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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 USC 119 (a)-(d) to application JP2021-199680 filed 12/08/2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. As such, the effective filing date of the application is 12/08/2021.
Status of Claims
Claims 1-8 are now pending.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mohri et al. (US 20220097178 A1), hereinafter Mohri, which claims priority to JP 2019-111619 filed 2019-06-14, in view of Wang et al. (US 20210252707 A1), hereinafter Wang.
Regarding claim 1, Mohri discloses:
An offline teaching device comprising:
an acquisition unit configured to acquire welding line information indicating welding lines on a workpiece on which welding is executed (see at least [0055]: “The shape detection unit 500 includes a laser light source (not shown) configured to be able to scan the welded portion on the workpiece Wk based on position information of the welded portion received from the robot control device 2, and a camera (not shown) disposed to be able to image an imaging region including the periphery of the welded portion and configured to image a reflection trajectory (that is, a shape line of the welded portion) of the reflected laser light among the laser light emitted to the welded portion.”)
sensor information indicating a measurement region of a sensor that measures an appearance shape of a bead formed on the workpiece based on the welding (see at least [0055]: “The shape detection unit 500 included in the robot MC detects a shape of a welding bead in the welded portion based on the control signal received from the robot control device 2, and acquires shape data for each welding bead based on a detection result. The robot MC transmits the acquired shape data for each welding bead to the inspection device 3.”)
and obstacle information including at least a position of an obstacle disposed between the sensor and the workpiece (see at least [0145]: “However, an obstacle such as a jig or a pillar is already present at the position of the point P1. Therefore, it is impossible to perform the repair welding such that the welding is ended at the point P1. Therefore, in the third determination mode, the processor 31 determines a position, that is, a point P′, rounded to the point B which is an end point on the operation trajectory of the welding robot in the main welding as a welding end point. Since the end point B (point P′) is a point on the operation trajectory of the welding robot in the main welding, it is guaranteed that the welding robot does not collide with the obstacle, and it is possible to perform the repair welding such that the welding is ended at the end point.”)
and an output unit configured to generate a calculation result of the inclusion rate and output the calculation result to a screen (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
Mohri does not explicitly disclose, but Wang, in an analogous field of endeavor, teaches:
a calculation unit configured to calculate an inclusion rate indicating a rate of measurable welding line in which measurement of the appearance shape is not impossible due to the obstacle during the measurement by the sensor based on the welding line information, the sensor information, and the obstacle information (see at least Fig. 1. See further [0036]: “The next step in the process, also shown on FIG. 3, is worst state search. Worst state search involves finding the location along the trajectory 330, in between each adjacent pair of the waypoints q.sup.r, having the worst state distance relative to the obstacles—which could be the worst amount of interference with one of the obstacles 310/312/314, or the smallest distance to one of the obstacles 310/312/314. For the trajectory 330, worst state points 352, 354, 356 and 358 are the worst states for the first, second, third and fourth sections of the trajectory 330, respectively. Each of the worst state points is designated as q.sub.worst,i, where i=(1, . . . , 4).”)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Mohri with the method of calculation as taught by Wang because, as stated by [0006] of Wang’s disclosure: “… there is a need for an improved robot motion optimization technique which does not require dense waypoint spacing but still reliably identifies and automatically resolves any collisions or minimum distance threshold violations along the robot's trajectory.”
Regarding claim 2, the combination of Mohri and Wang teaches the offline teaching device according to claim 1.
Mohri further discloses wherein the measurement region has a three-dimensional shape formed based on a scanning distance of the sensor during the measurement (see at least [0056]: “The shape detection unit 500 is, for example, a three-dimensional shape measurement sensor. The shape detection unit 500 includes a laser light source (not shown) configured to be able to scan the welded portion on the workpiece Wk based on position information of the welded portion received from the robot control device 2, and a camera (not shown) disposed to be able to image an imaging region including the periphery of the welded portion and configured to image a reflection trajectory (that is, a shape line of the welded portion) of the reflected laser light among the laser light emitted to the welded portion. The shape detection unit 500 transmits, to the inspection device 3, the shape data (image data) of the welded portion based on the laser light imaged by the camera.”)
Mohri does not explicitly disclose, but Wang, in an analogous field of endeavor, teaches:
and the calculation unit specifies an effective measurement region based on an overlap between the measurement region and the obstacle, and calculates the inclusion rate based on the effective measurement region and the welding line information (see at least Fig. 1. See further [0036]: “The next step in the process, also shown on FIG. 3, is worst state search. Worst state search involves finding the location along the trajectory 330, in between each adjacent pair of the waypoints q.sup.r, having the worst state distance relative to the obstacles—which could be the worst amount of interference with one of the obstacles 310/312/314, or the smallest distance to one of the obstacles 310/312/314. For the trajectory 330, worst state points 352, 354, 356 and 358 are the worst states for the first, second, third and fourth sections of the trajectory 330, respectively. Each of the worst state points is designated as q.sub.worst,i, where i=(1, . . . , 4).”)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Mohri with the method of calculation as taught by Wang because, as stated by [0006] of Wang’s disclosure: “… there is a need for an improved robot motion optimization technique which does not require dense waypoint spacing but still reliably identifies and automatically resolves any collisions or minimum distance threshold violations along the robot's trajectory.”
Regarding claim 3, the combination of Mohri and Wang teaches:
The offline teaching device according to claim 2.
Mohri further discloses wherein the output unit outputs a calculated value of the inclusion rate to the screen (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
Regarding claim 4, the combination of Mohri and Wang teaches:
The offline teaching device according to claim 2.
Mohri further discloses wherein the output unit outputs a first welding line and a second welding line of the welding lines to the screen in a distinguishable manner (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
the first welding line being located within the effective measurement region, and the second welding line being located outside the effective measurement region (see at least Figures 3 and 4.)
Regarding claim 5, the combination of Mohri and Wang teaches the offline teaching device according to claim 1.
Mohri does not explicitly disclose, but Wang, in a similar field of endeavor, teaches:
wherein the measurement region of the sensor includes at least a first measurement region based on a first arrangement position of the sensor and a second measurement region based on a second arrangement position of the sensor different from the first measurement region (see at least [0061]: “An optional computer 630, in communication with the controller 620, may be used for several different tasks—including providing obstacle geometry data in the form of CAD solid or surface models. The computer 630, if used, communicates with the controller 620 via any suitable wireless or hardwire network connection. As an alternative to using CAD data to define the obstacles 610, one or more sensors, such as a sensor 640, may be used. The sensor(s) 640 may be a camera or any type of object sensor capable of providing 3D geometry of the obstacles 610 in the workspace 602. The sensor(s) 640 could be one or more 3D cameras, or a plurality of 2D cameras whose data is combined into 3D obstacle data. The sensor(s) 640 could also include other types of devices such as radar, LiDAR and/or ultrasonic. The sensor(s) 640 also communicate with the controller 620 and/or the computer 630 via any suitable wireless or hardwire network connection.”)
and the calculation unit calculates a first inclusion rate indicating a rate of a first measurable welding line in which the measurement of the appearance shape is not impossible due to the obstacle during the measurement in the first measurement region, a second inclusion rate indicating a rate of a second measurable welding line in which the measurement of the appearance shape is not impossible due to the obstacle during the measurement in the second measurement region, and a whole inclusion rate indicating a ratio of a sum of the first measurable welding line and the second measurable welding line to the welding lines (see at least Fig. 1. See further [0036]: “The next step in the process, also shown on FIG. 3, is worst state search. Worst state search involves finding the location along the trajectory 330, in between each adjacent pair of the waypoints q.sup.r, having the worst state distance relative to the obstacles—which could be the worst amount of interference with one of the obstacles 310/312/314, or the smallest distance to one of the obstacles 310/312/314. For the trajectory 330, worst state points 352, 354, 356 and 358 are the worst states for the first, second, third and fourth sections of the trajectory 330, respectively. Each of the worst state points is designated as q.sub.worst,i, where i=(1, . . . , 4).”)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Mohri with the method of calculation as taught by Wang because, as stated by [0006] of Wang’s disclosure: “… there is a need for an improved robot motion optimization technique which does not require dense waypoint spacing but still reliably identifies and automatically resolves any collisions or minimum distance threshold violations along the robot's trajectory.”
Regarding claim 6, the combination of Mohri and Wang teaches the offline teaching device according to claim 5.
Mohri further discloses wherein the output unit outputs the first inclusion rate, the second inclusion rate, and the whole inclusion rate to the screen as the calculation result of the inclusion rate (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
Regarding claim 7, Mohri discloses:
An offline teaching method performed by an offline teaching device including one or more computers, the offline teaching method comprising:
acquiring welding line information indicating welding lines on a workpiece on which welding is executed (see at least [0055]: “The shape detection unit 500 includes a laser light source (not shown) configured to be able to scan the welded portion on the workpiece Wk based on position information of the welded portion received from the robot control device 2, and a camera (not shown) disposed to be able to image an imaging region including the periphery of the welded portion and configured to image a reflection trajectory (that is, a shape line of the welded portion) of the reflected laser light among the laser light emitted to the welded portion.”)
sensor information indicating a measurement region of a sensor that measures an appearance shape of a bead formed on the workpiece based on the welding (see at least [0055]: “The shape detection unit 500 included in the robot MC detects a shape of a welding bead in the welded portion based on the control signal received from the robot control device 2, and acquires shape data for each welding bead based on a detection result. The robot MC transmits the acquired shape data for each welding bead to the inspection device 3.”)
and obstacle information including at least a position of an obstacle disposed between the sensor and the workpiece (see at least [0145]: “However, an obstacle such as a jig or a pillar is already present at the position of the point P1. Therefore, it is impossible to perform the repair welding such that the welding is ended at the point P1. Therefore, in the third determination mode, the processor 31 determines a position, that is, a point P′, rounded to the point B which is an end point on the operation trajectory of the welding robot in the main welding as a welding end point. Since the end point B (point P′) is a point on the operation trajectory of the welding robot in the main welding, it is guaranteed that the welding robot does not collide with the obstacle, and it is possible to perform the repair welding such that the welding is ended at the end point.”)
and generating a calculation result of the inclusion rate and outputting the calculation result to a screen (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
Mohri does not explicitly disclose, but Wang, in an analogous field of endeavor, teaches:
calculating an inclusion rate indicating a rate of a measurable welding line in which measurement of the appearance shape is not impossible due to the obstacle during the measurement by the sensor, based on the welding line information, the sensor information, and the obstacle information (see at least Fig. 1. See further [0036]: “The next step in the process, also shown on FIG. 3, is worst state search. Worst state search involves finding the location along the trajectory 330, in between each adjacent pair of the waypoints q.sup.r, having the worst state distance relative to the obstacles—which could be the worst amount of interference with one of the obstacles 310/312/314, or the smallest distance to one of the obstacles 310/312/314. For the trajectory 330, worst state points 352, 354, 356 and 358 are the worst states for the first, second, third and fourth sections of the trajectory 330, respectively. Each of the worst state points is designated as q.sub.worst,i, where i=(1, . . . , 4).”)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Mohri with the method of calculation as taught by Wang because, as stated by [0006] of Wang’s disclosure: “… there is a need for an improved robot motion optimization technique which does not require dense waypoint spacing but still reliably identifies and automatically resolves any collisions or minimum distance threshold violations along the robot's trajectory.”
Regarding claim 8, Mohri discloses:
An offline teaching method performed by an offline teaching device including one or more computers, the offline teaching method comprising:
inputting welding line information to the computer, the welding line information indicating welding lines on a workpiece on which welding is executed (see at least [0055]: “The shape detection unit 500 includes a laser light source (not shown) configured to be able to scan the welded portion on the workpiece Wk based on position information of the welded portion received from the robot control device 2, and a camera (not shown) disposed to be able to image an imaging region including the periphery of the welded portion and configured to image a reflection trajectory (that is, a shape line of the welded portion) of the reflected laser light among the laser light emitted to the welded portion.”)
inputting sensor information to the computer, the sensor information indicating a measurement region of a sensor that measures an appearance shape of a bead formed on the workpiece based on the welding (see at least [0055]: “The shape detection unit 500 included in the robot MC detects a shape of a welding bead in the welded portion based on the control signal received from the robot control device 2, and acquires shape data for each welding bead based on a detection result. The robot MC transmits the acquired shape data for each welding bead to the inspection device 3.”)
inputting obstacle information to the computer, the obstacle information including at least a position of an obstacle disposed between the sensor and the workpiece (see at least [0145]: “However, an obstacle such as a jig or a pillar is already present at the position of the point P1. Therefore, it is impossible to perform the repair welding such that the welding is ended at the point P1. Therefore, in the third determination mode, the processor 31 determines a position, that is, a point P′, rounded to the point B which is an end point on the operation trajectory of the welding robot in the main welding as a welding end point. Since the end point B (point P′) is a point on the operation trajectory of the welding robot in the main welding, it is guaranteed that the welding robot does not collide with the obstacle, and it is possible to perform the repair welding such that the welding is ended at the end point.”)
and outputting the calculation result to a screen (see at least [0091]-[0092]: “ Further, the data processing unit 35 counts the number of times of inspection for each welded portion, and when a welding inspection result is not good even if the number of times of inspection exceeds the number of times of inspection stored in the determination threshold storage unit 36, the data processing unit 35 determines that it is difficult or impossible to repair the defective portion by the repair welding. In this case, the determination unit 37 generates the alert including the position of the defective portion and the defect factor, and transmits the generated alert to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1. The inspection device 3 may generate an alert having contents other than those described above. The alert is also transmitted to the host device 1 via the robot control device 2. The alert transmitted to the host device 1 is transmitted to and displayed on the monitor MN1.”)
Mohri does not explicitly disclose, but Wang, in an analogous field of endeavor teaches:
generating a calculation result of an inclusion rate indicating a rate of measurable welding lines in which measurement of the appearance shape is not impossible due to the obstacle during the measurement by the sensor, based on the welding line information, the sensor information, and the obstacle information (see at least Fig. 1. See further [0036]: “The next step in the process, also shown on FIG. 3, is worst state search. Worst state search involves finding the location along the trajectory 330, in between each adjacent pair of the waypoints q.sup.r, having the worst state distance relative to the obstacles—which could be the worst amount of interference with one of the obstacles 310/312/314, or the smallest distance to one of the obstacles 310/312/314. For the trajectory 330, worst state points 352, 354, 356 and 358 are the worst states for the first, second, third and fourth sections of the trajectory 330, respectively. Each of the worst state points is designated as q.sub.worst,i, where i=(1, . . . , 4).”)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Mohri with the method of calculation as taught by Wang because, as stated by [0006] of Wang’s disclosure: “… there is a need for an improved robot motion optimization technique which does not require dense waypoint spacing but still reliably identifies and automatically resolves any collisions or minimum distance threshold violations along the robot's trajectory.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH NELESKI whose telephone number is (571)272-6064. The examiner can normally be reached 10 - 6.
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/E.R.N./Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658