DETAILED ACTION
Information Disclosure Statement
The information disclosure statements submitted on 08/24/2023, 02/06/2024 and 08/08/2024 have been considered by the Examiner and made of record in the application file.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Torrione (US 2019/0385298 A1) in view of Zheng (US 2017/0167853 A1).
Regarding claim 1, Torrione discloses a method for conducting subterranean operations comprising:
engaging a tubular with a pipe handler; (paragraph 11: The “Pipe Tally System” system, PTS, consists of several parts. In one preferred embodiment, one or more video cameras positioned so as to be able to see the drilling pipe 106 as it is attached to, or removed from the drill-string.)
moving the tubular with the pipe handler to a new location; (paragraph 24: If the aggregate motion of the pipe is “in well” (down), the pipe is considered added to the drill-string, and this is marked in the pipe tally. If the aggregate motion of the pipe is “out of well” (up), the pipe is considered removed from the drill-string, and this is marked in the pipe tally.)
disengaging from the tubular at the new location; (paragraph 24: in-well/out-of-well)
determining, via a machine learning module of the rig controller, (paragraph 22: SVM) a deviation parameter of the tubular by determining a deviation from the estimated location based on processing collected images from one or more imaging sensors that contain the tubular. (figure 4; paragraphs 21-24: Adaptive background estimation and subtraction models perform foreground/background segmentation. Since the background should be relatively stable over the time-frames during which each object is in-frame, adaptive background updating can be halted when a specific object is detected. Shape and size constraints can be applied to reduce false-alarms due to other non-pipe related changes in the scene. Pipes used in drilling are long and narrow, and the diameters of the pipes under consideration are tightly constrained. As a result, object aspect ratio and object size can be used to reduce non-pipe false alarms. Changes in the background that are approximately the correct size and shape are then sent to a confirmation step, which takes into account features extracted from the detected regions. A support vector machine trained to recognize pipe, roughneck, and/or elevator regions is then applied to the features extracted from each foreground region. The detections may be input into a finite state machine.)
Torrione fails to specifically disclose determining, via a rig controller, an estimated location of the tubular based on the new location at which the pipe handler disengaged from the tubular.
In related art, Zheng discloses determining, via a rig controller, an estimated location of the tubular based on the new location at which the pipe handler disengaged from the tubular. (paragraph 59: detector detects markers on the rig structure to determine elevation of the travelling block. System uses cameras and optical detectors to determine block elevation, compute distance, and calculate tubular string length)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Zheng into the teachings of Torrione to effectively improve drilling efficiency and equipment reliability.
Regarding claim 15, Torrione discloses a method for conducting a subterranean operation comprising:
retrieving a known length of a tubular from a unique data record in a first database, (paragraph 14: pipe tally, information about the pipe lengths and diameters) wherein the unique data record is associated with a unique record identification (ID) of the tubular; (paragraph 14: information about pipes may be amalgamated into an automatically generated well-state report which may include a pipe tally (information about the pipe lengths and diameters, time the pipe was added to or removed from the drill-string, or any other pipe specific information)
detecting, via an imaging sensor, a location of a first detectable feature of a tubular string; (paragraph 27: The camera may be capable of gathering visual data regarding detecting and/or localizing com ponents of a drilling rig which may include pipes, drill pipes, roughnecks, elevators, drill-string components)
connecting the tubular to the tubular string; (paragraph 11: attached to, or removed from the drill-string)
lowering the tubular string along with the tubular a pre-determined distance into a wellbore; (paragraph 24: If the aggregate motion of the pipe is “in well” (down), the pipe is considered added to the drill-string, and this is marked in the pipe tally. If the aggregate motion of the pipe is “out of well” (up), the pipe is considered removed from the drill-string, and this is marked in the pipe tally.)
detecting, via a machine learning module, a deviation of the second detectable feature from the estimated location; and (figure 4; paragraphs 21-24: Adaptive background estimation and subtraction models perform foreground/background segmentation. Since the background should be relatively stable over the time-frames during which each object is in-frame, adaptive background updating can be halted when a specific object is detected. Shape and size constraints can be applied to reduce false-alarms due to other non-pipe related changes in the scene. Pipes used in drilling are long and narrow, and the diameters of the pipes under consideration are tightly constrained. As a result, object aspect ratio and object size can be used to reduce non-pipe false alarms. Changes in the background that are approximately the correct size and shape are then sent to a confirmation step, which takes into account features extracted from the detected regions. A support vector machine trained to recognize pipe, roughneck, and/or elevator regions is then applied to the features extracted from each foreground region. The detections may be input into a finite state machine.)
storing the deviation as a deviation parameter in a second database associated with the unique record ID of the tubular. (paragraph 14: well-state report and pipe tally stores pipe-specific data; abstract and paragraph 9: logging system)
Torrione fails to specifically disclose determining, via a rig controller, an estimated location of a second detectable feature of the tubular by adding the known length to the location of the first detectable feature and subtracting the pre-determined distance.
In related art, Zheng discloses determining, via a rig controller, an estimated location of a second detectable feature of the tubular by adding the known length to the location of the first detectable feature and subtracting the pre-determined distance. (Zheng: paragraph 59: detector detects markers on the rig structure to determine elevation of the travelling block. System uses cameras and optical detectors to determine block elevation, compute distance, and calculate tubular string length; paragraphs 59-63: cameras determine block elevation and compute distance; paragraphs 63-67: controller calculates tubular position and length from elevation change)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Zheng into the teachings of Torrione to effectively improve drilling efficiency and equipment reliability.
For the rejection of claim 18, see the rejections of claim 1 and claim 15 above.
Regarding claim 2, Torrione, as modified by Zheng, discloses the claimed invention wherein determining, via the machine learning module, a confidence score for the deviation parameter, wherein the confidence score represents a confidence in an accuracy of the deviation parameter. (paragraphs 21-22 and 26)
Regarding claim 3, Torrione, as modified by Zheng, discloses the claimed invention wherein determining, via the rig controller, the estimated location of the tubular while the tubular is being moved by the pipe handler. (Zheng: abstract)
Regarding claim 4, Torrione, as modified by Zheng, discloses the claimed invention wherein determining a plurality of deviation parameters as the tubular is being moved based on a plurality of estimated locations on a path along which the tubular is being moved; and determining, via the machine learning module, a plurality of deviation parameters of the tubular by determining a deviation from each of the plurality of estimated locations based on processing collected images from one or more imaging sensors that contain the tubular. (paragraph 25; Zheng: paragraphs 63 and 71)
Regarding claim 5, Torrione, as modified by Zheng, discloses the claimed invention wherein determining, via the machine learning module, a confidence score for each of the plurality of deviation parameters, wherein each of the confidence scores represent a confidence in an accuracy of the corresponding one of the plurality of deviation parameters. (paragraphs 22-26)
Regarding claim 6, Torrione, as modified by Zheng, discloses the claimed invention wherein each of the plurality of deviation parameters is substantially equal to the other ones of the plurality of deviation parameters, thereby indicating a high confidence score for each of the plurality of deviation parameters. (paragraphs 22-26)
Regarding claim 7, Torrione, as modified by Zheng, discloses the claimed invention wherein storing the deviation parameter in a unique entry in an error database, wherein the unique entry is associated with a unique record identification (ID) of the tubular; and storing the estimated location in a tubular database in a unique data record entry that is associated with the unique record ID of the tubular. (paragraphs 9 and 14)
Regarding claim 8, Torrione, as modified by Zheng, discloses the claimed invention wherein retrieving, via the rig controller and based on the unique record ID, the deviation parameter from the error database and the estimated location from the tubular database; and controlling the pipe handler to engage the tubular based on the estimated location and the deviation parameter. (paragraph 27)
Regarding claim 9, Torrione, as modified by Zheng, discloses the claimed invention wherein the new location is a resulting location when the tubular is connected to a tubular string at well center and is lowered into a wellbore, wherein the pipe handler is a top drive. (Zheng: paragraphs 56-59)
Regarding claim 10, Torrione, as modified by Zheng, discloses the claimed invention wherein the tubular string is at a known location prior to connection of the tubular to the tubular string, wherein after connection of the tubular to the tubular string, lowering the tubular string, via the top drive, a pre-determined distance; and determining the estimated location of the tubular by adding a known length of the tubular to the known location of the tubular string and subtracting the pre-determined distance. (Zheng: paragraphs 63-67)
Regarding claim 11, Torrione, as modified by Zheng, discloses the claimed invention wherein the deviation parameter indicates slippage of the tubular string after the top drive hands off the tubular string to a retention feature at well center on a rig floor. (Zheng: paragraph 75)
Regarding claim 12, Torrione, as modified by Zheng, discloses the claimed invention wherein the new location is an estimated location where the tubular is disengaged from the pipe handler in a vertical storage area. (paragraph 18)
Regarding claim 13, Torrione, as modified by Zheng, discloses the claimed invention wherein the pipe handler communicates the estimated location to the rig controller, the method further comprising: determining, via the machine learning module processing the collected images, the deviation from the estimated location of the tubular in the vertical storage area; storing the deviation in an error database as a deviation parameter; and controlling, via the rig controller, the pipe handler to engage the tubular based on the deviation parameter. (paragraph 27)
Regarding claim 14, Torrione, as modified by Zheng, discloses the claimed invention wherein the deviation parameter comprises a deviation in a linearity of the tubular from the estimated location. (paragraph 25)
Regarding claim 16, Torrione, as modified by Zheng, discloses the claimed invention wherein storing the estimated location of the second detectable feature in the first database; retrieving the estimated location of the second detectable feature from the first database; retrieving the deviation parameter from the second database; and controlling, via the rig controller, a pipe handler to engage the tubular based on the estimated location of the second detectable feature and the deviation parameter. (paragraphs 14 and 17; Zheng: paragraph 53)
Regarding claim 17, Torrione, as modified by Zheng, discloses the claimed invention wherein adding the tubular to a pipe tally and adding the known length to a pipe tally length when a pipe handler engages the tubular to add the tubular to the tubular string. (paragraph 24)
Regarding claim 19, Torrione, as modified by Zheng, discloses the claimed invention wherein storing the estimated location of the second detectable feature in the first database; retrieving the estimated location of the second detectable feature from the first database; retrieving the deviation parameter from the second database; and controlling, via the rig controller, a pipe handler to engage the second tubular based on the estimated location of the second detectable feature and the deviation parameter. (paragraphs 14 and 27)
Regarding claim 20, Torrione, as modified by Zheng, discloses the claimed invention wherein subtracting the first tubular from a pipe tally and subtracting the known length from a pipe tally length when a pipe handler removes the first tubular from the tubular string. (paragraph 24)
Conclusion
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/BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665