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 .
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered but they are not persuasive.
On page 7 of the reply, applicant argues “Okamato is directed to an automatic welding system that can perform welding control using images obtain by captured images of a welding point during arc welding. Okamato notes that different areas, including the area of the welding wire 201 and the area of the molten pool 203 are identified. See Fig. 11. However, Okamato does not disclose or suggest "inputting, via a user of the welding equipment, welding information during the welding, wherein the welding information includes information related to a position of the welding wire with respect to the molten pool," as recited in amended Claim 1. “
However, Okamato discloses that changes to the welding information are made during the welding process by the robot control device 30 controls the operation of the welding robot 20, and the power supply device 40 executes welding. Further, the correction information generation apparatus 100 monitors a welding image obtained by the camera 60 and sequentially calculates correction information of physical quantities related to arc welding. Okamato fails to disclose the welding information is input by a user.
It would have been obvious to have the user input the welding information during welding since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art.
No argument against claim 9 is made.
Response to Amendment
The amendment filed 12/29/2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: Claims 1 and 11 recite “inputting via a user of the welding equipment, welding information during welding”. However, the specification, page 10 lines 30-35 state the welding information is prepared in advance, by the operator. The specification does not specifically state the welding information is input by a user (operator) during welding. It seems some things may be done during welding, but this limitation does not have clear support in the specification as originally filed.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 11 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 11 recite “inputting via a user of the welding equipment, welding information during welding”. However, the specification, page 10 lines 30-35 state the welding information is prepared in advance, by the operator. The specification does not specifically state the welding information is input by a user (operator) during welding. It seems some things may be done during welding, but this limitation does not have clear support in the specification as originally filed.
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.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Okamato et al (JP 2018192524) as cited by applicant.
Regarding claim 1, Okamato discloses, a method of constructing a machine learning model for outputting information used in welding control, and specifically discloses (see paragraphs 33-188, Figures 1-38): a machine learning model includes an image and state association information related to a state of the arc weld, the machine learning model takes the image as an input and the state association information as an output, a step of photographing a puddle or arc in electric arc welding, providing the captured image as an input to the machine learning model, (See Paragraphs [0122]-[0123]) a step of retrieving state association information output from the machine learning model, and the step of controlling a welder performing the arc welding based on the retrieved status association information, the machine learning model is constructed by supervised learning based on a plurality of teaching data (See Paragraphs [0124]-[0128]), the plurality of teaching data includes images and status association information related to a status of the arc weld, the image includes an image of the puddle, arc, or wire in the arc weld using the wire, and the image obtained by imaging the puddle, arc, or wire in the arc weld using the wire by the camera is taken as an input and condition-associated information related to the condition of the arc weld is taken as an output under control of the welding mechanism. (See Paragraphs [0130]-[0132]) As teaching data, a relationship between sets of output values of input data such as image data photographed in a case where a welded part is welded at a wide variety of welding conditions (a plurality of settings of at least 1 welding condition) may be prepared.
While Okamato discloses that changes to the welding information are made during the welding process by the robot control device 30 controls the operation of the welding robot 20, and the power supply device 40 executes welding. Further, the correction information generation apparatus 100 monitors a welding image obtained by the camera 60 and sequentially calculates correction information of physical quantities related to arc welding. Okamato fails to disclose the welding information is input by a user.
However, it would have been obvious to have the user input the welding information during welding since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art.
Regarding claims 2-3, the images under various imaging conditions are used as teacher data (paragraph 00 75) is also described, and in view of outputting the tip of the wire (paragraph 0068), the torch position (paragraph 0096). The robot control 30 adjusts the position of the welding torch 21 and the protruding length of the welding wire 24 from the welding torch 21 by using feedback control of the welding voltage value, on the basis of which the welding voltage value becomes appropriate. It would have been obvious to use an extension of 10 to 40mm since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. Regarding claims 4-6, the image characteristic information is arc center (ArcX, ArcY), wire tip (WireX, WireY), puddle front left end (Pool Lead Lx, Pool Lead Ly), puddle front right end (Pool Lead Rx, Pool Lead Ry), puddle left end (Pool Lx), and puddle right end (Pool Rx).
Regarding claims 7 and 8, Okamato discloses, a method of constructing a machine learning model for outputting information used in welding control, and specifically discloses (see paragraphs 33-188, Figures 1-38): a machine learning model includes an image and state association information related to a state of the arc weld, the machine learning model takes the image as an input and the state association information as an output, a step of photographing a puddle or arc in electric arc welding, providing the captured image as an input to the machine learning model, (See Paragraphs [0122]-[0123]) a step of retrieving state association information output from the machine learning model, and the step of controlling a welder performing the arc welding based on the retrieved status association information, the machine learning model is constructed by supervised learning based on a plurality of teaching data (See Paragraphs [0124]-[0128]), the plurality of teaching data includes images and status association information related to a status of the arc weld, the image includes an image of the puddle, arc, or wire in the arc weld using the wire, and the image obtained by imaging the puddle, arc, or wire in the arc weld using the wire by the camera is taken as an input and condition-associated information related to the condition of the arc weld is taken as an output under control of the welding mechanism. (See Paragraphs [0130]-[0132]) As teaching data, a relationship between sets of output values of input data such as image data photographed in a case where a welded part is welded at a wide variety of welding conditions (a plurality of settings of at least 1 welding condition) may be prepared.
Okamato discloses, regarding claim 9, an automatic welding system and specifically discloses: comprising a welding device which welds a weldment; a camera (vision sensor) that images the condition of welding; a machine learning model includes an image and state association information related to a state of the arc weld, the machine learning model takes the image as an input and the state association information as an output, a step of photographing a puddle or arc in electric arc welding, providing the captured image as an input to the machine learning model, a step of retrieving state association information output from the machine learning model, and the step of controlling a welder performing the arc welding based on the retrieved status association information, the machine learning model is constructed by supervised learning based on a plurality of teaching data, the plurality of teaching data includes images and status association information related to a status of the arc weld, the image includes an image of a puddle, an arc, or a wire in the arc weld using the wire, the image being taken by the camera imaging the puddle, the arc, or the wire in the arc weld using the wire as an input and the status association information related to the status of the arc weld as an output under control of the welding mechanism; further comprising an information acquiring unit that provides an image obtained by the camera as an input to the machine learning model, acquires state association information output from the machine learning model; further included is a control unit that controls a welding mechanism performing the arc welding based on the state association information acquired by the information acquiring unit. However, as teaching data, a relationship between sets of output values of input data such as image data photographed in a case where a welded part is welded at a wide variety of welding conditions (a plurality of settings of at least 1 welding condition) may be prepared. (See Paragraphs [0122]-[0128], Paragraphs [0130]-[0132]) The wire length in relation to the molten pool is considered. Regarding claim 10, a model constructing unit is configured to construct the machine learning model outputting, as the state association information, correction information for correcting physical quantities related to the arc welding, the control unit is configured to control the welding machine such that the physical quantities are corrected in accordance with the correction information acquired by the information acquisition unit. Okamato discloses the model performs a correction. As the learning model is abstract, performing the correction could be considered a model in and of itself. Either way it would have been obvious to use the first learning model or an additional learning model to output a correction as each step is output into the next step.
Regarding claim 11, Okamato discloses, a method of constructing a machine learning model for outputting information used in welding control, and specifically discloses (see paragraphs 33-188, Figures 1-38): a machine learning model includes an image and state association information related to a state of the arc weld, the machine learning model takes the image as an input and the state association information as an output, a step of photographing a puddle or arc in electric arc welding, providing the captured image as an input to the machine learning model, (See Paragraphs [0122]-[0123]) a step of retrieving state association information output from the machine learning model, and the step of controlling a welder performing the arc welding based on the retrieved status association information, the machine learning model is constructed by supervised learning based on a plurality of teaching data (See Paragraphs [0124]-[0128]), the plurality of teaching data includes images and status association information related to a status of the arc weld, the image includes an image of the puddle, arc, or wire in the arc weld using the wire, and the image obtained by imaging the puddle, arc, or wire in the arc weld using the wire by the camera is taken as an input and condition-associated information related to the condition of the arc weld is taken as an output under control of the welding mechanism. (See Paragraphs [0130]-[0132]) As teaching data, a relationship between sets of output values of input data such as image data photographed in a case where a welded part is welded at a wide variety of welding conditions (a plurality of settings of at least 1 welding condition) may be prepared. Regarding claims 2-3, the images under various imaging conditions are used as teacher data (paragraph 00 75) is also described, and in view of outputting the tip of the wire (paragraph 0068), the torch position (paragraph 0096). The robot control 30 adjusts the position of the welding torch 21 and the protruding length of the welding wire 24 from the welding torch 21 by using feedback control of the welding voltage value, on the basis of which the welding voltage value becomes appropriate. It would have been obvious to use an extension of 10 to 40mm since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art.
While Okamato discloses that changes to the welding information are made during the welding process by the robot control device 30 controls the operation of the welding robot 20, and the power supply device 40 executes welding. Further, the correction information generation apparatus 100 monitors a welding image obtained by the camera 60 and sequentially calculates correction information of physical quantities related to arc welding. Okamato fails to disclose the welding information is input by a user.
However, it would have been obvious to have the user input the welding information during welding since it has been held that broadly providing a mechanical or automatic means to replace manual activity which has accomplished the same result involves only routine skill in the art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN W JENNISON whose telephone number is (571)270-5930. The examiner can normally be reached M-Th 9-5.
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/BRIAN W JENNISON/Primary Examiner, Art Unit 3761 3/26/2026