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 Rejections - 35 USC § 103
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.
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, 2, 4-6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 4491996B2 (‘996) in view of Ume et al. (2011/0023610).
With respect to claim 1, ‘996 teaches a determination method for determining a processing state (i.e. an abnormal or normal weld based on a detected blowhole) in laser processing for lap welding (as ‘996 teaches using a laser welder; Abstract), the determination method comprising: detecting, using an optical sensor (plasma light sensor 7), at least visible light, generated at a welded portion (i.e. a melted part 5 of a workpiece 4; [0015]) formed at a surface of a workpiece (4) by emission of a laser beam (13) on the workpiece (4); obtaining, from the optical sensor (7), a signal indicating a change in the visible light in a time section corresponding to a welding time of the workpiece (as the intensity-time curve determined from the data collected by the sensor used to detect a change; [0018]); determining, as the processing state (the abnormal or normal weld based on a detected blowhole), a position and a number of molten shape abnormality (i.e. a blowhole) in a welded region (seen in Fig. 2a) having a molten length (depicted in Fig. 2a) and a molten width (depicted in Fig. 2a) by inputting a feature quantity (i.e. a temperature value) to a determination model (as ‘996 teaches in [0020-0022] a system of equations using temperature features values, thereby reading on “a determination model”) that determines the processing state (the abnormal or normal weld based on a detected blowhole), the feature quantity (i.e. temperature) including signal intensity (as sensed by sensor 7) of the signal based on the signal (from the sensor), the molten shape abnormality (i.e. blowhole) occurring when a foreign substance exists at an overlapping surface of the workpiece (as a detected blowhole occurs when foreign matter adheres to the material being welded; [0004]); and outputting, as a determination result, the position and the number of the molten shape abnormality that are determined (as the determination model outputs , in real-time, temperature control, based on the position and number of molten shape abnormalities; note: insofar, as how “outputting” is structurally recited; the feedback control loop using the model determines the number of abnormalities and their respective locations so to controlling the welding process in real-time).
‘996 remains silent regarding the determination model is constructed based on training data including the feature quantity calculated under a condition where the molten shape abnormality occurs and the processing state in the condition where the molten shape abnormality occurs.
Ume et al. teaches a similar method that includes a determination model is constructed based on training data including feature quantity calculated under a condition where molten shape abnormalities occurs and the processing state in the condition where the molten shape abnormality occurs (as Ume et al. teaches in [0088], utilizing the weight matrices determined during training of the ANN 304 and the activation function associated with each node of each layer, the ANN 304 performs a series of mathematical operations on the input vector A and outputs a coded defect vector B which relates to the identity and/or severity classification of the defect corresponding to the defect signal. In the embodiments described herein, the defect vector B is coded to identify one of four different types of defects and/or combinations thereof. The defect vector also contains the severity classification of the identified defect, as described above; hence the model is trained with data that includes a variety of defect).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the determination model of ‘996 to include the trained neural network of Ume et al. such that the model is trained with data that includes defects, as such a modification improves the ability to detect and classify defects; thereby improving the accuracy and effectiveness of ‘996.
With respect to claim 2, ‘996 as modified by Ume et al. teaches the determination method according to Claim 1, wherein the determining (using the modified determination model of Ume et al.) includes detecting a peak (as seen in Fig. 10, according to the algorithmic process disclosed in [00710073]) in a signal and determining a size of the molten shape abnormality as the processing state (as Ume et al. discloses the signal allows the method to determine a type of the defect and if its outside acceptable tolerances, thereby indirectly teaching a size is determined), the outputting includes outputting, as the determination result, the size of the molten shape abnormality that is determined (as the resulting output to modify the control of the laser welder with be based on the outputted size determination by the modified model), and the feature quantity includes an intensity value based on the signal intensity of the signal at the peak (as the combination, as whole, collects intensity data from the sensor 7, plots the peaks of those values, to determine the defect size and location such that corrective actions can occur).
With respect to claim 4, ‘996 as modified by Ume et al. teaches the determination method wherein the modified determination model includes a learned model (as Ume et al. teaches the model is trained to identify types of defects) generated by machine learning using training data, the training data including (i) a feature quantity calculated from a signal based on the at least one visible light (as the trained model in Ume et al. is trained using light intensity data and types of defects) detected during the laser processing under each condition of a plurality of conditions (as the training data is constructed such to allow the ANN to classify defects via the trained data set which includes the different types, condition, and severities) where the processing state changes (during welding) and (ii) the processing state that is determined by appearance measurement of the welded region (as collected by the sensor 7 and temperature sensor, taught in ‘996), the feature quantity (temperature value and light intensity) and the processing state (during welding) being associated with each other (as the interaction when the measured data and the processing state aid in making determinations of the weld).
With respect to claim 5, ‘996 teaches a determination device for determining a processing state (i.e. an abnormal or normal weld based on a detected blowhole) in laser processing for lap welding (as ‘996 teaches using a laser welder; Abstract), the determination device comprising: an arithmetic circuit (found in both 10 and 11); and a communication circuit (i.e. a circuit within 9; Fig. 1) that receives a signal generated by detecting, by an optical sensor (7), visible light, generated at a welded portion (i.e. a melted part 5 of a workpiece 4; [0015]) formed at a surface (seen in Fig. 2a) of a workpiece (4) by emission of a laser beam (13) on the workpiece (4), wherein the signal indicates a change in the visible light in a time section corresponding to a welding time of the workpiece (as the intensity-time curve determined from the data collected by the sensor used to detect a change; [0018]), the arithmetic circuit (found in both 10 and 11) obtains the signal by the communication circuit (as 9 receives the signal data from the sensors seen in Fig. 1), determines, as the processing state (the abnormal or normal weld based on a detected blowhole), a position and a number of molten shape abnormality (i..e blowhole) in a welded region (seen in Fig. 2a) having a molten length (depicted in Fig. 2a) and a molten width (depicted in Fig. 2a) by inputting a feature quantity (i.e. a temperature value) to a determination model (as ‘996 teaches in [0020-0022] a system of equations using temperature features values, thereby reading on “a determination model”) that determines the processing state (the abnormal or normal weld based on a detected blowhole), the feature quantity (i.e. temperature) including signal intensity of the signal based on the signal (as sensed by sensor 7), the molten shape abnormality (i.e. blowhole) occurring when a foreign substance exists at an overlapping surface of the workpiece (as a detected blowhole occurs when foreign matter adheres to the material being welded; [0004]), and outputs by the communication circuit (seen in Fig. 1), as a determination result, the position and the number of the molten shape abnormality that are determined (as the determination model outputs , in real-time, temperature control, based on the position and number of molten shape abnormalities; note: insofar, as how “outputting” is structurally recited; the feedback control loop using the model determines the number of abnormalities and their respective locations so to controlling the welding process in real-time).
‘996 remains silent regarding the determination model is constructed based on training data including the feature quantity calculated under a condition where the molten shape abnormality occurs and the processing state in the condition where the molten shape abnormality occurs.
Ume et al. teaches a similar device that includes a determination model is constructed based on training data including feature quantity calculated under a condition where molten shape abnormalities occurs and the processing state in the condition where the molten shape abnormality occurs (as Ume et al. teaches in [0088], utilizing the weight matrices determined during training of the ANN 304 and the activation function associated with each node of each layer, the ANN 304 performs a series of mathematical operations on the input vector A and outputs a coded defect vector B which relates to the identity and/or severity classification of the defect corresponding to the defect signal. In the embodiments described herein, the defect vector B is coded to identify one of four different types of defects and/or combinations thereof. The defect vector also contains the severity classification of the identified defect, as described above; hence the model is trained with data that includes a variety of defect).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the determination model of ‘996 to include the trained neural network of Ume et al. such that the model is trained with data that includes defects, as such a modification improves the ability to detect and classify defects; thereby improving the accuracy and effectiveness of ‘996.
With respect to claim 6, ‘996 as modified by Ume et al. teaches the determination device according to Claim 5, wherein the arithmetic circuit detects a peak (as seen in Fig. 10, according to the algorithmic process disclosed in [00710073]) in a signal and determining a size of the molten shape abnormality as the processing state (as Ume et al. discloses the signal allows the method to determine a type of the defect and if its outside acceptable tolerances, thereby indirectly teaching a size is determined), and outputs by the communication circuit, as the determination result, the size of the molten shape abnormality that is determined (as the resulting output to modify the control of the laser welder with be based on the outputted size determination by the modified model), and the feature quantity includes an intensity value based on the signal intensity of the signal at the peak (as the combination, as whole, collects intensity data from the sensor 7, plots the peaks of those values, to determine the defect size and location such that corrective actions can occur).
With respect to claim 8, ‘996 as modified by Ume et al. teaches the determination device wherein the modified determination model includes a learned model (as Ume et al. teaches the model is trained to identify types of defects) generated by machine learning using training data, the training data including (i) a feature quantity calculated from a signal based on the at least one visible light (as the trained model in Ume et al. is trained using light intensity data and types of defects) detected during the laser processing under each condition of a plurality of conditions (as the training data is constructed such to allow the ANN to classify defects via the trained data set which includes the different types, condition, and severities) where the processing state changes (during welding) and (ii) the processing state that is determined by appearance measurement of the welded region (as collected by the sensor 7 and temperature sensor, taught in ‘996), the feature quantity (temperature value and light intensity) and the processing state (during welding) being associated with each other (as the interaction when the measured data and the processing state aid in making determinations of the weld).
Allowable Subject Matter
Claims 3 and 7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (2015/0001196) which teaches monitoring a laser welding bead for defects.
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/MATTHEW G MARINI/Primary Examiner, Art Unit 2853