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 § 102
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 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.
Claim(s) 1, 2, 9-11, 13, 14 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Boualleg et al. (WO2020191360A1).
With respect to claim 13, Boualleg et al. teaches in Fig. 7 a system (791) comprising one or more computers (792) and one or more storage devices (794) on which are stored instructions that are operable (796), when executed by the one or more computers (792), to cause the one or more computers (792) to perform operations comprising: obtaining sensor data (s710 and s720) from at least one sensor attached to a surface of a drill bit (as Boualleg et al. teaches in in [00163]; a MWD module 256 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device. The MWD and/or LWD modules may be configured to obtain formation information); providing (s4110; Fig. 41) the sensor data from the at least one sensor (i.e. one of the sensors taught in [00163]) to one or more machine learning models (s4118), wherein the one or more machine learning models are trained using a library of (i) sensor signatures (s4118; time-series data) and (ii) geological formations ([00118]; [i]n such an approach, machine learning (ML) to train a machine learning (ML) model can be related to the formation that causes the drillstring to under-perform. For example, under-performance can be added to the drillstring (e.g., past data/model) plus location information (e.g. GR minimum) to adjust one or more steering targets downhole (e.g., if a rate of penetration (ROP) estimate/downlinked/measured is present) to output one or more drilling parameters (for a closed-loop steering controller; [00119]); and adjusting one or more parameters of an operation performed by a drill controlling the drill bit based on the one or more drilling parameters output by the one or more machine learning models (as the output in the machine learning output, disturbances can be accounted for and adjusted, for example steering; [00119] [00122] [00159]).
With respect to claim 1, during operation, the rejected structure of claim 13 performs the recited steps of claim 1.
With respect to claim 20, Boualleg et al. teaches in Fig. 7 one or more computer storage media (794) encoded with instructions (796) that, when executed by one or more computers (792), cause the one or more computers (792) to perform the recited operational steps during the operation of the rejected structure of claim 13.
With respect to claims 2 and 14, Boualleg et al. teaches in Fig. 4 the system (791) wherein adjusting the one or more parameters of the operation performed by the drill comprises: adjusting parameters that control one or more of: drill steering (i.e. a trajectory based on outputted results; [00161] [00174]).
Note: during operation, the rejected structure of claim 14 performs the recited steps of claim 2.
With respect to claim 9, Boualleg et al. teaches in Fig. 4 the method wherein adjusting the one or more parameters of the operation performed by the drill (the drill taught in [0056]) comprises: obtaining (via 344) the output from the one or more machine learning models (as read in [00118]); and adjusting, using one or more updated parameters (related to the drill bit) included in the output from the one or more machine learning models (as Boualleg et al. teaches updating existing data using the output from the models), the one or more parameters of the operation performed by the drill ([00124]; [a]s an example, during drilling, a method can include utilizing measured depth to trigger one or more drilling parameter changes).
With respect to claim 10, Boualleg et al. teaches in Fig. 4 the method wherein obtaining the sensor data from the at least one sensor attached to the surface of the drill bit comprises (as rejected above): obtaining, during operation of the drill [0056], the sensor data from the at least one sensor attached to the surface of the drill bit (as Boualleg et al. teaches using the real-time data from drilling operations; [00125] [00223]).
With respect to claim 11, Boualleg et al. teaches in Fig. 4 the method wherein the one or more machine learning models are trained to predict one or more geological formations (as Boualleg et al. teaches the ML framework using predictive models of a reservoir; [00194]).
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) 3-4 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boualleg et al. (WO2020191360A1) in view of Zhan et al. (2021/0032936).
With respect to claims 3 and 15, Boualleg et al. teaches all that is claimed in the above rejection of claims 1 and 13 but remains silent regarding obtaining the sensor data from the at least one sensor attached to the surface of the drill bit comprises: obtaining the sensor data from (i) an acoustic sensor and (ii) a pressure sensor.
Zhan et al. teaches a similar system having at least one sensor (302) attached to a surface of a drill bit (300) comprises: obtaining the sensor data from (i) an acoustic sensor and (ii) a pressure sensor (as read in the Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the drill bit taught in Boualleg et al. to include the plurality of acoustic and pressure sensors, as taught in Zhan et al., because Zhan et al. teaches such a modification allows for in real-time alterations based on the received data from the sensors coupled to the drill bit during formation of a wellbore, thereby improving the overall system of Boualleg et al.
With respect to claims 4 and 16, Boualleg et al. teaches all that is claimed in the above rejection of claims 1 and 13 but remains silent regarding wherein obtaining the sensor data from the at least one sensor attached to the surface of the drill bit comprises: obtaining the sensor data from an accelerometer.
Zhan et al. teaches a similar system having at least one sensor (302) attached to a surface of a drill bit (300) comprises: obtaining the sensor data from an accelerometer (as read in the Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the drill bit taught in Boualleg et al. to include the plurality of acoustic and pressure sensors, as taught in Zhan et al., because Zhan et al. teaches such a modification allows for in real-time alterations based on the received data from the sensors coupled to the drill bit during formation of a wellbore, thereby improving the overall system of Boualleg et al.
Claim(s) 5-8 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boualleg et al. (WO2020191360A1) in view of Yang et al. (2021/0389492).
With respect to claims 5 and 17, Boualleg et al. teaches all that is claimed in the above rejection of claims 1 and 13, but remains silent regarding wherein providing the sensor data from the at least one sensor to the one or more machine learning models comprises: generating a transformed version of the sensor data; and providing the transformed version of the sensor data to the one or more machine learning models.
Yang et al. teaches a similar system that includes generating a transformed version of sensor data (as Yang et al. teaches FFT transferring the acoustic signals into the frequency domain; [0031]; and providing the transformed version of the sensor data to models (as seen in s900, Fig. 9).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the time-series data collected in Boualleg et al into the frequency domain, as taught in Yang et al., because Yang et al. teaches such transformations allow for de-noising background noise and thereby improving the overall system and accuracy of Boualleg et al.
With respect to claims 6 and 18, Boualleg et al. as modified teaches wherein generating the transformed version of the sensor data comprises: performing a fast Fourier transform (FFT) on the sensor data from the at least one sensor (as taught in Yang et al.; [0031]).
With respect to claims 7 and 19, Boualleg et al. as modified teaches wherein generating the transformed version of the sensor data (as modified by Yang et al.) comprises: extracting attributes from the sensor data (s903 of Yang et al.); and generating, using the extracted attributes (at s903), the transformed version of the sensor data (for input into the trained ML models of Boualleg et al.).
With respect to claim 8, Boualleg et al. as modified teaches wherein the attributes include amplitude (s903 of Yang et al.).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boualleg et al. (WO2020191360A1) in view of Holtz (WO2016/043723A1).
With respect to claim 12, Boualleg et al. teaches all that is claimed in the above rejection of claim 1, but remains silent regarding generating the library of sensor signatures and geological formations as an electronic database using records from known geological formations.
Holtz teaches generating a library (66) of sensor signatures (page 5, lines 2-9) and geological formations (Fig. 2B, which depicts the signatures relative to formations, i.e. rock, rock properties) as an electronic database using records from known geological formations (as the sound signatures and their relative geological features are in a database for use a model 102).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modified by Boualleg et al. to utilize the library of data, as taught by Holtz, because Holtz teaches such a modification aids in improving the development of borehole trajectories, thereby improving the planning method of Boualleg et al..
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Haugen et al. (2015/0177403) which teaches using acoustic sensors for monitoring subsea equipment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853
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