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 the request for continued examination filed on 06/18/2026.
Claims 1, 3-4, 6-8, 15-17, 20, 22-24, and 26-31 have been amended.
Claim 32 has been added.
Claim 25 has been cancelled.
Claims 1, 3-4, 6-8, 15-17, 20, 22-24, and 26-32 are currently pending and have been examined.
A request for continued examination under 37 CFR 1.114, including the fee set forth in
37 CFR 1.17(e), was filed in this application after final rejection. Since this application is
eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e)
has been timely paid, the finality of the previous Office action has been withdrawn pursuant to
37 CFR 1.114. Applicant's submission filed on 06/18/2026 has been entered.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 02/18/2026, 04/16/2026, and 6/18/2026 have been received and considered.
Examiner Notes
4. Examiner cites particular paragraphs (or columns and lines) in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. See MPEP §2111.01. For purpose of examination Roy (US 20240198519 A1) provisional date is 12/19/2022 and this date will be used for examination.
Response to Amendment
5. Applicant' s amendments to the Claims have overcome each and every 103 rejection
previously set forth in the Final Office Action mailed 04/20/2026.
Applicant’s arguments, see page 7-9 filed 06/09/2026, with respect to the rejections(s)
of claim(s) 1, 3-4, 6-8, 15-17, 20, and 22-31 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
However, upon further consideration, a new grounds for rejection is made under 35 USC 103 as necessitated by amendment as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) further in view of Asmari (US 20200111222 A1) further in view of Baxter (US 20220397224 A1) further in view of Vallapuzha (US 8170715 B1) further in view of Leomy (WO 2017167982 A1) further in view of Hu (CN 110260095 A) further in view of Fekrmandi (US 20210148503 A1) further in view of Weisenberg (US 20180326439 A1) further in view of Abdelkader (US20220001548 A1) and further in view of Zhang (CN 117006349 A).
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.
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.
6. Claim(s) 1, 3, 7, 15, 23, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20240198519 A1) to Roy et al. (hereinafter Roy) in view of (US 20060290779 A1) to Reverte et al. (hereinafter Reverte).
Regarding claim 1, Roy discloses A robot sensor system, the robot sensor system comprising: a plurality of sensors attached to a member of a robot sized and shaped for reception in a pipe, wherein the plurality of sensors include: (Roy Paragraph 0210: “It can be seen that various embodiments herein provide for an inspection robot capable to inspect a surface such as an interior of a pipe”) 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; (Roy Paragraph 0177: “Without limitation to any other aspect of the present disclosure, a position sensor onboard the inspection robot 223102 may include one or more of an encoder, an inertial measurement unit (IMU), a rangefinding device (e.g., a laser, Lidar, radar, etc.),”) and processing circuitry configured to: determine confidence levels of data from each of the plurality of sensors; (Roy Paragraph 0185: “Certain embodiments herein reference a competence value. A competence value, as utilized herein, includes any indication that a given position source (e.g., a position sensor and/or positioning algorithm that provides a position value) is providing a proper position value, and/or that the position source is not providing a proper position value. The competence value may be applied quantitatively (e.g., weighting the particular position value in a Kalman filter or other combining methodology between various sensors) and/or qualitatively (e.g., the related position value is ignored, mode switched between weightings, the real position estimate is reset to a position indicated by the position value, the real position estimate is reset to a value based on the position value, for example exponentially decaying toward a position indicated by the position value, etc.).”) (Note: Competence value = Confidence level) adjust, based on the confidence levels, a weight assigned to data from one or more of the plurality of sensors; (Roy Paragraph 0181: “With reference to FIG. 28, in certain embodiments, in the inspection robot positioning system 222100, each one of the first position sensor 222102 and the second position sensor 222108 may include at least one of: an inertial measurement unit (IMU) 224102, 224120, a camera 224104, 224122, a range finder 224108, 224124, a triangulation assembly 224110, 224128, an encoder 224112, 224130”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the first position sensor 222102 may include an inertial measurement unit (IMU), e.g., the IMU 224102 of FIG. 28; an inspection surface, e.g., the inspection surface 223104 of FIG. 27, may include a substantially vertical surface; and the controller 222112 may be further configured to determine the first position value 222104 in response to a gravity vector 226118.”) (Roy Paragraph 0210: “It can be seen that various embodiments herein provide for an inspection robot capable to inspect a surface such as an interior of a pipe”) determine, based on one or more machine learning models and the weight assigned to the data, a position of the robot within the pipe; (Roy Paragraph 0016: “the position description comprising a robot position value of the inspection robot on an inspection surface;”) (Roy Paragraph 0087: “The utilization of aligned payloads 2 provides for a number of capabilities for the inspection robot 100, including at least: redundancy of sensing values (e.g., to develop higher confidence in a sensed value);”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0180: “comparison between similar locations of offset inspection facilities, which can enhance detection of anomalies and/or outliers, and/or increase the capability of iterative improvement operations such as machine learning and/or artificial intelligence operations to enhance inspections, plan repair or maintenance cycles, improve confidence in certifications or risk management operations, or the like.”)
Roy does not disclose […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe.
However, Reverte does teach […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe. (Reverte Paragraph 0088: “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) (Reverte Paragraph 0089: “As the robot moves, the features will move within the scan and their motion will be measured to compute robot pose and odometry.”)
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 Roy to include […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of 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. 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 detecting roots within pipes. [Reverte Paragraph 0033]
Regarding claim 3, Roy discloses The robot sensor system of claim 1, wherein the plurality of sensors further include a camera, and wherein the processing circuitry is further configured to map an inside of the pipe. (Roy Paragraph 0077: “The example controller 802 further includes an inspection visualization circuit 810 that determines the inspection map 818 in response to the inspection data 812 and the position data 814,”) (Roy Paragraph 0080: Referencing FIG. 6, an example inspection map 818 is depicted. In the example, the inspection surface 500 may be similar to that depicted in FIG. 2—for example the interior surface of tower formed by a number of pipes to be inspected. The example inspection map 818 includes an azimuthal indication 902 and a height indication 904, with data from the inspection depicted on the inspection map 818”) (Roy Paragraph 0177: “Without limitation to any other aspect of the present disclosure, a position sensor onboard the inspection robot 223102 may include one or more of an encoder, an inertial measurement unit (IMU), a rangefinding device (e.g., a laser, Lidar, radar, etc.), actuator feedback values (e.g., motor commands and/or direct feedback values), a transceiver (e.g., configured to provide position information via range and/or direction information to an external device such as a wireless router, dedicated signal device, or the like), a global positioning system (GPS) device, a local positioning device, a camera and/or imaging device,”)
Regarding claim 7, Roy discloses A method comprising: moving, based on a robot sensor system attached to a member of a robot, the robot within a pipe, the robot sensor system comprising: (Roy Paragraph 0014: “Embodiments of the present disclosure provide for systems and methods that improve localization of an inspection robot”) (Paragraph 0062: “Without limiting any other disclosures or embodiments herein, inspection operations herein include operating one or more sensors in relation to an inspected surface,”) 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; (Roy Paragraph 0177: “Without limitation to any other aspect of the present disclosure, a position sensor onboard the inspection robot 223102 may include one or more of an encoder, an inertial measurement unit (IMU), a rangefinding device (e.g., a laser, Lidar, radar, etc.),”) and processing circuitry configured to: determine confidence levels of data from each of the plurality of sensors; (Roy Paragraph 0185: “Certain embodiments herein reference a competence value. A competence value, as utilized herein, includes any indication that a given position source (e.g., a position sensor and/or positioning algorithm that provides a position value) is providing a proper position value, and/or that the position source is not providing a proper position value. The competence value may be applied quantitatively (e.g., weighting the particular position value in a Kalman filter or other combining methodology between various sensors) and/or qualitatively (e.g., the related position value is ignored, mode switched between weightings, the real position estimate is reset to a position indicated by the position value, the real position estimate is reset to a value based on the position value, for example exponentially decaying toward a position indicated by the position value, etc.).”) (Note: Competence value = Confidence level) adjust, based on the confidence levels, a weight assigned to data from one or more of the plurality of sensors; (Roy Paragraph 0181: “With reference to FIG. 28, in certain embodiments, in the inspection robot positioning system 222100, each one of the first position sensor 222102 and the second position sensor 222108 may include at least one of: an inertial measurement unit (IMU) 224102, 224120, a camera 224104, 224122, a range finder 224108, 224124, a triangulation assembly 224110, 224128, an encoder 224112, 224130”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the first position sensor 222102 may include an inertial measurement unit (IMU), e.g., the IMU 224102 of FIG. 28; an inspection surface, e.g., the inspection surface 223104 of FIG. 27, may include a substantially vertical surface; and the controller 222112 may be further configured to determine the first position value 222104 in response to a gravity vector 226118.”) (Roy Paragraph 0210: “It can be seen that various embodiments herein provide for an inspection robot capable to inspect a surface such as an interior of a pipe”) determine, based on one or more machine learning models and the weight assigned to the data, a position of the robot within the pipe; (Roy Paragraph 0016: “the position description comprising a robot position value of the inspection robot on an inspection surface;”) (Roy Paragraph 0087: “The utilization of aligned payloads 2 provides for a number of capabilities for the inspection robot 100, including at least: redundancy of sensing values (e.g., to develop higher confidence in a sensed value);”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0180: “comparison between similar locations of offset inspection facilities, which can enhance detection of anomalies and/or outliers, and/or increase the capability of iterative improvement operations such as machine learning and/or artificial intelligence operations to enhance inspections, plan repair or maintenance cycles, improve confidence in certifications or risk management operations, or the like.”)
Roy does not disclose […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe.
However, Reverte does teach […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe. (Reverte Paragraph 0088: “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) (Reverte Paragraph 0089: “As the robot moves, the features will move within the scan and their motion will be measured to compute robot pose and odometry.”)
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 Roy to include […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of 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. 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 detecting roots within pipes. [Reverte Paragraph 0033]
Regarding claim 15, Roy discloses The robot sensor system of claim 1, wherein the processing circuitry is further operable configured to control operation of the plurality of the sensors. (Roy Paragraph 0135: “For example, the inspection robot controller 802 may store a number of command sets thereon, wherein the first hardware controller 8910 selects one of the number of command sets as the local command set 8920 based on the type of hardware component being controlled, a function of the hardware component (e.g., sensing, a type of sensor, actuating a payload, actuating a sensor position, actuating a down force value, actuating a drive wheel, etc.) and/or the type of command present in the first command set 8904.”)
Regarding claim 23, Roy discloses Non-transitory computer readable media comprising instructions that when executed cause processing circuitry of a robot sensor system to: (Roy Paragraph 0210: “It can be seen that various embodiments herein provide for an inspection robot capable to inspect a surface such as an interior of a pipe”) (Roy Paragraph 0232: “The methods, program code, instructions, and/or programs may be stored and/or accessed on machine readable transitory and/or non-transitory media”) determine confidence levels of data from each of a plurality of sensors of the robot sensor system, the 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 a pipe, and an encoder; (Roy Paragraph 0177: “Without limitation to any other aspect of the present disclosure, a position sensor onboard the inspection robot 223102 may include one or more of an encoder, an inertial measurement unit (IMU), a rangefinding device (e.g., a laser, Lidar, radar, etc.),”) (Roy Paragraph 0185: “Certain embodiments herein reference a competence value. A competence value, as utilized herein, includes any indication that a given position source (e.g., a position sensor and/or positioning algorithm that provides a position value) is providing a proper position value, and/or that the position source is not providing a proper position value. The competence value may be applied quantitatively (e.g., weighting the particular position value in a Kalman filter or other combining methodology between various sensors) and/or qualitatively (e.g., the related position value is ignored, mode switched between weightings, the real position estimate is reset to a position indicated by the position value, the real position estimate is reset to a value based on the position value, for example exponentially decaying toward a position indicated by the position value, etc.).”) (Note: Competence value = Confidence level) adjust, based on the confidence levels, a weight assigned to data from one or more of the plurality of sensors; (Roy Paragraph 0181: “With reference to FIG. 28, in certain embodiments, in the inspection robot positioning system 222100, each one of the first position sensor 222102 and the second position sensor 222108 may include at least one of: an inertial measurement unit (IMU) 224102, 224120, a camera 224104, 224122, a range finder 224108, 224124, a triangulation assembly 224110, 224128, an encoder 224112, 224130”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the first position sensor 222102 may include an inertial measurement unit (IMU), e.g., the IMU 224102 of FIG. 28; an inspection surface, e.g., the inspection surface 223104 of FIG. 27, may include a substantially vertical surface; and the controller 222112 may be further configured to determine the first position value 222104 in response to a gravity vector 226118.”) (Roy Paragraph 0210: “It can be seen that various embodiments herein provide for an inspection robot capable to inspect a surface such as an interior of a pipe”) determine, based on one or more machine learning models and the weight assigned to the data, a position of the robot within the pipe; (Roy Paragraph 0016: “the position description comprising a robot position value of the inspection robot on an inspection surface;”) (Roy Paragraph 0087: “The utilization of aligned payloads 2 provides for a number of capabilities for the inspection robot 100, including at least: redundancy of sensing values (e.g., to develop higher confidence in a sensed value);”) (Roy Paragraph 0187: “In certain embodiments, in the inspection robot positioning system 222100, the system layer 225112 may be further configured to determine the position description 222114 by weighting application of each of the first position value 222104 and the second position value 222110 in response to the corresponding first competence value 225118 and second competence value 225120.”) (Roy Paragraph 0180: “comparison between similar locations of offset inspection facilities, which can enhance detection of anomalies and/or outliers, and/or increase the capability of iterative improvement operations such as machine learning and/or artificial intelligence operations to enhance inspections, plan repair or maintenance cycles, improve confidence in certifications or risk management operations, or the like.”)
Roy does not disclose […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe.
However, Reverte does teach […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of a root infiltrating the pipe. (Reverte Paragraph 0088: “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) (Reverte Paragraph 0089: “As the robot moves, the features will move within the scan and their motion will be measured to compute robot pose and odometry.”)
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 Roy to include […] and detect, based on the data from one or more of the plurality of sensors and the position of the robot, a location of 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. 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 detecting roots within pipes. [Reverte Paragraph 0033]
Regarding claim 31, Roy in view of Reverte teaches claim 1, accordingly the rejection of claim 1 is incorporated above.
Roy does not disclose The robot sensor system 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 sensor system 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 Roy to include The robot sensor system 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]
7. Claim(s) 4, 6, 8, 22, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of (US 20200111222 A1) to Asmari et al. (hereinafter Asmari).
Regarding claim 4, Roy in view of Reverte teaches claim 3, accordingly, the rejection of claim 3 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 3, wherein the processing circuitry associates video data from the camera with the position of the robot to map the inside of the pipe.
However, Asmari does teach The robot sensor system of claim 3, wherein the processing circuitry 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.”)
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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 Roy in view of Reverte to include The robot sensor system of claim 3, wherein the processing circuitry associates video data from the camera with the position of the robot to map the inside of the pipe taught by Asmari. This would have been for the benefit to provide a combination hardware and software system that uses one or more cameras to determine the location of a robot inside a pipe and geotag detected features with limited assistance from sensors, devices, or extra operators above the ground in order to solve the issue of time-consuming, costly, and very challenging, localization of the robot especially when the asset is a pipe that crosses areas with obstructions over the ground such as roadways, parked cars, or buildings. [Asmari Paragraph 0006 and 0008]
Regarding claim 6, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the encoder is configured to measure a distance traveled by the robot.
However, Asmari does teach The robot sensor system 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,”)
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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the encoder is configured to measure a distance traveled by the robot taught by Asmari. This would have been for the benefit to provide a combination hardware and software system that uses one or more cameras to determine the location of a robot inside a pipe and geotag detected features with limited assistance from sensors, devices, or extra operators above the ground in order to solve the issue of time-consuming, costly, and very challenging, localization of the robot especially when the asset is a pipe that crosses areas with obstructions over the ground such as roadways, parked cars, or buildings. [Asmari Paragraph 0006 and 0008]
Regarding claim 8, Roy in view of Reverte teaches claim 7, accordingly, the rejection of claim 7 is incorporated above.
Roy in view of Reverte does not teach The method of claim 7, wherein the plurality of sensors further include a camera, and wherein the processing circuitry is further configured to map an inside of the pipe.
However, Asmari does teach The method of claim 7, wherein the plurality of sensors further include a camera, and wherein the processing circuitry is further configured 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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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 Roy in view of Reverte to include The method of claim 7, wherein the plurality of sensors further include a camera, and wherein the processing circuitry is further configured to map an inside of the pipe taught by Asmari. This would have been for the benefit to provide a combination hardware and software system that uses one or more cameras to determine the location of a robot inside a pipe and geotag detected features with limited assistance from sensors, devices, or extra operators above the ground in order to solve the issue of time-consuming, costly, and very challenging, localization of the robot especially when the asset is a pipe that crosses areas with obstructions over the ground such as roadways, parked cars, or buildings. [Asmari Paragraph 0006 and 0008]
Regarding claim 22, Roy in view of Reverte and further in view of Asmari teaches claim 8, accordingly, the rejection of claim 8 is incorporated above.
Roy in view of Reverte does not teach The method of claim 8, wherein the processing circuitry associates video data from the camera with the position of the robot to map the inside of the pipe.
However, Asmari does teach Asmari discloses The method of claim 8, wherein the processing circuitry 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.”)
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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 Roy in view of Reverte to include Asmari discloses The method of claim 8, wherein the processing circuitry associates video data from the camera with the position of the robot to map the inside of the pipe taught by Asmari. This would have been for the benefit to provide a combination hardware and software system that uses one or more cameras to determine the location of a robot inside a pipe and geotag detected features with limited assistance from sensors, devices, or extra operators above the ground in order to solve the issue of time-consuming, costly, and very challenging, localization of the robot especially when the asset is a pipe that crosses areas with obstructions over the ground such as roadways, parked cars, or buildings. [Asmari Paragraph 0006 and 0008]
Regarding claim 24, Roy in view of Reverte teaches claim 23, accordingly, the rejection of claim 23 is incorporated above.
Roy in view of Reverte does not teach 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 robot sensor system to map an inside of the pipe, wherein the robot sensor system associates video data from the camera with the position of the robot to map the inside of the pipe.
However, Asmari does teach 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 robot sensor system to map an inside of the pipe, wherein the robot sensor 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.”)
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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 Roy in view of Reverte to include 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 robot sensor system to map an inside of the pipe, wherein the robot sensor system associates video data from the camera with the position of the robot to map the inside of the pipe taught by Asmari. This would have been for the benefit to provide a combination hardware and software system that uses one or more cameras to determine the location of a robot inside a pipe and geotag detected features with limited assistance from sensors, devices, or extra operators above the ground in order to solve the issue of time-consuming, costly, and very challenging, localization of the robot especially when the asset is a pipe that crosses areas with obstructions over the ground such as roadways, parked cars, or buildings. [Asmari Paragraph 0006 and 0008]
8. Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of (US 20220397224 A1) to Baxter et al. (hereinafter Baxter).
Regarding claim 16, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the sensor system is attached to a tool of the robot.
However, Baxter does teach The robot sensor system of claim 1, wherein the sensor system is attached to a tool of the robot. (Baxter Paragraph 0072: “The robot 12 comprises a tractor 14 and a tool positioning mechanism 16 at a leading end of the tractor. The tool positioning mechanism 16 is configured to operatively connect a set 18 of interchangeable robotic tools 20, 22, 24 to the tractor 14.”) (Baxter Paragraph 0177: “Referring again to FIG. 36, the installation tool 22 can be equipped with various sensors that aid in navigating the robot 12 through the main pipe M and operatively aligning the cylinders 1138 with the corporation stops C. In the illustrated embodiment, the installation tool 22 comprises a sensor head 1180 on the leading end. The sensor head 1180 can include one or more imaging sensors (e.g., a camera) and/or positioning sensors (e.g., an accelerometer, a gyroscope, etc.) that aid in positioning the installation tool within the main pipe M.”)
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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the sensor system is attached to a tool of the robot taught by Baxter. This would have been for the benefit to provide a more efficient robot for use inside a main pipe with at least one branch conduit extending therefrom comprises a tractor configured for movement along an axis of the main pipe. [Baxter Paragraph 0012]
9. Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of Baxter and further view of (US 8170715 B1) Vallapuzha et al. (hereinafter Vallapuzha).
Regarding claim 17, Roy in view of Reverte and further in view of Baxter teaches claim 16, accordingly, the rejection of claim 16 is incorporated above.
Roy in view of Reverte further in view of Baxter does not teach The robot sensor system of claim 16, wherein the processing circuitry is further configured to determine a position of the tool.
However, Vallapuzha does teach The robot sensor system of claim 16, wherein the processing circuitry is further configured to determine a position of the tool. (Vallapuzha Column 5, line number 51-53: “The robot contains sensors for providing feedback regarding the position of the tool and impedances experienced by the tool.”)
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 Roy in view of Reverte further in view of Baxter to include The robot sensor system of claim 16, wherein the processing circuitry is further configured to determine a position of the tool 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]
10. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of Leomy (WO 2017167982 A1).
Regarding claim 20, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the robot is configured to position a tool at a predetermined point in the pipe based on the position of the robot within the pipe.
However, Leomy does teach The robot sensor system of claim 1, wherein the robot is configured to position a 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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the robot is configured to position a tool at a predetermined point in the pipe based on the position of the robot within the pipe by Leomy. This would have been for the benefit to provide a more efficient method for determining the position and / or orientation of a robot / carrier operating in a pipe. [Leomy Page 2, Paragraph 5]
11. Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of Hu (CN 110260095 A).
Regarding claim 26, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to determine, based on one or more properties associated with the pipe, a material hardness of the pipe.
However, Hu does teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to determine, 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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the processing circuitry is further configured to determine, based on one or more properties associated with the pipe, a material hardness of the pipe taught. This would have been for the benefit to provide a more efficient 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]
12. Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of (US 20210148503 A1) to Fekrmandi et al. (hereinafter Fekrmandi).
Regarding claim 27, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect, based on the data from the plurality of sensors indicating a diameter change of the pipe, offset segments of the pipe.
However, Fekrmandi does teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect, based on the data 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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect, based on the data 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]
13. Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of (US 20180326439 A1) to Weisenberg et al. (hereinafter Weisenberg).
Regarding claim 28, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to determine, based on the data 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 sensor system of claim 1, wherein the processing circuitry is further configured to determine, based on the data 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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the processing circuitry is further configured to determine, based on the data 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]
14. Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) further in view of Weisenberg (US 20180326439 A1) and further in view of Vallapuzha (US 8170715 B1).
Regarding claim 29, Roy in view of Reverte and further in view of Weisenberg teaches claim 28, accordingly, the rejection of claim 28 is incorporated above.
Roy in view of Reverte further in view of Weisenberg does not teach The robot sensor system of claim 28, wherein responsive to determining that there is a liner within the pipe, the robot sensor 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 sensor system of claim 28, wherein responsive to determining that there is a liner within the pipe, the robot sensor 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 Roy in view of Reverte further in view of Weisenberg to include The robot sensor system of claim 28, wherein responsive to determining that there is a liner within the pipe, the robot sensor 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]
15. Claim(s) 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of (US 20220001548 A1) to Abdelkader et al. (hereinafter Abdelkader).
Regarding claim 30, Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to: detect, based on the data from the one or more of the plurality of sensors, an anomaly; and responsive to detecting the anomaly, determine a new position of the robot within the pipe.
However, Abdelkader does teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to: detect, based on the data from the one or more of the plurality of sensors, an anomaly; and responsive to detecting the anomaly, determine a new position of the robot 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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the processing circuitry is further configured to: detect, based on the data from the one or more of the plurality of sensors, an anomaly; and responsive to detecting the anomaly, determine a new position of the robot 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]
16. Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy (US 20240198519 A1) in view of Reverte (US 20060290779 A1) and further in view of Zhang (CN 117006349 A).
Regarding claim 32, , Roy in view of Reverte teaches claim 1, accordingly, the rejection of claim 1 is incorporated above.
Roy in view of Reverte does not teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect the location of a root infiltrating the pipe based on a root recognizer model of the one or more machine learning models.
However, Zhang does teach The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect the location of a root infiltrating the pipe based on a root recognizer model of the one or more machine learning models. (Zhang Page 13, Paragraph 6:“However, with the development of technologies such as deep learning, more and more methods capable of automatically detecting the defects of the drainage pipeline instead of manual work, some scholars and others propose an image recognition algorithm of application feature extraction and machine learning method, and use support vector machine method (SVM) to classify the defects of the drainage pipeline. The invention claims a machine learning method for identifying fault type by using random forest classifier set, but the two machine learning methods need to be extracted manually,”) (Zhang Page 19, Paragraph 3: “That is, this application combines the two open source data sets of Storm drain model Dataset and Pipe root Dataset, after combining, there are a total of 1317 original images, wherein there are 8 kinds of detection targets, It is respectively buckling, cracking, chipping, hole, seam excursion, barrier, public facilities invading and tree root.”)
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 Roy in view of Reverte to include The robot sensor system of claim 1, wherein the processing circuitry is further configured to detect the location of a root infiltrating the pipe based on a root recognizer model of the one or more machine learning models taught by Zhang. This would have been for the benefit to provide a pipeline obstacle cleaning robot, a pipeline obstacle cleaning device and a drainage pipeline defect detection method to ensure the smoothness of the urban rainwater sewage drainage pipeline, which is good for draining the accumulated water of the urban road in time, and solve the problem that the domestic sewage is suddenly increased or the water outlet is blocked to cause the water immersion in the rainstorm weather, it is necessary to clean all kinds of obstacles in the pipeline in time and repair the pipeline. [Zhang Page 2, Paragraph 3 and Page 2, paragraph 2]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN J HARVEY whose telephone number is 571-272-5327. The examiner can normally be reached 8:00AM-5:00PM M-Th, 8:00AM-4:00PM F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kito Robinson can be reached at 571-270-3921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.J.H./Junior Patent Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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