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 .
Examiner notes the entry of the following papers:
Amended claims filed 12/8/2025.
Applicant’s arguments/remarks made in amendment filed 12/8/2025.
Claims 1-2, 4-6, and 16-20 are amended. Claims 3, and 7-15 are canceled. Claims 1-2, 4-6, and 16-20 are presented for examination.
Response to Arguments
Applicant makes arguments. Each is addressed.
Applicant argues “The amendments to independent claims 1 and 16 clearly require more than just an abstract idea or mental process. Therefore, withdrawal of the rejections under 35 U.S.C. § 101 is respectfully requested.” (Remarks, page 7, paragraph 1, line 3.) Examiner agrees. The rejections under 35 U.S.C. § 101 are withdrawn.
Applicant’s arguments that the prior art of record does not teach the amended claims are moot in view of new areas of the cited prior art found that teach the amended claims.
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.
Claims 1-2, and 4-5 are rejected under 35 U.S.C. § 103 as being unpatentable over Machocki, K., et al(A New Non-Intrusive Condition Monitoring System Designed to Improve Reliability of RCDs, herein Machocki), and Baumgartner, T. (Maximizing the Value of Information from High-Frequency Downhole Dynamics Data, herein Baumgartner).
Regarding claim 1,
Machocki teaches a system for use with a subterranean well (Machocki, abstract, line 2 “The Rotating Control Device (RCD) is a crucial part of the MPD equipment but is prone to failure. Therefore, a new condition monitoring system was developed to improve the reliability of RCDs and eliminate their catastrophic failures during MPD jobs.” In other words, MPD (managed pressure drilling) is for use with a subterranean well, and condition monitoring system is a system for use with a subterranean well.), the system comprising:
a rotating control device (Machocki, abstract, line 2 “The Rotating Control Device (RCD) is a crucial part of the MPD equipment but is prone to failure.” In other words, rotating control device is a rotating control device.);
a lifting apparatus configured to raise and lower a tubular string through the rotating control device (Machocki, Fig. 1, and, page 15, column 2, paragraph 4, line 1 “Rotational Speed Sensor – monitoring the rel-ative speed of rotation of the Top Drive, the drill pipe, and the RCD bearing assembly.” Examiner notes the specification of the instant application recites “The rotational speed of the seals 44, 46 can be measured by the sensor 62, and the rotational speed of the tubular string 12 can be measured by a sensor 74 incorporated into a lifting apparatus 76 (such as a top drive) used to raise and lower the tubular string (see FIG. 1 ), or incorporated into a rotary table.” Therefore, examiner is interpreting that a top drive is a lifting apparatus.
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In other words, top drive is lifting apparatus, and from Fig. 1, drill string is a tubular string through the rotating control device.) ;
[a load sensor] associated with the lifting apparatus (Machocki, page 15, column 2, paragraph 4, line 1 “Rotational Speed Sensor – monitoring the rel-ative speed of rotation of the Top Drive, the drill pipe, and the RCD bearing assembly.” See above mapping. In other words, Top Drive is lifting apparatus.); and
[an artificial intelligence device] which monitors data output by the [load sensor] and determines a condition of at least one seal of the rotating control device based on the [load sensor] data (Machocki, page 18, column 1, paragraph 1, line 3 “All sensors included within this system are non-intrusive sensors. They allow moni-toring temperatures, vibration, acoustic emissions, pipe-RCD misalignment, and RCD-pipe relative rotations. The readings are presented to the operator in an easy-to-un-derstand format to alert the operator about any substantial trend deviations and prevent catastrophic failure.” And, page 15, column 1, paragraph 1, line 7 “The following are the most common failures:
1. RCD sealing elements leak as a result of defective seals.
Seizures in RCD bearings, leading to seal deterio-ration and leakage.” And, page 15, column 2, paragraph 4, line 3 “The primary function of this sensor is to monitor for any rotational slip between these components. For example, a difference in rotation-al speed could suggest a problem with bearings or seals.” In other words, monitoring temperatures, vibration, acoustic emissions, etc. is monitors data output from sensors, suggest a problem with...seals is determine a condition of at least one seal, and presented to the operator in an easy-to-understand format to alert the operator about any substantial trend deviations is configured to determine a condition of at least one seal of the rotating control device.).
Thus far, Machocki does not explicitly teach a load sensor (associated with a lifting device) or an artificial intelligence device.
Baumgartner teaches a load sensor (Baumgartner, page 38, paragraph 2, line 2 “Numerous sensors continuously produce a variety of data from the rig site. Some sensors are concerned with the monitoring of tool conditions (e.g. sensors in the top drive), while others measure operational parameters. The most basic measurements are torque, tension or hookload, mud pressure, flow rates and rotational speed.” And, page 114, paragraph 2, line 1 “Weight on bit (WOB) is not directly measured at surface, instead it is inferred from
a hook load sensor located at the deadline anchor.” In other words, hookload sensor is a load sensor, top drive is lifting apparatus, and sensors in the top drive is sensors associated with a lifting apparatus.)
Baumgartner teaches an artificial intelligence device (Baumgartner, page vi, paragraph 2, line 1 “A novel kinematic model was developed that fully accounts for sensor position and measurement design. It supports the hypothesis that lateral vibrations cause high-frequency fluctuations of tangential accelerations. Hence, against currently prevailing scientific opinion, “high-frequency torsional oscillations” (HFTO) are not actually a torsional phenomenon, but the consequence of a lateral vibration. A downhole measurement tool under off-center rotation captures particular high-frequency data patterns that can be considered a sensor artifact. If ignored, these artifacts can impact the calculations of RPM and other derived measurements from downhole data.” And, page 130, paragraph 1, line 1 “The described pattern recognition approach is using supervised machine learning techniques that require labeled data for training an algorithm, as opposed to unsupervised learning techniques where the algorithm provides a classification automatically.” Examiner notes the specification of the instant application recites “The artificial intelligence 136 may comprise any type or combination of artificial intelligence devices and processes, implemented in hardware and/or software.” (Specification, page 15, paragraph 4, line 1.) Examiner notes that one of ordinary skill in the art would know that artificial intelligence logic is written in software and that in order to execute, the software must be loaded onto a device with a processor and memory. Based on this, and the description recited in the specification, examiner is interpreting supervised machine learning techniques as teaching an artificial intelligence device. In other words, supervised machine learning techniques is an artificial intelligence device.)
Both Machocki and Baumgartner are directed to drilling subterranean wells, among other things. Machocki teaches a system for use with a subterranean well, the system comprising a rotating control device, and at least one sensor configured to measure a parameter indicative of a condition of at least one seal of the rotating control device but does not explicitly teach a load sensor or using an artificial intelligence device which monitors data output. Baumgartner teaches a load sensor and using an artificial intelligence device which monitors data output.
In view of the teaching of Machocki it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Baumgartner into Machocki. This would result in a system for use with a subterranean well, the system comprising a rotating control device, at least one sensor configured to measure a parameter indicative of a condition of at least one seal of the rotating control device, a load sensor, and an artificial intelligence device which monitors data output.
One of ordinary skill in the art would be motivated to do this in order to minimize dysfunctions and improve downhole dynamics data collection. (Baumgartner, page vi, paragraph 2, line 1 “A novel kinematic model was developed that fully accounts for sensor position and measurement design. It supports the hypothesis that lateral vibrations cause high-frequency fluctuations of tangential accelerations. Hence, against currently prevailing scientific opinion, “high-frequency torsional oscillations” (HFTO) are not actually a torsional phenomenon, but the consequence of a lateral vibration. A downhole measurement tool under off-center rotation captures particular high-frequency data patterns that can be considered a sensor artifact. If ignored, these artifacts can impact the calculations of RPM and other derived measurements from downhole data. An extensive set of downhole data was analyzed to improve downhole dynamics data collection schemes for detecting drilling dysfunctions.)
Regarding claim 2,
The combination of Machocki and Baumgartner teaches the system of claim 1, in which
a change in load occurs when a radially enlarged tool joint passes through the at least one seal of the rotating control device (Baumgartner, page 38, paragraph 2, line 2 “Numerous sensors continuously produce a variety of data from the rig site. Some sensors are concerned with the monitoring of tool conditions (e.g. sensors in the top drive), while others measure operational parameters. The most basic measurements are torque, tension or hookload, mud pressure, flow rates and rotational speed.” Examiner notes that the limitation merely identifies a potential cause for a change in sensor data, not a method or solution for mitigation of the change. There is no mention of what, if anything, is done about the change in load outside of sensing it. Therefore, examiner is interpreting the limitation to mean detecting a change in load. In other words, measuring hookload is determining a change in load.)
Regarding claim 4,
The combination of Machocki and Baumgartner teaches the system of claim 1, in which
the artificial intelligence device looks for a change in a pattern of the load sensor data (Machocki, page 18, column 1, paragraph 1, line 3 “All sensors included within this system are non-intrusive sensors. They allow moni-toring temperatures, vibration, acoustic emissions, pipe-RCD misalignment, and RCD-pipe relative rotations. The readings are presented to the operator in an easy-to-un-derstand format to alert the operator about any substantial trend deviations and prevent catastrophic failure.” In other words, substantial trend deviations is a change in pattern. Machocki teaches presenting sensor data to alert the operator about trend deviations. From the mapping of claim 1, Baumgartner teaches load sensor data and artificial intelligence device. The motivation to combine load sensor data and artificial intelligence device in claim 1 applies here.).
Regarding claim 5,
The combination of Machocki and Baumgartner teaches the system of claim 4, in which the artificial intelligence device is configured to
provide an alert if the change the pattern of the load sensor data is outside of an acceptable range (Machocki, page 18, column 1, paragraph 1, line 3 “All sensors included within this system are non-intrusive sensors. They allow moni-toring temperatures, vibration, acoustic emissions, pipe-RCD misalignment, and RCD-pipe relative rotations. The readings are presented to the operator in an easy-to-un-derstand format to alert the operator about any substantial trend deviations and prevent catastrophic failure.” In other words, alert the operator about any substantial trend deviations is provide the alert if a change in a pattern is outside of the load sensor acceptable range. Machocki teaches presenting sensor data to the operator. From mapping of claims 1 and 4, Baumgartner teaches load sensor data. The motivation to combine load sensor data in claims 1 and 4 applies here.).
Claim 6 is rejected under 35 U.S.C. § 103 as being unpatentable over Machocki, Baumgartner, and Xu, Y., et al (Predict the Service Life of Rotary Lip Seals by Machine Learning Methods, herein Xu).
Regarding claim 6,
The combination of Machocki and Baumgartner teaches the system of claim 1, in which
Thus far, the combination of Machocki and Baumgartner does not explicitly teach the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the load sensor data.
Xu teaches the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the load sensor data (Xu, Figure 2., and, page 4, paragraph 1, line 3 “As for scenario 2, historical failure data is available in which supervised learning can be applied to predict the RUL of the seals.” And, abstract, line 7 “ The application of machine learning methods using actual testing data in order to estimate the useful life of the seals has been presented.”
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In other words, machine learning is artificial intelligence, historical failure data is data, and predict the RUL (remaining useful life) is evaluate whether the rotating control device should be taken out of service for remediation. Examiner notes rotating control device and load sensor data is previously mapped to Baumgartner in claim 1.)
Both Xu and the combination of Machocki and Baumgartner are directed to rotating equipment, among other things. The combination of Machocki and Baumgartner teaches a system for use with a subterranean well, the system comprising a rotating control device, a lifting apparatus configured to raise and lower a tubular string through the rotating control device, a load sensor associated with the lifting apparatus, and an artificial intelligence device which monitors data output by the load sensor and determines a condition of at least one seal of the rotating control device based on the load sensor data; but does not explicitly teach the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the load sensor data. Xu teaches the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the load sensor data.
In view of the teaching of Machocki and Baumgartner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Xu in the combination of Machocki and Baumgartner. This would result in a system for use with a subterranean well, the system comprising a rotating control device, a lifting apparatus configured to raise and lower a tubular string through the rotating control device, a load sensor associated with the lifting apparatus, an artificial intelligence device which monitors data output by the load sensor and determines a condition of at least one seal of the rotating control device based on the load sensor data, and the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the load sensor data.
One of ordinary skill in the art would be motivated to do this in order to aid manufacturers in improving maintenance procedures for high value engineering products. (Xu, abstract, line 1 “This paper aims to use machine learning methods to predict the service life of rotary lip seals to aid manufacturers and users improving the current maintenance procedures. Seals are widely used in most engineering applications. The knowledge of condition of seals throughout their working life is important due to the fact that they are often used on high value engineering products. As the current material properties of the seal and the working environment various, it is difficult to predict useful life of the rotary lip seal. In this paper, the factors relating to life of rotary lip seals are investigated and discussed.”)
Claims 16 - 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Machocki, Baumgartner, and Fossli, et al (US 2019/0145198 A1, System and Methods for Controlled Mud Cap Drilling, herein Fossli).
Regarding claim 16,
The combination of Machocki and Baumgartner teaches a system for use with a subterranean well, the system comprising:
a rotating control device (Machocki, see mapping of claim 1, office action, page 7.) ;
[a liquid level sensor] positioned in or on the rotating control device (Machocki, see mapping of claim 1, office action, page 7. And, page 15, column 2, paragraph 2, line 1 “Various non-intrusive sensors are installed directly on the RCD and related MPD drilling machinery.” In other words, sensors are installed directly on the RCD is positioned in or on the rotating control device.); and
an artificial intelligence device which monitors data output by the [liquid level sensor] and determines a condition of a seal of the rotating control device based on the [liquid level sensor] data (Machocki and Baumgartner, see mapping of claim 1, office action, page 7.)
Thus far, the combination of Machocki and Baumgartner does not explicitly teach a liquid level sensor.
Fossli teaches a liquid level sensor (Fossli, paragraph [0064, line 4 “By doing this and using the drilling unit conventional trip tank 31 closed circulation system, drilling mud
can be circulated from the trip tank 31, by the trip tank pump 30 into the RCD housing 45 thereby providing lubrication for the riser slip joint 11 and to monitor the effectiveness of
the RCD 18. Any leak in the RCD 18 may be monitored by measuring or observing the liquid level in the trip tank 31.” In other words, monitor by measuring or observing the liquid level is sensing the liquid level which requires a liquid level sensor.)
Both Fossli and the combination of Machocki and Baumgarten are directed to subterranean drilling, among other things. The combination of Machocki and Baumgarten teaches a system for use with a subterranean well, the system comprising a rotating control device, a lifting apparatus configured to raise and lower a tubular string through the rotating control device, a load sensor associated with the lifting apparatus, and an artificial intelligence device which monitors data output by the load sensor and determines a condition of at least one seal of the rotating control device based on the load sensor data; but does not explicitly teach a liquid level sensor. Fossli teaches a liquid level sensor.
In view of the teaching of the combination of Machocki and Baumgartner, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Fossli into the combination of Machocki and Baumgartner. This would result a system for use with a subterranean well, the system comprising a rotating control device, a lifting apparatus configured to raise and lower a tubular string through the rotating control device, a load sensor associated with the lifting apparatus, and an artificial intelligence device which monitors data output by the load sensor and determines a condition of at least one seal of the rotating control device based on the load sensor data and using a liquid level sensor.
One of ordinary skill in the art would be motivated to do this because marine drilling in deeper water is challenging and conventional drilling methods are insufficient requiring a plurality of sensors. (Fossli, paragraph [0004], line 1 “The present disclosure relates to systems, methods and arrangements for drilling subsea wells, while being able to manage and regulate the annular pressure profile in the well bore when there are no returns up the annulus of the well between the drill pipe and casing and/or open-hole section of the well. Marine drilling in deeper water, through depleted sub-bottom reservoir formations or into severely (naturally) fractured basement, fractured carbonate formations which often are karstified (containing karsts or caves), is a challenge and is impracticable to be performed with conventional drilling methods.”)
Regarding claim 17,
The combination of Machocki, Baumgartner, and Fossli teaches the system of claim 16, in which the
liquid level sensor detects leakage past the seal of the rotating control device(Fossli, paragraph [0064, line 4 “By doing this and using the drilling unit conventional trip tank 31 closed circulation system, drilling mud can be circulated from the trip tank 31, by the trip tank pump 30 into the RCD housing 45 thereby providing lubrication for the riser slip joint 11 and to monitor the effectiveness of the RCD 18. Any leak in the RCD 18 may be monitored by measuring or observing the liquid level in the trip tank 31.” In other words, monitor by measuring or observing the liquid level is sensing the liquid level which requires a liquid level sensor, and any leak in the RCD may be monitored is liquid level sensor detects leakage past the seal of the rotating control device.).
Regarding Claim 18
The combination of Machocki, Baumgartner, and Fossli teaches the system of claim 17, in which
an output of the liquid level sensor corresponds to a volume of liquid that has leaked past the seal (Fossli, See above mapping, and paragraph [0055], line 1 “Further, because the fluid level in the riser 1 is actively monitored by the control system, an accurate reading of mud losses and total volumes in the active mud tank.” In other words, fluid level is liquid level, from prior mapping, liquid level sensor is liquid level sensor, and accurate reading…of losses and total volumes is output corresponds to a volume of liquid that has leaked.)
Regarding claim 19,
The combination of Machocki, Baumgartner, and Fossli teaches the system of claim 18, in which
the artificial intelligence device is configured to provide an alert if the volume is outside of an acceptable range (Machocki, page 18, column 1, paragraph 1, line 3 “All sensors included within this system are non-intrusive sensors. They allow moni-toring temperatures, vibration, acoustic emissions, pipe-RCD misalignment, and RCD-pipe relative rotations. The readings are presented to the operator in an easy-to-un-derstand format to alert the operator about any substantial trend deviations and prevent catastrophic failure.” In other words, alert the operator about any substantial trend deviations is provide the alert if a change in a pattern is outside of the load sensor acceptable range. Machocki teaches presenting out of range sensor data to the operator. From mapping of claim 1, Baumgartner teaches artificial intelligence device, and from mapping of claim 18, Fossli teaches a volume of liquid.).
Claim 20 is rejected under 35 U.S.C. § 103 as being unpatentable over Machocki, Baumgartner, Fossli, and Xu.
Regarding claim 20,
The combination of Machocki, Baumgartner, and Fossli teaches the system of claim 16, in which
Thus far, the combination of Machocki, Baumgartner, and Fossli does not explicitly teach the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the liquid level sensor data.
Xu teaches the artificial intelligence device evaluates whether the rotating control device should be taken out of service for remediation based on the liquid level sensor data (Xu, Figure 2., and, page 4, paragraph 1, line 3 “As for scenario 2, historical failure data is available in which supervised learning can be applied to predict the RUL of the seals.” And, abstract, line 7 “ The application of machine learning methods using actual testing data in order to estimate the useful life of the seals has been presented.”
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In other words, supervised learning is artificial intelligence, historical failure data is data, and predict the RUL (remaining useful life) is evaluate whether the rotating control device should be taken out of service for remediation. Examiner notes liquid level sensor is previously mapped to Fossli in claim 16, from which claim 20 depends.)
One of ordinary skill in the art would be motivated to combine Xu into the combination of Machocki, Baumgartner and Fossli at least for the reasons used to combine Xu into the combination of Machocki, and Baumgartner described in claim 6.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.I.R./Examiner, Art Unit 2124
/MIRANDA M HUANG/ Supervisory Patent Examiner, Art Unit 2124