DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. Claims 1-14 have been examined and rejected. This Office action is responsive to the amendment filed on July 6, 2026, which has been entered in the above identified application.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
4. Claims 1 and 4-8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4 and 6-10 respectively of copending Application No. 18/570775 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 4 and 6-10 of co-pending application 18/570775 contain every element of claims 1 and 4-8 of the instant application and thus, anticipates the claims of the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Objections
5. The correction to claim 10 has been approved, and the objection to the claim is withdrawn.
Claim Rejections - 35 USC § 112
6. The correction to claim 1 has been approved, and the rejections to claims 1-14 are withdrawn.
Claim Rejections - 35 USC § 102
7. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
8. Claims 1, 2, 4, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mehr et al (Pub. No. US 2018/0341248).
8-1. Regarding claim 1, Mehr teaches the claim comprising: creating a machine code and transmitting the machine code to a controller, by disclosing implementing an additive manufacturing process by first creating a three-dimensional model of an object to be fabricated using a computer-aided design (CAD) software package [paragraph 83] and converting the model to a standard file format [paragraph 84]. A method is provided for automated classification of object defects and adaptive real-time control of the object to be fabricated using modular components that share and exchange process data, process control data, and process control instructions [paragraph 34]. In the disclosed adaptive process control method, real-time process monitoring data may be used by a processor running a machine learning algorithm to make adjustment(s) to one or more process control parameters [paragraphs 118]. One or more processors may be employed to implement the machine learning algorithms and additive manufacturing process control methods [paragraph 165].
Mehr teaches starting an additive manufacturing process to build the component using a print head; monitoring the process with sensors, by disclosing an additive manufacturing deposition apparatus having a printhead [paragraphs 22, 117; figure 13] that begins the process of fabricating the object and monitoring the process in real-time using a variety of sensors [paragraphs 111-112].
Mehr teaches evaluating sensor data to identify one or more anomalies in the component during the manufacturing process, by disclosing providing a classification of detected object defects using the real-time data from the sensors as input to a machine learning algorithm [paragraph 125].
Mehr teaches establishing a parallel digital twin of the component from sensor data comprising position data of the one or more anomaly, by disclosing using process simulation tools [paragraph 100], process control parameters [paragraphs 107], process monitoring tools [paragraph 111], and data from in-process inspection tools [paragraph 116, lines 15-21] to provide an indication of the current state of the fabrication process and/or the part being fabricated [paragraph 178]. The training data set used by the machine learning algorithm to enable automated classification of object defects, prediction of optimal sets or sequences of process control parameters , adjustment of process control parameters in real-time, or any combination thereof [paragraph 29], is updated with additional process simulation data, process characterization data, in-process inspection data , post-build inspection data, or any combination thereof, after each iteration of an additive manufacturing process that is performed iteratively [paragraph 32]. A current “state” of the part under fabrication is identified and compared to the design target (or reference “state”), and one or more process control parameters are adjusted in order to minimize the different between the two states [paragraph 139, lines 32-42].
Mehr teaches predicting a position of the print head at a specific time using the machine code, by disclosing predicting the next actions by the deposition process, including the indicated positions of the wire feed (i.e., the print head) [paragraphs 116-118].
Mehr teaches analyzing a working area around the predicted position with respect to anomalies present using the digital twin, by disclosing monitoring the dimensions and/or properties of the melt pool, the deposited layer downstream from the melt pool, or other features of the part being fabricated at one or more positions on the part [paragraph 119]. Real-time data is obtained for one or more object properties from sensors as input to the machine learning algorithm to allow for the classification of detected object defects for adjustment in real-time [paragraphs 125-126, 129]
Mehr teaches adjusting process parameters of the manufacturing process when the working area is reached to eliminate the anomaly, by disclosing determining 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 [paragraph 124, lines 23-29].
8-2. Regarding claim 2, Mehr teaches all the limitations of claim 1, further comprising using a model to evaluate the sensor data and, in a dynamic period of time, computing feedback data to the controller (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”).
8-3. Regarding claim 4, Mehr teaches all the limitations of claim 1, further comprising assigning a time stamp to each position to which the print head moves (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).
8-4. Regarding claim 14, Mehr teaches all the limitations of claim 1, wherein the additive method of manufacturing comprises a Laser Metal Deposition (LMD) method (See Mehr para. [0051]: the additive manufacturing processes and systems to which the disclosed defect classification and adaptive control methods may be applied is laser-metal wire deposition).
Claim Rejections - 35 USC § 103
9. 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.
10. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248) in view of Hsu et al (U.S. Patent No. 10,380,911).
10-1. Regarding claim 3, Mehr teaches all the limitations of claim 2. Mehr does not expressly teach wherein the dynamic period of time amounts to between 0.05 s and 3 s. Hsu teach wherein the dynamic period of time amounts to between 0.05 s and 3 s (See Hsu [col. 19, ln. 63 to col. 20, ln. 29]: a GPU processes data from a sensor (camera) for output to a processor, wherein processing data by the GPU comprises determining, in real-time (e.g., with latency less than 100 milliseconds or, more preferably, less than 20 milliseconds, or more preferably still, less than 5 milliseconds), one or more characteristics helpful in achieving a better product, for training purposes, calibrating the system, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide a processor for evaluating the sensor data of Mehr in real-time with a latency of less than 100 milliseconds, as taught by Hsu. This would help ensure that defects to the object will be identified in a timely manner.
11. Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248) in view of Sha et al (Pub. No. US 2022/0285009).
11-1. Regarding claim 5, Mehr teaches all the limitations of claim 1. Although Mehr discloses the digital twin as well as points in a coordinate system (See Mehr paras. [0084] and [0095]), Mehr does not explicitly teach wherein: the digital twin comprises a point cloud; and for each point an anomaly value is determined and a process state is stored. Sha teaches wherein digital twin includes a point cloud; and for each point an anomaly value is determined and 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 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]).
11-2. Regarding claim 6, Mehr-Sha teach all the limitations of claim 5, wherein determining the anomaly value includes data of a working area from neighboring points (See Mehr para. [119]: monitoring the dimensions and/or properties of the melt pool, the deposited layer downstream from the melt pool, or other features of the part being fabricated at one or more positions on the part). 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 Mehr with the teachings of Sha for at least the same reasons as discussed above in claim 5.
12. Claims 7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248), in view of Sha et al (Pub. No. US 2022/0285009), and further in view of Severson et al (Pub. No. US 2022/0114307).
12-1. Regarding claim 7, Mehr-Sha teach all the limitations of claim 6. Mehr-Sha do not expressly teach wherein the working area has a spatial dimension of a liquid phase at the point of observation. 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]).
12-2. Regarding claim 9, Mehr-Sha-Saverson teach all the limitations of claim 7, wherein the working area comprises 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 7.
12-3. Regarding claim 10, Mehr-Sha-Saverson teach all the limitations of claim 9, further comprising adjusting axes of the ellipsoid to a dimension of a weld pool comprising the liquid phase (see Mehr paras. [119], [136], [125], [180]: The dimensions and/or properties of the melt pool are monitored in real-time. 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. During the build, in addition to building a machine learning model that correlates process control parameters (e.g., laser power, feed rate, travel speed, etc.) and result of the deposition process (e.g., the shape of melt pool, defects in the melt pool, etc.), one may also create a mapping between the process control parameters and a specific location in the part.)
13. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248), in view of Sha et al (Pub. No. US 2022/0285009), and further in view of Oh (Pub. No. US 2022/0417557).
13-1. Regarding claim 8, Mehr-Sha teach all the limitations of claim 5. Mehr-Sha does not expressly teach wherein the point cloud comprises a spatially structured data structure in the form of an octree. 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 of Mehr with the point clouds of Oh. One would have been motivated to combine these references because both references disclose 3D modeling and imaging, 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]).
14. Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248) in view of MacNeish (Pub. No. US 2019/0099952).
14-1. Regarding claim 11, Mehr teaches all the limitations of claim 1. Mehr does not expressly teach wherein adjusting the process parameters comprises increasing an application of heat. MacNeish teaches wherein adjusting the process parameters comprises increasing an application of heat (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 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 by increasing the flexibility and options of the control actions of Mehr. 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]).
14-2. Regarding claim 12, Mehr teaches all the limitations of claim 1. Although Mehr discloses adjusting parameters as they relate to speed (see Mehr paras [0052] and [0067], Mehr does not expressly teach wherein adjusting the process parameters comprises reducing a print head speed. MacNeish teaches wherein adjusting the process parameters comprises reducing a print head 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 with the teachings 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 by increasing the flexibility and options of the control actions of Mehr. 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]).
15. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mehr et al (Pub. No. US 2018/0341248) in view of Nilakantan (Pub. No. US 2021/0178697).
15-1. Regarding claim 13, Mehr teaches all the limitations of claim 1. Although Mehr discloses wire additive manufacturing and arc welding (See Mehr paras. [0096]), Mehr does not expressly teach wherein the additive method of manufacturing comprises an arc wire cladding method. Nilakantan teaches wherein the additive method of manufacturing comprises an arc wire cladding method (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 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.
Response to Arguments
16. The Examiner acknowledges the Applicant’s amendments to claims 1 and 10.
Examiner notes that no terminal disclaimer has been filed. Therefore, the provisional nonstatutory double patent rejections of the claims still remain.
Regarding independent claim 1, Applicant alleges that Mehr et al (Pub. No. US 2018/0341248) does not teach "analyzing a working area around the predicted position with respect to anomalies present using the digital twin” because the cited portions of Mehr is focused on using object properties from sensors to analyze dimensions and/or properties of the melt pool, the deposited layer downstream from the melt pool, or other features of the part being fabricated at one or more positions of the part, as opposed to using the digital twin to analyze a working area around the predicted position.
Contrary to Applicant’s arguments, Mehr discloses implementing an additive manufacturing process by first creating a three-dimensional model of an object to be fabricated using a computer-aided design (CAD) software package [paragraph 83; paragraph 133, lines 1-5]. Based on the 3D model of the object, a machine learning algorithm is used to predict a set or sequence of one or more process control parameters for fabricating the object [paragraph 133, lines 11-16]. Process control parameters include the location of a deposition apparatus as a function of time (i.e., a tool path) [paragraph 108]. Thus, a prediction is made of a position of a print head at a specific time using the machine code. While performing the additive manufacturing process to fabricate the object, real-time process characterization data is provided by one or more sensors as input to the machine learning algorithm to adjust one or more process control parameters in real-time [paragraph 133, lines 16-21]. To make such adjustments, for a predicted position of a print head where a laser beam impinges on metal wire to create a melt pool at the point of intersection and deposit material on a substrate [paragraph 133, lines 11-16; paragraph 117, lines 1-6] and where one or more defects may arise [paragraph 117, lines 10-27], analysis is performed on the area around the predicted position by monitoring the dimensions and/or properties of the melt pool, or other features of the part being fabricated at one or more positions on the part [paragraphs 119, 129] and using such information as real-time process characterization data for input into a machine learning algorithm that has been trained on a training data set reflecting the same input design geometry of the object being fabricated to determine a set or sequence of process control parameter adjustments that will implement a corrective action [paragraphs 124-126]. To perform such real-time, adaptive process control, an artificial neural network (ANN) architecture is used that provides an indication of the current state of the fabrication process and/or the part being fabricated [paragraph 178]. Thus, Mehr teaches analyzing a working area around the predicted position with respect to anomalies present using the digital twin.
Applicant states that dependent claims 2-14 recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding independent claim 1. However, as discussed above, Mehr is considered to teach claim 1, and consequently, claims 2-14 are rejected.
Conclusion
17. THIS ACTION IS MADE FINAL. 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.
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN H TAN whose telephone number is (571)272-8595. The examiner can normally be reached M-F 10AM-6PM.
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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