DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to application filed on 08/02/2022.
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/02/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?): Yes, the claim recites a series of steps and, therefore, is a process.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 19:
A machine learning model for inspecting pipelines, the model configured to:
receive wheel rotation information and caliper arm measurement data from a processor of a caliper pig [insignificant extra solution, e.g. mere data-gathering];
combine the caliper arm measurement data for the different caliper arms that correspond to the same wheel rotation information [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)];
parse the caliper arm measurement data into separate pipeline sections based on the wheel rotation information [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)]; and
sequentially process each pipeline section using the machine learning model to detect pipeline features, assign a feature class to each pipeline feature, and determine an axial position, an angular position, linear dimensions, and a size for each pipeline feature [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites an additional element of “receive”. This additional element is insignificant pre-solution (i.e. data gathering). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Further, claim 19 recites “machine learning model”. The “machine learning model” is used to generally apply the abstract idea without placing any limits on how the machine learning model. The recitation of “using a machine learning model” merely indicates a field of use or technological environment in which the judicial exception is performed. Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “receive” is insignificant pre-solutions (i.e. data gathering). At most the additional elements are not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions and/or (ii)-printing or downloading generated menus and/or (iii)- presenting offers to potential customers. Further, as explained above with respect to Step 2A, Prong two, the additional element of “using a machine learning model” is at best mere instructions to “apply” the abstract ideas, which can not provide an inventive concept. See MPEP 2106.05(f).
As per claim 20, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 19 is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter since the claim merely drawn to computer software per se. The claim does not seem to require any hardware or physical component to perform its function. As such, the claim appears to be system software per se and are therefore non-statutory.
Claim 20 depends on independent claim 19 and dependent claim is also rejected under 35 USC 101 by virtue of its dependence on the rejected independent claim.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2011/0167914 A1 issued to Sutherland et al in view of US Patent No. 11, 156, 911 B2 issued to Tabaian et al and further in view of US Patent No. 4, 457, 073 issued to Payne et al.
1. Sutherland et al discloses a system for inspecting pipelines, the system including:
a caliper pig having a front section, a middle section, and a rear section, the caliper pig (See: Fig. 1A and corresponding texts) including:
a bumper located at the front section and configure to form a leading surface through a pipeline (See: Fig. 1 and corresponding texts),
at least two spring-supported odometer arms located at the middle section or the front section, each odometer arm including a wheel sensor configured to contact an inner surface of the pipeline for measuring a distance traveled (See: [0030] FIG. 1A depicts a side view of an illustrative pipeline inline inspection tool or pig 1 that may be implemented in accordance with some embodiments of the present invention. Pig 1 includes a plurality of a multi-sensor devices 5 arranged in a circular/ring 7 configuration, magnetizing brushes 15a and 15b respectively coupled to opposite poles of a magnet (not shown), odometer wheels 25, and an instrumentation vehicle 45. A magnified view of the rearward portion of the inline inspection tool of FIG. 1A is depicted in FIG. 1B, showing in more detail a rearward sensor that comprises a sensor head 12 attached to an armature 14 (which is rotatably attached coaxially with an odometer wheel 25) and that comprises one or more sensors (e.g., caliper, EMAT, EC, etc.) as will be understood by those skilled in the art in view of the herein disclosure; par [0038] For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm),
a ring of caliper arms located in the middle section, the ring configured to cover a circumference of the inner surface of the pipeline (See: par [0032] FIG. 3 also schematically illustrates three of the integrated multi-sensor devices 5 of pig 1 moving along the axial direction to acquire various signals, described further hereinbelow, for sensing topological/topographic/geometric features, mechanical properties, and/or material properties at a down pipe sampling rate (schematically indicated by indicia 28) that depends on the acquisition rate and the spatial resolution of the sensors and at a circumferential sampling rate that depends on sensor device (head) density and the number of sensors of a given type per sensor head. While the circumferential distance between heads may be designed to be small or negligible, in alternative embodiments, rather than providing a single circumferential ring 7 of integrated multi-sensor devices 5, two or more circumferential rings may be provided with the sensors from different rings offset in the circumferential direction (i.e., azimuthally) to provide a desired circumferential spatial resolution (e.g., without necessarily requiring a particularly close circumferential packing of the multi-sensor devices in a given ring)), each caliper arm configured to move upward and downward to measure surface features of the pipeline and including a movement sensor to detect the upward and downward movement of the caliper arm (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm),
a memory device (See: par [0035] a memory 80 for storing acquired signal data and/or programs executed by microprocessor 75; and a power supply 90 to power the microprocessor 75 and other components that may require power (e.g., memory 80, coil driver, etc.)), and
a processor communicatively coupled to the movement sensors and the memory device (See: par [0035] a memory 80 for storing acquired signal data and/or programs executed by microprocessor 75; and a power supply 90 to power the microprocessor 75 and other components that may require power (e.g., memory 80, coil driver, etc.)) a processor configured to:
store wheel rotation information from the odometer wheel sensors to the memory device (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted; [0055] After the signals from each sensor in the multi-sensor device are collected, the acquired signals may be individually processed (optionally) and stored, step 120. For example, in some embodiments, microprocessor 75 and/or a processor in instrumentation vehicle 45 may be operable in performing error correction or compensation or other appropriate processing (e.g., based on normalization, or calibration, etc.); and
store caliper arm measurement data for each of the caliper arms to the memory device in association with the wheel rotation information (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted; [0055] After the signals from each sensor in the multi-sensor device are collected, the acquired signals may be individually processed (optionally) and stored, step 120. For example, in some embodiments, microprocessor 75 and/or a processor in instrumentation vehicle 45 may be operable in performing error correction or compensation or other appropriate processing (e.g., based on normalization, or calibration, etc.)); and
a computer (See: [0035] FIG. 5 is an illustrative block diagram of a multi-sensor device 5 in accordance with some embodiments of the present invention, schematically representing that each of the sensors in one multi-sensor device are connected to a microprocessor 75)
receive the wheel rotation information and the caliper arm measurement data from the processor of the caliper pig (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm),
combine the caliper arm measurement data for the different caliper arms that correspond to the same wheel rotation information (See: [0044] As will be further understood in view of the ensuing description, the EMAT, EC, MFL, and caliper sensors may be operable to acquire signals at the same sampling rate (though different sampling rates are possible), and information from various combinations of the acquired signals may be processed to provide for improved feature detection; [0048] As indicated above, acquisition of the signals from the various sensors provides for many embodiments for processing the acquired signals in various combinations to provide for improved characterization of the pipeline integrity (e.g., discerning features with greater sensitivity, greater accuracy, greater confidence levels, etc.). FIG. 6 is an operational flow diagram illustrating various methods for processing signals acquired from a multi-sensor device, in accordance with some embodiments of the present invention. Signals acquired (step 63) individually from the EC, EMAT, caliper, and MFL sensors 61 at respective desired sampling rates (e.g., at the same sampling rate) are stored (step 65), typically as values reflecting a calibration of the sensor (e.g., the acquired signal may be scaled or normalized according to a calibration factor to provide the stored value)),
parse the caliper arm measurement data into separate pipeline sections based on the wheel rotation information (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm), and
sequentially process each pipeline section using the machine learning model to detect pipeline features, assign a feature class to each pipeline feature, and determine an axial position, an angular position, linear dimensions, and a size for each pipeline feature (See: [0031] More specifically, FIG. 2A schematically depicts a pipeline portion comprising several straight segments separated by several bends, FIG. 2B depicts an expanded view of one of the straight segment portions 27 (e.g., a spool), and FIG. 2C shows an expanded view of a section 29 thereof (i.e., Region of Interest (ROI)). Coordinates are schematically depicted with respect to the spool, with the z-axis being oriented along the axial direction corresponding to the scan direction, the radial direction being oriented normal to the z-axis, and the azimuthal angle corresponding to the angular rotation about the z-axis, with the azimuthal (or circumferential) direction being oriented in a direction mutually perpendicular to the radial and axial directions. As schematically depicted in FIG. 2C, by way of example, the ROI includes a narrow, elongated axial feature ("feature" also referred to herein as an attribute or characteristic) 21 and a circumferentially and axially extending feature 23. Such features (or attributes or characteristics) may represent one or more of at least the following: topological/topographical/geometric variations (e.g., dents, scratches, peeling, wall thickness, etc.), material property (e.g., compositional) variations (e.g., surface and/or bulk property variations, such as due to corrosion or to differences between bulk material and surface coating material, etc.), and mechanical property (e.g., stress/strain) variations).
Sutherland et al does not specify but Tabaian et al discloses a transmitter configured to transmit a wireless signal to enable locating the caliper pig (See: Abstract, a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is configured to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels), a machine learning model (See: Col. 15 lines 30-52, The obtained pipeline image data can be sent to processor 140 and/or storage device 150 to be processed. Memory 160 can also aid in the image processing. The pipeline image data can be scanned to obtain pipeline corrosion data using machine learning algorithm 165. Machine learning algorithm 165 can also detect if a defect is present in pipeline image data. Machine learning algorithm 165 can compare the pipeline image data to a labeled defect library, where a library of images with labeled defects are stored. Machine learning algorithm can also label the defect in the pipeline image data, and the labeled pipeline image data can then be stored in the labeled defect library to expand the library. Machine learning algorithm 165 can also be trained using the labeled defect library, or other machine learning algorithms can be trained using the labeled defect library. In this manner, machine learning algorithm 165 can be constantly trained and improved in defect detection by training using an ever-expanding labeled defect library. Machine learning algorithm 165 can also utilized such methods to adapt to new environments and constantly improve. Once processed for corrosion data and detected defects, the pipeline imaging data can be store or transmitted out of pipeline 200).
It would have been obvious before the effective filing date to combine pipeline inspection devices as taught by Tabaian et al to integrated multi-sensor of Sutherland et al would be able to reliably inspect pipe walls and produce accurate results for detecting defects and corrosion levels (Tabaian et al, Col. 1 lines 44-46).
Further, neither the references disclose but Payne et al discloses a first cup located between the front section and the middle section, the first cup configured to have a diameter that is less than an inner diameter of the pipeline, a second cup located at the rear section, the second cup configured to have a diameter that is less than the inner diameter of the pipeline (See: Col. 2 lines 51-61, The caliper pig 10 includes a body 16 which is longitudinal and is shown to include a forward cup 18 and a rearward cup 20. The illustrations of the body 16, cups 18 and 20 are merely exemplary as the actual appearance of the body 10 may vary considerably, and more than one forward cup 18 may be employed, if desired. Each of the cups 18 and 20 is of an exterior circumferential configuration engaging the interior wall 12A of the pipeline. The cups cause the caliper pig 10 to move through the pipeline 12 by the flow of fluid through it, whether liquid or gas; Col. 3 lines 8-19, The caliper pig body 16 includes a rearward end portion 30 which has the rearward cup 20 affixed to it. In the illustrated arrangement, the rearward portion 30 includes an integral reduced diameter portion 32. Affixed exteriorly of this reduced diameter portion is the cup 20 having an opening 34 therein which receives the reduced diameter portion 32. A flat, annular retainer plate 36 having opening 38 therein, is also received on the reduced diameter end portion 32 and engages the rearward cup 20. By means of a plurality of bolts 40 the plate 36 holds the rearward cup 20 in position on the rearward end of the caliper pig body).
It would have been obvious before the effective filing date to combine internal configuration of a pipeline as taught by Payne et al to integrated multi-sensor of Sutherland et al would be to sense changes in the internal configuration of the pipeline through which the pig passes (Payne et al, Abstract).
2. Tabaian et al discloses the system of Claim 1, wherein the computer is communicatively coupled to the processor via at least one of a wired connection or a wireless connection (See: Abstract, a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is configured to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels).
3. Sutherland et al discloses the system of Claim 1, wherein the computer is integrated with the processor (See: Fig. 5 and corresponding texts).
4. Sutherland et al discloses the system of Claim 1, wherein each of the caliper arms includes a current sensor configured to detect a current within of the pipeline and generate current sense data, and wherein the processor is configured to store the current sense data to the memory device in conjunction with the caliper arm measurement data (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted; [0055] After the signals from each sensor in the multi-sensor device are collected, the acquired signals may be individually processed (optionally) and stored, step 120. For example, in some embodiments, microprocessor 75 and/or a processor in instrumentation vehicle 45 may be operable in performing error correction or compensation or other appropriate processing (e.g., based on normalization, or calibration, etc.).
5. Sutherland et al discloses the system of Claim 4, wherein the machine learning model is configured to additionally use the current sense data to detect pipeline features and assigning the feature class to each pipeline feature (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted).
6. Tabaian et al discloses the system of Claim 1, wherein the caliper pig further includes at least one of a clock generating time data, a temperature sensor generating temperature data, a pressure sensor generating pressure data, or an inertial measurement unit configured to generate angular acceleration data (See: Col. 8 lines 47-60, sensor 170 can comprise various components to measure a variety of parameters. For example, sensor 170 can comprise an acoustic emission sensor to measure acoustic transmission in pipeline 200 and detect leaks. Additionally, the acoustic emission sensor can be provided samples to establish a baseline behavior for pipeline 200. Acoustic data obtained from sensor 170 can also be processed by processors 140. For example, acoustic data can be processed using fast Fourier transforms and the like. In some embodiments, sensor 170 can further comprise temperature sensors and pressure transducers to keep track of conditions in the pipeline. Anomalies in the temperature and pressure data can indicate a defect and can alert imaging device 130 to begin imaging pipeline 200; Col. 12 lines 40-43, In block 650, the defect can be labeled as a defect in the pipeline image data. The defect can further be labeled with additional information, such as the type of defect, a timestamp, a defect position, and the like).
7. Tabaian et al discloses the system of Claim 6, wherein the processor is configured to store the at least one of the time data, the temperature data, the pressure data, or the angular acceleration data to the memory device in conjunction with the caliper arm measurement data (See: Col. 8 lines 47-60, sensor 170 can comprise various components to measure a variety of parameters. For example, sensor 170 can comprise an acoustic emission sensor to measure acoustic transmission in pipeline 200 and detect leaks. Additionally, the acoustic emission sensor can be provided samples to establish a baseline behavior for pipeline 200. Acoustic data obtained from sensor 170 can also be processed by processors 140. For example, acoustic data can be processed using fast Fourier transforms and the like. In some embodiments, sensor 170 can further comprise temperature sensors and pressure transducers to keep track of conditions in the pipeline. Anomalies in the temperature and pressure data can indicate a defect and can alert imaging device 130 to begin imaging pipeline 200; Col. 12 lines 40-43, In block 650, the defect can be labeled as a defect in the pipeline image data. The defect can further be labeled with additional information, such as the type of defect, a timestamp, a defect position, and the like).
8. The system of Claim 7, wherein the machine learning model is configured to additionally use the at least one of the time data, the temperature data, the pressure data, or the angular acceleration data to detect pipeline features and assigning the feature class to each pipeline feature (See: Col. 8 lines 47-60, sensor 170 can comprise various components to measure a variety of parameters. For example, sensor 170 can comprise an acoustic emission sensor to measure acoustic transmission in pipeline 200 and detect leaks. Additionally, the acoustic emission sensor can be provided samples to establish a baseline behavior for pipeline 200. Acoustic data obtained from sensor 170 can also be processed by processors 140. For example, acoustic data can be processed using fast Fourier transforms and the like. In some embodiments, sensor 170 can further comprise temperature sensors and pressure transducers to keep track of conditions in the pipeline. Anomalies in the temperature and pressure data can indicate a defect and can alert imaging device 130 to begin imaging pipeline 200; Col. 12 lines 40-43, In block 650, the defect can be labeled as a defect in the pipeline image data. The defect can further be labeled with additional information, such as the type of defect, a timestamp, a defect position, and the like).
9. Tabaian et al discloses the system of Claim 1, wherein the machine learning model is configured to use the caliper arm measurement data to determine pipeline joint lengths (See: Abstract, A pipeline inspection device including a housing, an antenna, an imaging device having one or more lenses, two diaphragms extending from the housing and distal to one another along the length of the housing, the two diaphragms sharing a longitudinal axis with the housing, a processor, a storage device in communication with the processor, and a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is configured to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels. Also disclosed herein are systems and methods for using the same).
10. Tabaian et al discloses the system of Claim 9, wherein at least one of the computer or the machine learning model is configured to use at least one of the determined pipeline joint lengths, time measurements from a clock, and angular acceleration data from at least one inertial measurement unit to check data quality of the caliper arm measurement data that was acquired at high speed areas of the caliper pig while inspecting the pipeline (See: Abstract, A pipeline inspection device including a housing, an antenna, an imaging device having one or more lenses, two diaphragms extending from the housing and distal to one another along the length of the housing, the two diaphragms sharing a longitudinal axis with the housing, a processor, a storage device in communication with the processor, and a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is configured to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels. Also disclosed herein are systems and methods for using the same).
11. Tabaian et al discloses the system of Claim 9, wherein at least one of the computer or the machine learning model is configured to use the determined pipeline joint lengths to classify corresponding caliper arm measurement data as a pipeline joint (See: Abstract, A pipeline inspection device including a housing, an antenna, an imaging device having one or more lenses, two diaphragms extending from the housing and distal to one another along the length of the housing, the two diaphragms sharing a longitudinal axis with the housing, a processor, a storage device in communication with the processor, and a memory in communication with the processor, storing a machine learning algorithm and instructions to be executed by the processor, wherein the antenna is configured to communicate with a remote transceiver, the remote transceiver being located on a pipeline through which the pipeline inspection device travels. Also disclosed herein are systems and methods for using the same).
12. Sutherland et al discloses the system of Claim 1, wherein at least one of the computer or the machine learning model is configured to create an electronic report that includes the identified pipeline features specifying the assigned feature class, the axial position, the angular position, the linear dimensions, and the size (See: [0053] Based on the further analysis performed in step 73, the resulting data is analyzed or interpreted to identify or extract a spatial representation of physical attributes characterizing the pipeline structure (step 75) and such attributes are provided to a user (step 77) according to various representations (e.g., user-selectable graphics/visual representations). Based on the further analysis performed in step 73, the range or degree of error, +/-.delta..sub.f, associated with each of the determined physical attributes in step 75 is less than the range or degree of error, +/-.delta..sub.a, associated with the physical attribute as determined in step 69).
13. Sutherland et al discloses the system of Claim 1, wherein at least one of the computer or the machine learning model is configured to cause the electronic report to be displayed to transmit the electronic report to a client device for display (See: [0053] Based on the further analysis performed in step 73, the resulting data is analyzed or interpreted to identify or extract a spatial representation of physical attributes characterizing the pipeline structure (step 75) and such attributes are provided to a user (step 77) according to various representations (e.g., user-selectable graphics/visual representations). Based on the further analysis performed in step 73, the range or degree of error, +/-.delta..sub.f, associated with each of the determined physical attributes in step 75 is less than the range or degree of error, +/-.delta..sub.a, associated with the physical attribute as determined in step 69).
14. Sutherland et al discloses the system of Claim 1, wherein at least one of the computer or the machine learning model is configured to generate a three-dimensional model of the pipeline (See: [0058] For illustration purposes, FIG. 8 shows a representation of MFL and caliper sensor signals juxtaposed after each acquired sensor signal has been mapped onto a three-dimensional grid representative of the inner pipeline wall) using the identified pipeline features specifying the assigned feature class, the axial position, the angular position, the linear dimensions, and the size (See: [0031] More specifically, FIG. 2A schematically depicts a pipeline portion comprising several straight segments separated by several bends, FIG. 2B depicts an expanded view of one of the straight segment portions 27 (e.g., a spool), and FIG. 2C shows an expanded view of a section 29 thereof (i.e., Region of Interest (ROI)). Coordinates are schematically depicted with respect to the spool, with the z-axis being oriented along the axial direction corresponding to the scan direction, the radial direction being oriented normal to the z-axis, and the azimuthal angle corresponding to the angular rotation about the z-axis, with the azimuthal (or circumferential) direction being oriented in a direction mutually perpendicular to the radial and axial directions. As schematically depicted in FIG. 2C, by way of example, the ROI includes a narrow, elongated axial feature ("feature" also referred to herein as an attribute or characteristic) 21 and a circumferentially and axially extending feature 23. Such features (or attributes or characteristics) may represent one or more of at least the following: topological/topographical/geometric variations (e.g., dents, scratches, peeling, wall thickness, etc.), material property (e.g., compositional) variations (e.g., surface and/or bulk property variations, such as due to corrosion or to differences between bulk material and surface coating material, etc.), and mechanical property (e.g., stress/strain) variations).
15. Sutherland et al discloses the system of Claim 1, wherein at least one of the computer or the machine learning model is configured to highlight or tag the identified pipeline features on the three-dimensional model (See: [0058] For illustration purposes, FIG. 8 shows a representation of MFL and caliper sensor signals juxtaposed after each acquired sensor signal has been mapped onto a three-dimensional grid representative of the inner pipeline wall).
16. Sutherland et al discloses the system of Claim 1, wherein the feature class includes at least one of a deposit, a buckle, a dent, a presence of an ovality, a presence of an offtake, a presence of a fixture, a presence of a tee, a presence of a valve, a bend, a diameter change, a wall thickness change, a presence of a through-hole, a presence of a stress/strain zone, a presence of a weld, and/or a hard spot (See: par [0008] he respective signals representative of the spatial position of the housings of different ones of the multi-sensor assemblies are capable of being processed to provide a measurement of the inner diameter of said pipeline; par [0031] Such features (or attributes or characteristics) may represent one or more of at least the following: topological/topographical/geometric variations (e.g., dents, scratches, peeling, wall thickness, etc.), material property (e.g., compositional) variations (e.g., surface and/or bulk property variations, such as due to corrosion or to differences between bulk material and surface coating material, etc.), and mechanical property (e.g., stress/strain) variations; [0062] In the region between regions a and b, the relative changes in MFL, EC, and possibly EMAT signals while the caliper signal does not change (e.g., insubstantial change), implies or may be inferred as meaning that the region is at a transition to a deformed region and is associated with stress/strain, which may be estimated based on the local changes in geometry/curvature.
17. Sutherland et al discloses the system of Claim 1, wherein the ring of caliper arms is a first ring of caliper arms, the caliper pig including a second ring of caliper arms located at the rear section, each caliper arm configured to move upward and downward to measure surface features of the pipeline and including a movement sensor to detect the upward and downward movement of the caliper arm (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm).
18. Sutherland et al discloses the system of Claim 1, further comprising at least one support ring located at the front section or the middle section, the support ring including wheeled arms for supporting the caliper pig (See: Fig. 1 and corresponding texts).
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2011/0167914 A1 issued to Sutherland et al in view of US Patent No. 11, 156, 911 B2 issued to Tabaian et al.
19. Sutherland et al discloses inspecting pipelines, the model configured to:
receive wheel rotation information and caliper arm measurement data from a processor of a caliper pig (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm);
combine the caliper arm measurement data for the different caliper arms that correspond to the same wheel rotation information (See: [0044] As will be further understood in view of the ensuing description, the EMAT, EC, MFL, and caliper sensors may be operable to acquire signals at the same sampling rate (though different sampling rates are possible), and information from various combinations of the acquired signals may be processed to provide for improved feature detection; [0048] As indicated above, acquisition of the signals from the various sensors provides for many embodiments for processing the acquired signals in various combinations to provide for improved characterization of the pipeline integrity (e.g., discerning features with greater sensitivity, greater accuracy, greater confidence levels, etc.). FIG. 6 is an operational flow diagram illustrating various methods for processing signals acquired from a multi-sensor device, in accordance with some embodiments of the present invention. Signals acquired (step 63) individually from the EC, EMAT, caliper, and MFL sensors 61 at respective desired sampling rates (e.g., at the same sampling rate) are stored (step 65), typically as values reflecting a calibration of the sensor (e.g., the acquired signal may be scaled or normalized according to a calibration factor to provide the stored value));
parse the caliper arm measurement data into separate pipeline sections based on the wheel rotation information (See: [0038] The caliper or deformation sensor 10 measures a rotation about a pivot axis where the sensor arm and head are mounted. Rotational movement about the pivot axis generates a signal in the sensor which then can be interpreted. The caliper sensor 10 may be implemented using any of a variety of transducer types (e.g., optical, electrical, magnetic, electromechanical (such as a rotary variable differential transformer (RVDT), magnetic, etc.) to convert rotational motion into a relative or proportional measurable signal reflecting a change in strain, capacitance, resistance, etc. The known dimensions of the sensor head 50 and arm 40 can be used to determine a deflection distance of the head 50. When considered in context of a plurality of circumferentially arranged integrated multi-sensor devices around an axis-symmetric tool in a pipe, this allows for measurement of inner diameter of the pipe. Additionally, as will be further understood below, the determined deflection of the head may be used to correct or compensate acquired signals (e.g., their magnitudes) and/or the spatial position associated with the acquired signals. For instance, if the head is angled as it traverses the sloped wall of a depression in the axial direction, then the actual displacement in the axial direction for the sampled signals may not equal the linear displacement determined from, for example, the odometer wheels, but may be corrected for the angle of the sensor head. One or more additional sensors may be provided to determine the head orientation; for example, an additional rotational transducer may be provided to measure the rotation about the pivot that joins the head to the arm); and
sequentially process each pipeline section to detect pipeline features, assign a feature class to each pipeline feature, and determine an axial position, an angular position, linear dimensions, and a size for each pipeline feature (See: [0031] More specifically, FIG. 2A schematically depicts a pipeline portion comprising several straight segments separated by several bends, FIG. 2B depicts an expanded view of one of the straight segment portions 27 (e.g., a spool), and FIG. 2C shows an expanded view of a section 29 thereof (i.e., Region of Interest (ROI)). Coordinates are schematically depicted with respect to the spool, with the z-axis being oriented along the axial direction corresponding to the scan direction, the radial direction being oriented normal to the z-axis, and the azimuthal angle corresponding to the angular rotation about the z-axis, with the azimuthal (or circumferential) direction being oriented in a direction mutually perpendicular to the radial and axial directions. As schematically depicted in FIG. 2C, by way of example, the ROI includes a narrow, elongated axial feature ("feature" also refer