applicant respectfully requests the Office to withdraw the § 103 rejection of Claims 1-2, 4, 8-11, 13, and 17-19 and allow issuance thereof.
V. Rejection of Claims 3, 5-6, 12, and 14-15 under 35 U.S.C. § 103
Claims 3, 5-6, 12, and 14-15 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Yu, Kristjansson and Dursun in view of:
U.S. Patent Application Publication No. 2020/0110943 by Gunawardena ("Gunawardena") for Claims 3 and 12; and
U.S. Patent Application Publication No. 2020/0234471 by Lu, et al. ("Lu") for Claims 5-6
and 14-15.
Applicant respectfully traverses these rejections based at least on the reasons given below.
As established above, the applied combination of the cited portions of Yu, Kristjansson, and Dursun does not provide a prima facie case of obviousness for pending Claims 1 and 10.
Gunawardena and Lu have not been cited to cure the above-noted deficiencies of the applied combination of the cited portions of Yu, Kristjansson, and Dursun. Instead, Gunawardena and Lu have been cited to teach the features of the above-mentioned dependent claims. As such, the cited portions of Yu, Kristjansson, and Dursun in combination with either of the cited portions of Gunawardena or Lu, as applied by the Office Action, do not provide a prima facie case of obviousness for pending Claims 1 and 10 and claims that depend thereon.
For at least this reason, the § 103 rejections of Claims 3, 5-6, 12, and 14-15 should be overturned and the claims set to issue. Accordingly, Applicant respectfully requests the Office to withdraw the § 103 rejections of Claims 3, 5-6, 12, and 14-15 and allow issuance thereof.
(see Response filed 12/17/2025 [pages 12-15]).
Applicant’s arguments with respect to claim(s) 1-6, 8-15 and 17-19 have been considered but they are not persuasive. The reference Yu US20200370409A1 is relied only to teach a trained model to automatically determine a mud motor drilling mode for a mud motor in real time without manual monitoring and adjustment; actuating the mud motor … steering a drill bit …; the reference Dursun US20180025269A1 is relied only to teach accessing historical run information … determining drilling measurements … training at least one initial model with a machine learning method using the determined drilling measurements. The combination of the prior art would not change the principle of operation of the prior art invention being modified, since the trained agent as a neural network (Yu) can be modified to incorporate with Dursun’s trained model by utilizing historical data with a filtering process to train and generate predictive model with machine learning method, while still using neural network and sensor data for determining drilling mode. The modification of Yu in view of Dursun merely enhances the data used for training and improving the trained model, rather than replacing or altering Yu’s use of neural network and sensor data, and does not change the principle of operation of Yu. Therefore, the combination of Yu in view of Dursun teach or suggest the amended limitations of claims 1, 10 and 19, and the rejection of claims 1-6, 8-15 and 17-19 under 35 U.S.C. §103 is maintained.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites “utilizing the trained at least one initial model to automatically determine a mud motor drilling mode for a mud motor” should read as “utilizing the trained at least one initial model to automatically determine the mud motor drilling mode for a mud motor.”
Appropriate correction is required.
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 1-6, 8-15 and 17-19 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.
The term “in real time” (and “in real-time”) in claims 1, 9, 10, 18 and 19 is relative term which renders the claim indefinite. The term “in real time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For example, “in real time” could reasonably encompass a wide range of time intervals, including milliseconds, seconds, minutes, or longer operational delays, and may vary depending on system constraints or drilling conditions. The claim does not specify whether “real time” requires immediate response, near instantaneous processing, processing within a predefined threshold, or merely during ongoing drilling operations. Therefore, the term “in real time” renders the metes and bounds of the claims unclear, and the claims are indefinite.
Claim 19 recites “… automatically determine a mud motor drilling mode for a mud motor …,” which renders the claim indefinite because it is unclear if “a mud motor drilling mode” refers to the “determining a mud motor historical drilling mode” recited in line 1 of claim 19. For the purpose of substantive examination, the examiner presumes that “determining a mud motor historical drilling mode” is interpreted as “determining a mud motor drilling mode,” and “… automatically determine a mud motor drilling mode for a mud motor …” is interpreted as “… automatically determine the mud motor drilling mode for a mud motor …”.
The remaining claims are dependent upon one of the claims listed above and are rejected for the same reason.
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.
The claims 1-6, 8-15 and 17-19 are rejected under 35 USC § 101 because the claimed invention is
directed to judicial exception, an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-6 and 8-9 are directed to method and fall within the statutory category of process;
Yes, Claims 10-15 and 17-18 are directed to non-transitory computer-readable medium and fall within the statutory category of article of manufacture;
Yes, Claim 19 is directed to system and fall within the statutory category of machine.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 1: The limitations of “determining drilling measurements based on the historical run information,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind. For example a person is capable of observing and evaluating historical run information, mentally determining parameters associated with historical run information. The steps include observation, evaluation, judgment, and opinion processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). Examiner note: the limitation recites at a high level of generality and does not provide any detail of how the drilling measurements are determined, nor does it recite a specific computer component, technological mechanism, or algorithm that limits how the determination is performed. Therefore, the limitation does not include a constraint that would preclude performance in the human mind or with pen and paper, and is reasonably considered as a mental process. See MPEP 2106.4(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
In MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
As explained in MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
MPEP 2106.04(a)(2)(I)(A): A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
MPEP 2106.04(a)(2)(I)(C): “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitations of “training at least one initial model with a machine learning method using the determined drilling measurements…,” when given its with broadest reasonable interpretation (BRI) in light of specification, can be considered to recite mathematical concepts. For example, page.10, line 21-31 and page.11, line 1-15 “a machine learning method may be selected to train the initial model. For example, a deep neural network model with the size of a row vector L = (11, 12,..., 1n), where 11 is the number of neurons in layer 1, and l is the number of neurons in layer n, may be trained using a scaled conjugate gradient algorithm with cross entropy as the performance function … Additionally, dropout may be used to regularize and help reducing interdependent learning amongst the neurons” and Equations (1) – (3). The specification discloses the training step includes mathematic relationships, mathematical formulas or equations, mathematical calculations., which fall within the category of mathematical concepts. See MPEP 2106.4(a)(2)(I).
The elements of claims 10 and 19 are substantially the same as those of claim 1. Therefore, the elements of claims 10 and 19 are rejected due to the same reasons as outlined above for claim 1.
Therefore, claims 1, 10 and 19 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 10 and 19: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements: “A non-transitory computer-readable medium comprising instructions that are configured, when executed by a processor, to:” and “A system for determining a mud motor historical drilling mode, comprising: a controller comprising: a memory operable to store historical run information; and a processor operable to:,” which are mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the following additional elements: “accessing historical run information stored in a memory of a controller” and “access historical run information stored in a memory of a controller communicatively coupled to the processor,“ are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., accessing data stored in a memory of a controller), which does not integrate a judicial exception into practical application. See MPEP 2106.05(g).
Further, the following additional limitation “utilizing the trained at least one initial model to automatically determine a mud motor drilling mode for a mud motor in real time without manual monitoring and adjustment; actuating the mud motor or a rotary table operable to provide rotation to a conveyance based, at least in part, on the automatically determined mud motor drilling mode, wherein the mud motor is coupled to the conveyance; and steering a drill bit coupled to the conveyance based on the actuation of the mud motor or rotation of the rotary table” and “utilize the trained at least one initial model to automatically determine a mud motor drilling mode for a mud motor in real time without manual monitoring and adjustment, wherein the mud motor disposed in a bottom hole assembly on a conveyance communicatively coupled to the controller; and transmit an instruction to actuate the mud motor or a rotary table operable to provide rotation to the conveyance based, at least in part, on the automatically determined mud motor drilling mode, wherein a drill bit coupled to the conveyance is steered based on the actuation of the mud motor or rotation of the rotary table,” are merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The limitation is recited at a high level of generality and does not provide detail of how the trained model is utilized to automatically determine the mud motor drilling mode, nor how the actuation of the mud motor or rotary table is technically performed based on the determination, nor how the steering of the drill bit is achieved. Instead, the limitation broadly recites that the result of the abstract idea is used to cause determination, actuation, and steering, without specifying any particular control technique, algorithm, or technical implementation. Although the limitation recites automatically determination of mud motor drilling mode, actuation of drilling components and steering of a drill bit, these actions are functionally described as outcomes of applying the abstract idea (i.e., mental process and mathematical concepts), and are not tied to any specific improvement in the functioning of a computer, drilling device or control system. The limitation does not recite any particular machine configuration or specialized control technique, but instead relies on generic components operating in the ordinary capacity to apply the abstract idea. Therefore, this additional limitation merely applies the generic computer components with judicial exception, and does not integrate judicial exception into practical application. see MPEP 2106.05(f). Furthermore, as explained in MPEP2106.05(a)(I): “… the courts have indicated may not be sufficient to show an improvement in computer-functionality: Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).”
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 10 and 19 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 10 and 19: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea.
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); …
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016); iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014); v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
The additional limitations of “utilizing the trained at least one initial model to automatically determine a mud motor drilling mode …; actuating the mud motor or rotary table …; and steering a drill bit …” do not amount to significantly more than the judicial exception. They are recited at a high level of generality and merely apply the result of the abstract idea to a drilling environment. The claim does not recite any specific control technique, algorithm, or technological mechanism for how the determination is used to control actuation or steering, but instead broadly recites that those actions are performed based on the recited abstract idea. Therefore, the additional elements, when considered individually and in combination, merely apply the judicial exception using conventional computing components and do not provide significantly more than the judicial exception.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 10 and 19 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-6, 8-9, 11-15 and 17-18 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-6, 8-9, 11-15 and 17-18 are also rejected for incorporating the deficiency of their independent claims 1 and 10.
Claim 2 recites “The method of claim 1, further comprising processing the historical run information to remove ancillary data and determine statistical information associated with the determined drilling measurements.”
The limitation merely specifies historical run information is processed to remove ancillary data and determine data associated with data has been determined; therefore, it is merely an extension of a mental process (i.e., mentally determine statistical information and filtering historical run information). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The method of claim 1, further comprising: determining hyper-parameters of the at least one initial model; and re-training the at least one initial model using the determined hyper-parameters.”
The limitation merely specifies parameters such as number of layers, number of neurons in each layer, learning rate, or regularization parameter of initial model is determined and train the initial model based on the determined parameters; therefore, it is merely an extension of a mental process (i.e., determine parameter) and a mathematical concept (i.e., train the model by using mathematical equations). Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The method of claim 1, wherein the at least one initial model is selected from a group consisting of a neural network model, Random Forest, Decision Tree, K-nearest neighbors, Naive Bayes Classifier, and any combination thereof.”
The limitation merely defines initial model. It is merely generically links the judicial exception to a particular technological environment or field of use. Therefore, limiting an abstract idea to a type of machine learning model does not integrate the exception into a practical application. See MPEP § 2106.05(h). Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The method of claim 1, further comprising using a scaled conjugate gradient algorithm with cross entropy as a performance function for evaluating a performance of the at least one initial model, wherein a cost function is calculated as a sum of cross-entropy loss.”
The limitation merely specifies the use of a scaled conjugate gradient algorithm together with cross entropy as a performance function to evaluate the initial model, and a cost function be calculated as the sum of cross-entropy loss; therefore, it is merely a mathematical concept. Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The method of claim 5, wherein the cost function comprises a regularization term to prevent overfitting or an over-complicated model.”
The limitation merely further defines cost function comprises a regularization term as mathematical adjustment to a formular to prevent overfitting or an over-complicated model; therefore, it is merely a mathematical concept. Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 8 recites “The method of claim 1, wherein the automatically determined mud motor drilling mode is determined as one selected from a group consisting of rotating, sliding, sliding without pipe rocking, sliding with pipe rocking, a derivative thereof, and any combination thereof.”
The limitation further defines mud motor drilling mode is determined; therefore, it is merely adding the words "apply it" (or an equivalent) with the judicial exception, or 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). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The method of claim 1, further comprising selecting one of one or more trained initial models for utilization in real-time.”
The limitation specifies the trained initial models is used for selecting one trained model in real-time; therefore, it is merely adding the words "apply it" (or an equivalent) with the judicial exception, or 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). Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claims 11-15 and 17-18 recites substantially the same elements as claims 2-6 and 8-9, are rejected for the same reasons under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and
103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set
forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 8-11, 13 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yu
US20200370409A1 in view of Dursun US20180025269 A1.
Claim 1, Yu teaches A method for identifying a mud motor drilling mode (abstract, “…determining a drilling mode from a plurality of drilling modes using a trained neural network…”; [0441], “determining a drilling mode from a plurality of drilling modes using a trained neural network and at least a portion of the sensor data; … the plurality of drilling modes can include a rotary drilling mode and a sliding drilling mode.” [0198], “a mud motor can be capable of delivering a desired well curvature via operations that can include switching between rotating and sliding modes (e.g., rotate mode and slide mode).”), comprising:
training at least one initial model with a machine learning method ([0412], “… a train block 2940 for, using the reward, training the agent component to generate a trained agent component …” [0425], “a trained agent component can include a trained value-based network as a trained neural network.”)
utilizing the trained at least one initial model to automatically determine a mud motor drilling mode for a mud motor in real time without manual monitoring and adjustment ([0160], “Through use of a mud motor, a directional drilling operation can alternate between rotating and sliding modes of drilling.” [0198], “a mud motor can be capable of delivering a desired well curvature via operations that can include switching between rotating and sliding modes (e.g., rotate mode and slide mode).” [0415], “FIG. 30 shows an example of a method 3000 … a determination block 3020 for determining a drilling mode from a plurality of drilling modes using a trained neural network and at least a portion of the sensor data; and an issuance block 3030 for issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode.” [0425], “… a trained agent component can include a trained value-based network as a trained neural network …” [0452], “As an example, a controller can include an agent component that selects a drilling mode using sensor data. In such an example, the drilling mode can be selected from a plurality of drilling modes, which may include one or more of a sliding mode (e.g., sliding up, sliding down, etc.), a rotary mode, a survey interval, etc.” Examiner note: the reference teaches automated control framework in which a trained neural network (determination block 3020) automatically determines a drilling mode based on sensor data and an issuance block issues control instructions for drilling using the determined mode, without requiring user intervention ([0415]), and a controller including an agent component, where the agent is a trained neural network ([0425]) that selects the drilling mode ([0452]). A POSITA would understand that the agent selection and control instruction framework constitutes automated determination and execution of drilling modes to perform the determining step without manual monitoring and adjustment.);
actuating the mud motor or a rotary table operable to provide rotation to a conveyance based, at least in part, on the automatically determined mud motor drilling mode, wherein the mud motor is coupled to the conveyance (Fig.2., drillstring 225, rotary table 220; motor 260, drill bit 226; [0054], “… the drillstring 225 pass through an opening in the rotary table 220.” [0055], “The kelly 218 can be used to transmit rotary motion from the rotary table 220 via the kelly drive bushing 219 to the drillstring 225, while allowing the drillstring 225 to be lowered or raised during rotation.” [0056], “As to a top drive example, the top drive 240 can provide functions performed by a kelly and a rotary table. The top drive 240 can turn the drillstring 225 …”; [0058], “The mud can then flow via a passage (e.g., or passages) in the drillstring 225 (examiner note: i.e. conveyance) and out of ports located on the drill bit 226 ...”; [0158], “A mud motor can include a bend in a motor bearing housing that provides for steering a bit toward a desired target.” [0194], “… rotation from the surface rig (e.g., table or top drive) may be stopped such that circulation of mud (e.g., drilling fluid) acts to drive the mud motor to rotate the bit downhole. As mentioned, in some instances, there can be a combination of surface rotation and downhole rotation. In general, where surface rotation is not provided, the drill string is in a sliding mode as it slides downward as drilling ahead occurs via rotation of the bit via operation of the mud motor. Such an operation can be referred to as a sliding operation (e.g., sliding mode). Another mode can be for holding the borehole direction tangent where surface equipment rotates the drillstring such that the motor bend also rotates with drillstring. In such a mode, the BHA does not have a particular drill-ahead direction. Such an operation can be referred to as a rotating operation (e.g., a rotating mode or rotary mode).” [0441], “determining a drilling mode from a plurality of drilling modes using a trained neural network … issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode. In such an example, the plurality of drilling modes can include a rotary drilling mode and a sliding drilling mode” See also [0415], [0425] and [0452]); and
steering a drill bit coupled to the conveyance based on the actuation of the mud motor or rotation of the rotary table ([0158], “A mud motor can include a bend in a motor bearing housing that provides for steering a bit toward a desired target.” [0163], “When transitioning from the rotating mode to the sliding mode, … orienting a bit to drill, … to steer the bit as appropriate to keep the trajectory on course. [0194], “… the bend can be pointed to a desired orientation while rotation from the surface rig (e.g., table or top drive) may be stopped such that circulation of mud (e.g., drilling fluid) acts to drive the mud motor to rotate the bit downhole.” Examiner note: for the limitation of “actuating the mud motor .. steering a drilling bit …”, Yu teaches multiple alternative actuation mechanisms, including rotation from the surface rig via a rotary table or top drive, and actuation of a mud motor via circulation of drilling fluid, the mud motor can provide directional drilling capability, including a bend in the motor housing for steering the drill bit toward a desire target, and issuing a control instruction for drilling using the determined drill mode. A POSITA would understand that provides different way to achieve rotation of drillstring (i.e., conveyance) by actuating the mud motor or rotary table, and to steer a drill bit coupled to the conveyance based on the actuation of the mud motor or rotation of the rotary table based on the control instruction using the determined drilling mode).
However, Yu fails to teach accessing historical run information stored in a memory of a controller; determining drilling measurements based on the historical run information; training at least one initial model with a machine learning method using the determined drilling measurements, wherein the at least one initial model comprises one or more inputs selected from a group consisting of revolutions per minute, tool-face, torque, flowrate, weight on bit, rate of penetration, differential pressure, a derivative thereof, and any combination thereof.
Dursun teaches accessing historical run information stored in a memory of a controller ([0023] “… the drilling system 100 may comprise a control unit 160 … The control unit 160 may comprise an information handling system …” [0010], “…The information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory.” [0022], “… The output of the sensors may be collected at the surface and stored, for example, in a database or data warehouse to be retrieved later.” [0025], “… the dataset comprises dynamic data 250 and static data 250. The dynamic data 250 may comprise drilling parameters, … including, … WOB, rotary speed, drill bit RPM, hook load, surface torque and torque on bit, downhole mud flow rate, return mud flow rate, SPP, and ROP. [0027], “ … an information handling system may include software executable by a processor … including accessing or otherwise receiving raw data from a remote data storage facility through a data network, manipulating the raw data, generating one or more predictive models, …” [0026], “These datasets may be retrieved and segregated according to the types of drilling operations and formations from which they were produced …”);
determining drilling measurements based on the historical run information ([0029], “Step 302 may comprise pre-processing steps to eliminate noisy, corrupted, or missing data from the received data 301. For example, the pre-processing steps may include the application of one or more thresholds, data filters and noise reduction algorithms, to alter or remove specific data entries or entire data sets.” [0030], “Step 303 comprises a feature extraction step that may be used to reduce the dimensionality of the training data sets T1-Tn before they are used to generate predictive models.”);
training at least one initial model with a machine learning method using the determined drilling measurements, wherein the at least one initial model comprises one or more inputs selected from a group consisting of revolutions per minute, tool-face, torque, flowrate, weight on bit, rate of penetration, differential pressure, a derivative thereof, and any combination thereof ([0036], “Step 305 comprises a training step, in which at least one learning algorithm 305 a with associated parameters 305b may be trained with the training data sets T1-Tn to produce one or more context-specific predictive models M1-Mn. For instance, a learning algorithm may receive as an input training data set T1 and determine a relationship between the drilling parameters and operational conditions within training data set T1 and the ROP values within training data set T1 that result from the associated drilling parameters and operational conditions.” [0037], “… the learning algorithm 305a may comprise supervised and unsupervised learning algorithms and may include a decision tree, a Bayesian belief networks, a genetic algorithms, an artificial neural network, and/or a support vector machines. Each of the above learning algorithms may “learn” by generating and refining an internal model based on the training data set. This internal model may be the context-specific predictive model corresponding to the training data set.” [0029], “Each of the training data sets T1-Tn may be associated with one or more different static variables, identified either through the binarized variables in the received data 301, or through nominal values 350 received at the pre-processing step 302. For instance, one of the training data sets T1-Tn may comprise all of the pre-processed data entries from the received data 301 …” [0030], “… a combination of drilling parameters and operating conditions are used as an input to the model …” Examiner note: the reference teaches that the training data set (T1-Tn) are formed from pre-processed data entries (i.e., data subjected to filtering and preprocessing in Step 302), which correspond to the determined drilling measures, and the training step uses these training data sets as input; therefore, the training is performed using data derived from the determined drilling measurement. The reference further teaches the drilling parameters , including RPM, torque, flow rate, WOB, and ROP, are included in the training data sets and are used as input to the predictive model, and correspond to the group of inputs. Therefore, the reference teaches that the at least one model comprises one or more inputs selected from the recited group).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu to incorporate the teachings of Dursun, and apply accessing stored historical drilling data, preprocessing the data to generate processed drilling measurements, and training predictive models using the processed data as input in order to enable model training based on historical drilling measurements and improve the data used for training the model. In this cases, Yu teaches training a neural network model, and using the trained model to determine drilling mode. Dursun teaches accessing historical drilling datasets, preprocessing the datasets including filtering and noise reduction to generate processed drilling parameters, segregating the processed data into training data sets, and training machine learning models using the training data sets that include drilling parameters as inputs. The combination of teachings would predictably provide the benefit of improving the accuracy and robustness of the trained model by incorporating training based on processed historical drilling datasets, thereby enabling more reliable and data driven drilling operation decisions.
Claim 2, Yu fails to teach processing the historical run information to remove ancillary data and determine statistical information associated with the determined drilling measurements.
Dursun teaches processing the historical run information to remove ancillary data ([0043], “Step 402 comprises a filtering step in which data entries may be removed based on a qualitative assessment of the data entry (note: i.e., remove ancillary data). For example, certain raw data sets include ROP values that were measured during the drilling operation, but calculated after the fact, and data entries or entries raw data sets containing these ROP values may be removed from the received data.”) and determine statistical information associated with the determined drilling measurements ([0032], “… principal component analysis may comprise a statistical algorithm in which a set of observations of possibly correlated variables, e.g., the dynamic variables and the ROP for a drilling operation, are converted using an orthogonal transformation into a set of values of linearly uncorrelated variables referred to as principal components … the variables with high variance with the ROP may be determined and selected … The processor may then determine a linear regression model that identifies the covariance structures” ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu to incorporate the teachings of Dursun, and apply preprocessing including filtering to remove noisy or invalid data entries and determining statistical information from drilling measurements using statistical algorithms to identify relationships and covariance structures among drilling parameters and operational conditions in order to improve the quality and reliability of input data and extract statistical relationships for use in generating a predictive model, and the combination of teachings would predictably provide the benefit of improving predictive accuracy and robustness of the predictive model by reducing noise and leveraging statistical features derived from the drilling data.
Claim 4, Yu further teaches The method of claim 1, wherein the at least one initial model is selected from a group consisting of a neural network model ([0425], “a trained agent component can include a trained value-based network as a trained neural network.”), Random Forest, Decision Tree, K-nearest neighbors, Naive Bayes Classifier, and any combination thereof.
Claim 8, Yu further teaches The method of claim 1,wherein the automatically determined mud motor drilling mode is determined as one selected from a group consisting of rotating, sliding, sliding without pipe rocking, sliding with pipe rocking, a derivative thereof, and any combination thereof ([0452], “As an example, a controller can include an agent component that selects a drilling mode … the drilling mode can be selected from a plurality of drilling modes, which may include one or more of a sliding mode (e.g., sliding up, sliding down, etc.), a rotary mode, a survey interval, etc.”.
Claim 9, Yu further teaches The method of claim 1 further comprising selecting one of one or more trained initial models for utilization in real-time ([0005], “… determining a drilling mode from a plurality of drilling modes using a trained neural network … issuing a control instruction for drilling an additional portion of the borehole using the determined drilling mode (note: i.e., utilization in real time).”).
The elements of claims 10-11, 13 and 17-19 are substantially the same as those of claims 1-2, 4 and 8-9. Therefore, the elements of claims 10-11, 13 and 17-19 are rejected due to the same reasons as outlined above for claims 1-2, 4 and 8-9. Further, the additional limitations of claims 10 and 19, “A system for determining a mud motor historical drilling mode, comprising: … and a processor operable to:” and “A non-transitory computer-readable medium comprising instructions that are configured, when executed by a processor, to:” (See Yu; [0455]).
Claim(s) 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yu and Dursun
as applied to claims 1 and 10 above, and further in view of Gunawardena US20200110943A1.
Claim 3, Yu teaches The method of claim 1, further comprising: (see Yu; i.e., trained neural network); and (see Yu; i.e., trained neural network).
However, Yu and Dursun fail to teach determining hyper-parameters of model; and re-training the model using the determined hyper-parameters.
Gunawardena teaches determining hyper-parameters of model; and re-training the model using the determined hyper-parameters. ([0051], “a trained neural network and related machine assisted technologies for each discipline and sub-discipline. The system and method further include using a specific training dataset for each discipline, refining the neural network by tuning hyper-parameters, and retraining the neural network based on new data.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu and Dursun to incorporate the teachings of Gunawardena, and apply method include using a specific training dataset for each discipline, refining the neural network by tuning hyper-parameters, and retraining the neural network based on new data in order to minimize the error rate to obtain a desired accuracy [0105]. In this case, the hyper-parameters would optimize the neural network model performance with the new data by re-training, therefore, it can significantly impact its accuracy on different data distributions.
The elements of claim 12 is substantially the same as those of claim 3. Therefore, the elements of claim 12 is rejected due to the same reasons as outlined above for claim 3.
Claim(s) 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yu
and Dursun as applied to claims 1 and 10 above, and further in view of Lu US20200234471A1 and Kanevsky US20160180214A1.
Claim 5, Yu and Dursun fail to teach, but Lu teaches The method of claim 1, further comprising using a scaled conjugate gradient algorithm with cross entropy as a performance function for evaluating a performance of the at least one initial model, wherein a cost function is calculated as a sum of cross-entropy loss ([0088], “an error is calculated (e.g., using a loss function or a cost function) to represent a measure of the difference (e.g., a distance measure) between the RTE-method generated data (i.e., reference scatter profile, or ground truth) and the output data of the 3D CNN as applied in a current iteration of the 3D CNN. The error can be calculated using any known cost function or distance measure between the image data, including those cost functions described above. Further, in certain implementations the error/loss function can be calculated using one or more of a hinge loss and a cross-entropy loss.” [0085], “the optimization method used in training the 3D CNN can use a form of gradient descent incorporating backpropagation to compute the actual gradients. This is done by taking the derivative of the cost function with respect to the network parameters and then changing those parameters in a gradient-related direction. The backpropagation training algorithm can be: a steepest descent method (e.g., with variable learning rate, with variable learning rate and momentum, and resilient backpropagation), a quasi-Newton method (e.g., Broyden-Fletcher-Goldfarb-Shanno, one step secant, and Levenberg-Marquardt), or a conjugate gradient method (e.g., Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart, and scaled conjugate gradient).”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu and Dursun to incorporate the teachings of Lu, and apply optimization method used in training the 3D CNN can use a form of gradient descent incorporating backpropagation to compute the actual gradients and the error/loss function can be calculated using one or more of a hinge loss and a cross-entropy loss in order to minimizes the cost criterion (i.e., the error value calculated using the cost function) [0084] and provides an optimization method for training the 3D CNN [0088].
However, Yu and Dursun and Lu fail to teach a cost function is calculated as a sum of cross-entropy loss.
Kanevsky teaches a cost function is calculated as a sum of cross-entropy loss ([0005], “… calculating the gradient for the neural network by applying a sharp discrepancy output layer objective function to the output layer may comprise calculating the gradient of a cross-entropy function.” [0026,] “… The sharp discrepancy objective function is obtained from a typical objective function, for example log-likelihood or cross entropy, comprising a sum of terms for … data in a training dataset.” Equation (7).)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu and Dursun and Lu to incorporate the teachings of Kanevsky, and apply a cross-entropy objective function as a summation over training data in order to improve the training objective of the neural network by aggregating perdition errors across multiple training samples for optimization, thereby improving model training accuracy and convergence performance.
Claim 6, Yu and Dursun fail to teach, but Lu teaches The method of claim 5, wherein the cost function comprises a regularization term to prevent overfitting or an over-complicated model ([0089], “the loss function can be combined with a regularization approach to avoid overfitting the network to the particular instances represented in the training data.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yu and Dursun to incorporate the teachings of Lu, and apply the combining a loss function with a regularization term to prevent overfitting of the model (see, e.g., [0089]) in order to reduce overfitting during training and improve generalization of the predictive model to new data, and the combination would predicably provide the benefit of improving model robustness and accuracy by preventing the model from fitting noise or specific instance in the training data.
The elements of claims 14-15 are substantially the same as those of claims 5-6. Therefore, the elements of claims 14-15 are rejected due to the same reasons as outlined above for claims 5-6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen US20210230998A1 teaches method can include receiving data for a borehole trajectory … and a bottom hole assembly that includes a mud motor … generating a sequence for operation of the mud motor using a model of at least the bit, where the sequence includes a sliding mode and a rotary mode for drilling the borehole in the formation … ([0004]).
Any inquiry concerning this communication or earlier communications from the examiner should be
directed to YI HAO whose telephone number is (571)270-1303. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at (571)272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YI . HAO/
Examiner, Art Unit 2187
/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 March 19, 2026