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 non-final and is in response to the claims filed June 10, 2026 and entered with the RCE on July 21, 2026. Claims 1-7 and 9-15 are currently pending, of which claims 1, 9, and 15 are currently amended. Claim 8 was previously cancelled.
Examiner notes that Applicant’s Specification does not contain any paragraph numbers. In an effort to easily track locations in the Specification, Examiner will refer to the pre-grant publication for the pending Application as the Specification (U.S. 2024/0286198 A1).
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 21, 2026 has been entered.
Response to Arguments
Prior Art Rejections
Applicant’s arguments regarding the previously cited prior art have been fully considered and are not persuasive. Specifically, Applicant argues that the amendments related to the “process data and sensor data” are not taught by any of the previously cited art. See Remarks 8-9. Applicant’s arguments with respect to claim(s), especially as it relates to Mehr and Sha, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, new reference Zhang has been introduced to disclose determining sensor data values and manufacturing process data. The sensor values can be correlated with the structure and manufacture of the 3D model parts, including with “the closest corresponding point/element of the 3D model”. See Zhang Fig. 5 and paras. [0004-05] and [0037-40].
It is for at least these reasons, and the reasons cited below, that the claims remain rejected in this Action.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 9-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “including process data and sensor data from adjacent points…” and the metes and bounds of the claim are unclear. Specifically, Applicant states that “Process data is all the data that accrues during the process. This includes the sensor signals and at least in part the process parameter set.” See Specification para. [0033]. Therefore, the scope of the process data is unclear because the specification states that it explicitly includes sensor data, but then sensor data is being claimed as a separate element from the process data. It is thus unclear what the process data includes. Claim 15 recites similar language and is rejected for at least the same reasons therein.
Claims 2-7 and 9-14 depend from above-rejected claim and are therefore rejected based on this dependency.
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 1-6, 9, 11, 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 Zhang et al. (U.S. Publication No. 2018/0314234; hereinafter “Zhang”) and Severson et al. (U.S. Publication No. 2022/0114307; hereinafter “Severson”).
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 as well as the detection of anomalies, Mehr does not teach adjacent points to determine those anomalies.
Zhang further teaches including process data and sensor data from adjacent points within a working area to determine the anomaly value (See Zhang Fig. 5 and paras. [0004-05] and [0037-40]: determining sensor data values and manufacturing process data. The sensor values can be correlated with the structure and manufacture of the 3D model parts, including with “the closest corresponding point/element of the 3D model”. Furthermore, this is being done to reduce defects and predict material properties that could cause such defects to bring the models within tolerance).
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. Zhang enhances the printing and anomaly detection of Mehr by more explicitly monitoring the sensor values and the manufacturing process, giving the data more context and reference points that allow for more accurate and complete models (See Zhang para. [0006]).
Moreover, while Mehr/Zhang teaches monitoring the liquid phase/melt pool (See Mehr paras. [0118-119]), Mehr/Zhang does not teach or suggest matching the liquid phase and a spatial extent.
Severson teaches wherein the working area shares a spatial extent with a liquid phase prevailing at an 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/Zhang 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/Zhang 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/Zhang by improving the efficiency and accuracy of the heat distribution of the additive manufacturing of Mehr/Zhang (See Severson paras. [0008-09]).
As per claim 2, Mehr/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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 9, Mehr/Zhang/Severson teaches the method as claimed in claim 1. However, neither Mehr nor Zhang 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/Zhang with the teachings of Severson for at least the same reasons as discussed above in claim 1.
As per claim 11, Mehr/Zhang/Severson teaches the method as claimed in claim 7. However, neither Mehr nor Zhang 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/Zhang with the teachings of Severson for at least the same reasons as discussed above in claim 1.
As per claim 15, Mehr/Zhang/Severson 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]).
Claim 7 is rejected under 35 U.S.C. §103 as being unpatentable over Mehr/Zhang/Severson as applied above, and further in view of Sha et al. (U.S. Publication No. 2022/0285009; hereinafter “Sha”).
As per claim 7, Mehr/Zhang/Severson teaches the method as claimed in claim 4. However, while Mehr/Zhang/Severson teaches the digital twin as well as point cloud systems (See Mehr paras. [0084] and [0095]; see also Zhang paras. [0039-40]), Mehr/Zhang/Severson does not explicitly teach a point cloud by means of the anomaly detection models.
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, with a reasonable expectation of success, the point clouds of Mehr with the point cloud anomaly values 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 point cloud analysis of Mehr/Zhang/Severson by “improv[ing] the overall accuracy and feature density of the system as 3D images of the subject are captured” while also increasing their computational efficiency through its image processing methods (See Sha paras. [0078] and [0100]).
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehr/Zhang/Severson as applied above, and further in view of Oh et al. (U.S. Publication No. 2022/0417557; hereinafter “Oh”).
As per claim 10, Mehr/Zhang/Severson teaches the method as claimed in claim 1. However, while Mehr/Zhang/Severson teaches a point cloud, Mehr/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson by increasing the flexibility and options of the control actions of Mehr/Zhang/Severson. 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/Zhang/Severson teaches the method as claimed in claim 1. However, while Mehr teaches adjusting parameters as they relate to speed (See Mehr paras. [0052] and [0067]), Mehr/Zhang/Severson 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/Zhang/Severson 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/Zhang/Severson as applied above, and further in view of Nilakantan (U.S. Publication No. 2021/0178697).
As per claim 14, Mehr/Zhang/Severson 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/Zhang/Severson 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118
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