DETAILED ACTION
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8, and 10 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by
U.S. Patent Application Publication No. 2024/0100626 (Schwarz).
Claim 1:
The cited prior art describes a method for determining a machining condition in laser machining for lap welding, the method comprising: (Schwarz: “The present disclosure relates to a laser working system for performing a working process on a workpiece, in particular on a metal workpiece, using a laser beam and a method for monitoring a working process on a workpiece, in particular on a metal workpiece, using a laser beam.” Paragraph 0002; “The first step 601 includes radiating a laser beam into a working region on a workpiece.” Paragraph 0095; “The laser working system 1 is controlled by a control unit (not shown) configured to control the working head 20, the sensor unit 10 and/or the laser device. The laser working system 1 may also include a computing unit (not shown) configured to determine information about the working process. According to one embodiment, the computing unit is combined with the control unit (not shown). In other words, the functionality of the computing unit may be combined with that of the control unit in a common processing unit.” Paragraph 0070)
detecting, by using an optical sensor, at least one component of heat radiation, visible light, and reflected light generated at a welded portion provided on a surface of a workpiece by emission of a laser beam on the workpiece; (Schwarz: see the sensor 100 as illustrated in figure 2 and as described in paragraphs 0071, 0072; “The hyperspectral sensor 100 is therefore configured to sense radiation and to output it as 25 bandpass-filtered individual images 301.” Paragraph 0061; “Radiation 205 emanating from the workpiece 2 is produced during the working process or during the working of the workpiece 2 by means of the laser beam 201. The radiation 205 may be light of the laser beam 201 reflected or scattered back from a surface of the workpiece 2, plasma radiation, thermal radiation, or visible light. The radiation 205 may also include light from a lighting source (not shown) radiated onto the workpiece 2 and reflected back.” Paragraph 0072; “In a second step 602, a hyperspectral image of a region of the workpiece with N times M pixels is acquired.” Paragraph 0095)
acquiring a signal indicating a change in the at least one component in a time section from a start of welding to an end of welding of the workpiece; (Schwarz: “In order to enable continuous monitoring of the laser working process, the sensor unit may be configured to capture hyperspectral images continuously or to capture one hyperspectral image per predefined time interval.” Paragraph 0026)
calculating a feature quantity based on a signal intensity of the signal in a predetermined section in the time section; (Schwarz: “determine an input tensor based on the hyperspectral image” paragraph 0035; “The method may further include a third step 603, in which an input tensor is determined based on the hyperspectral image” paragraph 0096; “Therefore, the captured hyperspectral image preferably corresponds to an image of the workpiece region that is both spatially resolved and wavelength-resolved at a specific point in time.” Paragraph 0027; “Accordingly, each pixel of the hyperspectral sensor detects an intensity of the outgoing radiation filtered by the respective bandpass filter.” Paragraph 0028)
determining, as the machining condition, presence or absence of a gap generated between superposed surfaces of the workpiece in an irradiation direction of the laser beam by inputting the calculated feature quantity to a determination model for determining the machining condition; and (Schwarz: “determine an output tensor based on the input tensor by means of a transfer function containing information about the working process, wherein the transfer function between the input tensor and the output tensor is formed by a deep neural network, in particular by a deep convolutional neural network” paragraph 0035; “The working errors may be classified in particular as follows: gap, offset, lack of welding penetration, lack of welding, ejections, pore formation.” Paragraph 0038; “Anomaly detection may be performed using standard techniques, building a model from the training data, and computing the deviation of the features from the model in the inference.” Paragraph 0078; “the method may include a fourth step 604, in which an output tensor containing information about a working result of a laser working process is determined based on the input tensor and by means of a transfer function. The transfer function between the input tensor and the output tensor may be formed by a trained neural network.” Paragraph 0096)
outputting the determined presence or absence of the gap as a determination result, (Schwarz: “the method may include a fourth step 604, in which an output tensor containing information about a working result of a laser working process is determined based on the input tensor and by means of a transfer function. The transfer function between the input tensor and the output tensor may be formed by a trained neural network.” Paragraph 0096; “The working errors may be classified in particular as follows: gap, offset, lack of welding penetration, lack of welding, ejections, pore formation.” Paragraph 0038; “Anomaly detection may be performed using standard techniques, building a model from the training data, and computing the deviation of the features from the model in the inference.” Paragraph 0078)
wherein the determination model is constructed based on training data including the feature quantity calculated under a plurality of conditions in which the machining condition changes and observed presence or absence of the gap, the feature quantity and the presence or absence of the gap being associated with each other. (Schwarz: see the training of the neural network as described in paragraphs 0036, 0039, 0040, 0041; “determining, based on the input tensor and by means of a transfer function, an output tensor containing information about the working process, wherein the transfer function between the input tensor and the output tensor is formed by a trained neural network, for example by a deep neural network or by a deep convolutional neural network. The output tensor may be formed in real time” paragraph 0036)
Claim 8:
The cited prior art describes the method according to Claim 1, wherein the determination model includes a trained model generated by machine learning using training data including a feature quantity calculated from a signal based on the at least one component detected by performing the laser machining under each condition of a plurality of conditions in which the machining condition changes and the presence or absence of the gap under the each condition, the feature quantity and the presence or absence of the gap being associated with each other. (Schwarz: “In order to train a monitored machine learning method, e.g. a neural network, both a large number of working processes free of errors, for example weldings, and a large number of working processes including errors are carried out and hyperspectral images are captured in each case. The training data may be used for anomaly detection, in particular when few weldings with errors can be produced. The training of the neural networks may be performed using standard methods.” Paragraph 0039; see the training of the neural network as described in paragraphs 0036, 0039, 0040, 0041; “determining, based on the input tensor and by means of a transfer function, an output tensor containing information about the working process, wherein the transfer function between the input tensor and the output tensor is formed by a trained neural network, for example by a deep neural network or by a deep convolutional neural network. The output tensor may be formed in real time” paragraph 0036)
Claim 10:
Claim 10 is substantially similar to claim 1 and is rejected based on the same reasons and rationale.
10. A device that determines a machining condition in laser machining for lap welding, the device comprising:
an arithmetic circuit; and a
communication circuit that receives a signal generated by an optical sensor detecting at least one component of heat radiation, visible light, and reflected light generated at a welded portion provided on a surface of a workpiece by emission of a laser beam on the workpiece, wherein the signal is a signal indicating a change in the at least one component in a time section from a start of welding to an end of welding of the workpiece,
the arithmetic circuit
acquires the signal by the communication circuit,
calculates a feature quantity based on a signal intensity of the signal in a predetermined section in the time section,
determines, as the machining condition, presence or absence of a gap generated between superposed surfaces of the workpiece in an irradiation direction of the laser beam by inputting the calculated feature quantity to a determination model for determining the machining condition, and
outputs the determined presence or absence of the gap as a determination result, and
the determination model is constructed based on training data including the feature quantity calculated under a plurality of conditions in which the machining condition changes and observed presence or absence of the gap, the feature quantity and the presence or absence of the gap being associated with each other.
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 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2024/0100626 (Schwarz) in view of
U.S. Patent Application Publication No. 2021/0107096 (Funami) (cited by Applicant).
Claim 2:
Schwarz does not explicitly describe a feature quantity as described below. However, Funami teaches the feature quantity as described below.
The cited prior art describes the method according to Claim 1, wherein the feature quantity includes at least one of a signal intensity decrease amount and an integral value of the signal intensity, the signal intensity decrease amount indicating a degree of decrease in the signal intensity of the reflected light. (Funami: “Therefore, the disclosure is made to solve the above-described problem of the related art; and an object of the disclosure is to provide a laser welding quality inspection method and a laser welding quality inspection apparatus that can determine a welding abnormality with higher accuracy.” Paragraph 0004; “However, in a method of performing a determination of a welding defect during laser welding by a peak intensity of welding light (thermal radiation light, plasma light and laser reflection light) generated during the laser welding of the related art, or an integrated value of the intensity of those types of welding light, when there is a clear welding abnormality, it is possible to determine the welding defect, but when there is a minute welding abnormality, there is a problem that the welding defect cannot be accurately determined.” Paragraph 0003)
One of ordinary skill in the art would have recognized that applying the known technique of Schwarz, namely, a laser working system, with the known techniques of Funami, namely, a laser welding system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Schwarz to analyze data for a laser system to detect anomalies with the teachings of Funami to determine laser welding quality would have been recognized by those of ordinary skill in the art as resulting in an improved laser welding system. In other words, the combination of references provides for a laser welding system to detect anomalies based on various types of data and processing techniques based on the teachings of a laser welding system to detect anomalies in Schwarz and the teachings of a laser quality system using various types of data and processing techniques in Funami.
Claim 3:
Schwarz does not explicitly describe a feature quantity as described below. However, Funami teaches the feature quantity as described below.
The cited prior art describes the method according to Claim 2, wherein the calculating of the feature quantity includes calculating an average intensity of a signal corresponding to a change in the reflected light in a time section at a peak output after rising of a laser output at which the laser beam oscillates and before falling of the laser output, the predetermined section includes a section calculated with a time when the signal of the reflected light first reaches the average intensity from a peak intensity as an initial time and with a time when the signal of the reflected light again reaches the average intensity as a termination time, and the signal intensity decrease amount is a value obtained by subtracting, from the average intensity, a minimum value of the calculated signal intensity in the section. (Funami: see the calculations using average intensity of the signal as described in paragraphs 0041, 0042; “According to a fifth aspect of the fourth aspect of the disclosure, there is provided the laser welding quality inspection method, in which in the calculating a normalization signal of the thermal radiation light and a normalization signal of the plasma light, an average value m.sub.av of the signal intensity of the thermal radiation light within the determination period, an average value n.sub.av of the signal intensity of the plasma light within the determination period, a time function H(t) of the signal intensity of the thermal radiation light before being normalized within the determination period, a time function S(t) of the signal intensity of the plasma light before being normalized within the determination period, a time function Hm(t) of the normalization signal of the thermal radiation light within the determination period, and a time function Sn(t) of the normalization signal of the plasma light within the determination period respectively satisfy the following expressions.” Paragraph 0041)
Schwarz and Funami are combinable for the same rationale as set forth above with respect to claim 2.
Claim 4:
Schwarz does not explicitly describe a feature quantity as described below. However, Funami teaches the feature quantity as described below.
The cited prior art describes the method according to Claim 1, wherein the feature quantity includes a feature quantity corresponding to a time fluctuation in the signal intensity of the reflected light in a time section at a peak output after rising of a laser output at which the laser beam oscillates and before falling of the laser output. (Funami: “FIG. 6A illustrates an external appearance of melted and solidified portion 18 during the abnormal welding according to the present exemplary embodiment. During the abnormal welding, perforation 25 is seen in a central portion of melted and solidified portion 18. During the welding, it is considered that at this location, the resin foreign matter at the joining interface is rapidly sublimated by the irradiation of the laser beam, and a molten material is blown off, whereby perforation 25 is formed. The signal intensity of the thermal radiation light obtained at this time is illustrated in a lower part of FIG. 6B. As clearly illustrated by a lower graph of FIG. 6B, abnormal peak 26 appears in the signal intensity of the thermal radiation light when the abnormal welding occurs.” Paragraph 0076)
Schwarz and Funami are combinable for the same rationale as set forth above with respect to claim 2.
Claim 5:
Schwarz does not explicitly describe a feature quantity as described below. However, Funami teaches the feature quantity as described below.
The cited prior art describes the method according to Claim 1, wherein the feature quantity includes an integral value of the signal intensity of at least one of the heat radiation and the visible light in a time section at a peak output after rising of a laser output at which the laser beam oscillates and before falling of the laser output. (Funami: “Therefore, the disclosure is made to solve the above-described problem of the related art; and an object of the disclosure is to provide a laser welding quality inspection method and a laser welding quality inspection apparatus that can determine a welding abnormality with higher accuracy.” Paragraph 0004; “However, in a method of performing a determination of a welding defect during laser welding by a peak intensity of welding light (thermal radiation light, plasma light and laser reflection light) generated during the laser welding of the related art, or an integrated value of the intensity of those types of welding light, when there is a clear welding abnormality, it is possible to determine the welding defect, but when there is a minute welding abnormality, there is a problem that the welding defect cannot be accurately determined.” Paragraph 0003; “FIG. 6A illustrates an external appearance of melted and solidified portion 18 during the abnormal welding according to the present exemplary embodiment. During the abnormal welding, perforation 25 is seen in a central portion of melted and solidified portion 18. During the welding, it is considered that at this location, the resin foreign matter at the joining interface is rapidly sublimated by the irradiation of the laser beam, and a molten material is blown off, whereby perforation 25 is formed. The signal intensity of the thermal radiation light obtained at this time is illustrated in a lower part of FIG. 6B. As clearly illustrated by a lower graph of FIG. 6B, abnormal peak 26 appears in the signal intensity of the thermal radiation light when the abnormal welding occurs.” Paragraph 0076)
Schwarz and Funami are combinable for the same rationale as set forth above with respect to claim 2.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2024/0100626 (Schwarz) in view of
U.S. Patent Application Publication No. 2013/0178952 (Wersborg).
Claim 6:
Schwarz does not explicitly describe a melting width as described below. However, Wersborg teaches the melting width as described below.
The cited prior art describes the method according to Claim 1, wherein the training data further includes a numerical value related to a melting width calculated by measuring an appearance shape of the welded portion after welding or an image obtained by capturing the welded portion after welding, in association with the presence or absence of the gap. (Wersborg: “he same would be possible with an RL agent learning to achieve a predefined meltpool size, kerf width, or cutting quality. The RL agent could learn from features generated from different sensor data sources such as photodiode data, camera sensors, acoustic sensor, processing gas values, etc. . . . Another exemplary welding setup would be to have in-process photodiode sensors and a post-process triangulation sensor giving a reward signal for an RL agent for a specific welding seam width. Another exemplary cutting setup would be to have in-process features from a camera or photodiodes for an RL agent learning how to control processing gas pressure. It is furthermore applicable to give the RL agents action boundaries, limiting their range of actions but also increasing process stability.” paragraph 0121; “The in-process pictures taken at different processing times show that the variance within the video pictures mainly relies on weld seam width, melt pool size and form, and the front of the heat affected zone. Furthermore, information about the keyhole seems to be present in slight intensity variations within this spot size, and yet this is not visible from the pictures shown. The agent abstracts these process characteristics by extracting the learned camera features displayed in FIG. 13. These features are sensitive to variations in keyhole size, melt pool form or size, seam width, and other characteristics. Of course, the features from the photodiodes are collected as well.” Paragraph 0185)
One of ordinary skill in the art would have recognized that applying the known technique of Schwarz, namely, a laser working system, with the known techniques of Wersborg, namely, a laser processing system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Schwarz to analyze data for a laser system to detect anomalies with the teachings of Wersborg to control a laser working system would have been recognized by those of ordinary skill in the art as resulting in an improved laser welding system. In other words, the combination of references provides for a laser welding system to detect anomalies based on various types of data and processing techniques based on the teachings of a laser welding system to detect anomalies in Schwarz and the teachings of a laser control system using various types of data and processing techniques in Wersborg.
Claim 7:
Schwarz does not explicitly describe training data as described below. However, Wersborg teaches the training data as described below.
The cited prior art describes the method according to Claim 1, wherein the training data further includes both a numerical value related to a calculated by measuring an appearance shape of the welded portion after welding and an image obtained by capturing the welded portion after welding, in association with the presence or absence of the gap. (Funami: see the images and the feature values for training as illustrated in figures 5, 8, 11, 13) (Schwarz: “In order to train a monitored machine learning method, e.g. a neural network, both a large number of working processes free of errors, for example weldings, and a large number of working processes including errors are carried out and hyperspectral images are captured in each case. The training data may be used for anomaly detection, in particular when few weldings with errors can be produced. The training of the neural networks may be performed using standard methods.” Paragraph 0039; see the training of the neural network as described in paragraphs 0036, 0039, 0040, 0041; “determining, based on the input tensor and by means of a transfer function, an output tensor containing information about the working process, wherein the transfer function between the input tensor and the output tensor is formed by a trained neural network, for example by a deep neural network or by a deep convolutional neural network. The output tensor may be formed in real time” paragraph 0036)
Schwarz and Wersborg are combinable for the same rationale as set forth above with respect to claim 6.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2024/0100626 (Schwarz) in view of
JP 4873854 (citations to English translation) (Nippon) (cited by Applicant).
Claim 9:
Schwarz does not explicitly describe a gap amount as described below. However, Nippon teaches the gap amount as described below.
The cited prior art describes the method according to Claim 1, wherein the determining of the presence or absence of the gap includes determining a gap amount indicating a size of the gap in the irradiation direction of the laser beam, the gap amount of the gap includes a numerical value indicating a displacement amount with a state where the gap is not generated between the superposed surfaces of the workpiece as a reference, and the training data of the determination model includes the feature quantity calculated under a situation where the gap is generated and the gap amount of the generated gap in association with each other. (Nippon: “FIG. 5 is a diagram conceptually showing a welding change state due to a difference in the overlap gap in laser welding of galvanized materials together with an image of a laser welded portion. As shown in FIG. 5, the lap welding with the laser beam is performed by irradiating the galvanized steel plates M <b> 1 and M <b> 2 that are materials to be welded while moving the laser beam. As shown in FIGS. 5A to 5H, the light generated from the laser welded portion is the size of the overlap gap PG between the two galvanized steel plates M1 and M2 to be subjected to lap welding. Thus, it can be seen that the shape becomes different with respect to the moving direction of the laser beam (the direction in which laser welding proceeds: from the left to the right indicated by the arrows in FIGS. 5A to 5H). In particular, it can be seen that the tail of the light (light emitting part) from the laser welding part MP is different with respect to the moving direction of the laser light.”) (Schwarz: “In order to train a monitored machine learning method, e.g. a neural network, both a large number of working processes free of errors, for example weldings, and a large number of working processes including errors are carried out and hyperspectral images are captured in each case. The training data may be used for anomaly detection, in particular when few weldings with errors can be produced. The training of the neural networks may be performed using standard methods.” Paragraph 0039; see the training of the neural network as described in paragraphs 0036, 0039, 0040, 0041; “determining, based on the input tensor and by means of a transfer function, an output tensor containing information about the working process, wherein the transfer function between the input tensor and the output tensor is formed by a trained neural network, for example by a deep neural network or by a deep convolutional neural network. The output tensor may be formed in real time” paragraph 0036)
One of ordinary skill in the art would have recognized that applying the known technique of Schwarz, namely, a laser working system, with the known techniques of Nippon, namely, a laser welding system, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Schwarz to analyze data for a laser system to detect anomalies with the teachings of Nippon to determine laser welding quality would have been recognized by those of ordinary skill in the art as resulting in an improved laser welding system. In other words, the combination of references provides for a laser welding system to detect anomalies based on various types of data and processing techniques based on the teachings of a laser welding system to detect anomalies in Schwarz and the teachings of a laser quality system using various types of data and processing techniques in Nippon.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent No. 8,149,409 describes a welding detection system.
U.S. Patent No. 11,660,706 describes a welding quality system.
U.S. Patent No. 5,272,312 describes a laser welding quality system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E EVERETT whose telephone number is (571)272-2851. The examiner can normally be reached Monday-Friday 8:00 am to 5:00 pm (Pacific).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117
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