DETAILED ACTION
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/15/2023 and 04/10/2025 is being considered by the examiner.
Claim Objections
Claim 64 is objected to because of the following informalities: “cable” should read as “capable.” Appropriate correction is required.
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 36, 41-58 and 60-67 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 36:
Step 1: The Claim is directed to a method.
Step 2A, Prong One: The Claim recites the abstract idea in the grouping of a mathematical concept of estimating wall thickness information using a model.
Step 2A, Prong Two: The Claim adds the insignificant extra-solution activity to the judicial exception of mere data gathering by measuring magnetic characteristic values of a magnetic tube. This does not integrate the judicial exception into a practical application
Step 2B: The additional elements do not amount to significantly more for the same reasons as stated in Step 2A, Prong Two and because they are well-understood, routine, conventional (WURC) activity previously known to the industry, specified at a high level of generality, to the judicial exception.
The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 41:
The Claim is directed to a method. Estimating the wall thickness information along the pipe is a mathematical concept. Obtaining data to input it into a model is mere data gathering and is WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 42:
The Claim is directed to a method. Taking of a moving average, extracting data based on calculating a difference and comparison to a threshold is a mathematical concept. Obtaining data to input it into a model is mere data gathering and is WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 43:
The Claim is directed to a method. Obtaining data based on a longitudinal position and inputting that data into a model is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 44:
The Claim is directed to a method. Mere conversion of data into an image using math is a mathematical concept. Obtaining data based on a longitudinal position and inputting that data into a model is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 45:
The Claim is directed to a method. Assigning colors to pixel values and merely generating an image and inputting that image into a model is an insignificant application and WURC idea. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 46:
The Claim is directed to a method. Acquiring information is mere data gathering, selecting a model based on that information is selecting a particular type of data to be manipulated and inputting measurement data into a model is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 47:
The Claim is directed to a method. The mathematical models, which are abstract ideas of mathematical concepts are being defined. Inputting measurement data into models is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 48:
The Claim is directed to a method. Taking the sum of a Euclidean distance is a mathematical concept. Inputting data into a model is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 49:
The Claim is directed to a method. Performing an arithmetic operation and executing an analysis is a mathematical concept. Inputting data into a model is mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim 50 is patent ineligible for the same reasons as Claim 36.
Claim 51 is patent ineligible for the same reasons as Claim 36.
Claim 52 is patent ineligible for the same reasons as Claim 36.
Claims 53-57 are patent ineligible for the same reasons as Claims 41-49.
Claim 58 is patent ineligible for the same reasons as Claim 36.
Claims 60-62 are patent ineligible for the same reasons as Claims 41-49.
Claim 63:
The Claim is directed to a method. Determining a fee is a mathematical concept. Imposing a fee is an insignificant application and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claims 64-66 are patent ineligible for the same reasons as Claims 60-63.
Claim 67:
The Claim is directed to a device. The Claim recites the abstract idea in the grouping of a mathematical concept of estimating wall thickness information using a model. Acquiring measurement data and transmitting thickness information are mere data gathering and WURC activity. The Claim elements individually and as a whole do not integrate the judicial exception into a practical application or amount to significantly more.
The Claim is patent ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 36-41, 49-53, 55 and 68 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dutta et al. (US 2009/0243604 A1, Pub Oct 1, 2009, herein Dutta).
Regarding Claim 36, Dutta teaches:
An information processing method in which a computer (Figure 8 shows a wall thickness measurement (WTM) method for calculating - "information processing method" - wall thickness using processing body 240 - "computer" [0040,0063].; see Fig 2 & 8) executes:
processing of acquiring measurement data obtained by measuring magnetic characteristic values of a magnetic tube (In Step 840, magnetic flux leakage (MFL) signals are detected [0067] using external magnetization and sensor tool 100 - "acquiring measurement data by measuring magnetic characteristic values" - from a pipe 150 "magnetic tube" [0040]. These signals are processed - "processing" - by processor 246 [0041].; see Fig 1-3 & 8); and
processing of estimating wall thickness information by inputting the acquired measurement data to a model trained for estimating the wall thickness information relating to the wall thickness of the magnetic tube in a case where the measurement data is input (In Step 850, the pipe's wall thickness is calculated - "estimated wall thickness information" - using the MFL signals - "inputting the acquired measurement data" - and matching them to the stored calibrated simulated measurements from Step 820, which were obtained using a mathematical model - "a model trained for estimating the wall thickness information relating to the wall thickness of the magnetic tube" [0063-0069].; see Fig 3 & 8).
Regarding Claim 37, Dutta teaches:
wherein the magnetic characteristic values are measurement values measured by using an inspection probe (External magnetization and sensor tool 100 [0032]) including a magnet (magnets 120 and 122 [0032]) that generates a magnetic field, a yoke (core 110 [0032]) that is disposed on an opposite side of the magnetic tube (pipe 150 [0032]) with respect to the magnet (120, 322), and a magnetic sensor (sensor 130 [0032]) that is disposed between the yoke (110) and the magnetic tube (150) and measures a magnetic flux density (detect MFL signals [0032]) passing through the yoke (110), the magnet (120,122), and the magnetic tube (150), and is an output voltage of the magnetic sensor which is proportional to the magnetic flux density (Magnetic sensor 130 outputs the MFL signal that is indicative of the pipe profile [0004,0037].; see Fig 3).
Regarding Claim 38, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions where a cross-section of the magnetic tube is equally divided along a peripheral direction at respective positions of the magnetic tube having a cylindrical shape along a longitudinal direction by using the inspection probe in which the magnet and the magnetic sensor are periodically attached onto an outer periphery of the yoke (External magnetization and sensor tool 300 may be part of an automated robotic system, which may be translated along the length of the pipe 350 to obtain a plurality of WTMs and estimate a portion of the pipe's profile at a space interval [0042-0043].; see Fig 3),
the magnetic characteristic values are normalized so that the magnetic characteristic values at the outside of the magnetic tube match each other at all positions along the peripheral direction, and the magnetic characteristic values at sound portions of the magnetic tube where thickness reduction does not occur match each other at all positions along the peripheral direction (Simulated wall thinning reference curve 1120 is modeled and matched to the empirical wall thinning reference curve 1110 using a scaling factor - "normalized" [0072].; see Fig 11 & 12), and
the measurement data obtained by normalizing the magnetic characteristic values is input to the model to estimate the wall thickness information (MFL data is used to generate data that is used to generate a predicted wall thickness profile curve through method 800 [0073].; see Fig 8, 11 & 12).
Regarding Claim 39, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at the respective positions where the cross-section of the magnetic tube having a cylindrical shape is equally divided along the peripheral direction by using the inspection probe in which the magnet and the magnetic sensor are periodically attached onto an outer periphery of the yoke (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043].),
the magnetic characteristic values at the respective positions are corrected to values in a case where the inspection probe passes a central axis of the magnetic tube (Data is scaled and estimated WTM are generated [0071-0073].; see Fig 10-12), and
the measurement data in which the magnetic characteristic values are corrected is input to the model to estimate the wall thickness information at the respective positions (Step 850 [0067].; see Fig 8).
Regarding Claim 40, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions where a cross-section of the magnetic tube is equally divided along a peripheral direction at respective positions of the magnetic tube having a cylindrical shape along a longitudinal direction by using the inspection probe in which the magnet and the magnetic sensor are periodically attached onto an outer periphery of the yoke (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043].),
an average value, a standard deviation, the degree of distortion, or kurtosis of the magnetic characteristic values along the longitudinal direction or the peripheral direction is calculated from the magnetic characteristic values at the respective positions in the longitudinal direction or the peripheral direction (Each sensor 130 may detect an average MFL signal along the length of the pipe [0036,0043].; see Fig 1), and
the measurement data to which the average value, the standard deviation, the degree of distortion, or the kurtosis is added is input to the model to estimate the wall thickness information (The data from sensors 130 is detected in Step 840, processed and then input into the model in Step 850 that is used to calculate the wall thickness [0063-0067].; see Fig 1 & 8).
Regarding Claim 41, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions of the magnetic tube along a longitudinal direction (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043].), and
the measurement data is input to the model to estimate the wall thickness information of respective portions obtained by partitioning the magnetic tube for a certain length along the longitudinal direction (In Step 850, the MFL signals detected from Step 840 are input in the model to estimate the wall thickness [0063-0067].; see Fig 1 & 8).
Regarding Claim 49, Dutta teaches:
wherein the measurement data includes sensor data obtained from a plurality of sensors provided in a peripheral direction of the magnetic tube, first group data obtained by performing preprocessing including arithmetic operation processing on the sensor data, and second group data obtained by executing main component analysis on the sensor data and the first group data, and the sensor data, the first group data, and the second group data are input to the model to estimate the wall thickness information (The measurement data is the MFL signals detected from a pipe in Step 840. The first group data is the averaged signals from sensor 130 [0036]. The second group data is the matching analysis of the averaged signals to determine the wall thickness measurement in Step 850.; see Fig 1 & 8).
Claim 50 is rejected on the same grounds as Claim 36.
Claim 51 is rejected on the same grounds as Claim 36.
Regarding Claim 52, Dutta teaches:
A model generation method (Fig 8) in which a computer (processor 246 [0041]; see Fig 2) executes:
processing of acquiring training data associated with a correct value of wall thickness information relating to the wall thickness of a magnetic tube with respect to measurement data obtained by measuring magnetic characteristic values of the magnetic tube (In Step 810, a set of calibration measurements - "acquiring training data" - is obtained using a sensor 100 or 300 [0064]. In Step 840, MFL signals - "measurement data" - are detected from the pipe [0063-0067].; see Fig 1 & 8); and
processing of generating a trained model that estimates the wall thickness information in a case where the measurement data is input on the basis of the training data (In Step 820, a model is generated from a set of simulated measurements. In Step 830 the model is trained based on the calibration measurements of Step 810. In Step 850, the data from Step 840 is matched with the trained model of Step 830 to generate wall thickness data based on the data from Step 840 [0063-0067].; see Fig 1 & 8).
Regarding Claim 53, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions where a cross-section of the magnetic tube having cylindrical shape is equally divided along a peripheral direction, the positions on the cross-section of the magnetic tube where the magnetic characteristic values are measured are shifted along the peripheral direction to generate a plurality of patterns of measurement data from the training data, and the trained model is generated by using the plurality of patterns of measurement data (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043]. Sensors 103 are at various circumferential positions of pipe 150 [0036]. In step 810 average measurements are taken from each of sensors 103 [0036, 0063-0067].; see Fig 1 & 8).
Regarding Claim 55, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions of the magnetic tube along a longitudinal direction (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043]. Sensors 103 are at various circumferential positions of pipe 150 [0036].; see Fig 1),
Regarding Claim 68, Dutta teaches:
A non-transitory computer-readable storage medium storing a program that causes a computer to execute:
processing (Processor 246 [0041]; see Fig 2) of acquiring measurement data obtained by measuring magnetic characteristic values (Detect a MFL signal from a pipe [0063-0067]; see Fig 8) of a magnetic tube (Pipe 250 [0041]; see Fig 2) from a measurement device (sensor 230 [0041]; see Fig 2);
processing of transmitting the measurement data to an information processing device that estimates wall thickness information by using a model that is trained for estimating the wall thickness information relating to the wall thickness of the magnetic tube in a case where the measurement data is input (Sensor 230 sends the measurement data to processor 246 [0041].; see Fig 2);
processing of acquiring the wall thickness information estimated by inputting the measurement data to the model from the information processing device (In Step 850, processor 246 calculates the wall thickness using the MFL signals from Step 840 and the model developed in Step 830 [0063-0067].; see Fig 8); and
processing of displaying the acquired wall thickness information on a display unit (Output device 244 displays the wall thickness measurements [0041].; see Fig 2).
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.
Claims 42-43 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Winslow et al. (US 6,359,434 B1, Pub Mar 19, 2002, herein Winslow).
Regarding Claim 42, Dutta does not teach the limitations.
However, Winslow teaches:
wherein a moving average of the magnetic characteristic values along the longitudinal direction is taken to specify a base line of the measurement data, data of a peak portion where a difference between the base line and the magnetic characteristic values is equal to or more than a predetermined threshold value is extracted from the measurement data, and the extracted data of the peak portion is input to the model to estimate the wall thickness information of the peak portion (Data is smoothed by applying a moving block average filter that removes noise spikes, while allowing actual defect signal spikes to be found [10:30-40]. A threshold is set to determine whether the thickness is nominal or out of range [17:26-41]. The thickness of the wall is determined [22:44-54]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Winslow by having wherein a moving average of the magnetic characteristic values along the longitudinal direction is taken to specify a base line of the measurement data, data of a peak portion where a difference between the base line and the magnetic characteristic values is equal to or more than a predetermined threshold value is extracted from the measurement data, and the extracted data of the peak portion is input to the model to estimate the wall thickness information of the peak portion because it prevents noise peaks from being detected as defects as taught by Winslow [10:30-40].
Regarding Claim 43, Dutta does not teach the limitations.
However, Winslow teaches:
wherein data of respective data sections obtained by slightly shifting data sections having a predetermined length along the longitudinal direction is extracted from the measurement data, and the extracted data of the respective data sections is input to the model to estimate the wall thickness information of the peak portion (The thickness of the wall at any point can be determined [22:44-54]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Winslow by having wherein data of respective data sections obtained by slightly shifting data sections having a predetermined length along the longitudinal direction is extracted from the measurement data, and the extracted data of the respective data sections is input to the model to estimate the wall thickness information of the peak portion because it prevents noise peaks from being detected as defects as taught by Winslow [10:30-40].
Claims 44-45 and 70 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Babcock et al. (US 2018/0259486 A1, Pub Sep 13, 2018, herein Babcock).
Regarding Claim 44, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions on a cross-section orthogonal to the longitudinal direction at respective positions of the magnetic tube along the longitudinal direction (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043]. Sensors 103 are at various circumferential positions of pipe 150 [0036].; see Fig 1),
Dutta does not teach:
the measurement data is converted into an image in which a first axis of the image is set to a position along the longitudinal direction, a second axis of the image is set to a position on the cross-section, and pixel values of respective pixels are allocated in correspondence with the magnetic characteristic values at respective positions of the magnetic tube, and
the image is input to the model to estimate the wall thickness information.
However, Babcock teaches:
the measurement data is converted into an image in which a first axis of the image is set to a position along the longitudinal direction, a second axis of the image is set to a position on the cross-section, and pixel values of respective pixels are allocated in correspondence with the magnetic characteristic values at respective positions of the magnetic tube, and the image is input to the model to estimate the wall thickness information (The display screen 1102/1104 shows the longitudinal axis of the pipe and the circumferential axis of the pipe and uses colors to indicate defect depths that is input into the defect detection system 112 [0066, 0078].; see Fig 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Babcock by having the measurement data is converted into an image in which a first axis of the image is set to a position along the longitudinal direction, a second axis of the image is set to a position on the cross-section, and pixel values of respective pixels are allocated in correspondence with the magnetic characteristic values at respective positions of the magnetic tube, and the image is input to the model to estimate the wall thickness information because it allows a user to quickly assess the location and severity of a defect as taught by Babcock [0064].
Regarding Claim 45, Dutta does not teach the limitations.
However, Babcock teaches:
wherein a plurality of hue images in which exponentiation values of a plurality of patterns of magnetic characteristic values which are different each other in an exponent are allocated to hues different from each other are generated from the measurement data, a synthetic image obtained by synthesizing the plurality of hue images is generated, and the generated synthetic image is input to the model to estimate the wall thickness information (The display screen 1102/1104 shows the longitudinal axis of the pipe and the circumferential axis of the pipe and uses colors to indicate defect depths that is input into the defect detection system 112 [0066, 0078].; see Fig 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Babcock by having wherein a plurality of hue images in which exponentiation values of a plurality of patterns of magnetic characteristic values which are different each other in an exponent are allocated to hues different from each other are generated from the measurement data, a synthetic image obtained by synthesizing the plurality of hue images is generated, and the generated synthetic image is input to the model to estimate the wall thickness information because it allows a user to quickly assess the location and severity of a defect as taught by Babcock [0064].
Regarding Claim 70, Dutta teaches:
wherein the measurement data is data obtained by measuring the magnetic characteristic values at respective positions on a cross-section orthogonal to a longitudinal direction at respective positions of the magnetic tube along the longitudinal direction (In an embodiment, the magnetization and sensor tool may be part of an automated robotic system, which may be controlled via software and/or hardware to detect a plurality of MFL signals along the pipe. For instance, the magnetization and sensor tool may be translated at a predetermined speed along a predetermined pipe length or predetermined period of time to detect the MFL signals along the length of the pipe. The MFL signals may be detected at a predetermined time or space interval to obtain a plurality of WTMs at a predetermined resolution along the pipe. The MFL signals may also be detected at a continuous time or space interval to obtain a continuous pipe wall thickness profile [0043]. Sensors 103 are at various circumferential positions of pipe 150 [0036].; see Fig 1),
a graph showing the wall thickness at the respective positions along the longitudinal direction is displayed on the basis of the wall thickness information representing the wall thickness at the respective positions on the cross-section at the respective positions along the longitudinal direction (Figure 12 is a wall thickness profile graph that shows the wall thickness on the y-axis and the longitude along the x-axis for a particular sensor 130 [0073].; see Fig 12),
a designation input for designating a position along the longitudinal direction is accepted on the graph (On the x-axis, various positions along the x-axis are shown [0073].; see Fig 12), and
Dutta does not teach:
a cross-sectional image of the magnetic tube which reproduces the wall thickness at the respective positions on the cross-section at the designated position is displayed
However, Babcock teaches:
a cross-sectional image of the magnetic tube which reproduces the wall thickness at the respective positions on the cross-section at the designated position is displayed (The display screen 1102/1104 shows the longitudinal axis of the pipe and the circumferential axis of the pipe and uses colors to indicate defect depths that is input into the defect detection system 112 [0066, 0078].; see Fig 11).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Babcock by having a cross-sectional image of the magnetic tube which reproduces the wall thickness at the respective positions on the cross-section at the designated position is displayed because it allows a user to quickly assess the location and severity of a defect as taught by Babcock [0064].
Claims 46-47, 56 and 69 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Kayoko et al. (JP 2007225564 A, Pub September 6, 2007, herein Kayoko).
Regarding Claim 46, Dutta teaches:
wherein magnetic tube information relating to the magnetic tube that is a measurement target in the measurement data is acquired (In Step 840, MFL signals are detected from a pipe.; see Fig 8),
Dutta does not teach:
a model corresponding to the acquired magnetic tube information is selected among a plurality of the models trained by a plurality of pieces of different training data in correspondence with the magnetic tube information, and
the measurement data is input to the selected model to estimate the wall thickness information.
However, Kayoko teaches:
a model corresponding to the acquired magnetic tube information is selected among a plurality of the models trained by a plurality of pieces of different training data in correspondence with the magnetic tube information, and the measurement data is input to the selected model to estimate the wall thickness information (Different models are used depending on the types of materials being evaluated [0076-0079].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Kayoko by having a model corresponding to the acquired magnetic tube information is selected among a plurality of the models trained by a plurality of pieces of different training data in correspondence with the magnetic tube information, and the measurement data is input to the selected model to estimate the wall thickness information because different types of flaws require different models to accurately determine as taught by Kayoko [0042].
Regarding Claim 47, Dutta does not teach the limitations.
However, Kayoko teaches:
wherein the models include a first model trained mainly based on the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is smaller than a predetermined value, a second model trained mainly based on the measurement data and wall thickness information in which the residual wall thickness of the magnetic tube is larger than the predetermined value, and a third model trained to output wall thickness information in a case where the wall thickness information output from the first model and the wall thickness information output from the second model are input, and the acquired measurement data is input to the first model and the second model, and outputs from the first model and the second model are input to the third model to estimate the wall thickness information (Multiple models can be selected based on the depth of the defect which is dependent on the thickness of the wall of the pipe [0057-0060].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Kayoko by having wherein the models include a first model trained mainly based on the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is smaller than a predetermined value, a second model trained mainly based on the measurement data and wall thickness information in which the residual wall thickness of the magnetic tube is larger than the predetermined value, and a third model trained to output wall thickness information in a case where the wall thickness information output from the first model and the wall thickness information output from the second model are input, and the acquired measurement data is input to the first model and the second model, and outputs from the first model and the second model are input to the third model to estimate the wall thickness information because it improves evaluation accuracy when evaluating flaws at different depths as taught by Kayoko [0059-0060].
Regarding Claim 56, Dutta does not teach the limitations.
However, Kayoko teaches:
first training data that mainly includes the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is smaller than a predetermined value is acquired, a first model that outputs first wall thickness information in a case where the measurement data is input is generated on the basis of the acquired first training data, second training data that mainly includes the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is larger than the predetermined value is acquired, a second model that outputs second wall thickness information in a case where the measurement data is input is generated on the basis of the acquired second training data, third training data that includes the first wall thickness information output from the first model, the second wall thickness information output from the second model, and the wall thickness information is acquired, and a third model that outputs wall thickness information in a case where the first wall thickness information and the second wall thickness information are input is generated on the basis of the acquired third training data (Multiple models can be selected based on the depth of the defect which is dependent on the thickness of the wall of the pipe [0057-0060].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Kayoko by having first training data that mainly includes the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is smaller than a predetermined value is acquired, a first model that outputs first wall thickness information in a case where the measurement data is input is generated on the basis of the acquired first training data, second training data that mainly includes the measurement data and wall thickness information in which a residual wall thickness of the magnetic tube is larger than the predetermined value is acquired, a second model that outputs second wall thickness information in a case where the measurement data is input is generated on the basis of the acquired second training data, third training data that includes the first wall thickness information output from the first model, the second wall thickness information output from the second model, and the wall thickness information is acquired, and a third model that outputs wall thickness information in a case where the first wall thickness information and the second wall thickness information are input is generated on the basis of the acquired third training data because it improves evaluation accuracy when evaluating flaws at different depths as taught by Kayoko [0059-0060].
Regarding Claim 69, Dutta does not teach the limitations.
However, Kayoko teaches:
wherein second measurement data obtained by measuring the wall thickness of the magnetic tube by a measurement method different from a measurement method of the measurement data is transmitted to the information processing device as second measurement data representing a correct value of the wall thickness information, and the wall thickness information estimated by using the model that is updated on the basis of the measurement data and the second measurement data is acquired from the information processing device (Different models are applied based on different conditions that require different evaluation parameters that are fed into a neural network that performs multiple regression analysis to update the model [0037-0047].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Kayoko by having wherein second measurement data obtained by measuring the wall thickness of the magnetic tube by a measurement method different from a measurement method of the measurement data is transmitted to the information processing device as second measurement data representing a correct value of the wall thickness information, and the wall thickness information estimated by using the model that is updated on the basis of the measurement data and the second measurement data is acquired from the information processing device because it allows for increasingly accurate measurements of defects of differing sizes as taught by Kayoko [0047].
Claims 48 and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Liu et al. ("Matching pipeline In-line inspection data for corrosion characterization"; Pub Oct 11, 2018; NDT and E International; 101; herein Liu).
Regarding Claim 48, Dutta does not teach the limitations.
However, Liu teaches:
wherein the measurement data is corrected so that the sum of a Euclidean distance between the measurement data and wall thickness information corresponding to the measurement data becomes the minimum, and the model is trained on the basis of the measurement data after correction, and wall thickness information (The amount of corrosion on a pipe is determined using a machine learning model that takes in data using the Euclidean distance between points in Euclidean space [pp.44-46].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Liu by having wherein the measurement data is corrected so that the sum of a Euclidean distance between the measurement data and wall thickness information corresponding to the measurement data becomes the minimum, and the model is trained on the basis of the measurement data after correction, and wall thickness information because it allows for pattern matching to be performed to identify patterns of corrosion as taught by Liu [pp.45-46].
Regarding Claim 57, Dutta does not teach the limitations.
However, Liu teaches:
wherein the measurement data and the wall thickness information corresponding to the measurement data are acquired,
the measurement data is corrected so that the sum of a Euclidian distance between the acquired measurement data and the acquired wall thickness information becomes the minimum, and the model is trained on the basis of the measurement data after correction and the wall thickness information (The amount of corrosion on a pipe is determined using a machine learning model that takes in data using the Euclidean distance between points in Euclidean space [pp.44-46].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Liu by having wherein the measurement data and the wall thickness information corresponding to the measurement data are acquired, the measurement data is corrected so that the sum of a Euclidian distance between the acquired measurement data and the acquired wall thickness information becomes the minimum, and the model is trained on the basis of the measurement data after correction and the wall thickness information because it allows for pattern matching to be performed to identify patterns of corrosion as taught by Liu [pp.45-46].
Claim 54 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Zhang et al. (CN 107941899 A, Pub Apr 20, 2018, herein Zhang).
Dutta does not teach the limitations.
However, Zhang teaches:
wherein a plurality of patterns of the measurement data are generated from the training data by adding a plurality of patterns of predetermined noise, and
the trained model is generated by using the plurality of patterns of measurement data (Gaussian white noise is added to magnetic field signal data to eventually denoise the signals. These signals are introduced into a neural network that determines irregularities in the target being inspected [0060-0089].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Zhang by having wherein a plurality of patterns of the measurement data are generated from the training data by adding a plurality of patterns of predetermined noise, and the trained model is generated by using the plurality of patterns of measurement data because it is critical to a denoising algorithm so that defects can be more accurately identified as taught by Zhang [0060].
Claims 58-59 and 67 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia et al. (US 2017/0372196 A1, Pub Dec 28, 2017, herein Traidia).
Regarding Claim 58, Dutta teaches:
An information processing method in which a computer executes:
processing of acquiring measurement data obtained by measuring magnetic characteristic values of a magnetic tube from each of user terminal (Processor 246 acquires sensor data from sensor 230 - "acquiring measurement data obtained by measuring magnetic characteristic values" - of pipe 250 - "magnetic tube" - from a "user terminal" consisting of input device 242 and output device 244 [0041].; see Fig 2);
processing of estimating (Processor 246 does the calculations - "estimating" [0041].; see Fig 2) wall thickness information by inputting the acquired measurement data to a model that is trained for estimating the wall thickness information relating to the wall thickness of the magnetic tube in a case where the measurement data is input (In Steps 840 and 850, measurement data is put through a learning model to generate wall thickness measurements about pipe 250 [0063-0067].; see Fig 8); and
processing of transmitting (Processor 246 also sends the processed data to output device 244 [0041].; see Fig 2) the estimated wall thickness information to the user terminal (Input device 242 and output device 244 [0041]; see Fig 2) that is an acquisition source of the measurement data (The user terminal includes the processor 246 that acquires measurement data from sensor 230 [0041].; see Fig 2).
Dutta does not teach:
each of user terminals connected for communication through a network
However, Traidia teaches:
each of user terminals connected for communication through a network (Data regarding faults in a pipe is communicated to individual computer workstations or operator terminals over a network [0051].; see Fig 5);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Traidia by having each of user terminals connected for communication through a network because it is applying a known technique to a known device ready for improvement to yield the predictable result to share and process data with users in many different locations.
Regarding Claim 59, Dutta teaches:
wherein the magnetic characteristic values are measurement values measured by using an inspection probe (External magnetization and sensor tool 100 [0032]) including a magnet (magnets 120 and 122 [0032]) that generates a magnetic field, a yoke (core 110 [0032]) that is disposed on an opposite side of the magnetic tube (pipe 150 [0032]) with respect to the magnet (120, 322), and a magnetic sensor (sensor 130 [0032]) that is disposed between the yoke (110) and the magnetic tube (150) and measures a magnetic flux density (detect MFL signals [0032]) passing through the yoke (110), the magnet (120,122), and the magnetic tube (150), and is an output voltage of the magnetic sensor that becomes lower as the magnetic flux density is larger (Magnetic sensor 130 outputs the MFL signal that is indicative of the pipe profile [0004,0037].; see Fig 3).
Regarding Claim 67, Dutta teaches:
An information processing device, comprising:
an acquisition unit that acquires measurement data obtained by measuring magnetic characteristic values of a magnetic tube from each of user terminal (Processor 246 - "acquisition unit" - acquires sensor data from sensor 230 - "measurement data obtained by measuring magnetic characteristic values" - of pipe 250 - "magnetic tube" - from a "user terminal" consisting of input device 242 and output device 244 [0041].; see Fig 2);
an estimation unit (Processor 246 also does the calculations [0041].; see Fig 2) that estimates wall thickness information by inputting the acquired measurement data to a model that is trained for estimating the wall thickness information relating to the wall thickness of the magnetic tube in a case where the measurement data is input (In Steps 840 and 850, measurement data is put through a learning model to generate wall thickness measurements about pipe 250 [0063-0067].; see Fig 8); and
a transmission unit (Processor 246 also sends the processed data to output device 244 [0041].; see Fig 2) that transmits the estimated wall thickness information to the user terminal (Input device 242 and output device 244 [0041]; see Fig 2) that is an acquisition source of the measurement data (The user terminal includes the processor 246 that acquires measurement data from sensor 230 [0041].; see Fig 2).
Dutta does not teach:
each of user terminals connected for communication through a network
However, Traidia teaches:
each of user terminals connected for communication through a network (Data regarding faults in a pipe is communicated to individual computer workstations or operator terminals over a network [0051].; see Fig 5)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta in view of Traidia by having each of user terminals connected for communication through a network because it is applying a known technique to a known device ready for improvement to yield the predictable result to share and process data with users in many different locations.
Claim 60 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia and further in view of Kayoko.
Dutta and Traidia do not teach the limitations.
However, Kayoko teaches:
wherein a designation input of magnetic tube information relating to the magnetic tube that is a measurement target is accepted, the model corresponding to the acquired magnetic tube information among a plurality of the models which have learned training data different in correspondence with the magnetic tube information is selected, and the measurement data is input to the selected model to estimate the wall thickness information (Different models are used depending on the types of materials being evaluated [0076-0079].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta and Traidia in view of Kayoko by having wherein a designation input of magnetic tube information relating to the magnetic tube that is a measurement target is accepted, the model corresponding to the acquired magnetic tube information among a plurality of the models which have learned training data different in correspondence with the magnetic tube information is selected, and the measurement data is input to the selected model to estimate the wall thickness information because different types of flaws require different models to accurately determine as taught by Kayoko [0042].
Claims 61-62 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia and further in view of Khalaj et al. (US 2020/0284137 A1, Pub Sep 10, 2020, herein Khalaj).
Regarding Claim 61, Dutta and Traidia do not teach the limitations.
However, Khalaj teaches:
wherein the measurement data acquired from the user terminal is stored in a storage unit (The data collected by tool 970 can be stored with the tool [0077].; see Fig 9),
a correct value of the wall thickness information corresponding to the measurement data is acquired (In Step 710 measurements are acquired [0045].; see Fig 7), and
the model is updated on the basis of the measurement data stored in the storage unit, and the correct value (In Step 720, the model is updated based on the measurement data [0046].; see Fig 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta and Traidia in view of Khalaj by having wherein the measurement data acquired from the user terminal is stored in a storage unit, a correct value of the wall thickness information corresponding to the measurement data is acquired, and the model is updated on the basis of the measurement data stored in the storage unit, and the correct value because it provides for pipe characterization that can be performed faster and with higher precision as taught by Khalaj [0016].
Regarding Claim 62, Dutta and Traidia do not teach the limitations.
However, Khalaj teaches:
wherein the model is updated by using the measurement data acquired from a user terminal of a user for every user who is an acquisition source of the measurement data (A user interface can be connected wirelessly with the tool and data processing unit to update the model [0045-0047,0077].; see Fig 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta and Traidia in view of Khalaj by having wherein the model is updated by using the measurement data acquired from a user terminal of a user for every user who is an acquisition source of the measurement data because it provides for pipe characterization that can be performed faster and with higher precision as taught by Khalaj [0016].
Claims 63-64 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia and further in view of Khalaj and further in view of Shimomura (US 2021/0264164 A1, PCT Filed Nov 12, 2019).
Regarding Claim 63, Dutta, Traidia and Khalaj do not teach the limitations.
However, Shimomura teaches:
wherein in a case of estimating the wall thickness information, a usage fee of the model which is imposed on a user is determined in correspondence with the amount of calculation of the processing of estimating the wall thickness information using the model (A data use fee is charged based on the aggregation of data used for each sensor device [0069]. The sensor is equipped with an AI function and uses machine learning [0054,0086].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta, Traidia and Khalaj in view of Shimomura by having wherein in a case of estimating the wall thickness information, a usage fee of the model which is imposed on a user is determined in correspondence with the amount of calculation of the processing of estimating the wall thickness information using the model because it is applying a known technique to a known device ready for improvement to yield predictable results of monetizing the processing of data and increasing profit.
Regarding Claim 64, Dutta, Traidia and Khalaj do not teach the limitations.
However, Shimomura teaches:
wherein in a case where the measurement data is acquired from the user terminal, a selection input of usage availability of the measurement data according to updating of the model is accepted from a user,
in a case of accepting a selection input indicating that the measurement data is cable of being used, the measurement data is stored in the storage unit, and
the usage fee of the model which is imposed on the user is decreased in correspondence with usage availability of the measurement data (A data use fee is charged based on the aggregation of data used for each sensor device [0069]. The sensor is equipped with an AI function and uses machine learning [0054,0086].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta, Traidia and Khalaj in view of Shimomura by having wherein in a case where the measurement data is acquired from the user terminal, a selection input of usage availability of the measurement data according to updating of the model is accepted from a user, in a case of accepting a selection input indicating that the measurement data is cable of being used, the measurement data is stored in the storage unit, and the usage fee of the model which is imposed on the user is decreased in correspondence with usage availability of the measurement data because it is applying a known technique to a known device ready for improvement to yield predictable results of monetizing the processing of data and increasing profit.
Claim 65 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia and further in view of Khalaj and further in view of Shimomura and further in view of Kayoko.
Dutta, Traidia and Khalaj do not teach:
the usage fee of the model which is imposed on the user is decreased in correspondence with presence or absence of acquisition of the second measurement data.
However, Shimomura teaches:
the usage fee of the model which is imposed on the user is decreased in correspondence with presence or absence of acquisition of the second measurement data (A data use fee is charged based on the aggregation of data used for each sensor device [0069]. The sensor is equipped with an AI function and uses machine learning [0054,0086]. If the data is less the fee changes in a corresponding way.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta, Traidia and Khalaj in view of Shimomura by having the usage fee of the model which is imposed on the user is decreased in correspondence with presence or absence of acquisition of the second measurement data because it is applying a known technique to a known device ready for improvement to yield predictable results of monetizing the processing of data and increasing profit.
Dutta, Traidia, Khalaj and Shimomura do not teach:
wherein second measurement data obtained by measuring the wall thickness of the magnetic tube by a measurement method different from a measurement method of the measurement data is acquired as second measurement data representing the correct value from the user terminal, the model is updated on the basis of the measurement data and the second measurement data
However, Kayoko teaches:
wherein second measurement data obtained by measuring the wall thickness of the magnetic tube by a measurement method different from a measurement method of the measurement data is acquired as second measurement data representing the correct value from the user terminal, the model is updated on the basis of the measurement data and the second measurement data (Different models are applied based on different conditions that require different evaluation parameters that are fed into a neural network that performs multiple regression analysis to update the model [0037-0045].),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta, Traidia, Khalaj, Shimomura and in view Kayoko of by having wherein second measurement data obtained by measuring the wall thickness of the magnetic tube by a measurement method different from a measurement method of the measurement data is acquired as second measurement data representing the correct value from the user terminal, the model is updated on the basis of the measurement data and the second measurement data because it allows for increasingly accurate measurements of defects of differing sizes as taught by Kayoko [0047].
Claim 66 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta in view of Traidia and further in view of Khalaj and further in view of Shimomura and further in view of Kayoko and further in view of Babcock.
Dutta, Traidia, Khalaj, Shimomura and Kayoko do not teach the limitations.
However, Babcock teaches:
wherein the second measurement data is measurement data obtained by an internal rotary inspection system (Further testing of the pipe can be performed with an ultrasonic tester [0072].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dutta, Traidia, Khalaj, Shimomura and in view Kayoko of by having wherein the second measurement data is measurement data obtained by an internal rotary inspection system because it allows for an additional level of testing to be performed to assess defects [0072].
Conclusion
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/R.M/Examiner, Art Unit 2858 03/09/2026
/ALESA ALLGOOD/Primary Examiner, Art Unit 2858