DETAILED ACTION
Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are currently pending in this application.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 03/01/2024 and 07/31/2024 have been received. The submission is in compliance with the provisions of 37 CFR 1.97 and 37 CFR 1.98. Accordingly, the information disclosure statement has being considered by the examiner.
Drawings
4. The drawings submitted on 03/01/2024 are in compliance with 37 CFR § 1.81 and 37 CFR § 1.83 and have been accepted by the examiner.
Claim Rejections - 35 USC § 101 Non-Statutory
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Specifically, representative Claim 1 recites:
A method, comprising:
receiving a first set of data from one or more downhole sensors disposed in one or more wellbores of one or more wells extending through one or more subterranean formations, wherein the first set of data relates to operating parameters of one or more fracturing operations being performed on the one or more subterranean formations;
training machine learning algorithms using the first set of data as a first set of inputs to the machine learning algorithms;
receiving a second set of data from the one or more downhole sensors disposed in the one or more wellbores of the one or more wells extending through the one or more subterranean formations, wherein the second set of data relates to the operating parameters of the one or more fracturing operations being performed on the one or more subterranean formations; and
identifying one or more locations of one or more fractures through the one or more subterranean formations using the second set of data as a second set of inputs to the machine learning algorithms.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations comprise the abstract ideas of Claims 8 and 14.
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Likewise, claim 8 a non-transitory computer readable medium claim and claim 14 is a system claim.
Under Step 2A, prong 1, claim 1 is found to include at least one judicial exception, that being a mathematical concept and/or mental process. This can be seen in the claim limitation of “training machine learning algorithms using the first set of data as a first set of inputs to the machine learning algorithms; identifying one or more locations of one or more fractures through the one or more subterranean formations using the second set of data as a second set of inputs to the machine learning algorithms.”, which is the judicial exception of a mental process and/or a mathematical concept because it is merely a data evaluation including calculations, and/or judgements capable of being performed mentally.
Similar limitations comprise the abstract ideas of Claims 8 and 14.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “receiving a first set of data from one or more downhole sensors disposed in one or more wellbores of one or more wells extending through one or more subterranean formations, wherein the first set of data relates to operating parameters of one or more fracturing operations being performed on the one or more subterranean formations; receiving a second set of data from the one or more downhole sensors disposed in the one or more wellbores of the one or more wells extending through the one or more subterranean formations, wherein the second set of data relates to the operating parameters of the one or more fracturing operations being performed on the one or more subterranean formations” (claims 1, 8, and 14) which are merely data gathering steps recited at a high level of generality and therefore merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. The claim also recites “machine learning algorithms” (claims 1, 8, and 14) however the “machine learning” is recited at a high level of generality in applicant’s specification, describing a variety of different types of “machine learning” models that may be used, and merely amounts to the use of computer technology as a tool to apply the abstract idea (see MPEP 2106.05(f)) and/or the use of “machine learning” to perform the predictions, that are otherwise abstract, is merely an attempt at limiting the abstract to a particular field of use (See MPEP 2106.05(h)).
The generic data gathering, processing, and output steps, and other elements, are recited so generically (no details whatsoever are provided) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the identified fracture location data.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity (claims 1, 8, and 15). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claims 8 and 14, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-7, 9-13, and 15-20, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements therefore these claims are found ineligible for the reasons described for independent claims 1, 8, and 14.
See Supreme court decision in Alice Corporation Pty. Ltd. V. CLS Bank International, et al.
Claim Rejections - 35 USC § 102
7. 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 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.
8. 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.
9. Claims 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by AlTammar et al. US Pat Pub # 2022/0243568
With regards to claims 1, 8 and 14 AlTammar et al. US Pat Pub # 2022/0243568 teaches a method, system and computer readable instructions comprising:
receiving a first set of data from one or more downhole sensors disposed in one or more wellbores of one or more wells extending through one or more subterranean formations, wherein the first set of data relates to operating parameters of one or more fracturing operations being performed on the one or more subterranean formations; (Paragraph 0009)
training machine learning algorithms using the first set of data as a first set of inputs to the machine learning algorithms; (hydraulic stimulation manager; Paragraph 0009 and 0033)
receiving a second set of data from the one or more downhole sensors disposed in the one or more wellbores of the one or more wells extending through the one or more subterranean formations, wherein the second set of data relates to the operating parameters of the one or more fracturing operations being performed on the one or more subterranean formations; (Paragraph 0009)and
identifying one or more locations of one or more fractures through the one or more subterranean formations using the second set of data as a second set of inputs to the machine learning algorithms. (Paragraph 0045)
With regards to claims 2, 9 and 15, AlTammar et al. US Pat Pub # 2022/0243568 teaches predicting one or more fracture heights of the one or more fractures based at least in part on the identified one or more locations of the one or more fractures. (Paragraph 0040)
With regards to claims 3, 10 and 16, AlTammar et al. US Pat Pub # 2022/0243568 teaches receiving a third set of data from the one or more downhole sensors disposed in the one or more wellbores of the one or more wells extending through the one or more subterranean formations, wherein the third set of data relates to the operating parameters of the one or more fracturing operations being performed on the one or more subterranean formations; and predicting an operating parameter of the one or more fracturing operations being performed on the one or more subterranean formations using the third set of data and the identified one or more locations of the one or more fractures as a third set of inputs to the machine learning algorithms. (real-time data is continuous and maybe interpreted as an additional set of data; Paragraph 0009)
With regards to claims 4, 11 and 17, AlTammar et al. US Pat Pub # 2022/0243568 teaches training the machine learning algorithms comprises transforming the first set of data into particular features using feature engineering. (DAS inversion may identify various hydraulic stimulation features such as tubing expansion, fluid-to-fluid interfaces, an adjacent hydraulic fracture, presence of a porous reservoir, and/or an annular compartment; Paragraph 0045)
With regards to claims 7 and 20, AlTammar et al. US Pat Pub # 2022/0243568 teaches automatically adjusting at least one of the operating parameters of the one or more fracturing operations based at least in part on the identification of the one or more locations of the one or more fractures through the one or more subterranean formations. (Paragraph 0033)
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. Claim(s) 5-6, 12-13 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over AlTammar et al. US Pat Pub # 2022/0243568 in view of Kong et al. NPL: A novel fracture prediction model using machine learning in a community based cohort.
With regards to claims 5-6, 12-13 and 18-19, AlTammar et al. US Pat Pub # 2022/0243568 teaches the claimed invention but does not appear to teach dividing the particular features into a training data set, a validation data set, and a test data set and a k-fold cross-validation to the machine learning algorithms.
Kong et al. teaches dividing the particular features into a training data set, a validation data set, and a test data set and a k-fold cross-validation to the machine learning algorithms. (Page 3 of 9, performance evaluation paragraph)
It would’ve been obvious to one of ordinary skill in the art at the time of the invention to modify the AlTammar et al. invention to include the training, validation, k-fold validation and test data taught by Kong et al. to arrive at the claimed invention as it would improve fracture prediction. (Page 1 of 9, introduction, column 2)
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Balan et al. US Pat # 11,401,803 teaches determining fracture surface area in a well.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADITYA S BHAT whose telephone number is (571)272-2270. The examiner can normally be reached on Monday-Friday 8 am-6pm.
15. 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.
16. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
17. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ADITYA S BHAT/Primary Examiner, Art Unit 2857 June 20, 2026
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