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 .
Claim 1-17, and 19-21 are pending.
Claim 18 is cancelled.
Response to Amendment
The amendment filed January 9th, 2026 has been entered. Claims 1-17, and 19-21 remain pending in the application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-17, and 19-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-9, 12-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over HIROSE et al. JP 2020/157317 A (hereinafter “HIROSE”), in view of SHINAGAWA et al. WO 2022/085153 A1 (hereinafter “SHINAGAWA”), further in view of Aoki et al. USPGPUB 2018/0056434 (hereinafter “Aoki”).
Regarding claim 1, HIROSE teaches a system for estimating an abnormality ([Abstract], and [Control unit configuration] “the abnormality determination unit 85 may compare the reference data with the process data to estimate the time when the abnormality occurs”), comprising:
an industrial device configured to control at least one jig such that the at least one jig presses an object to perform a work process ([Welding equipment configuration] “The welding control unit 47 controls the welding unit 49 based on the control signal received from the communication circuit 41 for executing the process associated with the welding work”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted shanks as jig that presses on an object to perform a work process, wherein examiner interpreted workpieces to be the object, and wherein examiner interpreted welding control unit as an industrial device);
a torque data acquisition sensor configured to acquire torque data of torque of a motor configured to move the jig ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like”, [Welding equipment configuration] “The measurement circuit 43 is connected to the sensor 45, and acquires data on the state of the welding device 40 as operation data of the welding device 40 via the sensor 45”, [Welding equipment configuration] “The sensor 45 is installed in the vicinity of the welding portion 49 (welding working portion) or the welding portion 49 in order to detect the state of the welding device 40”, and [Control unit configuration] “In order to pressurize the workpieces W1 and W2 in the "pressurization process", when pressure is applied to the shanks S1 and S2 using a clamp (not shown), vibration caused by the motor that rotates the clamp and the workpieces W1 and W2 and the shank S1”, wherein examiner interpreted sensor attached to welding portion which acquires data on state of the welding device, and wherein the welding device includes a motor that is rotating the clam and workpieces, and shanks, wherein the sensor detects torque as being a torque data acquisition sensor configured to acquire torque data of torque of a motor configured to move the jig);
a position data acquisition sensor configured to acquire position data on a position of the jig ([Welding equipment configuration] “the sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49”, [Welding equipment configuration] “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted sensor measuring position of the welded portion, wherein the welded portion includes shanks as a position data acquisition sensor configured to acquire position data on a position of the jig); and
perform an estimation estimating an abnormality based on the position data ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Control unit configuration] “In particular, if the welding tips T1 and T2 are welded to the workpieces W1 and W1, an extra force is applied to separate the welding tips T1 and T2 from the workpieces W1 and W1 in the "opening process", and the welding tips T1 and T1 are separated. T2 or shanks S1 and S2 will be distorted”, “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, “according to the abnormality determination method and the abnormality determination device according to the present embodiment, the group of processes includes an opening step of reducing the pressure applied to the work, and determines the abnormality in the opening process. You may. As a result, it is possible to detect an abnormality in the welding operation or a sign of the abnormality based on the characteristics of the opening process in the group of processes”, wherein examiner interpreted estimating the cause of defect in the welding state based on presence or absence of abnormalities in the each welding processes which includes the opening process as performing an estimation estimating an abnormality based on the position data, wherein examiner interpreted shanks, and welding tips separating from the workpieces during the opening process as using the position data when the jig is separated from the object).
HIROSE does not explicitly teach processing circuitry configured to detect that the jig is separated from the object based on the torque data, the position data being acquired based on detection that the jig is separated from the object.
However, SHINAGAWA teaches processing circuitry configured to detect that the jig is separated from the object based on the torque data ([FIG. 4, and FIG. 5 Description] “From the state shown in FIG. 4, the gun control unit 101a of the CPU 101 drives the servomotor 25 so that the movable electrode 23 approaches the fixed electrode 22. As a result, as shown in FIG. 5, the work W is sandwiched between the fixed electrode 22 and the movable electrode 23. At this time, whether or not the work W is sandwiched between the fixed electrode 22 and the movable electrode 23 is determined by whether or not the torque detected by the torque sensor 24 reaches a predetermined torque. Specifically, when the torque detected by the torque sensor 24 reaches a predetermined torque, the CPU 101 determines that the work W is sandwiched between the fixed electrode 22 and the movable electrode 23”, wherein examiner interpreted determining whether or not the work W is sandwiched between the fixed electrode and movable electrode based on torque detected by torque sensor as processing circuitry configured to detect that the jig is separated from the object based on the torque data).
HIROSE, and SHINAGAWA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and incorporating detecting jig is separated based on torque data, as taught by SHINAGAWA.
One of ordinary skill in the art would have been motivated to improve detecting when work W is sandwiched between electrodes, as suggested by SHINAGAWA (see [Plate thickness inspection]).
HIROSE, and SHINAGAWA does not explicitly teach the position data being acquired based on detection that the jig is separated from the object.
However, Aoki teaches the position data being acquired based on detection that the jig is separated from the object (Paragraph [0041] “The welding gun control apparatus 18 also includes a position detection part 67 for detecting the positions of the electrodes 30, 32”, and Paragraph [0035] “The movable electrode 30 moves along the axis of the movable electrode 30. The electrode drive motor 34 is driven, whereby the movable electrode 30 moves toward the opposite electrode 32 or moves in a direction which is away from the movable electrode 30”, and Paragraph [0047], wherein examiner interpreted position detection part detecting positions of electrodes as acquiring position data based on detection that the jig is separated from the object, wherein examiner interpreted electrodes being movable and position detection part detecting positions of the electrodes to include detecting positions of electrodes when moving towards the other or away from the other, and therefore, to include acquiring position data based on detection that the jig is separated from the object, and in combination with HIROSE teaches determining abnormality based on position data).
HIROSE, SHINAGAWA, and Aoki are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and SHINAGAWA, and incorporating acquiring jig position data, as taught by Aoki.
One of ordinary skill in the art would have been motivated to improve detecting and storing position data, and determining abnormality in welding process based on the pressurizing force applied by electrodes to a workpiece, as suggested by Aoki (see Paragraph [0044], Paragraphs [0005-0008]).
Regarding claim 2, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
SHINAGAWA further teaches wherein the processing circuitry is further configured to detect that the jig has come into contact with the object based on the torque data ([FIG. 4, and FIG. 5 Description] “From the state shown in FIG. 4, the gun control unit 101a of the CPU 101 drives the servomotor 25 so that the movable electrode 23 approaches the fixed electrode 22. As a result, as shown in FIG. 5, the work W is sandwiched between the fixed electrode 22 and the movable electrode 23. At this time, whether or not the work W is sandwiched between the fixed electrode 22 and the movable electrode 23 is determined by whether or not the torque detected by the torque sensor 24 reaches a predetermined torque. Specifically, when the torque detected by the torque sensor 24 reaches a predetermined torque, the CPU 101 determines that the work W is sandwiched between the fixed electrode 22 and the movable electrode 23”, wherein examiner interpreted determining whether or not the work W is sandwiched between the fixed electrode and movable electrode based on torque detected by torque sensor as processing circuitry is further configured to detect that the jig has come into contact with the object based on the torque data).
HIROSE further teaches to perform the estimation further estimating the abnormality based on the position data when the jig is detected to be in contact with the object ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, “In the "pressurizing step" (step P1 of FIG. 4), the welded portion 49 moves the shank S1 downward and the shank S2 upward, and as a result, the pressure F applied to the workpieces W1 and W2 increases. When the pressure F reaches a predetermined magnitude, the pressure F is maintained at a predetermined magnitude in the "pressurization maintenance step" (step P2 of FIG. 4)”, [Control unit configuration] “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, “according to the abnormality determination method and the abnormality determination device according to the present embodiment, the group of processes includes an opening step of reducing the pressure applied to the work, and determines the abnormality in the opening process. You may. As a result, it is possible to detect an abnormality in the welding operation or a sign of the abnormality based on the characteristics of the opening process in the group of processes”, wherein examiner interpreted estimating the cause of defect in the welding state based on presence or absence of abnormalities in the each welding processes which includes the pressurizing step of shanks moving towards workpiece as performing the estimation includes further estimating the abnormality based on the position data when the jig is detected to be in contact with the object).
Regarding claim 3, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a second acquisition process that acquires the torque data or position data including measurement results at a first time point when a first event occurs and a second time point when a second event occurs, and perform a second estimation process that estimates the abnormality based on the torque data or position data acquired by the second acquisition process ([Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein examiner interpreted determining abnormality based on data measured by measurement circuitry via senor during these different steps/processes including sensors that measure torque, and position as processing circuitry is configured to perform a second acquisition process that acquires the torque data or position data including measurement results at a first time point when a first event occurs and a second time point when a second event occurs, and perform a second estimation process that estimates the abnormality based on the torque data or position data acquired by the second acquisition process).
Regarding claim 4, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 3 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a third acquisition process that acquires the torque data including measurement results at the first time point when the first event, in which the at least one jig touches the object, occurs and at the second time point when the second event, in which the at least one jig separates from the object, occurs, and perform a third estimation process that estimates the abnormality based on the torque data acquired by the third acquisition process ([Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein examiner interpreted determining abnormality based on data measured by measurement circuitry via senor during these different steps/processes including measuring torque as the processing circuitry is configured to perform a third acquisition process that acquires the torque data including measurement results at the first time point when the first event, in which the at least one jig touches the object, occurs and at the second time point when the second event, in which the at least one jig separates from the object, occurs, and perform a third estimation process that estimates the abnormality based on the torque data acquired by the third acquisition process).
Regarding claim 5, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 4 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a fourth acquisition process that acquires the position data indicating a position at which the at least one jig touches the object and a position at which the at least one jig separates from the object, and perform a fourth estimation process that estimates the abnormality based on the position data acquired by the fourth acquisition process ([Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein examiner interpreted determining abnormality based on data measured by measurement circuitry via senor during these different steps/processes including measuring position as the processing circuitry is configured to perform a fourth acquisition process that acquires the position data indicating a position at which the at least one jig touches the object and a position at which the at least one jig separates from the object, and perform a fourth estimation process that estimates the abnormality based on the position data acquired by the fourth acquisition process).
Regarding claim 6, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a fifth estimation process that estimates the abnormality based on normal data related to a normal operation of the industrial device ([Control unit configuration] “the abnormality determination unit 85 may compare the reference data with the process data to estimate the time when the abnormality occurs. Specifically, the deviation amount of the feature amount in the process data with respect to the feature amount in the reference data may be calculated, and it may be estimated that the larger the deviation amount is, the more an abnormality may occur in the near future”, wherein examiner interpreted determining abnormality by comparing reference data with process data as processing circuitry is configured to perform a fifth estimation process that estimates the abnormality based on normal data related to a normal operation of the industrial device).
Regarding claim 7, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a sixth estimation process that estimates an abnormality that has occurred in the object based on the position data acquired ([Control unit configuration] “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85”. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, [Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein examiner interpreted detecting defect in welding state based on measured data from the measurement circuitry via sensors that measure position as processing circuitry configured to perform a sixth estimation process that estimates an abnormality that has occurred in the object based on the position data acquired).
Regarding claim 8, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a seventh estimation process that estimates the abnormality related to a width of the object as an abnormality that has occurred in the object ([Control unit configuration] “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85”, wherein examiner interpreted detecting defect in the welding state to include a seventh estimation process that estimates the abnormality related to a width of the object as an abnormality that has occurred in the object).
Regarding claim 9, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform an eighth estimation process that estimates an abnormality related to a predetermined device that is associated with the work process, based on the position data ([Control unit configuration] “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85”. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, [Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein examiner interpreted detecting defect in welding state based on measured data from the measurement circuitry via sensors that measure position as the processing circuitry is configured to perform an eighth estimation process that estimates an abnormality related to a predetermined device that is associated with the work process, based on the position data).
Regarding claim 12, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to determine a parameter used in estimation of the abnormality based on a learning model in which an estimation result of the abnormality executed in a past and an inspection result of an object for which the work process has been performed in the past are learned; and perform a ninth estimation process that estimates the abnormality based on the parameter ([Control unit configuration] “the abnormality determination unit 85 may determine the presence or absence of an abnormality by distinguishing between the normal state and the abnormal state in the process data based on deep learning using a neural network”, and “deep learning (deep learning, machine learning) using a neural network, a pair of input data and correct answer output (pair of process data and corresponding process) was prepared as training data, and the input data was input. Using a method that separates process data by calculating the output error between the output of the neural network and the correct output and adjusting various parameters of the neural network so that the error is minimized (supervised learning)”, “In addition, a self-encoder is used as a neural network, learning (unsupervised learning) is performed using training data of only input data (process data), and the self-encoder is made to acquire features that well represent the input data, so that the input data You may use the method of classifying and separating process data”, wherein examiner interpreted determining abnormality using deep learning as processing circuitry is configured to determine a parameter used in estimation of the abnormality based on a learning model in which an estimation result of the abnormality executed in a past and an inspection result of an object for which the work process has been performed in the past are learned; and perform a ninth estimation process that estimates the abnormality based on the parameter).
Regarding claim 13, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the at least one jig comprises multiple jigs and the object is pressed by the multiple jigs, and the processing circuitry is configured to perform a fifth acquisition process that acquires torque data and position data sets that respectively correspond to the multiple jigs, and perform a tenth estimation process that estimates the abnormality based on the torque data and position data sets acquired by the fifth acquisition process ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, wherein examiner interpreted shanks to be multiple jigs that presses on the object and measurement circuitry performs the acquiring of operation data and abnormality determination unit determines abnormality based on the torque data and position data sets, which is the fifth acquisition process, and tenth estimation process).
Regarding claim 14, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a sixth acquisition process that acquires torque data and position data sets that respectively correspond to multiple objects, and perform an eleventh estimation process that estimates the abnormality based on the torque data and position data sets acquired by the sixth acquisition process ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, wherein examiner interpreted workpieces to be the multiple objects and measurement circuitry performs the acquiring of torque data and position data sets and abnormality determination unit determines abnormality, which is the sixth acquisition process, and eleventh estimation process).
Regarding claim 15, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to register object identification information, which identifies the object, and an estimation result of the abnormality in association with each other in a database ([Control unit configuration] “The control unit 80 (an example of a controller) is a general-purpose microcomputer including a CPU (central processing unit), a memory, and an input / output unit. A computer program (abnormality determination program) for functioning as a part of the abnormality determination device 100 for determining an abnormality of the welding device 40 is installed in the control unit 80. By executing the computer program, the control unit 80 functions as a plurality of information processing circuits (81, 83, 85, 87, 89) included in the abnormality determination device 100”, wherein examiner interpreted memory within a controller/microcomputer that determines abnormality as to be registering object identification information which identifies object and an estimation result of the abnormality in association with each other in a database).
Regarding claim 16, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a twelfth estimation process that estimates a change in cycle time as the abnormality ([Abnormality judgement processing procedure] “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, wherein examiner interpreted abnormality in processes/steps of a work process as processing circuitry configured to perform a twelfth estimation process that estimates a change in cycle time as the abnormality, wherein examiner interpreted each process/steps as cycle time).
Regarding claim 17, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches wherein the processing circuitry is configured to perform a seventh acquisition process that acquires torque data and position data, and perform a thirteenth estimation process that estimates the abnormality based on the torque data and position data acquired by the seventh acquisition process ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, and [Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, wherein sensor detects multiple kinds of operation data including torque data and position data which is the seventh acquisition process, and determining abnormality based on these data as thirteenth estimation process).
Regarding claim 19, HIROSE teaches a method for estimating abnormality ([Abstract], and [Control unit configuration] “the abnormality determination unit 85 may compare the reference data with the process data to estimate the time when the abnormality occurs”), comprising:
controlling a jig by an industrial device such that the jig presses an object to perform a work process ([Welding equipment configuration] “The welding control unit 47 controls the welding unit 49 based on the control signal received from the communication circuit 41 for executing the process associated with the welding work”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted shanks as jig that presses on an object to perform a work process, wherein examiner interpreted workpieces to be the object, and wherein welding control unit controls the welding unit to press on the object);
acquiring torque data of torque of a motor configured to move the jig using a torque data acquisition sensor ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like”, [Welding equipment configuration] “The measurement circuit 43 is connected to the sensor 45, and acquires data on the state of the welding device 40 as operation data of the welding device 40 via the sensor 45”, [Welding equipment configuration] “The sensor 45 is installed in the vicinity of the welding portion 49 (welding working portion) or the welding portion 49 in order to detect the state of the welding device 40”, and [Control unit configuration] “In order to pressurize the workpieces W1 and W2 in the "pressurization process", when pressure is applied to the shanks S1 and S2 using a clamp (not shown), vibration caused by the motor that rotates the clamp and the workpieces W1 and W2 and the shank S1”, wherein examiner interpreted sensor attached to welding portion which acquires data on state of the welding device, and wherein the welding device includes a motor that is rotating the clam and workpieces, and shanks, wherein the sensor detects torque as being a torque data acquisition sensor configured to acquire torque data of torque of a motor configured to move the jig);
acquiring position data on a position of the jig using a position data acquisition sensor ([Welding equipment configuration] “the sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49”, [Welding equipment configuration] “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted sensor measuring position of the welded portion, wherein the welded portion includes shanks as a position data acquisition sensor configured to acquire position data on a position of the jig);
estimating an abnormality based on the operation position data when the jig is separated from the object ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Control unit configuration] “In particular, if the welding tips T1 and T2 are welded to the workpieces W1 and W1, an extra force is applied to separate the welding tips T1 and T2 from the workpieces W1 and W1 in the "opening process", and the welding tips T1 and T1 are separated. T2 or shanks S1 and S2 will be distorted”, “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, “according to the abnormality determination method and the abnormality determination device according to the present embodiment, the group of processes includes an opening step of reducing the pressure applied to the work, and determines the abnormality in the opening process. You may. As a result, it is possible to detect an abnormality in the welding operation or a sign of the abnormality based on the characteristics of the opening process in the group of processes”, wherein examiner interpreted estimating the cause of defect in the welding state based on presence or absence of abnormalities in the each welding processes which includes the opening process as performing an estimation estimating an abnormality based on the position data when the jig is separated from the object, wherein examiner interpreted shanks, and welding tips separating from the workpieces during the opening process as using the position data when the jig is separated from the object).
HIROSE does not explicitly teach detecting that the jig is separated from the object based on the torque data; the position data being acquired based on detection that the jig is separated from the object.
However, SHINAGAWA teaches detecting that the jig is separated from the object based on the torque data ([FIG. 4, and FIG. 5 Description] “From the state shown in FIG. 4, the gun control unit 101a of the CPU 101 drives the servomotor 25 so that the movable electrode 23 approaches the fixed electrode 22. As a result, as shown in FIG. 5, the work W is sandwiched between the fixed electrode 22 and the movable electrode 23. At this time, whether or not the work W is sandwiched between the fixed electrode 22 and the movable electrode 23 is determined by whether or not the torque detected by the torque sensor 24 reaches a predetermined torque. Specifically, when the torque detected by the torque sensor 24 reaches a predetermined torque, the CPU 101 determines that the work W is sandwiched between the fixed electrode 22 and the movable electrode 23”, wherein examiner interpreted determining whether or not the work W is sandwiched between the fixed electrode and movable electrode based on torque detected by torque sensor as processing circuitry configured to detect that the jig is separated from the object based on the torque data).
HIROSE, and SHINAGAWA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and incorporating detecting jig is separated based on torque data, as taught by SHINAGAWA.
One of ordinary skill in the art would have been motivated to improve detecting when work W is sandwiched between electrodes, as suggested by SHINAGAWA (see [Plate thickness inspection]).
HIROSE, and SHINAGAWA does not explicitly teach the position data being acquired based on detection that the jig is separated from the object.
However, Aoki teaches the position data being acquired based on detection that the jig is separated from the object (Paragraph [0041] “The welding gun control apparatus 18 also includes a position detection part 67 for detecting the positions of the electrodes 30, 32”, and Paragraph [0035] “The movable electrode 30 moves along the axis of the movable electrode 30. The electrode drive motor 34 is driven, whereby the movable electrode 30 moves toward the opposite electrode 32 or moves in a direction which is away from the movable electrode 30”, and Paragraph [0047], wherein examiner interpreted position detection part detecting positions of electrodes as acquiring position data based on detection that the jig is separated from the object, wherein examiner interpreted electrodes being movable and position detection part detecting positions of the electrodes to include detecting positions of electrodes when moving towards the other or away from the other, and therefore, to include acquiring position data based on detection that the jig is separated from the object, and in combination with HIROSE teaches determining abnormality based on position data).
HIROSE, SHINAGAWA, and Aoki are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and SHINAGAWA, and incorporating acquiring jig position data, as taught by Aoki.
One of ordinary skill in the art would have been motivated to improve detecting and storing position data, and determining abnormality in welding process based on the pressurizing force applied by electrodes to a workpiece, as suggested by Aoki (see Paragraph [0044], Paragraphs [0005-0008]).
Regarding claim 20, HIROSE teaches a non-transitory computer-readable storage medium including computer executable instructions, wherein the instructions, when executed by a computer, cause the computer to perform a method ([Control unit configuration] “The control unit 80 (an example of a controller) is a general-purpose microcomputer including a CPU (central processing unit), a memory, and an input / output unit. A computer program (abnormality determination program) for functioning as a part of the abnormality determination device 100 for determining an abnormality of the welding device 40 is installed in the control unit 80. By executing the computer program, the control unit 80 functions as a plurality of information processing circuits (81, 83, 85, 87, 89) included in the abnormality determination device 100”), the method comprising:
controlling a jig by an industrial device such that the jig presses an object to perform a work process ([Welding equipment configuration] “The welding control unit 47 controls the welding unit 49 based on the control signal received from the communication circuit 41 for executing the process associated with the welding work”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted shanks as jig that presses on an object to perform a work process, wherein examiner interpreted workpieces to be the object, and wherein welding control unit controls the welding unit to press on the object);
acquiring torque data of torque of a motor configured to move the jig using a torque data acquisition sensor ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like”, [Welding equipment configuration] “The measurement circuit 43 is connected to the sensor 45, and acquires data on the state of the welding device 40 as operation data of the welding device 40 via the sensor 45”, [Welding equipment configuration] “The sensor 45 is installed in the vicinity of the welding portion 49 (welding working portion) or the welding portion 49 in order to detect the state of the welding device 40”, and [Control unit configuration] “In order to pressurize the workpieces W1 and W2 in the "pressurization process", when pressure is applied to the shanks S1 and S2 using a clamp (not shown), vibration caused by the motor that rotates the clamp and the workpieces W1 and W2 and the shank S1”, wherein examiner interpreted sensor attached to welding portion which acquires data on state of the welding device, and wherein the welding device includes a motor that is rotating the clam and workpieces, and shanks, wherein the sensor detects torque as being a torque data acquisition sensor configured to acquire torque data of torque of a motor configured to move the jig);
acquiring position data on a position of the jig using a position data acquisition sensor ([Welding equipment configuration] “the sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49”, [Welding equipment configuration] “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, wherein examiner interpreted sensor measuring position of the welded portion, wherein the welded portion includes shanks as a position data acquisition sensor configured to acquire position data on a position of the jig);
estimating an abnormality based on the position data when the jig is separated from the object ([Welding equipment configuration] “The sensor 45 may be a camera that captures an image or a moving image, or is between an optical sensor, a sound sensor, a position sensor that measures the position of the welded portion 49, and a work that is the target of welding work in the welded portion 49. It may be a sensor that measures the generated pressure, torque, or the like. In addition, when the welding device is a production robot, the sensor 45 may include a position sensor for quantitatively monitoring a state such as an arm position and an operation of the production robot”, “As shown in FIG. 3, the welded portion 49 includes, for example, shanks S1 and S2 and welding tips T1 and T2 attached to the tips of the shanks S1 and S2, respectively. Welding tips T1 and T2 are arranged so as to sandwich the workpieces W1 and W2 to be welded”, [Control unit configuration] “In particular, if the welding tips T1 and T2 are welded to the workpieces W1 and W1, an extra force is applied to separate the welding tips T1 and T2 from the workpieces W1 and W1 in the "opening process", and the welding tips T1 and T1 are separated. T2 or shanks S1 and S2 will be distorted”, “The correlation calculation unit 87 estimates the cause of the defect in the welding state (cause of the abnormality in the welding state) based on the combination of the determination results in the plurality of steps in which the presence / absence of the abnormality is determined by the abnormality determination unit 85. Taking the case where the welding apparatus 40 performs resistance welding as an example, the correlation calculation unit 87 is based on the combination of the presence or absence of abnormalities in each of the "pressurization process", "pressurization maintenance process", and "opening process". , As shown in Table 1 below, the cause of the defective welding condition is estimated”, “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, “according to the abnormality determination method and the abnormality determination device according to the present embodiment, the group of processes includes an opening step of reducing the pressure applied to the work, and determines the abnormality in the opening process. You may. As a result, it is possible to detect an abnormality in the welding operation or a sign of the abnormality based on the characteristics of the opening process in the group of processes”, wherein examiner interpreted estimating the cause of defect in the welding state based on presence or absence of abnormalities in the each welding processes which includes the opening process as performing an estimation estimating an abnormality based on the position data when the jig is separated from the object, wherein examiner interpreted shanks, and welding tips separating from the workpieces during the opening process as using the position data when the jig is separated from the object).
HIROSE does not explicitly teach detecting that the jig is separated from the object based on the torque data; the position data being acquired based on detection that the jig is separated from the object.
However, SHINAGAWA teaches detecting that the jig is separated from the object based on the torque data ([FIG. 4, and FIG. 5 Description] “From the state shown in FIG. 4, the gun control unit 101a of the CPU 101 drives the servomotor 25 so that the movable electrode 23 approaches the fixed electrode 22. As a result, as shown in FIG. 5, the work W is sandwiched between the fixed electrode 22 and the movable electrode 23. At this time, whether or not the work W is sandwiched between the fixed electrode 22 and the movable electrode 23 is determined by whether or not the torque detected by the torque sensor 24 reaches a predetermined torque. Specifically, when the torque detected by the torque sensor 24 reaches a predetermined torque, the CPU 101 determines that the work W is sandwiched between the fixed electrode 22 and the movable electrode 23”, wherein examiner interpreted determining whether or not the work W is sandwiched between the fixed electrode and movable electrode based on torque detected by torque sensor as processing circuitry configured to detect that the jig is separated from the object based on the torque data).
HIROSE, and SHINAGAWA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and incorporating detecting jig is separated based on torque data, as taught by SHINAGAWA.
One of ordinary skill in the art would have been motivated to improve detecting when work W is sandwiched between electrodes, as suggested by SHINAGAWA (see [Plate thickness inspection]).
HIROSE, and SHINAGAWA does not explicitly teach the position data being acquired based on detection that the jig is separated from the object.
However, Aoki teaches the position data being acquired based on detection that the jig is separated from the object (Paragraph [0041] “The welding gun control apparatus 18 also includes a position detection part 67 for detecting the positions of the electrodes 30, 32”, and Paragraph [0035] “The movable electrode 30 moves along the axis of the movable electrode 30. The electrode drive motor 34 is driven, whereby the movable electrode 30 moves toward the opposite electrode 32 or moves in a direction which is away from the movable electrode 30”, and Paragraph [0047], wherein examiner interpreted position detection part detecting positions of electrodes as acquiring position data based on detection that the jig is separated from the object, wherein examiner interpreted electrodes being movable and position detection part detecting positions of the electrodes to include detecting positions of electrodes when moving towards the other or away from the other, and therefore, to include acquiring position data based on detection that the jig is separated from the object, and in combination with HIROSE teaches determining abnormality based on position data).
HIROSE, SHINAGAWA, and Aoki are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to welding system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, and SHINAGAWA, and incorporating acquiring jig position data, as taught by Aoki.
One of ordinary skill in the art would have been motivated to improve detecting and storing position data, and determining abnormality in welding process based on the pressurizing force applied by electrodes to a workpiece, as suggested by Aoki (see Paragraph [0044], Paragraphs [0005-0008]).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over HIROSE et al. JP 2020/157317 A (hereinafter “HIROSE”), in view of SHINAGAWA et al. WO 2022/085153 A1 (hereinafter “SHINAGAWA”), further in view of Aoki et al. USPGPUB 2018/0056434 (hereinafter “Aoki”) as applied to claims 1-9, 12-17, and 19-21 above, further in view of TSUJIKAWA et al. JP H0621700 A (hereinafter “TSUJIKAWA”).
Regarding claim 10, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
The combination does not explicitly teach wherein the processing circuitry is configured to analyze pre-process data related to a pre-process performed prior to the work process, based on an abnormality estimation result.
However, TSUJIKAWA teaches wherein the processing circuitry is configured to analyze pre-process data related to a pre-process performed prior to the work process, based on an abnormality estimation result ([0011] “In FIG. 1, 6 is a mounting machine in the previous step, 7 is a mounting inspection machine, 8 is a mounting machine in the next step, and these are arranged on the mounting line. The board (work) 9 processed by the mounting machine in the previous step 6 is inspected by the mounting inspection machine 7 for the quality of the mounted state, and a good product is sent to the next step 8 and a defective product is sent to a board stocker or the like”, [0011] “As shown in FIG. 2, the mounting inspection machine 7 is provided with an absolute judgment standard A having a predetermined acceptance range for judging the quality of the mounting state, and within the acceptance range of the absolute judgment standard A”, wherein examiner interpreted inspecting the board processed by mounting machine to determine if it is within a range as processing circuitry is configured to analyze pre-process data related to a pre-process performed prior to the work process, based on an abnormality estimation result).
HIROSE, SHINAGAWA, Aoki, and TSUJIKAWA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to manufacturing system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, SHINAGAWA, and Aoki, and incorporating analyzing pre-process of prior work process, as taught by TSUJIKAWA.
One of ordinary skill in the art would have been motivated to improve judging the working condition of a machine and determining whether a product is defective by performing a pre-process inspection, as suggested TSUJIKAWA (see [0002-0008]).
Regarding claim 11, HIROSE, SHINAGAWA, and Aoki teaches all of the features with respect to claim 1 as outlined above.
HIROSE further teaches a post-process, which is performed after the work process, based on an abnormality estimation result ([Abnormality judgement processing procedure] “First, in step S101, the measurement circuit 43 acquires the operation data of the welding device 40 via the sensor 45, and the communication circuit 50 receives the operation data of the welding device 40 from the welding device 40”, “In step S105, the abnormality determination unit 85 determines the abnormality in the “pressurization step” based on the process data in the “pressurization step””, “In step S107, the abnormality determination unit 85 determines the abnormality in the “pressurization maintenance step” based on the process data in the “pressurization maintenance step””, and “In step S109, the abnormality determination unit 85 determines the abnormality in the “opening process” based on the process data in the “opening process””, wherein examiner interpreted determining abnormality during any of the processes or steps as a post-process which is performed after the work process based on abnormality estimation result).
The combination does not explicitly teach wherein the processing circuitry is configured to control at least one of a pre-process, which is performed prior to the work process.
However, TSUJIKAWA teaches wherein the processing circuitry is configured to control at least one of a pre-process, which is performed prior to the work process ([0011] “In FIG. 1, 6 is a mounting machine in the previous step, 7 is a mounting inspection machine, 8 is a mounting machine in the next step, and these are arranged on the mounting line. The board (work) 9 processed by the mounting machine in the previous step 6 is inspected by the mounting inspection machine 7 for the quality of the mounted state, and a good product is sent to the next step 8 and a defective product is sent to a board stocker or the like”, [0011] “As shown in FIG. 2, the mounting inspection machine 7 is provided with an absolute judgment standard A having a predetermined acceptance range for judging the quality of the mounting state, and within the acceptance range of the absolute judgment standard A”, wherein examiner interpreted inspecting board to determine if they are defective as controlling of pre-processing performed prior to the work process).
HIROSE, SHINAGAWA, Aoki, and TSUJIKAWA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to inspecting system.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above system for estimating an abnormality as taught by HIROSE, SHINAGAWA, and Aoki, and incorporating controlling of pre-process, as taught by TSUJIKAWA.
One of ordinary skill in the art would have been motivated to improve judging the working condition of a machine and determining whether a product is defective by performing a pre-process inspection, as suggested TSUJIKAWA (see [0002-0008]).
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Kobayashi et al. [USPGPUB 2021/0229283] teaches an abnormality determination apparatus.
MURASE et al. [JP 2013/205894 A] teaches an abnormality detection device.
KAMIGUCHI et al. [USPGPUB 2019/0369596] teaches an analysis device, an analysis method, and an analysis program capable of analyzing a machining state.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DHRUVKUMAR PATEL whose telephone number is (571)272-5814. The examiner can normally be reached 7:30 AM to 5:30 AM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571)272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.P./ Examiner, Art Unit 2119
/MOHAMMAD ALI/ Supervisory Patent Examiner, Art Unit 2119