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 Action is FINAL and is in response to the claims filed April 16, 2025. Claims 1-7 and 9-15 are currently pending, of which claims 1, 2, 4, 9, 10, and 15 are currently amended. Claim 8 has been cancelled.
Response to Arguments
Claim Objections
Applicant has amended the claims at issue and the previous objections have therefore been withdrawn.
Specification
While not addressed in Applicant’s remarks, Examiner notes that the new title has been accepted and entered, and therefore the previous objection to the Specification has been withdrawn.
Rejections Under 35 USC §112
Applicant has amended the claims at issue and the previous rejections under 35 USC 112(b) have therefore been withdrawn.
Prior Art Rejections
Applicant’s arguments regarding the previously cited prior art have been fully considered and are not persuasive. Specifically, Applicant argues that Sha has nothing to do with detecting anomalies using data of a working area from adjacent points. See Remarks 9. Examiner respectfully disagrees with both these arguments as well as Applicant’s characterization of the claim language and prior art.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Examiner respectfully notes that this is a rejection of obviousness under 35 USC 103. Examiner explicitly delineated which references teach which limitations, and why the two references are obvious to combine in this case. Mehr teaches the vast majority of the claims, including anomaly detection (See Mehr para. [0137]). Sha clearly teaches point cloud adjacency, while also using “points in the point cloud to determine an amount of error in between the features in the point cloud”. The amount of error between the features in the point cloud is being combined with the error detection and plotted coordinates of Mehr. See Sha paras. [0122-126] and [0178]; see also Mehr paras. [0118], [0131-132] and [0137].
Furthermore, in response to applicant's argument that Sha is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, both references disclose 3D modeling and imaging, including with additive manufacturing/3D printing (See Sha paras. [0004] and [0210]).
It is for at least these reasons, and the reasons cited below, that the claims remain rejected in this Action.
Claim Interpretation
Claim 2 is/are directed to a method that recites “when the process parameter is reused”. The conditional nature of this claim language allows for an interpretation where any prior art meets the broadest reasonable interpretation of the claim without having the “storing” limitation of claim 2. See MPEP 2111.04(II); see also Ex parte Schulhauser.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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 § 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.
Claims 1-7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al. (U.S. Publication No. 2018/0341248; hereinafter “Mehr”), and further in view of Sha et al. (U.S. Publication No. 2022/0285009; hereinafter “Sha”).
As per claim 1, Mehr teaches a method for the additive manufacture of a component, the method comprising
creating a machine code (See Mehr paras. [0034], [0118], and [0165]: process control instructions executed);
creating a representation of a generalized anomaly detection model; training the representation with training data originating from datasets of sensor data from a previously executed manufacturing process with a known process result (See Mehr para. [0133-134]: initial model provided via an input design geometry for an object; para. [0131]: sensor data can be used with a reference data set);
calculating output data from input data (See Mehr para. [0052]: prior to deposition, process parameters are chosen; paras. [0133]: input design geometry);
creating an adaptive anomaly detection model trained on a parameter-set-specific basis with available training data (See Mehr paras. [0124-125]: simulation data used and trained to create defect classification models);
transferring the machine code and the detection models to a control system; starting the manufacturing process; monitoring the process with sensors (See Mehr Fig. 13 and paras. [0022] and [0111-112]: monitoring the process in real-time using a variety of sensors. The deposition process begins and process control instructions may be shared and exchanged);
evaluating sensor signals of the manufacturing process using the generalized anomaly detection model (See Mehr paras. [0111-112] and [0125]: “herein 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”);
training a specialized anomaly detection model in parallel from an adaptive anomaly detection model using process data of the running manufacturing process (See Mehr paras. [0134]: “the training data set may be updated in real-time using process simulation data, process control data, process characterization data, in-process inspection data, and/or post-build inspection data as fabrication is performed on a given system”);
detecting anomalies in the manufacture of the component using the specialized anomaly detection model during the manufacturing process (See Mehr paras. [0131-132] and [0137]: “the real-time process characterization data that is fed to the machine learning algorithm used to 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”).
However, while Mehr teaches points in a coordinate system, Mehr does not teach adjacent points to determine anomalies.
Sha further teaches including data of a working area from adjacent points to determine the anomaly value (See Sha paras. [0122-126] and [0178]: point cloud adjacency, while also using “points in the point cloud to determine an amount of error in between the features in the point cloud”. This would apply to the defect detection in Mehr).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection of Mehr with the point clouds of Sha. One would have been motivated to combine these references because both references disclose 3D modeling and imaging, including with additive manufacturing/3D printing. Moreover, Sha enhances the printing of Mehr by “improv[ing] the speed at which image data from multiple sources is processed and aligned, which can improve performance and reduce processing hardware requirements for achieving desired performance benchmarks…” while also “improv[ing] the overall accuracy and feature density of the system as 3D images of the subject are captured” (See Sha paras. [0057] and [0078]).
As per claim 2, Mehr/Sha further teaches the method as claimed in claim 1, further comprising:
storing an adaptive anomaly detection model trained based on a process parameter set as a specialized anomaly detection model (See Mehr paras. [0133-134]: employing specific type of machine learning algorithm with parameters associated with an optimal control sequence, where the parameters can be adjusted in real-time; paras. [0168-169]: storage units); and
when the process parameter set is reused, using the specialized anomaly detection model (See Mehr Fig. 8 and para. [0178]: predicted future build states based on current build state and a set of actions).
As per claim 3, Mehr/Sha further teaches the method as claimed in claim 1, further comprising using the specialized anomaly detection model at a start of a second manufacturing process with a second process parameter set; and training the specialized anomaly detection model with the adaptive anomaly detection model (See Mehr Fig. 8 and para. [0178]: predicted future build states based on current build state and a set of actions; paras. [0126-127]: “the training data set may be updated in real-time with object defect and object classification date as it is performed on a given system. In some instances, the training data may be updated with object defect data and object classification data drawn from a plurality of automated defect classification systems”; para. [0027]: models can be deployed across multiple workspaces and work sites).
As per claim 4, Mehr/Sha further teaches the method as claimed in claim 1, further comprising:
developing a digital twin of the resulting component in parallel during the process from the sensor data comprising position data of detected anomalies (See Mehr paras. [0032], [0139], and [0178]: compare current state against target fabrication data/state in real-time. This is a digital twin);
predicting via a position of a printhead at a particular time using the machine code (See Mehr paras. [0116-118]: predicting next actions of the deposition process, including the indicated positions of the wire feed (i.e., the print head));
analyzing a working area around the position based on anomalies present in the digital twin (See Mehr paras. [0125-126] and [0129]: sensors to monitor used in the defect classification); and
adjusting the process parameters on reaching the working area for the elimination of the anomaly (See Mehr para. [0124]: “determine a set or sequence of process control parameter adjustments that will implement a corrective action, e.g., to adjust a layer dimension or thickness, so as to correct a defect when first detected”).
As per claim 5, Mehr/Sha further teaches the method as claimed in claim 4, further comprising introducing the anomalies determined by the anomaly detection models into the digital twin (See Mehr paras. [0125-126]: training sets using simulated data and real-time sensor data to classify defects in real-time).
As per claim 6, Mehr/Sha further teaches the method as claimed in claim 4, further comprising assigning a timestamp to each position approached by the printhead (See Mehr paras. [0108] and [0139]: parameters can include “the location of a deposition apparatus as a function of time” while the current state is compared to the design target and adjust the control parameters accordingly).
As per claim 7, Mehr teaches the method as claimed in claim 4. However, while Mehr teaches the digital twin as well as points in a coordinate system (See Mehr paras. [0084] and [0095]), Mehr does not explicitly teach a point cloud.
Sha teaches wherein digital twin includes a point cloud and an anomaly value is determined for each point by means of one of the anomaly detection models for which a process state is stored (See Sha paras. [0088], [0100], and [0178]: point cloud used to determine an amount of error, where values are provided).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr with the teachings of Sha for at least the same reasons as discussed above in claim 1.
As per claim 15, Mehr/Sha teaches an additive manufacturing device that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. Furthermore, Mehr teaches a robot arm; a control system; a printhead; sensors to detect process parameters; wherein the control system is configured to implement said method (See Mehr paras. [0034], [0051], [01112], and [0117]).
Claims 9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Sha as applied above, and further in view of Severson et al. (U.S. Publication No. 2022/0114307; hereinafter “Severson”).
As per claim 9, Mehr/Sha teaches the method as claimed in claim 8. However, while Mehr/Sha teaches monitoring the liquid phase/melt pool (See Mehr paras. [0118-119]), Mehr/Sha does not teach or suggest matching the liquid phase and a spatial extent.
Severson teaches wherein the working area has a spatial extent matching a liquid phase prevailing at the observation time point (See Severson para. [0099]: “adjust the scaling factor C so that the temperature at the periphery of the 1.5-mm (dia.) melt pool matches the liquidus temperature (1830° C.).” Therefore, the melt pool dimensions of Mehr/Sha can be matched using the scaling factor and temperatures of Severson).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection of Mehr/Sha with the double ellipsoid of Severson. One would have been motivated to combine these references because both references modeling in additive manufacturing, and Severson enhances the models of Mehr/Sha by improving the efficiency and accuracy of the heat distribution of the additive manufacturing of Mehr/Sha (See Severson paras. [0008-09]).
As per claim 11, Mehr/Sha teaches the method as claimed in claim 7. However, Mehr/Sha does not teach or suggest where the working area is represented by a double ellipsoid.
Severson teaches where the working area is represented by a double ellipsoid (See Severson paras. [0045] and [0091-93]: double ellipsoid modeling for the manufacturing of Mehr).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr/Sha with the teachings of Severson for at least the same reasons as discussed above in claim 9.
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Sha as applied above, and further in view of Oh et al. (U.S. Publication No. 2022/0417557; hereinafter “Oh”).
As per claim 10, Mehr/Sha teaches the method as claimed in claim 1. However, while Mehr/Sha teaches a point cloud, Mehr/Sha does not teach an octree structure.
Oh teaches wherein the point cloud is represented in a spatially structured data structure in the form of an octree (See Oh paras. [0369] and [0380]: octree structure with the point cloud).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the digital twin and defect detection and point clouds of Mehr/Sha with the point octree point clouds of Oh. One would have been motivated to combine these references because both references disclose 3D modeling and imaging with the use of point clouds, and Oh enhances the models of Mehr by increasing their processing efficiency as there can be a large amount of point data (See Oh paras. [0002-03]).
Claims 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Sha as applied above, and further in view of MacNeish, III et al. (U.S. Publication No. 2019/0099952; hereinafter, “MacNeish”).
As per claim 12, Mehr/Sha teaches the method as claimed in claim 1. However, while Mehr teaches adjusting parameters as they relate to heat flux and other temperatures (See Mehr paras. [0108] and [0124]), Mehr does not explicitly teach higher heat input.
MacNeish teaches wherein adjustment of the process parameters for the elimination of the anomaly includes a higher heat input (See MacNeish paras. [0038], [0042], and [0044]: “a difference in temperate at the hot end 106 from a desired set point may represent a necessary adjustment by the PID controller of the thermocouple connectively associated with the heating element 303 of the hot end 106.” This includes increasing “the delivery of power to the heating element 303 of the hot end 106”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the additive manufacturing corrective actions of Mehr/Sha with the corrective action adjustments of MacNeish. One would have been motivated to combine these references because both references disclose corrective actions in additive manufacturing, and MacNeish enhances the manufacturing of Mehr/Sha by increasing the flexibility and options of the control actions of Mehr/Sha. This would further decrease significant or fatal print flaws, as well as their frequency of occurrence of these printing breakdowns, while minimizing the number of settings needed to engage in the additive manufacturing (See MacNeish para. [0006]).
As per claim 13, Mehr/Sha teaches the method as claimed in claim 1. However, while Mehr/Sha teaches adjusting parameters as they relate to speed (See Mehr paras. [0052] and [0067]), Mehr/Sha does not explicitly teach higher heat input.
MacNeish further teaches wherein adjustment of the process parameters for the elimination of the anomaly includes a lower printhead speed (See MacNeish paras. [0044] and [0049-50]: “the controller 310 may indicate an increase or decrease in the print head hob speed…” Additionally, “[a] motor having encoding 1004 may be provided so that filament pull, grabbing, jamming, or crimping may be sensed to allow for ultimate adjustment of motor speed”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Mehr/Sha with the teachings of MacNeish for at least the same reasons as discussed above in claim 12.
Claim 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Sha as applied above, and further in view of Nilakantan (U.S. Publication No. 2021/0178697).
As per claim 14, Mehr/Sha teaches the method as claimed in claim 1. However, while Mehr teaches wire additive manufacturing and arc welding (See Mehr paras. [0096]), Mehr does not explicitly teach wire arc additive manufacturing.
Nilakantan teaches the additive manufacturing method comprises wire arc additive manufacturing (See Nilakantan para. [0002]: additive manufacturing technology can include wire arc additive manufacturing).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the wire additive manufacturing of Mehr/Sha with the wire arc manufacturing of Nilakantan. One would have been motivated to combine these references because both references disclose modeling in wire additive manufacturing, and Nilakantan enhances the manufacturing of Mehr by increasing the flexibility by expanding the types of environments that the modeling of Mehr can apply to.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118
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