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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Step 1 of the USPTO’s eligibility analysis entails considering whether the claimed subject matter
falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101:
Process, machine, manufacture, or composition of matter.
Claims 1, 8, and 14 are directed to, a system (machine or manufacture),
One or more non-transitory machine-readable media (manufacture), and a method (process) respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step
2A. Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance is a two-prong inquiry. In
Prong One, examiners evaluate whether the claim recites a judicial exception.
Claims 1, 11, and 16 recite abstract limitations, including “compare an input image of a resistivity inversion against an archive of historical data; generate, based on the comparison, a real-time well path suggestion; and output the real-time well path suggestion to a user interface”, “input an image of a resistivity inversion …. compare …. the input image of the resistivity inversion against an archive of historical data ….generate, based on the comparison, a real-time well path suggestion …. output the real-time well path suggestion to a user interface” and “ inputting an image of a resistivity inversion …. Comparing …. the input image of the resistivity inversion against an archive of historical data; generating, based on the comparison, a real-time well path suggestion; and outputting the real-time well path suggestion to a user interface.”
These limitations, as drafted, are a process that, under its broadest reasonable interpretation,
cover performance of the limitations in the mind, or by a human using pen and paper, and therefore
recite mental processes. The mere recitation of generic computing elements does not take the claim
out of the mental process grouping. Furthermore, as discussed in MPEP §2106.04(a)(2)(III)(A), claims
directed toward a mental process include claims to “‘collecting information, analyzing it, and displaying
certain results of the collection and analysis,’ where the data analysis steps are recited at a high level of
generality such that they could practically be performed in the human mind, Electric Power Group v.
Alstom, S.A., 830 F.3d 1350, 1353-54, 199 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)”. Thus, the claims
recite an abstract idea.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019
Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim
requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites
additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites the elements of “a downhole tool including a resistivity sensor” “a drilling assembly coupled to a drill bit” and “a learning machine”. Claim 8 recites the additional elements of “one or more non-transitory machine-readable media”, “a processor” and “a learning machine”. Claim 14 also recites the additional elements of “a learning machine”.
The functions of the sensor, drilling assembly, learning machine and processor and supporting system components (i.e. non-transitory machine-readable media) are additional elements
whose functions are recited at a high level of generality and are merely invoked as tools to perform the
abstract idea.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a
practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application, then the
claim is directed to the recited judicial exception, and requires further analysis under Step 2B to
determine whether they provide an inventive concept (i.e., whether the additional elements amount to
significantly more than the exception itself).
As discussed above, the additional elements of the learning machine, processor and supporting system components (i.e. non-transitory machine-readable media) amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification notes that the processing is a generic computer (Fig. 4; para 41, 42). Furthermore, the drilling system is recited at a high level of generality, and, as applied, is a system used in their ordinary capacity to perform drilling operations, and therefore amount to “apply it.”
Thus, even when viewed as an ordered combination, nothing in the claims add significantly
more (i.e. an inventive concept) to the abstract idea.
Regarding claims 2, 5, 6, 9, 12, 13, 15, 18 and 19 The limitations of “output a resistivity inversion interpretation of the input image to the user interface; and output one or more annotations of the resistivity inversion interpretation to the user interface“, “output …. a decision to a user as whether to implement the real-time well path suggestion”, “update the archive of historical data based on a result of the decision” are further directed to a method encompassing a mental process, as described above in regards to claim 1.
Regarding claims 3, 10 and 17, the recitation of the specific variables and data
limitations are insufficient as “merely selecting information, by content or source, for collection,
analysis, and display does nothing significant to differentiate a process from ordinary mental processes,
whose implicit exclusion from §101 undergirds the information-based category of abstract ideas
(See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claims 1
and 11, this recitation does not provide a practical application of the abstract idea, and is not
significantly more.
Regarding claims 4, 7, 11, 16 and 20, the additional limitation “the learning machine is trained using the archive of historical data”, “alter a well path of the wellbore based, at least in part, on the real-time well path suggestion” amounts to little more than instruction to apply the exception. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boualleg et al. (US 2022/0170359, hereafter Boualleg) in view of the Chinese document to Li et al. (CN 113156538, hereafter Li).
With regards to claims 1, 8 and 14, Boualleg discloses a system configured for real-time well path optimization (para 120) of a wellbore being drilled into a subsurface formation, the system comprising: a downhole tool including a resistivity sensor (para 152); a drilling assembly coupled to a drill bit (226), the drilling assembly including the downhole tool; and a learning machine configured to, compare measured data against an archive of historical data (para 319); generate, based on the comparison, a real-time well path suggestion; and output the real-time well path suggestion to a user interface (para 233. 256, ). Boualleg discloses one or more non-transitory machine-readable media including instructions executable by a processor to cause the processor to execute the method of the system in addition to the learning machine (para 5). Boualleg shows all the limitation of the present invention except, it fails to explicitly disclose comparing an input image of a resistivity inversion. Li discloses using the image of a resistivity inversion in the training of a model and predictive neural network (clm. 1; abstract). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the image of resistivity inversion as disclosed in Li with the system and method taught in Boualleg with a reasonable expectation of success. This would be done because resistivity inversion is a common method of imaging a subsurface formation for predictive drilling.
With regards to claims 2, 9 and 15, Boualleg discloses the learning machine is further configured to: output an interpretation of the input data to the user interface; and output one or more annotations of the data to the user interface (para 269, 277). It fails to disclose the data is a resistivity inversion image. Li discloses using the image of a resistivity inversion in the training of a model and predictive neural network (clm. 1; abstract). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the image of resistivity inversion as disclosed in Li with the system and method taught in Boualleg with a reasonable expectation of success. This would be done because resistivity inversion is a common method of imaging a subsurface formation for predictive drilling.
With regards to claim 3, 10 and 17, Boualleg discloses the real-time well path suggestion includes text readable by a user (para 280).
With regards to claim 4, 11 and 16, Boualleg discloses the learning machine is trained using the archive of historical data (para 244, 319, 369).
With regards to claim 5, 12 and 18, Boualleg discloses the learning machine is further configured to: output, via the user interface, a decision to a user as whether to implement the real-time well path suggestion (para 280).
With regards to claim 6, 13 and 19, Boualleg discloses the learning machine is further configured to: update the archive of historical data based on a result of the decision (para 319).
With regards to claim 7 and 20 Boualleg discloses alter a well path of the wellbore based, at least in part, on the real-time well path suggestion (para 208).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art cited all show similar features to those of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P STEPHENSON whose telephone number is (571)272-7035. The examiner can normally be reached M-F 10am-6pm.
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/DANIEL P STEPHENSON/ Primary Examiner, Art Unit 3676