DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is in response to Applicant’s arguments and amendments filed on 07/02/2026 amending Claims 1, 5, 9, 12, 15, and 18, canceling Claims 2, 10, and 16 and adding new Claims 21 - 23. Claims 1, 3 – 9, 11 – 15, and 17 – 23 are examined
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, 3, 4, 6 – 9, 11, 13 – 15, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi et al. (2021/0299788A1) as evidenced by Wittwer (11,467,561) in view of Etter et al. (10,695,867) in view of Dallarosa et al. (9,636,776) in view of Zimmermann et al. (6,666,630) in view of Kleinert et al. (11,077,526).
Regarding Claim 1, Kobayashi teaches, in Figures 1 – 3 and 9 and Paras. [0011], [0021], [0037], [0039], [0041], [0043], and [0045], the invention as claimed including a method, comprising: determining first data (Para. [0045] “…are given as first input data…”.) indicative of processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing a material (10 - Para. [0037]) in a laser processing system (20 – Para. [0037]) comprising a digital twin (54 and 56); providing the first data (Para. [0045] “…are given as first input data…”.) as input to a trained machine learning model (50, 54, and 56), wherein the digital twin (54 and 56) comprises the trained machine learning model (54 and 56); obtaining one or more outputs of the trained machine learning model (54 and 56), the one or more outputs indicating predicted performance data (Para. [0045] “…a relationship used for estimating first output data that is three-dimensional shape data of the processed part after irradiation with the laser beam, …a relationship used for estimating second output data that is laser beam parameters of laser beam which the processed part of the processing object 10 is to be irradiated with…”) associated with the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing the material (10); and causing (Para. [0058] “After termination of the learning process of FIG. 3, machine learning may be performed by using learning data accumulated by that time, every time laser processing is performed or at a predetermined timing.”), based on the predicted performance data, the material (10) to be processed according to the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]).
The Claim 1 limitations recited, “the digital twin comprises the trained machine learning model”; therefore, the “digital twin” was just a different name for the “machine learning model” disclosed by Kobayashi, as evidenced by Wittwer. Wittwer teaches, in Figs. 1 – 12, Col. 2, ll. 40 – 50, Col. 12, l. 55 - Col. 13, l. 30, Col. 13, ll. 45 – 65, Col. 14, ll. 10 – 30, Col. 15, ll. 10 – 35, and Col. 18, ll. 10 – 50, a similar laser processing system (L) with a “digital twin”, i.e., machine learning model, with “The data structures, application cases, algorithms, and data flow form the digital twin of the laser material machining (application). The digital twin is modular and uses, among other things, empirical values to keep the algorithms simple and fast and to ensure the comparability between digital twin and real laser material machining (application). The model includes at least three data structures: i. Data structure 1 contains the data of the parameter set P; ii. Data structure 2 contains the process characteristics PKG; iii. Data structure 3 contains the data of the machining result E.”
Kobayashi, as evidenced by Wittwer, is silent on wherein the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern. Kobayashi further teaches, in Paras. [0021], [0037], [0041], [0043], and [0045], a plurality of processing parameters.
Etter teaches, in Figs. 1 – 7 and Col. 11, ll. 30 – 40, a similar laser processing system having hatch distance as a processing parameter. Dallarosa teaches, in sole figure, Col. 2, ll. 55 – 65, Col. 3, ll. 5 – 15, and Col. 4, ll. 20 – 25, a similar laser processing system having marking speed as a processing parameter. Zimmermann teaches, in Figs. 1 – 3b, Col. 2, ll. 1 – 15, and Col. 4, ll. 45 – 65, a similar laser processing system having milling strategy as a processing parameter. Kleinert teaches, in Figs. 1 – 37, Col. 44, ll. 40 – 50, Col. 51, ll. 25 – 30, and Col. 55, ll. 20 – 35, a similar laser processing system having scan pattern as a processing parameter.
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kobayashi, as evidenced by Wittwer, with the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, because all the claimed elements, i.e., the method comprising: determining first data indicative of processing parameters for processing a material in a laser processing system comprising a digital twin; providing the first data as input to a trained machine learning model, wherein the digital twin comprises the trained machine learning model; obtaining one or more outputs of the trained machine learning model, the one or more outputs indicating predicted performance data associated with the processing parameters for processing the material; and causing, based on the predicted performance data, the material to be processed according to the processing parameters, and processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, were known in the art, and one skilled in the art could have substituted the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, for the processing parameter(s) of Kobayashi, as evidenced by Wittwer, with no change in their respective functions, to yield predictable results, i.e., the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern would have been utilized by the laser processing system operating method for processing a material. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1395; MPEP 2143(I)(B).
Regarding Claim 9, Kobayashi teaches, in Figures 1 – 3 and 9 and Paras. [0011], [0021], [0037], [0039], [0041], [0043], and [0045], the invention as claimed including a system (20 – Fig. 1) comprising: a memory (Paras. [0041] and [0044]); and a processing device (38 and 50) coupled to the memory, the processing device (38 and 50) to: determining first data (Para. [0045] “…are given as first input data…”.) indicative of processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing a material (10 - Para. [0037]) in a laser processing system (20 – Para. [0037]) comprising a digital twin (54 and 56); providing the first data (Para. [0045] “…are given as first input data…”.) as input to a trained machine learning model (50, 54, and 56), wherein the digital twin (54 and 56) comprises the trained machine learning model (54 and 56); obtaining one or more outputs of the trained machine learning model (54 and 56), the one or more outputs indicating predicted performance data (Para. [0045] “…a relationship used for estimating first output data that is three-dimensional shape data of the processed part after irradiation with the laser beam, …a relationship used for estimating second output data that is laser beam parameters of laser beam which the processed part of the processing object 10 is to be irradiated with…”) associated with the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing the material (10); and causing (Para. [0058] “After termination of the learning process of FIG. 3, machine learning may be performed by using learning data accumulated by that time, every time laser processing is performed or at a predetermined timing.”), based on the predicted performance data, the material (10) to be processed according to the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]).
The Claim 9 limitations recited, “the digital twin comprises the trained machine learning model”; therefore, the “digital twin” was just a different name for the “machine learning model” disclosed by Kobayashi, as evidenced by Wittwer. Wittwer teaches, in Figs. 1 – 12, Col. 2, ll. 40 – 50, Col. 12, l. 55 - Col. 13, l. 30, Col. 13, ll. 45 – 65, Col. 14, ll. 10 – 30, Col. 15, ll. 10 – 35, and Col. 18, ll. 10 – 50, a similar laser processing system (L) with a “digital twin”, i.e., machine learning model, with “The data structures, application cases, algorithms, and data flow form the digital twin of the laser material machining (application). The digital twin is modular and uses, among other things, empirical values to keep the algorithms simple and fast and to ensure the comparability between digital twin and real laser material machining (application). The model includes at least three data structures: i. Data structure 1 contains the data of the parameter set P; ii. Data structure 2 contains the process characteristics PKG; iii. Data structure 3 contains the data of the machining result E.”
Kobayashi, as evidenced by Wittwer, is silent on wherein the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern. Kobayashi further teaches, in Paras. [0021], [0037], [0041], [0043], and [0045], a plurality of processing parameters.
Etter teaches, in Figs. 1 – 7 and Col. 11, ll. 30 – 40, a similar laser processing system having hatch distance as a processing parameter. Dallarosa teaches, in sole figure, Col. 2, ll. 55 – 65, Col. 3, ll. 5 – 15, and Col. 4, ll. 20 – 25, a similar laser processing system having marking speed as a processing parameter. Zimmermann teaches, in Figs. 1 – 3b, Col. 2, ll. 1 – 15, and Col. 4, ll. 45 – 65, a similar laser processing system having milling strategy as a processing parameter. Kleinert teaches, in Figs. 1 – 37, Col. 44, ll. 40 – 50, Col. 51, ll. 25 – 30, and Col. 55, ll. 20 – 35, a similar laser processing system having scan pattern as a processing parameter.
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kobayashi, as evidenced by Wittwer, with the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, because all the claimed elements, i.e., the system comprising: a memory; and a processing device coupled to the memory, the processing device to: determine first data indicative of processing parameters for processing a material in a laser processing system comprising a digital twin; provide the first data as input to a trained machine learning model, wherein the digital twin comprises the trained machine learning model; obtain one or more outputs of the trained machine learning model, the one or more outputs indicating predicted performance data associated with the processing parameters for processing the material; and cause, based on the predicted performance data, the material to be processed according to the processing parameters, and processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, were known in the art, and one skilled in the art could have substituted the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, for the processing parameter(s) of Kobayashi, as evidenced by Wittwer, with no change in their respective functions, to yield predictable results, i.e., the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern would have been utilized by the laser processing system for processing a material. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1395; MPEP 2143(I)(B).
Regarding Claim 15, Kobayashi teaches, in Figures 1 – 3 and 9 and Paras. [0011], [0021], [0037], [0039], [0041], [0043], and [0045], the invention as claimed including a non-transitory machine-readable storage medium (Paras. [0041] and [0044] – “memory”) comprising instructions (Paras. [0008] and [0060] “When the present disclosure is implemented as an aspect of a program that causes a computer to serve as a machine learning apparatus, the program may be performed by the system controller 50…”) that, when executed by a processing device (38 and 50), cause the processing device (38 and 50) to: determining first data (Para. [0045] “…are given as first input data…”.) indicative of processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing a material (10 - Para. [0037]) in a laser processing system (20 – Para. [0037]) comprising a digital twin (54 and 56); providing the first data (Para. [0045] “…are given as first input data…”.) as input to a trained machine learning model (50, 54, and 56), wherein the digital twin (54 and 56) comprises the trained machine learning model (54 and 56); obtaining one or more outputs of the trained machine learning model (54 and 56), the one or more outputs indicating predicted performance data (Para. [0045] “…a relationship used for estimating first output data that is three-dimensional shape data of the processed part after irradiation with the laser beam, …a relationship used for estimating second output data that is laser beam parameters of laser beam which the processed part of the processing object 10 is to be irradiated with…”) associated with the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]) for processing the material (10); and causing (Para. [0058] “After termination of the learning process of FIG. 3, machine learning may be performed by using learning data accumulated by that time, every time laser processing is performed or at a predetermined timing.”), based on the predicted performance data, the material (10) to be processed according to the processing parameters (Paras. [0021], [0037], [0039], [0041], [0043], and [0045]).
The Claim 15 limitations recited, “the digital twin comprises the trained machine learning model”; therefore, the “digital twin” was just a different name for the “machine learning model” disclosed by Kobayashi, as evidenced by Wittwer. Wittwer teaches, in Figs. 1 – 12, Col. 2, ll. 40 – 50, Col. 12, l. 55 - Col. 13, l. 30, Col. 13, ll. 45 – 65, Col. 14, ll. 10 – 30, Col. 15, ll. 10 – 35, and Col. 18, ll. 10 – 50, a similar laser processing system (L) with a “digital twin”, i.e., machine learning model, with “The data structures, application cases, algorithms, and data flow form the digital twin of the laser material machining (application). The digital twin is modular and uses, among other things, empirical values to keep the algorithms simple and fast and to ensure the comparability between digital twin and real laser material machining (application). The model includes at least three data structures: i. Data structure 1 contains the data of the parameter set P; ii. Data structure 2 contains the process characteristics PKG; iii. Data structure 3 contains the data of the machining result E.”
Kobayashi, as evidenced by Wittwer, is silent on wherein the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern. Kobayashi further teaches, in Paras. [0021], [0037], [0041], [0043], and [0045], a plurality of processing parameters.
Etter teaches, in Figs. 1 – 7 and Col. 11, ll. 30 – 40, a similar laser processing system having hatch distance as a processing parameter. Dallarosa teaches, in sole figure, Col. 2, ll. 55 – 65, Col. 3, ll. 5 – 15, and Col. 4, ll. 20 – 25, a similar laser processing system having marking speed as a processing parameter. Zimmermann teaches, in Figs. 1 – 3b, Col. 2, ll. 1 – 15, and Col. 4, ll. 45 – 65, a similar laser processing system having milling strategy as a processing parameter. Kleinert teaches, in Figs. 1 – 37, Col. 44, ll. 40 – 50, Col. 51, ll. 25 – 30, and Col. 55, ll. 20 – 35, a similar laser processing system having scan pattern as a processing parameter.
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kobayashi, as evidenced by Wittwer, with the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, because all the claimed elements, i.e., the non-transitory machine-readable storage medium comprising instructions that, when executed by a processing device, cause the processing device to: determine first data indicative of processing parameters for processing a material in a laser processing system comprising a digital twin, and processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, were known in the art, and one skilled in the art could have substituted the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern, taught by Etter, Dallarosa, Zimmermann, and Kleinert, respectively, for the processing parameter(s) of Kobayashi, as evidenced by Wittwer, with no change in their respective functions, to yield predictable results, i.e., the processing parameters comprise at least one of hatch distance, marking speed, milling strategy, or scan pattern would have been utilized by the processing device connected to the non-transitory machine-readable storage medium. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1395; MPEP 2143(I)(B).
Re Claim 3, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, discloses the invention as claimed and as discussed above, and Kobayashi further teaches, in Para. [0063], wherein the predicted performance data comprises a predicted profile (“…computes three-dimensional shape data of the processed part (a shape in the course of processing) after irradiation with laser beam based on the input data. The optimization simulation process subsequently computes a difference between the computed three-dimensional shape data of the processed part (the shape in the course of processing) and the target shape and determines whether this difference is within an allowable range (step S330).”) of the processed material (10).
Re Claims 4, 11, and 17, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, discloses the invention as claimed and as discussed above, and Kobayashi further teaches, in Fig. 9 and Para. [0063], determining preferred processing parameters (“After setting the initial values of the processing conditions, the optimization simulation process specifies the material of the processing object, the laser beam parameters, and the laser irradiation locations as input data and performs a processing simulation based on the input data (step S320).”), wherein the preferred processing parameters are determined based on the predicted performance data meeting a performance criterion (“…computes a difference between the computed three-dimensional shape data of the processed part (the shape in the course of processing) and the target shape and determines whether this difference is within an allowable range (step S330).”).
Re Claim 6, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, discloses the invention as claimed and as discussed above, and Kobayashi further teaches, in Fig. 1 and Para. [0044], wherein the processing parameters for processing the material (10) in the laser processing system (20 - Fig. 1) are determined based on user input (52 – keyboard and mouse). Kobayashi further discloses, in Para. [0021], “The “deep learning” uses the pre-processed part data and the post-processed part data and obtains the first relationship having the output data that is the post-processed part data, as the learning result. This “deep learning” is thus basically supervised learning.”
Re Claims 7, 13, and 19, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, discloses the invention as claimed and as discussed above, and Kobayashi further teaches, in Para. [0060], further comprising: training the machine learning model using training input data comprising historical processing parameters data and training target output data comprising historical performance data associated with the historical processing parameters. Kobayashi teaches, in Para. [0060], “In the case where the machine learning apparatus is not provided with the processing laser irradiation device 30, the motor-driven stage 46, the three-dimensional data measuring instrument 48 or the like but is provided with only a microcomputer corresponding to the system controller 50, the processing of steps S100 to S170 in the learning process of FIG. 3 may be performed in advance by another device to obtain the material of the processing object 10, the laser beam parameters showing the properties of the laser beam which the processing object 10 is irradiated with, and the three-dimensional shape data of the processed part before and after irradiation of the processing object 10 with laser beam.” In other words, “another device” generated and collected the historical processing parameters and the historical performance data associated with the historical processing parameters in advance, i.e., before the collected historical data was provided to a microcomputer to train the machine learning model.
Re Claims 8, 14, and 20, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, discloses the invention as claimed and as discussed above, and Kobayashi further teaches, in Figs. 2 and 3, Paras. [0045], [0046], and [0049], further comprising: determining second data (Paras. [0045], [0046], and [0049] “second input data, based on the first learning result”) indicative of updated processing parameters for processing the material (10) in the laser processing system (20), wherein the second data (Paras. [0045], [0046], and [0049] “second input data, based on the first learning result”) is based on performance data of the material (10) processed according to the processing parameters; providing the second data (Paras. [0045], [0046], and [0049] “second input data, based on the first learning result, a relationship used for estimating second output data that is laser beam parameters of laser beam which the processed part of the processing object 10 is to be irradiated with, is obtained as a second learning result and is also stored as the learning results 56.”) as input to the trained machine learning model (54 and 56); obtaining one or more second outputs (Paras. [0045], [0046], and [0049] “second output data”) of the trained machine learning model (54 and 56), the one or more second outputs (Paras. [0045], [0046], and [0049] “second output data”) indicating updated predicted performance data associated with updated processing parameters for processing the material (10); and causing, based on the updated predicted performance data, the material (10) to be processed according to the updated processing parameters (Para. [0058] “After termination of the learning process of FIG. 3, machine learning may be performed by using learning data accumulated by that time, every time laser processing is performed or at a predetermined timing.”).
Claims 21 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi et al. (2021/0299788A1) as evidenced by Wittwer (11,467,561) in view of Etter et al. (10,695,867) in view of Dallarosa et al. (9,636,776) in view of Zimmermann et al. (6,666,630) in view of Kleinert et al. (11,077,526) as applied to Claim 1 above, and further in view of Andrei et al. (9,533,370).
Re Claim 21, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, teaches the invention as claimed and as discussed above; except, wherein the laser processing system comprises an aperture galvo-scan head comprising an F-Theta lens.
Andrei teaches, in Figs. 1 – 10 and Col. 10, ll. 1 – 5, a similar laser processing system including an aperture galvo-scan head (2) comprising an F-Theta lens (3).
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, with the aperture galvo-scan head comprising an F-Theta lens, taught by Andrei, because all the claimed elements, i.e., the method comprising: determining first data indicative of processing parameters for processing a material in a laser processing system comprising a digital twin; providing the first data as input to a trained machine learning model, wherein the digital twin comprises the trained machine learning model; obtaining one or more outputs of the trained machine learning model, the one or more outputs indicating predicted performance data associated with the processing parameters for processing the material; and causing, based on the predicted performance data, the material to be processed according to the processing parameters, and an aperture galvo-scan head comprising an F-Theta lens, were known in the art, in combination each one of the components would perform the same function as it did separately, and one skilled in the art could have combined the elements as claimed by known methods, with no change in their respective functions, to yield predictable results, i.e., integrating the aperture galvo-scan head comprising an F-Theta lens in the laser processing system would have facilitated focusing the laser beam onto the surface of the workpiece, Andrei Abstract. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1395; MPEP 2143(I)(A).
Claims 22 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi et al. (2021/0299788A1) as evidenced by Wittwer (11,467,561) in view of Etter et al. (10,695,867) in view of Dallarosa et al. (9,636,776) in view of Zimmermann et al. (6,666,630) in view of Kleinert et al. (11,077,526) as applied to Claim 1 above, and further in view of Liu et al. (6,433,303).
Re Claim 22, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, teaches the invention as claimed and as discussed above; except, wherein the laser processing system comprises a quarter-wave plate configured to convert a laser beam into circularly polarized light.
Liu teaches, in Figs. 1 – 7, Abstract, Col. 1, ll. 60 – 65, and Col. 7, ll. 15 – 20, a similar laser processing system including a quarter-wave plate configured to convert a laser beam into circularly polarized light.
It would have been obvious, to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, with the quarter-wave plate configured to convert a laser beam into circularly polarized light, taught by Liu, because all the claimed elements, i.e., the method comprising: determining first data indicative of processing parameters for processing a material in a laser processing system comprising a digital twin; providing the first data as input to a trained machine learning model, wherein the digital twin comprises the trained machine learning model; obtaining one or more outputs of the trained machine learning model, the one or more outputs indicating predicted performance data associated with the processing parameters for processing the material; and causing, based on the predicted performance data, the material to be processed according to the processing parameters, and a quarter-wave plate configured to convert a laser beam into circularly polarized light, were known in the art, in combination each one of the components would perform the same function as it did separately, and one skilled in the art could have combined the elements as claimed by known methods, with no change in their respective functions, to yield predictable results, i.e., integrating the quarter-wave plate in the laser processing system would have facilitated converting the laser beam into circularly polarized light. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1395; MPEP 2143(I)(A).
Claims 23 is rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi et al. (2021/0299788A1) as evidenced by Wittwer (11,467,561) in view of Etter et al. (10,695,867) in view of Dallarosa et al. (9,636,776) in view of Zimmermann et al. (6,666,630) in view of Kleinert et al. (11,077,526) as applied to Claim 1 above, and further in view of Gourdine (4,555,909).
Re Claim 23, Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, teaches the invention as claimed and as discussed above; except, wherein the digital twin further comprises a physics-based model comprising at least a heat conduction equation to simulate propagation and dissipation of thermal energy within the material, and wherein the machine learning model is informed by the physics-based model to model at least one of a temperature gradient, heat-affected zone, melting, recrystallization, or phase transformation in the material.
Gourdine teaches, in Col. 5, ll. 20 – 45, a physics-based model comprising at least a heat conduction equation to simulate propagation and dissipation of thermal energy within the material to model at least one of a temperature gradient.
Thus, improving a particular method (for laser processing a material), based upon the teachings of such improvement in Gourdine, would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, i.e., applying this known improvement technique in the same manner to the method for laser processing a material of Kobayashi, i.v., Etter, Dallarosa, Zimmermann, and Kleinert, a.e., Wittwer, and the results would have been predictable and readily recognized, that integrating a physics-based model comprising at least a heat conduction equation to simulate propagation and dissipation of thermal energy within the material would have facilitated informing the machine learning model to use the physics-based model to model at least a temperature gradient. KSR, 550 U.S. 398 (2007), 82 USPQ2d at 1396; MPEP 2143(I)(C).
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.
Response to Arguments
Applicant's arguments filed 07/02/2026 have been fully considered and to the extent possible have been addressed in the rejections above, at the appropriate locations.
Correspondence
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/LORNE E MEADE/Primary Examiner, Art Unit 3741
