DETAILED ACTION
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.
Claim(s) 1-25 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1, 13, 25 recites
store a machine learning algorithm;
receive one or more data records representing previous drilling operations,
the one or more data records including data representing one or more downlinks to a downhole tool;
determine, for each data record, whether each respective downlink of the one or more downlinks successfully communicated a command to the downhole tool;
identify one or more variables to a machine learning algorithm, at least one of the one or more variables corresponding to a drilling condition;
train the machine learning algorithm using at least a first subset of the received data and data indicating downlink success,
the machine learning algorithm training by identifying correlations between the one or more variables and the downlink success;
and
execute the machine learning algorithm,
where
the machine learning algorithm is configured to:
receive drilling condition data;
predict downlink success probability for each of a plurality of downlink parameter combinations based on the drilling condition data;
and
recommend one of the plurality of downlink parameter combinations for communication with the downhole tool based on the predicted downlink success probability
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim(s) 1, 13, Claim(s) 2-12, 14-24 recite(s)
the machine learning algorithm is selected from the group consisting of logical regression, random forest, or gradient boosting.
the drilling condition data comprises
geographic area for drilling,
a wellbore diameter,
mud rheology,
mud density,
type of bottom hole assembly,
rate of penetration,
flow amplitude,
rotations per minute amplitude,
bit period,
shock and vibrations,
true vertical depth of a point in a drilling path,
measured depth of the point in the drilling path,
and
stick/slip vibration.
preprocessing the received data.
preprocessing the data further comprises
removing fantom downlinks, encoding categorical values, and scaling numerical features.
preprocessing the data further comprises
appending the received data to include data indicating whether each downlink was successful or unsuccessful.
training the machine learning algorithm further comprises
assigning weights to the one or more variables and tuning hyperparameters using grid search or random search.
determines, for each data record, whether each respective downlink of the one or more downlinks successfully communicated the command to the downhole tool by reviewing subsequent downlinks to determine whether the subsequent downlinks indicate corrective actions taken due to an unsuccessful downlink.
the machine learning algorithm
predicting, for each record in a second subset of the received data, a probability of successful downlinking for each of the plurality of downlink parameter combinations based on the drilling condition data included in the second subset of received data.
calculating or estimating, for each respective downlink of the one or more downlinks,
a rate of penetration,
a measured depth,
a true vertical depth,
and
a stick/slip value,
calculates or estimates the rate of penetration by calculating a time duration of a power-up session and dividing a predetermined drilling distance for each power-up session by the time duration of the power-up session,
calculates or estimates the measured depth by multiplying the rate of penetration by the time duration of the powerup session,
calculates or estimates the true vertical depth by multiplying the measured depth for the powerup session and a cosine value of an inclination angle of the downhole tool,
and
calculates or estimates the stick/slip value by calculating the difference between a maximum turbine rotations per minute value and a minimum turbine rotations per minute value, dividing the difference by a mean turbine rotations per minute value to generate a quotient, and multiplying the quotient by 100 to generate an estimated stick/slip value.
drilling condition data is generated by one or more sensors included in the downhole tool during a drilling operation.
drilling condition data comprises a planned well drill path,
the planned well drill path including a plurality of points having a measured depth coordinate and a true vertical depth coordinate
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. A non-transitory machine-readable medium comprising instructions, which, when executed by one or more processors, cause the one or more processors to perform the following operations: A method comprising: A system comprising: a storage device configured to; a processor in communication with the storage device and configured to:);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. the one or more data records including data representing one or more downlinks to a downhole tool; drilling condition data is generated by one or more sensors included in the downhole tool during a drilling operation).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append 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., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
Examiner’s Note - 35 USC § 101
Examiner advises applicant that performing a controlling action with a downlink communication component to improve the success rate of a downlink communication based on the claimed recommendation would add a practical application, therefore curing the above rejection(s).
Allowable Subject Matter (over prior art)
The following is a statement of reasons for the indication of allowable subject matter over prior art:
Examiner’s closest prior art to the claimed subject matter:
WANG ET AL. (US 20250215789 A1) teaches ‘systems and techniques for improving the transmission of data up a wellbore such that wellbore operations may be performed or controlled more efficiently. When a wellbore is drilled or otherwise used, sensors may be deployed in the wellbore to measure parameters that may include borehole pressure, annular pressure, weight-on-bit, torque-on-bit, temperature, or other borehole logging data, for example. Data sensed by the sensors or interpreted from received sensor data may be sent up-hole to electronics that may reside at the surface of the Earth. Since wellbore (borehole) logging data usually have moderate levels of continuity or consistency between respective samples, differences between data samples acquired over a period of time may be expected to have smaller amplitudes/deviations than the data samples themselves. As such, data compression can be achieved by transmitting data that identifies a difference between an initial data sample and a subsequent reference data sample’;
BOUALLEG ET AL. (US 20220170359 A1) teaches ‘A method can include acquiring drilling performance data for a downhole tool; modeling drilling performance of the downhole tool to generate results; training a machine learning model using the drilling performance data and the results to generate a trained machine learning model; and predicting behavior of the downhole tool using the trained machine learning model’.
None of the cited prior art alone or in combination provides motivation to explicitly teach:
A non-transitory machine-readable medium comprising instructions, which, when executed by one or more processors, cause the one or more processors to perform the following operations:
A method comprising:
A system comprising:
a storage device
configured to
store a machine learning algorithm;
and
a processor in communication with the storage device
and configured to:
receive one or more data records representing previous drilling operations,
the one or more data records including data representing one or more downlinks to a downhole tool;
determine, for each data record, whether each respective downlink of the one or more downlinks successfully communicated a command to the downhole tool;
identify one or more variables to a machine learning algorithm, at least one of the one or more variables corresponding to a drilling condition;
train the machine learning algorithm using at least a first subset of the received data and data indicating downlink success,
the machine learning algorithm training by identifying correlations between the one or more variables and the downlink success;
and
execute the machine learning algorithm,
where
the machine learning algorithm is configured to:
receive drilling condition data;
predict downlink success probability for each of a plurality of downlink parameter combinations based on the drilling condition data;
and
recommend one of the plurality of downlink parameter combinations for communication with the downhole tool based on the predicted downlink success probability
of claim(s) 1, 13, 25 (including dependent claim(s)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW SCHECHTER can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
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