DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. This office action is in response to application number 18/885,389 filed on 09/13/2024,
and the amendments and arguments filed on 02/18/2026.
Claims 1, 3, 4, 6, 7, 8, 15, 16, 17, and 20 have been amended.
Claims 22-31 have been added.
Claims 2, 5, 9-14, 18-19, and 21 have been cancelled.
Claims 1, 3, 4, 6, 7, 8, 15, 16, 17, 20, and 22-31 are currently pending and have been examined.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 02/18/2026 has been received
and considered.
Response to Amendment
4. Applicant' s amendments to the Claims have overcome each and every rejection
previously set forth in the Non-Final Office Action mailed 11/20/2025.
Applicant’s arguments, see page 7-9 filed 02/18/2026, with respect to the rejections(s)
of claim(s) 1, 7, and 10 under 35 USC 102 and of claim(s) 2-6, 8, 9, and 11-21 under 35 USC 103 have been fully considered and are persuasive. Therefore, each and every rejection has been withdrawn. However, upon further consideration, a new grounds for rejection as necessitated by amendment is made under 35 USC 103 over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) further in view of Zhu (CN 110159869 A) further in view of Abdelkader (US 20220001548 A1) further in view of Leomy (WO 2017167982 A1) further in view of Hu (CN 110260095 A) further in view of Fekrmandi (US 20210148503 A1) and further in view of Vallapuzha (US 8170715 B1).
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.
The factual inquiries 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.
5. Claim(s) 1, 3, 4, 6, 7, 8, 16, 22-25, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20200111222 A1) to Asmari et al. (hereinafter Asmari) in view of Lee (KR 102042440 B1) further in view of (US 20060290779 A1) to Reverte et al. (hereinafter Reverte).
Regarding claim 1, Asmari discloses A robot sized and shaped for reception in a pipe, the robot comprising: a chassis configured for movement of the robot within the pipe; (Asmari Paragraph 0055: “FIG. 11 provides a schematic diagram 146 illustrating the integration of the data from the in-pipe mapping and the above-ground mapping. “) (Asmari Paragraph 0046: “FIG. 8 shows an inside wall 60 of a pipeline 62 in which a robotic system 64 is traveling.”)
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[…] a plurality of sensors including: an inertial measurement unit (IMU); one or more lasers; one or more optical detectors that receive reflections of light emitted by the one or more lasers from the pipe; and an encoder; (Asmari Paragraph 0009: “Embodiments described herein may include a robotic system equipped with one or more cameras—potentially capable of stereo vison—along with software algorithms to create 3D point clouds, color, texture, or some combination of these. In addition to or in place of the cameras, LiDAR may be employed by embodiments described herein.”) (Asmari Paragraph 0048: “The encoders 80 are used to measure the distance of travel for the system when it is in the pipe, and the IMU 82 may provide such information as angular rate and orientation of the system, all of which provides useful data for mapping the location of the robotic system inside the pipe.”) (Asmari Paragraph 0050: “The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”) a sensor fusion system operable to determine, based on one or more machine learning models, a position of the robot within the pipe based on readings from: the IMU; the one or more optical detectors; and the encoder; (Asmari Paragraph 0049: “That portion of the system identified as software in FIG. 9 performs a number of steps based on information it receives from the hardware. For example, at step 84 various features are identified using machine learning. As described above, this may include starting with an initial database of features such as pipe connections, access points, etc. which provides the mechanism for identifying the features as the system travels through the pipe. Then the database is augmented with additional information as new features are imaged and identified, which improves the accuracy for future mapping. The cameras 76 provide both low-frequency, high-resolution images 86 and high-frequency, low-resolution images 88. As described above, the GPS 78 provides launch location coordinates 90, and the wheel encoders 80 provide linear localization 92. The IMU 82 provides information related to the orientation of the system, which in this embodiment is a three-degree-of-freedom (DOF) orientation 94.”) (Asmari Paragraph 0050: “When the features are identified using machine learning at step 84, measurements can then be taken at step 96 based on these identified features. For example, the distance between the feature and the mobile system can be measured for each captured feature. The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”)
Asmari does not disclose […] a tool supported by the chassis for movement relative to the chassis; […] and processing circuitry that detects, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Lee does teach […] a tool supported by the chassis for movement relative to the chassis; (Lee Page 5, Paragraph 8:“In addition, although not shown in the drawings, one side end of the work platform 300, along with the chipping work tool 400, provided with a laser sensor (not shown), the straightness of the direction of the chipping work tool 400 It can be calibrated, and equipped with a front ultrasonic sensor (not shown), can protect the safety of the collaborating workers, and the IMU (Inertial Measurement Unit) for measuring the speed, direction, gravity, acceleration of the main body 110 ) With a sensor (not shown), it is possible to correct the orientation of the chipping work tool (400).”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari to include […] a tool supported by the chassis for movement relative to the chassis; taught by Lee. This would have been for the benefit to provide the chipping operation of the sewage pipe with a rock drill, by applying a passive compliance mechanism to the chipping work tool equipped with the rock drill. The sensor mechanism for measuring reaction force and the sensor mechanism for displacement measurement for controlling the reaction force are related to the sewer pipe repair robot which prevents damage of the sewer pipe structure due to the malfunction of the robot and the robot itself so the chipping tool that has an IMU attached to it can correct the orientation of the tool. [Lee Page 3, Paragraph 3]
Lee does not teach […] and processing circuitry that detects, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Reverte does teach […] and processing circuitry that detects, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe. (Reverte Paragraph 0088: “In addition to mechanical odometry, feature-based odometry can also be utilized. In this embodiment of the pose/odometry measurement, the robot locomotes down the pipe and can track its motion with respect to features that are observed in the robot's environment. These features may be inside or outside the pipe. For example, features that are commonly found inside pipes include lateral pipes, joints, manholes, reduction joints and defects such as cracks, collapses, roots, residue and debris. These features can be imaged and tracked with multiple sensing modes including: laser scanning, structured light, computer vision, and/or sonar for use in flooded pipes. Generally, this type of feature recognition is known in the art.”) (Note: Computer vision = Processing Circuitry) (Note: Computer vision typically uses some type of camera or lidar)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee to include […] and processing circuitry that detects, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe taught by Reverte. This would have been for the benefit to provide a streamlined and more efficient fleet of low-cost robots that can be deployed out of a pick up truck and managed with a laptop computer (see FIG. 2). The autonomy of these robots increases imaging throughput by removing the human from the information loop, allowing a single operator to deploy multiple robots that simultaneously map multiple pipes. Images collected during one or more pipe mapping runs are then stitched together by computer software to generate a synthetic, unwrapped pipe image that can quickly be reviewed by humans or computers, and can easily be archived for later use. Thus making it easier to detect any obstructions within pipes. [Reverte Paragraph 0033]
Regarding claim 3, Asmari discloses The robot of claim 1, wherein the plurality of sensors further include a camera, and wherein the sensor fusion system is operable to map an inside of the pipe. (Asmari Paragraph 0014: “ A “Simultaneous Localization and Mapping” (SLAM) algorithm may be used for locating a robot inside a pipe with limited, or in some cases without any, external signal transmitted from above the ground by an operator. Accurate locating of the robot inside the pipe may enable the system to create a detailed geotagged map of the inspected pipelines. Embodiments may include a multilayer deep learning algorithm that once trained properly, can detect different features inside the pipe and which is capable of improving its accuracy as it is used by trained operators.”) (Asmari Paragraph 0053: “a single-camera visual SLAM based on the high frequency and low resolution imagery of a single camera 128, respectively.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
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Regarding claim 4, Asmari discloses The robot of claim 3, wherein the sensor fusion system associates video data from the camera with the position of the robot to map the inside of the pipe. (Asmari Paragraph 0014: “ A “Simultaneous Localization and Mapping” (SLAM) algorithm may be used for locating a robot inside a pipe with limited, or in some cases without any, external signal transmitted from above the ground by an operator. Accurate locating of the robot inside the pipe may enable the system to create a detailed geotagged map of the inspected pipelines. Embodiments may include a multilayer deep learning algorithm that once trained properly, can detect different features inside the pipe and which is capable of improving its accuracy as it is used by trained operators.”) (Asmari Paragraph 0053: “a single-camera visual SLAM based on the high frequency and low resolution imagery of a single camera 128, respectively.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
Regarding claim 6, Asmari discloses The robot of claim 1, wherein the encoder is configured to measure a distance traveled by the robot. (Asmari Paragraph 0048: “The encoders 80 are used to measure the distance of travel for the system when it is in the pipe,”)
Regarding claim 7, Asmari discloses A method comprising: moving a robot within the pipe, the robot comprising: a chassis configured for movement of the robot within the pipe; (Asmari Paragraph 0014: “Accurate locating of the robot inside the pipe may enable the system to create a detailed geotagged map of the inspected pipelines.”) (Asmari Paragraph 0017: “Embodiments described herein may include a method for data acquisition that includes capturing video images with a first video camera as the first video camera is moved along a first path.”) (Asmari Paragraph 0046: “FIG. 8 shows an inside wall 60 of a pipeline 62 in which a robotic system 64 is traveling.”)
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[…] a plurality of sensors including an inertial measurement unit (IMU), an encoder, one or more lasers, and one or more optical detectors that receive reflections of light emitted by the one or more lasers from the pipe; (Asmari Paragraph 0009: “Embodiments described herein may include a robotic system equipped with one or more cameras—potentially capable of stereo vison—along with software algorithms to create 3D point clouds, color, texture, or some combination of these. In addition to or in place of the cameras, LiDAR may be employed by embodiments described herein.”) (Asmari Paragraph 0048: “The encoders 80 are used to measure the distance of travel for the system when it is in the pipe, and the IMU 82 may provide such information as angular rate and orientation of the system, all of which provides useful data for mapping the location of the robotic system inside the pipe.”) (Asmari Paragraph 0050: “The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”) and a sensor fusion system; and determining, via the sensor fusion system and based on one or more machine learning models, a position of the robot within the pipe based on readings from the IMU, the encoder, and the one or more optical detectors; (Asmari Paragraph 0049: “That portion of the system identified as software in FIG. 9 performs a number of steps based on information it receives from the hardware. For example, at step 84 various features are identified using machine learning. As described above, this may include starting with an initial database of features such as pipe connections, access points, etc. which provides the mechanism for identifying the features as the system travels through the pipe. Then the database is augmented with additional information as new features are imaged and identified, which improves the accuracy for future mapping. The cameras 76 provide both low-frequency, high-resolution images 86 and high-frequency, low-resolution images 88. As described above, the GPS 78 provides launch location coordinates 90, and the wheel encoders 80 provide linear localization 92. The IMU 82 provides information related to the orientation of the system, which in this embodiment is a three-degree-of-freedom (DOF) orientation 94.”) (Asmari Paragraph 0050: “When the features are identified using machine learning at step 84, measurements can then be taken at step 96 based on these identified features. For example, the distance between the feature and the mobile system can be measured for each captured feature. The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”)
Asmari does not disclose […] a tool supported by the chassis for movement relative to the chassis; […] and detecting, by processing circuitry and based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Lee does teach […] a tool supported by the chassis for movement relative to the chassis; (Lee Page 5, Paragraph 8:“In addition, although not shown in the drawings, one side end of the work platform 300, along with the chipping work tool 400, provided with a laser sensor (not shown), the straightness of the direction of the chipping work tool 400 It can be calibrated, and equipped with a front ultrasonic sensor (not shown), can protect the safety of the collaborating workers, and the IMU (Inertial Measurement Unit) for measuring the speed, direction, gravity, acceleration of the main body 110 ) With a sensor (not shown), it is possible to correct the orientation of the chipping work tool (400).”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari to include […] a tool supported by the chassis for movement relative to the chassis; taught by Lee. This would have been for the benefit to provide the chipping operation of the sewage pipe with a rock drill, by applying a passive compliance mechanism to the chipping work tool equipped with the rock drill. The sensor mechanism for measuring reaction force and the sensor mechanism for displacement measurement for controlling the reaction force are related to the sewer pipe repair robot which prevents damage of the sewer pipe structure due to the malfunction of the robot and the robot itself so the chipping tool that has an IMU attached to it can correct the orientation of the tool. [Lee Page 3, Paragraph 3]
Lee does not teach […] and detecting, by processing circuitry and based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Reverte does teach […] and detecting, by processing circuitry and based on readings from one or more of the plurality of sensors, a root infiltrating the pipe. (Reverte Paragraph 0088: “In addition to mechanical odometry, feature-based odometry can also be utilized. In this embodiment of the pose/odometry measurement, the robot locomotes down the pipe and can track its motion with respect to features that are observed in the robot's environment. These features may be inside or outside the pipe. For example, features that are commonly found inside pipes include lateral pipes, joints, manholes, reduction joints and defects such as cracks, collapses, roots, residue and debris. These features can be imaged and tracked with multiple sensing modes including: laser scanning, structured light, computer vision, and/or sonar for use in flooded pipes. Generally, this type of feature recognition is known in the art.”) (Note: Computer vision = Processing Circuitry) (Note: Computer vision typically uses some type of camera or lidar)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee to include […] and detecting, by processing circuitry and based on readings from one or more of the plurality of sensors, a root infiltrating the pipe taught by Reverte. This would have been for the benefit to provide a streamlined and more efficient fleet of low-cost robots that can be deployed out of a pick up truck and managed with a laptop computer (see FIG. 2). The autonomy of these robots increases imaging throughput by removing the human from the information loop, allowing a single operator to deploy multiple robots that simultaneously map multiple pipes. Images collected during one or more pipe mapping runs are then stitched together by computer software to generate a synthetic, unwrapped pipe image that can quickly be reviewed by humans or computers, and can easily be archived for later use. Thus making it easier to detect any obstructions within pipes. [Reverte Paragraph 0033]
Regarding claim 8, Asmari discloses The method of claim 7, wherein the plurality of sensors further include a camera, and wherein the sensor fusion system is operable to map an inside of the pipe. (Asmari Paragraph 0014: “ A “Simultaneous Localization and Mapping” (SLAM) algorithm may be used for locating a robot inside a pipe with limited, or in some cases without any, external signal transmitted from above the ground by an operator. Accurate locating of the robot inside the pipe may enable the system to create a detailed geotagged map of the inspected pipelines. Embodiments may include a multilayer deep learning algorithm that once trained properly, can detect different features inside the pipe and which is capable of improving its accuracy as it is used by trained operators.”) (Asmari Paragraph 0053: “a single-camera visual SLAM based on the high frequency and low resolution imagery of a single camera 128, respectively.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
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Regarding claim 16, Asmari in view of Lee and further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari does not disclose The robot of claim 1, wherein the IMU is mounted on the tool of the robot.
However, Lee does teach The robot of claim 1, wherein the IMU is mounted on the tool of the robot. (Lee Page 5, Paragraph 8:“In addition, although not shown in the drawings, one side end of the work platform 300, along with the chipping work tool 400, provided with a laser sensor (not shown), the straightness of the direction of the chipping work tool 400 It can be calibrated, and equipped with a front ultrasonic sensor (not shown), can protect the safety of the collaborating workers, and the IMU (Inertial Measurement Unit) for measuring the speed, direction, gravity, acceleration of the main body 110 ) With a sensor (not shown), it is possible to correct the orientation of the chipping work tool (400).”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari to include The robot of claim 1, wherein the IMU is mounted on the tool of the robot taught by Lee. This would have been for the benefit to provide the chipping operation of the sewage pipe with a rock drill, by applying a passive compliance mechanism to the chipping work tool equipped with the rock drill. The sensor mechanism for measuring reaction force and the sensor mechanism for displacement measurement for controlling the reaction force are related to the sewer pipe repair robot which prevents damage of the sewer pipe structure due to the malfunction of the robot and the robot itself so the chipping tool that has an IMU attached to it can correct the orientation of the tool. [Lee Page 3, Paragraph 3]
Regarding claim 22, Asmari discloses The method of claim 8, wherein the sensor fusion system associates video data from the camera with the position of the robot to map the inside of the pipe. (Asmari Paragraph 0045: “FIG. 6A shows the inside of a pipe 50 into which a system, such as the robotic system 40 can be employed. The data from the videos captured by the robotic system 40 can be analyzed as described above, and a high-density three-dimensional point cloud 52 of the pipe 50 can be created. From the point cloud 52, a high-resolution model 51 can be generated—see FIG. 6B. From the model 51 of the pipe 50, corroded areas 54, 56 can be identified.”) (Asmari Paragraph 0050: “The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
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Regarding claim 23, Asmari discloses Non-transitory computer readable media comprising instructions that when executed cause processing circuitry to: (Asmari Paragraph 0007: “Embodiments described herein may include a system having one or more processors configured to execute a software program or programs”) move a robot within a pipe, the robot comprising: a chassis configured for movement of the robot within the pipe; (Asmari Paragraph 0055: “FIG. 11 provides a schematic diagram 146 illustrating the integration of the data from the in-pipe mapping and the above-ground mapping. “)
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[…] a plurality of sensors including an inertial measurement unit (IMU), one or more lasers, one or more optical detectors that receive reflections of light emitted by the one or more lasers from the pipe, and an encoder; and a sensor fusion system; (Asmari Paragraph 0009: “Embodiments described herein may include a robotic system equipped with one or more cameras—potentially capable of stereo vison—along with software algorithms to create 3D point clouds, color, texture, or some combination of these. In addition to or in place of the cameras, LiDAR may be employed by embodiments described herein.”) (Asmari Paragraph 0048: “The encoders 80 are used to measure the distance of travel for the system when it is in the pipe, and the IMU 82 may provide such information as angular rate and orientation of the system, all of which provides useful data for mapping the location of the robotic system inside the pipe.”) (Asmari Paragraph 0050: “The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”) determine, via the sensor fusion system and based on one or more machine learning models, a position of the robot within the pipe based on readings from the IMU, the one or more optical detectors, and the encoder; (Asmari Paragraph 0049: “That portion of the system identified as software in FIG. 9 performs a number of steps based on information it receives from the hardware. For example, at step 84 various features are identified using machine learning. As described above, this may include starting with an initial database of features such as pipe connections, access points, etc. which provides the mechanism for identifying the features as the system travels through the pipe. Then the database is augmented with additional information as new features are imaged and identified, which improves the accuracy for future mapping. The cameras 76 provide both low-frequency, high-resolution images 86 and high-frequency, low-resolution images 88. As described above, the GPS 78 provides launch location coordinates 90, and the wheel encoders 80 provide linear localization 92. The IMU 82 provides information related to the orientation of the system, which in this embodiment is a three-degree-of-freedom (DOF) orientation 94.”) (Asmari Paragraph 0050: “When the features are identified using machine learning at step 84, measurements can then be taken at step 96 based on these identified features. For example, the distance between the feature and the mobile system can be measured for each captured feature. The images from the stereo cameras 76 are also used for simultaneous localization and mapping (SLAM)—the process described above—a stereo slam 98 and a single-camera visual slam 100, although in some embodiments only the stereo SLAM or the single-camera visual SLAM may be used. The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system. The visual slam using low-resolution and high-frequency imaging 100 generates a 3-D point cloud, which in this embodiment, is a sparse 3-D point cloud 106, which also receives information from the initial six DOF trajectory fusion 104. This allows the generation of a separate six DOF trajectory at 108, which is fed back into the six DOF trajectory fusion 104; this may significantly increase accuracy regarding the location of the system as it traverses the pipe.”)
Asmari does not disclose […] a tool supported by the chassis for movement relative to the chassis; […] and detect, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Lee does teach […] a tool supported by the chassis for movement relative to the chassis; (Lee Page 5, Paragraph 8:“In addition, although not shown in the drawings, one side end of the work platform 300, along with the chipping work tool 400, provided with a laser sensor (not shown), the straightness of the direction of the chipping work tool 400 It can be calibrated, and equipped with a front ultrasonic sensor (not shown), can protect the safety of the collaborating workers, and the IMU (Inertial Measurement Unit) for measuring the speed, direction, gravity, acceleration of the main body 110 ) With a sensor (not shown), it is possible to correct the orientation of the chipping work tool (400).”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari to include […] a tool supported by the chassis for movement relative to the chassis; taught by Lee. This would have been for the benefit to provide the chipping operation of the sewage pipe with a rock drill, by applying a passive compliance mechanism to the chipping work tool equipped with the rock drill. The sensor mechanism for measuring reaction force and the sensor mechanism for displacement measurement for controlling the reaction force are related to the sewer pipe repair robot which prevents damage of the sewer pipe structure due to the malfunction of the robot and the robot itself so the chipping tool that has an IMU attached to it can correct the orientation of the tool. [Lee Page 3, Paragraph 3]
Lee does not teach […] and detect, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe.
However, Reverte does teach […] and detect, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe. (Reverte Paragraph 0088: “In addition to mechanical odometry, feature-based odometry can also be utilized. In this embodiment of the pose/odometry measurement, the robot locomotes down the pipe and can track its motion with respect to features that are observed in the robot's environment. These features may be inside or outside the pipe. For example, features that are commonly found inside pipes include lateral pipes, joints, manholes, reduction joints and defects such as cracks, collapses, roots, residue and debris. These features can be imaged and tracked with multiple sensing modes including: laser scanning, structured light, computer vision, and/or sonar for use in flooded pipes. Generally, this type of feature recognition is known in the art.”) (Note: Computer vision = Processing Circuitry) (Note: Computer vision typically uses some type of camera or lidar)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee to include […] and detect, based on readings from one or more of the plurality of sensors, a root infiltrating the pipe taught by Reverte. This would have been for the benefit to provide a streamlined and more efficient fleet of low-cost robots that can be deployed out of a pick up truck and managed with a laptop computer (see FIG. 2). The autonomy of these robots increases imaging throughput by removing the human from the information loop, allowing a single operator to deploy multiple robots that simultaneously map multiple pipes. Images collected during one or more pipe mapping runs are then stitched together by computer software to generate a synthetic, unwrapped pipe image that can quickly be reviewed by humans or computers, and can easily be archived for later use. Thus making it easier to detect any obstructions within pipes. [Reverte Paragraph 0033]
Regarding claim 24, Asmari discloses The non-transitory computer readable media of claim 23, wherein the plurality of sensors further include a camera, and wherein the instructions further cause the processing circuitry to: operate the sensor fusion system to map an inside of the pipe. (Asmari Paragraph 0014: “ A “Simultaneous Localization and Mapping” (SLAM) algorithm may be used for locating a robot inside a pipe with limited, or in some cases without any, external signal transmitted from above the ground by an operator. Accurate locating of the robot inside the pipe may enable the system to create a detailed geotagged map of the inspected pipelines. Embodiments may include a multilayer deep learning algorithm that once trained properly, can detect different features inside the pipe and which is capable of improving its accuracy as it is used by trained operators.”) (Asmari Paragraph 0053: “a single-camera visual SLAM based on the high frequency and low resolution imagery of a single camera 128, respectively.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
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Regarding claim 25, Asmari discloses The non-transitory computer readable media of claim 24, wherein the sensor fusion system associates video data from the camera with the position of the robot to map the inside of the pipe. (Asmari Paragraph 0045: “FIG. 6A shows the inside of a pipe 50 into which a system, such as the robotic system 40 can be employed. The data from the videos captured by the robotic system 40 can be analyzed as described above, and a high-density three-dimensional point cloud 52 of the pipe 50 can be created. From the point cloud 52, a high-resolution model 51 can be generated—see FIG. 6B. From the model 51 of the pipe 50, corroded areas 54, 56 can be identified.”) (Asmari Paragraph 0050: “The position information regarding the initial launch location 90 is used to generate a three DOF localization at 102. This, along with the linear localization 92 and the three DOF orientation 94 are combined to create a six DOF fusion 104 of the trajectory of the system.”) (Asmari Paragraph 0054: “From the visual slam 128, a sparse 3-D point cloud is generated at 136 as is a six DOF trajectory at 138. The GPS coordinates of the scan path at 130 provide a three DOF localization of the system at 140, which is combined with the six DOF trajectory 138 to create a six DOF trajectory fusion at 142. This information is fed back into the 3-D point cloud 136, which helps to improve the accuracy of the six DOF trajectory 138. Output from the stereo SLAM process at 126 and information from the six DOF trajectory fusion 142 are combined at 144 to create a dense 3-D point cloud 144. The dense 3-D point cloud 144 may be conveniently referred to as a second 3-D point cloud because it is associated with the second transport module, but in systems using only a single camera, the sparse 3-D point cloud 136 generated by the visual SLAM 128 may be a second 3-D point cloud. The six DOF trajectory fusion 142, the GPS coordinates of the in-pipe launch location 132, and the localization information from the robot inside the pipe at 134 are used to correlate with the in-pipe mapping output from the steps shown in FIG. 9.”)
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Regarding claim 31, Asmari in view of Lee and further in view of Reverte teaches claim 1, accordingly the rejection of claim 1 is incorporated above.
Asmari does not teach The robot of claim 1, wherein the robot is attached to a first end of a tether that is within an inside of the pipe, and wherein a second end of the tether is not within the inside of the pipe.
However, Reverte does teach The robot of claim 1, wherein the robot is attached to a first end of a tether that is within an inside of the pipe, and wherein a second end of the tether is not within the inside of the pipe. (Reverte Paragraph 0014: “An autonomous robot, which may be untethered or tethered for mechanical, communications and/or power, is deployed within the pipe”) (Reverte Paragraph 0016: “FIGS. 1 and 2 show one particular advantage of this system. In the traditional method of FIG. 1, an operator at a surface-bound truck 120 controls a single inspection robot 100 via a communications and power tether 110.”)
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Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee to include The robot of claim 1, wherein the robot is attached to a first end of a tether that is within an inside of the pipe, and wherein a second end of the tether is not within the inside of the pipe taught by Reverte. This would have been for the benefit to provide a streamlined and more efficient fleet of low-cost robots that can be deployed out of a pick up truck and managed with a laptop computer (see FIG. 2). The autonomy of these robots increases imaging throughput by removing the human from the information loop, allowing a single operator to deploy multiple robots that simultaneously map multiple pipes. Images collected during one or more pipe mapping runs are then stitched together by computer software to generate a synthetic, unwrapped pipe image that can quickly be reviewed by humans or computers, and can easily be archived for later use. Thus making it easier to detect any obstructions within pipes. [Reverte Paragraph 0033]
6. Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of Zhu (CN 110159869 A).
Regarding claim 15, Asmari in view of Lee further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the sensor fusion system is further operable to control operation of the plurality of the sensors.
However, Zhu does teach The robot of claim 1, wherein the sensor fusion system is further operable to control operation of the plurality of the sensors. (Zhu Page 7, Paragraph 1: “then, a gyroscope, a mileage wheel, a laser radar, an infrared distance measuring sensor data parallel fusion, obtaining the robot self-state accurate data, according to the specific position, travel speed, and bend corner distance, camera, magnetic leakage module, a temperature and humidity sensor and a gas sensor data parallel fusion, to obtain accurate environmental data in the pipe.”) (Zhu Page 8, Paragraph 2: “pipeline detecting robot provided by the invention and its detection method, multi-sensor fusion with the robot using only one sensor compared, multi-sensor integrated with a more complete acquisition of the detected object information and increase the reliability of the system, even when one or more sensors fails, the system may still operate normally. the multi-sensor data fusion of data processing method, compared to only individual judging or simple data processing mode are added to the sensor information, has better fault tolerance, because noise of each sensor is not related, after fusion processing can obviously inhibit noise, reducing uncertainty. At the same time, fusion detection method of the present invention improves the complementarity between the information of each sensor, some sensor provides dense information, some other sensor providing sparse information, these complementary after fusion, can compensate for the limitation of the uncertainty and measurement range of a single sensor. providing optimal estimation of statistical significance by Kalman filter can predict the next state value for merging information according to the current time value, and the recursive characteristic of system information processing does not need a lot of information and storage operation.”) (Note: The sensor fusion system can reduce uncertainty and uses Kalman filters to predict next state value.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the sensor fusion system is further operable to control operation of the plurality of the sensors taught by Zhu. This would have been for the benefit to provide a pipeline detecting robot that has a plurality of brackets that has multiple sensors on the main bracket and a sensor fusion system to provide the solution to fix the problem of sensors that has less load, information detection not being complete, the error detection rate not being high, and processing the sensor return information is too simple thus the pipeline environment cannot be sensed properly. [Zhu Page 2, Paragraph 3-4]
7. Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of (US 20220001548 A1) to Abdelkader et al. (hereinafter Abdelkader).
Regarding claim 17, Asmari in view of Lee and further in view of Reverte teaches claim 16, accordingly, the rejection of claim 16 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 16, wherein the sensor fusion system is further operable to control movement of the tool.
However, Abdelkader does teach The robot of claim 16, wherein the sensor fusion system is further operable to control movement of the tool. (Abdelkader Paragraph 0007: “The system further includes a main controller for controlling movement of the system and for positioning the automated coating nozzle based on position measurements from the second positioning mechanism at a location that allows localized recoating of the anomaly contained in the coating.”) (Abdelkader Paragraph 0034: “The first robotic device 200 can thus be in the form of a multi-sensor platform that carries a variety of condition assessment tools inside the pipe string 10 in a single deployment and, as described herein, can also provide images and/or live video that can aid in detecting anomalies within the pipe string 10.”) (Abdelkader Paragraph 0058: “Both encoder measurements and LIDAR measurements can be fused using software filtering techniques to provide millimeter distance accuracy with respect to the pipe opening. The final position estimate can be used to: (1) provide accurate position estimates for detected holidays; and (2) automatically position the coating module for accurate and localized coating.”) (Abdelkader Paragraph 0089: “As discussed herein, the coating inspection operation involves the use of the coating inspection module 200 along with the coating inspection tool 230 which is particularly suited for discovering anomalies, such as holidays, that are within the coating. In the event that a holiday or the like is discovered, the precise location of the holiday is logged and recorded (based on the known location of the inspection module 200 and the deployment angle of the coating inspection module 200). Instead of a complete recoating of the entire weld joint, as discussed herein, only the localized area of the coating where the holiday is detected is recoated with the coating module. This can require movement of the system 100 (the robotic crawlers) so as to position the coating module relative to the weld joint and then recoat the specific localized area based on the stored information concerning the precise location of the holiday.”) (Note: Based on the readings fused sensor data the robot is moved to the position of the anomaly and the tool is moved to the position to recoat the surface.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 16, wherein the sensor fusion system is further operable to control movement of the tool taught by Abdelkader. This would have been for the benefit to provide an automated system that includes a second robotic device that is configured to controllably travel inside of the pipe string and includes an automated coating inspection tool for inspecting the coating on the weld joint. The automated inspection tool includes a second position detection mechanism for detecting a position of the coating inspection tool, thereby allowing a location of an anomaly in the coating to be determined. Thus solving the problem of an operator controlling the robot manually in order to inspect the coating in a weld joint. [Abdelkader Paragraph 0004 and 0006]
8. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of Leomy (WO 2017167982 A1).
Regarding claim 20, Asmari in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the robot is configured to position the tool at a predetermined point in the pipe based on the position of the robot within the pipe.
However, Leomy does teach The robot of claim 1, wherein the robot is configured to position the tool at a predetermined point in the pipe based on the position of the robot within the pipe. (Leomy Page 2, Paragraph 17 - Page 3, Paragraph 4: “ The invention relates, in a second aspect, to a method for monitoring maintenance of a pipework, comprising at least one campaign having the following steps: - introduction of a robot / carrier into the piping; determination of a position and / or a reference orientation of the robot / carrier, by a method as defined above; - Performing a maintenance operation of the pipe, using the robot / carrier, for example a measuring operation, control, machining or brushing. According to particular embodiments, the method for monitoring maintenance of a pipe involves one or more of the following characteristics: in the step of performing the maintenance operation of the pipework, the robot / carrier positions a tool at least one position with respect to an internal surface of the pipe, the robot / carrier determining the position of the tool using the reference position and / or orientation as a reference;”) (Leomy Page 8, Paragraph 1: “For example, the determined position of the tool 83 is expressed by a longitudinal distance from the reference longitudinal position, and by an angular orientation relative to the reference angular orientation.”)
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Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the robot is configured to position the tool at a predetermined point in the pipe based on the position of the robot within the pipe taught by Leomy. This would have been for the benefit to provide a method for determining the position and / or orientation of a robot / carrier operating in a pipe and a set and associated maintenance method. [Leomy Page 2, Paragraph 5]
9. Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of Hu (CN 110260095 A).
Regarding claim 26, Amari in view of Lee and further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the processing circuitry determines, based on one or more properties associated with the pipe, a material hardness of the pipe.
However, Hu does teach The robot of claim 1, wherein the processing circuitry determines, based on one or more properties associated with the pipe, a material hardness of the pipe. (Hu Page 4, Paragraph 13 - Page 5, Paragraph 1: “the ground controller 2, which is used for controlling the walking mechanism 12 drive the pipeline robot 1, is further used for detecting the information of receiving video information and sonar detection information and generating pipeline. hand-operated remote control device specifically, ground controller 2 comprising a computer host and a matched computer host is provided with a video processing module and the sonar processing module, both of which are software modules, the video processing module transmits the video information acquired in CCTV detection component 131 to generate detection information of pipeline on the water surface, sonar processing module detecting the sonar information acquired in sonar detection component 132 to generate detection information of the pipeline under the water surface, are combined to form a detection information of the complete pipeline; detecting information comprises a slope of the pipe, thickness of the pipeline, hardness of the pipeline type and pipe wall of the pipeline defect thickness and hardness and so on.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the processing circuitry determines, based on one or more properties associated with the pipe, a material hardness of the pipe taught by Hu. This would have been for the benefit to provide a detection system of municipal pipeline robot, there is no need for operation of cleaning accumulated silt, the pipeline can be completely detected. [Hu Page 2, Paragraph 5]
10. Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of (US 20210148503 A1) to Fekrmandi et al. (hereinafter Fekrmandi).
Regarding claim 27, Amari in view of Lee further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the processing circuitry detects, based on readings from the plurality of sensors indicating a diameter change of the pipe, offset segments of the pipe.
However, Fekrmandi does teach The robot of claim 1, wherein the processing circuitry detects, based on readings from the plurality of sensors indicating a diameter change of the pipe, offset segments of the pipe. (Fekrmandi Paragraph 0018: “It is a further object, feature, or advantage of the present disclosure that the legged mechanism of the proposed pipe inspection crawler is optimal for navigating changes in the internal profile of piping seen in joints, bands, valves and T-joints and step changes.”) (Fekrmandi Paragraph 0063: “While wheel-based robots have advantages such as easy speed and differential direction control, they suffer from the complexity of the steering mechanism and instability during navigation. In addition, the wheel-based robots get stuck inside the pipe when there are sharp corners, steps, and sudden changes in pipe diameter. Recently, combination of two or more locomotion systems have been implemented to pipe inspection robots for more advantages in term of robustness and flexibility. By using a hybrid locomotion system, the inspection robot 10 can adapt and navigate in a various pipe configuration. The robotic crawler 10 uses a hybrid of legged and inchworm types like peristaltic locomotion for navigating inside the pipe as shown in FIGS. 6A-6D. Peristalsis is common in small, limbless invertebrates such as worms, where they need to deform their body to create the essential processes of locomotion. In the design of the new crawler 10, two adjacent modules 14 are engaged in motion at any time.”) (Fekrmandi Paragraph 0071: “The system is also what allows each module to independently adapt to the diameter of the pipe surrounding it with no user input or specific mode changes within the software. The software includes a path planning algorithm, a mapping system based on known intersections and pipe geometry changes, and a machine vision system to allow the locomotion method cycle to preemptively adapt for sharp lips, long vertical shafts, and to detect the features identified in the path planning stage.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the processing circuitry detects, based on readings from the plurality of sensors indicating a diameter change of the pipe, offset segments of the pipe taught by Fekrmandi. This would have been for the benefit to provide an improved legged mechanism of the proposed pipe inspection crawler is optimal for navigating changes in the internal profile of piping seen in joints, bands, valves and T-joints and step changes. [Fekrmandi Paragraph 0018]
11. Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of (US 20180326439 A1) to Weisenberg et al. (hereinafter Weisenberg).
Regarding claim 28, Amari in view of Lee further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the processing circuitry determines, based on readings from the plurality of sensors indicating a diameter of the pipe, whether there is a liner within the pipe.
However, Weisenberg does teach The robot of claim 1, wherein the processing circuitry determines, based on readings from the plurality of sensors indicating a diameter of the pipe, whether there is a liner within the pipe. (Weisenberg Paragraph 0009: “It is an assumption within the industry that the liner operator is capable of accurately predetermining liner thickness by performing a simple calculation based on pipe diameter, lining device speed and material flow.”) (Weisenberg Paragraph 0012: “To accurately determine the thickness of the applied layer of polymer resin, two sensor assemblies are provided as part of or adjacent the spinner assembly of the applicator apparatus. One sensor assembly is a leading sensor assembly positioned forward of the spinner assembly (relative to the direction of travel of the applicator apparatus) and the other sensor assembly is a trailing sensor assembly. The leading sensor assembly measures the inner diameter of the pipe wall prior to application of the polymer resin. The trailing sensor assembly measures the inner diameter of the liner immediately after it has been applied to the pipe wall.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the processing circuitry determines, based on readings from the plurality of sensors indicating a diameter of the pipe, whether there is a liner within the pipe taught by Weisenberg. This would have been for the benefit to address the challenges associated with accurate lining thickness measurement by providing a cost effective, reliable and accurate method to measure the thickness of the lining material casted on the host pipe in real time, i.e., virtually simultaneously with the application of the liner material. [Weisenberg Paragraph 0010]
12. Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) further in view of Weisenberg (US 20180326439 A1) and further in view of (US 8170715 B1) to Vallapuzha et al. (hereinafter Vallapuzha).
Regarding claim 29, Asmari in view of Lee further in view of Reverte further in view of Weisenberg teaches claim 28, accordingly, the rejection of claim 28 is incorporated above.
Asmari in view of Lee further in view of Reverte further in view of Weisenberg does not teach The robot of claim 28, wherein responsive to determining that there is a liner within the pipe, the sensor fusion system determines an adjusted position of the robot within the pipe based on at least a thickness of the liner.
However, Vallapuzha does teach The robot of claim 28, wherein responsive to determining that there is a liner within the pipe, the sensor fusion system determines an adjusted position of the robot within the pipe based on at least a thickness of the liner. (Vallapuzha Column 3, line number 24-29: “These devices sense and determine various characteristics of their environment (e.g., sharpness/dullness of work tool on the robot, thickness of the pipe liner upon which work is being performed, type of material of which the pipe/liner is made, etc.) at the time and location that the work is to be performed.”) (Vallapuzha Column 6, line number 55-62: “The blocked lateral connection is located either automatically using previously input data regarding the location of service connections or some other sensing mechanism, or it may be located manually using a reel payout sensor and onboard video cameras. Previously input data for use in the automatic process may include information from pre-inspection reports that give the distance and clocking position of each lateral, and thickness of the installed liner.”) (Vallapuzha Column 9, line number 24-27: “FIG. 5A indicates the path 540 of the cutting tool. This measurement determines the depth required of the cutting tool to cut through the lining material 510 blocking a lateral connection.”) (Vallapuzha Column 6, line number 53-Column 7, line number 5: “After robot deployment, the location of the blocked lateral connection can be confirmed by recognizing a dimple by either visual inspection or automatic scanning to detect changes in the depth of the pipe lining material. The robot's cutting tool is then aimed approximately over the center of the blocked connection, and its position is manually confirmed either visually or by using a dimple map generated using scanning data. If not centered, the cutting tool may be manually or automatically centered over the blocked connection by an operator or using known software techniques.”)
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Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte further in view of Weisenberg to include The robot of claim 28, wherein responsive to determining that there is a liner within the pipe, the sensor fusion system determines an adjusted position of the robot within the pipe based on at least a thickness of the liner taught by Vallapuzha. This would have been for the benefit to provide an autonomous robot that uses impedance control to perform various types of work within a pipe provided with device that senses the characteristics of the environment and perform work according to the impedance based software control algorithm. Thus, solving the problem of adjusting robots according to the changing conditions in situations be measuring the impedance characteristics of a unique circumstance. [Vallapuzha Column 2, line number 59-Column 3, line number 2 to Column 3, line number 18-34]
13. Claim(s) 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asmari (US 20200111222 A1) in view of Lee (KR 102042440 B1) further in view of Reverte (US 20060290779 A1) and further in view of Abdelkader (US 20220001548 A1).
Regarding claim 30, Asmari in view of Lee further in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Asmari in view of Lee further in view of Reverte does not teach The robot of claim 1, wherein the sensor fusion system is further configured to: detect one or more readings from the plurality of sensors indicating an anomaly; and responsive to detecting the one or more readings, cause the robot to move to a new position within the pipe.
However, Abdelkader does teach The robot of claim 1, wherein the sensor fusion system is further configured to: detect one or more readings from the plurality of sensors indicating an anomaly; and responsive to detecting the one or more readings, cause the robot to move to a new position within the pipe. (Abdelkader Paragraph 0034: “The first robotic device 200 can thus be in the form of a multi-sensor platform that carries a variety of condition assessment tools inside the pipe string 10 in a single deployment and, as described herein, can also provide images and/or live video that can aid in detecting anomalies within the pipe string 10.”) (Abdelkader Paragraph 0058: “Both encoder measurements and LIDAR measurements can be fused using software filtering techniques to provide millimeter distance accuracy with respect to the pipe opening. The final position estimate can be used to: (1) provide accurate position estimates for detected holidays; and (2) automatically position the coating module for accurate and localized coating.”) (Abdelkader Paragraph 0089: “As discussed herein, the coating inspection operation involves the use of the coating inspection module 200 along with the coating inspection tool 230 which is particularly suited for discovering anomalies, such as holidays, that are within the coating. In the event that a holiday or the like is discovered, the precise location of the holiday is logged and recorded (based on the known location of the inspection module 200 and the deployment angle of the coating inspection module 200). Instead of a complete recoating of the entire weld joint, as discussed herein, only the localized area of the coating where the holiday is detected is recoated with the coating module. This can require movement of the system 100 (the robotic crawlers) so as to position the coating module relative to the weld joint and then recoat the specific localized area based on the stored information concerning the precise location of the holiday.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asmari in view of Lee further in view of Reverte to include The robot of claim 1, wherein the sensor fusion system is further configured to: detect one or more readings from the plurality of sensors indicating an anomaly; and responsive to detecting the one or more readings, cause the robot to move to a new position within the pipe taught by Abdelkader. This would have been for the benefit to provide an automated system that includes a second robotic device that is configured to controllably travel inside of the pipe string and includes an automated coating inspection tool for inspecting the coating on the weld joint. The automated inspection tool includes a second position detection mechanism for detecting a position of the coating inspection tool, thereby allowing a location of an anomaly in the coating to be determined. Thus solving the problem of an operator controlling the robot manually in order to inspect the coating in a weld joint. [Abdelkader Paragraph 0004 and 0006]
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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/K.J.H./Junior Patent Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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