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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2020 112 116.4, filed on 05/05/2020.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 8 is objected to because of the following informalities: claim 8, last two lines, “a steepness of the cutting front…”, is missing a modifier along the lines of, “and/or”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“sensor unit” in Claim 12
The generic placeholder is “sensor unit” and the functional language attributed the “sensor unit” includes: “configured to acquire the at least one sensor data set”.
“analysis unit” in Claim 12
The generic placeholder is “analysis unit” and the functional language attributed the “analysis unit” includes: “configured to determine the value of the at least one physical property”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Reference is made to the Specification filed on 11/04/2022.
Regarding the sensor unit, on Para. 0042, “The sensor unit may include a diode, a photodiode, an image sensor, a line sensor, a camera, a spectrometer, a multispectral sensor and/or a hyperspectral sensor.”, where the sensor unit is assumed to include one of those devices listed
Regarding the analysis unit, Para. 0056, “the analysis unit 220 includes a processor for determining the value of a physical unit according to embodiments of the present invention.”, where the analysis unit is assumed to include a processor capable using functions to do analysis
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 11 recites the limitations “the at least one control data set” in line 1. There is insufficient antecedent basis for these limitations in the claim. For examination purposes, claim 11 will be read as “the at least one control data set” equating to the “control data set” in claim 10.
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 1-2, 4-6, and 8-13 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawahito et al. (JP 2005021949 A, hereinafter Kawahito) in view of Coffman et al. (US 10061300 B1, hereinafter Coffman).
Regarding claim 1, Kawahito discloses a method for analyzing a laser machining process (Abstract, “a laser welding monitoring method with high reliability by which a welding state can be inspected with high accuracy”), said method comprising the steps of:
acquiring at least one sensor data set for the laser machining process (Para. 0051, “The thermal radiation light that has passed through the pinhole 18 passes through a condensing lens 36, a two-piece filter 19 of an interference filter that passes 1300 nm, and a notch filter for a wavelength of 1064 nm, and is composed of InGaAs by a condensing lens 37. An image was formed on the sensor 20, and measurement was performed with a measurement diameter 21 of 1.5 mm and sampling of 10 MHz.”, where the sensor 20 gathers data regarding a laser machining process, where that process is the thermal radiation light); and
determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function, said transfer function being formed by a trained neural network (Para. 0056, “time of reflected light based on the time of keyhole generation and the intensity of heat radiation light are used as input signals, and the tensile strength and the size of the bonding area on the surface of the workpiece 2 are used as output signals. Using an error propagation method, the input / output relationship was learned with 98% accuracy by the neural network provided in the signal processing device 28.”, where the thermal radiation signal is used as an input for a neural network that outputs a physical property of the tensile strength, where a transfer function is a function that converts an input to an output and is implied to be within a neural network).
Kawahito does not disclose:
explicitly disclosing a transfer function used within the neural network.
However, Coffman discloses, in the similar field of laser machining (Section 13, lines 24-25, “selective laser sintering machines”), where the neural network uses a transfer function (Section 20, lines 36-44, “The preprocessed datasets can be divided into a training dataset, a testing dataset, and/or a validation dataset to build an artificial neural network model (not shown in FIG. 7). Such an artificial neural network can include an input layer, at least one hidden layer, and output layer. Layers in the artificial neural network can be configured to compute outputs according to different transfer functions including log-sigmoid, tan-sigmoid, purelin, or other suitable transfer functions.”). It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the neural network in Kawahito to include the transfer function as taught by Coffman.
One of ordinary skill in the art would have been motivated to make this modification in order to gain the advantage of allowing a user to customize how outputs are computed, where different transfer functions can be used depending on a user’s design choices, as stated by Coffman, Section 20, lines 41-44, “Layers in the artificial neural network can be configured to compute outputs according to different transfer functions including log-sigmoid, tan-sigmoid, purelin, or other suitable transfer functions.”.
Regarding claim 2, modified Kawahito teaches the method according to claim 1, as set forth above, discloses wherein acquiring at least one sensor data set is based on a measurement of process radiation of the laser machining process and/or on a measurement of at least one process parameter of the laser machining process (Kawahito, Para. 0056, “time of reflected light based on the time of keyhole generation and the intensity of heat radiation light are used as input signals”, where the intensity of heat radiation light can be during the radiation process, Para. 0065, “to determine the joint strength and area of the weld in real time”).
Regarding claim 4, modified Kawahito teaches the method according to claim 1, as set forth above, discloses wherein the at least one sensor data set is based on a measurement of a radiation intensity of process radiation of the laser machining process (Kawahito, Para. 0056, “time of reflected light based on the time of keyhole generation and the intensity of heat radiation light are used as input signals”, where the intensity of heat radiation light can be during the radiation process, Para. 0065, “to determine the joint strength and area of the weld in real time”) and/or on an image of a machined surface of a workpiece (Kawahito, Para. 0052, “the condensing lens 26 forms an image of the processing point on the high-speed camera 27 to measure the image”, where data can be gathered from an image of the machined surface of the workpiece).
Regarding claim 5, modified Kawahito teaches the method according to claim 4, as set forth above, discloses wherein the radiation intensity is measured for a predetermined period of time and/or in at least one predetermined wavelength range and/or at least one predetermined wavelength (Kawahito, Para. 0074, “The thermal radiation light that has passed through the pinhole 18 is converted into a parallel beam by the condenser lens 36, passes through the notch filter 29 for a wavelength of 1064 nm, and is branched into two by the half mirror 30. One beam passed through the interference filter 31 and the condensing lens 37 that passed through 1200 nm, and formed an image on the InGaAs sensor 20, and measured with a measurement diameter 21 of 1.5 mm and sampling of 10 MHz. The other beam passed through the interference filter 32 passing through 2000 nm and the condensing lens 33 to form an image on the InGaAs sensor 34…From the measured signal, a high-frequency component was removed by a 1kHz low-pass filter in the signal processor 28.”, where the thermal radiation intensity has wavelengths restricted through filters) and/or in a spatially resolved manner and/or in a frequency-resolved manner.
Regarding claim 6, modified Kawahito teaches the method according to claim 2, as set forth above, discloses wherein the process radiation of the laser machining process comprises at least one of temperature radiation, plasma radiation, and laser radiation reflected from a surface of a workpiece (Kawahito, Para. 0079, “With respect to 50 processing points, the intensity of reflected light and the intensity of heat radiation light with respect to the time of keyhole generation are used as input signals”).
Regarding claim 8, modified Kawahito teaches the method according to claim 1, as set forth above, discloses wherein the physical property of the machining result is selected from a group comprising a tensile strength (Kawahito, Para. 0056, “time of reflected light based on the time of keyhole generation and the intensity of heat radiation light are used as input signals, and the tensile strength and the size of the bonding area on the surface of the workpiece 2 are used as output signals.”, where tensile strength of the bond can be determined; regarding the rest of the physical properties listed, it is the Examiner’s position that the claim limitation requires that at least one physical property is selected from the group, where tensile strength would satisfy this limitation), a compressive strength, an electrical conductivity, a keyhole depth, a welding depth, a gap size of a gap between two workpieces joined by the laser machining process, a roughness of a cut edge of a workpiece cut by the laser machining process, a burr of a cut edge of a workpiece cut by the laser machining process, a burr height of a cut edge of a workpiece cut by the laser machining process, a steepness of the cutting front and a perpendicularity of a cut edge of a workpiece cut by the laser machining process.
Regarding claim 9, modified Kawahito teaches the method according to claim 1, as set forth above, discloses wherein the at least one sensor data set is acquired during and/or after the execution of the laser machining process, and/or wherein the value of the physical property is determined while the laser machining process is performed (Kawahito, Para. 0065, “to determine the joint strength and area of the weld in real time. it can. As a result, a reliable laser welding monitoring method capable of inspecting the welding state can be obtained.”, where real time monitoring of the machining process is performed, where the inputs from the sensor data would be gather during the machining process and output tensile strength information in real time) and/or after the laser machining process has been completed.
Regarding claim 10, modified Kawahito teaches the method according to claim 1, as set forth above, discloses wherein the value of the physical property is further determined based on at least one control data set of the laser machining process (Kawahito, Para. 0080, “Since there was a case where the calculated surface temperature was lower than the previous temperature, correction was made to advance the reference time until the temperature was below the calculated surface temperature. The intensity of the reflected light and the heat radiation at the corrected reference time is set to 1, and is used as a reference. Further, the intensity of the heat radiation at the temperature obtained from the heat radiation having a different wavelength at each time. As a result of using the value obtained by dividing the value as the intensity of the heat radiation light, the accuracy was improved by several percent, and the tensile strength and the size of the bonding area on the surface of the workpiece 2 could be monitored.”, where a reference is used in order to improve the accuracy of the outputs).
Regarding claim 11, modified Kawahito teaches the method according to claim 9, as set forth above, discloses wherein the at least one control data set comprises control data for a laser power (Kawahito, Para. 0080, “Since there was a case where the calculated surface temperature was lower than the previous temperature, correction was made to advance the reference time until the temperature was below the calculated surface temperature. The intensity of the reflected light and the heat radiation at the corrected reference time is set to 1, and is used as a reference. Further, the intensity of the heat radiation at the temperature obtained from the heat radiation having a different wavelength at each time.”, where the heat radiation is the laser power, Para. 0085, “The larger the melt diameter, the stronger the intensity of the heat radiation light”), a distance between a laser machining head carrying out the laser machining process and the workpiece, a focus position, a focus diameter, a path signal, a workpiece material and/or a workpiece thickness.
Regarding claim 12, modified Kawahito teaches the method according to claim 1, as set forth above, discloses a system for analyzing a laser machining process, wherein said system is configured to carry the method according to claim 1 (Kawahito, Para. 0008, “a highly reliable laser welding monitoring method and laser welding monitoring apparatus capable of inspecting a welding state with high accuracy.”), said system comprising:
a sensor unit configured to acquire the at least one sensor data set for the laser machining process (Kawahito, Para. 0051, “The thermal radiation light that has passed through the pinhole 18 passes through a condensing lens 36, a two-piece filter 19 of an interference filter that passes 1300 nm, and a notch filter for a wavelength of 1064 nm, and is composed of InGaAs by a condensing lens 37. An image was formed on the sensor 20, and measurement was performed with a measurement diameter 21 of 1.5 mm and sampling of 10 MHz.”, where the sensor 20 gathers data regarding a laser machining process and ); and
an analysis unit configured to determine the value of the at least one physical property by means of the transfer function formed by the trained neural network (Kawahito, Para. 0056, “time of reflected light based on the time of keyhole generation and the intensity of heat radiation light are used as input signals, and the tensile strength and the size of the bonding area on the surface of the workpiece 2 are used as output signals. Using an error propagation method, the input / output relationship was learned with 98% accuracy by the neural network provided in the signal processing device 28.”, where the signal processing device 28 analyzes the data through the neural network).
Regarding claim 13, modified Kawahito teaches the system according to claim 12, as set forth above, discloses wherein said sensor unit comprises a diode, a photodiode (Kawahito, Para. 0074, “formed an image on the InGaAs sensor 20”, where an InGaAs sensor is made of photodiodes), an image sensor, a line sensor, a camera, a spectral sensor, a multispectral sensor and/or a hyperspectral sensor.
Claims 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawahito et al. (JP 2005021949 A, hereinafter Kawahito) in view of Coffman et al. (US 10061300 B1, hereinafter Coffman) in further view of Wang et al. (CN 101329169 B, hereinafter Wang).
Regarding claim 3, modified Kawahito teaches the method according to claim 2, as set forth above, discloses where a process parameter includes keyhole measurement to determine the radiation values (Kawahito, Para. 0054, “the maximum value of the intensity of the reflected light and the heat radiation light at the time when the keyhole was formed was set to 1, and used as a reference. In processing, when the intensity of reflected light and heat radiation light exceeds 1, it is considered that keyhole welding is started, and when the intensity of heat radiation light exceeds 10, it is determined that the workpiece 2 has started melting.”, where the depth of the keyhole is implied through whether or not the keyhole welding has started).
Modified Kawahito does not disclose:
explicitly wherein the at least one process parameter comprises a keyhole depth, a focus position, a focus diameter and/or a distance of a laser machining head carrying out the laser machining process from a workpiece.
However, Wang discloses, in the similar field of neural networks working with laser machining (Abstract, “The invention adopts the method of system neural network for building the electronic beam welding melting zone shape”), where the process parameter can include focus position and laser power as inputs and the melting depth as an output (Para. 0002, “Geng such as the BP network model establishing method, which effectively realizes the penetration prediction of A3 steel YAG laser penetration welding, relative error absolute value of an average value within 8%, mode to the laser output power, welding speed, shielding gas flow and a focus position are input to the melting depth is output (, , Xu-liang. YAG laser welding parameters of artificial neural network model [J]. Welding Journal, 2001, 6, 37-40).”). It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the neural network inputs and outputs in modified Kawahito to include the focus position as an input as taught by Wang.
One of ordinary skill in the art would have been motivated to make this modification in order to gain the advantage of being able to use previously known neural network calculations in order to determine welding physical properties, where a user could choose these neural networks for convenience as they have been established and known in the prior art, as stated by Wang, Para. 0003, “In summary, abroad scholars research mainly uses neural network for predicting process parameter factors of the appearance feature of the welding seam, the considered input layer includes only power (voltage and current), a welding speed and other process parameters, the output is only penetration, welding seam width, melting area and welding deformation, input and output factors are less.”.
Claims 7 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kawahito et al. (JP 2005021949 A, hereinafter Kawahito) in view of Coffman et al. (US 10061300 B1, hereinafter Coffman) in further view of Mehr et al. (US 20180341248 A1, hereinafter Mehr).
Regarding claim 7, modified Kawahito teaches the method according to claim 1, as set forth above.
Modified Kawahito does not disclose:
wherein the value of the at least one physical property is determined based on at least two sensor data sets that have been acquired by different sensors for the same period of time.
However, Mehr discloses, in the similar field of laser machining with neural networks (Abstract, “machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.”), where the output of a neural network (Para. 0005, “In some embodiments, the machine learning algorithm comprises an artificial neural network. In some embodiments, the artificial neural network comprises an input layer, an output layer, and at least 5 hidden layer.”) is determined based on at least two sensor data sets that are acquired by different sensors over the same period of time (Para. 0006, “providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time.”, where real time input of the sensor data would mean that the data set is gathered over the same time and for the same instantaneous period of data gathering). It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the sensor data from modified Kawahito to include additional sensor data from other sensors, where all the data is gathered in real time as taught by Mehr.
One of ordinary skill in the art would have been motivated to make this modification in order to gain the advantage of using the same time period of data collection in order to determine a physical property in real time, which can be helpful for making in process adjustments, as stated by Mehr, Para. 0006, “wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time.”.
Regarding claim 14, modified Kawahito teaches the system according to claim 12, as set forth above, discloses wherein said analysis unit is configured to determine the value of the at least one physical property in real time (Kawahito, Para. 0065, “to determine the joint strength and area of the weld in real time. it can. As a result, a reliable laser welding monitoring method capable of inspecting the welding state can be obtained.”).
Modified Kawahito does not disclose:
and to output control data to a laser machining system carrying out the laser machining process.
However, Mehr discloses where physical property analysis is done in real time (Para. 0006, “providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties”) and control data is output in order to correct for defects (Para. 0031, “in-process inspection data (e.g., automated defect classification data) may be used by the machine learning algorithm to determine a set or sequence of process control parameter adjustments that will implement a corrective action”). It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the analysis unit in modified Kawahito to include a further step of outputting control data to the laser machining system as taught by Mehr.
One of ordinary skill in the art would have been motivated to make this modification in order to gain the advantage of being ablet to issue a corrective action so that defects in the laser machining workpiece can be prevented, as stated by Mehr, Para. 0137, “run process control may comprise data supplied by an automated object defect classification system as described above, so that the deposition process control parameters may be adjusted in real-time to compensate or correct for part defects as they arise during the build process.”.
Regarding claim 15, modified Kawahito teaches the system according to claim 12, as set forth above, discloses a laser machining system for machining a workpiece by means of a laser beam (Kawahito, Abstract, “an irradiation process, in which a laser beam is irradiated from the side of a first welding material for welding the first welding material to a second welding material”).
Modified Kawahito does not disclose:
said laser machining system comprising: a laser machining head for radiating a laser beam onto a workpiece to be machined.
However, Mehr discloses where the laser beam includes a head that delivers the laser beam onto the workpiece (Para. 0051, “The laser generates a melt pool on the substrate material, into which the metal wire is fed and melted, forming a metallurgical bound with the substrate. By moving the laser processing head”). It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the laser beam in modified Kawahito to include a laser machining head as taught by Mehr.
One of ordinary skill in the art would have been motivated to make this modification in order to gain the advantage of being able to move the laser around, which can allow for different areas to be welded and controlled, as stated by Mehr, Para. 0051, “The relative motion of the deposition tool and the substrate may be controlled, for example, using a 6-axis industrial robot arm. The formation of a deposited layer is illustrated in FIG. 2, as will be described in more detail below.”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Takayama et al. (DE 102017005349 A1, hereinafter Takayama) discloses a similar neural network for laser machining, however the output is a change in the machining.
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/KEVIN GUANHUA WEN/Examiner, Art Unit 3761
12/15/2025
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