CTNF 18/593,417 CTNF 83009 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1,3-14, and 16-20 /are rejected under 35 U.S.C. 103 as being unpatentable over Vora et al. (PG/PUB 20210016509) in view over Nelson (PG/PUB 20230092671) . Claim 1: Vora et al. teaches an additive manufacturing system (ABSTRACT, summary of invention) but does not expressly teach the power delivery system limitations described below. Nelson teaches the power delivery system limitations described below, comprising: an energy delivery device configured to deliver energy to a build surface of a component to form a melt pool in the build surface of the component (Vora et al., ABSTRACT, 0019, 0021 e.g. “A first laser (e.g., a fiber laser) can be used for melting and consolidating the powder, and a second laser is utilized for dual purpose: (a) for metrology to measure the surface roughness, dimensional accuracy, material properties, etc., and (b) based on the evaluated measurements to take corrective actions (laser ablation, etc.) to attain the desired surface finish and dimensional accuracy. Exemplary embodiments can include computer-controlled fully automated Robotic arm with six-axis motion capability, which can accommodate multiple lasers. A first laser can be used for additive manufacturing, and a second laser can be used for corrective action (e.g., additive and subtractive manufacturing).” a powder delivery device configured to direct a powder stream toward the melt pool to form an as-deposited layer on the build surface (Nelson, ABSTRACT, summary of invention, Figure 1-14 e.g. “A powder flow monitoring system may include a computing device configured to receive image data representing illuminated powder of a powder stream between a powder delivery device and a build surface of a component, generate a representation of the powder stream based on the image data, and output the representation of the powder stream for display at a display device.”) a topology monitoring system configured to capture data indicative of a position of a surface of the as-deposited layer (Vora, 0023, 0025-26, Figure 6A e.g. “he Defect Exploration System 23 controls sensors (e.g., image capture sensors that capture photographic, video graphics, optical, scanning electron microscope (SEM), tunneling electron microscope (TEM) systems as well as thermal profile capture sensors, and sensors that capture or detect distortion or dimensional profile data. The Defect Identification System 25 includes image or sensor data recognition systems (e.g., feature recognition). Embodiments also can perform comparison of captured thermal profile sensor data with a database of stored defect data or comparator data. The Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment)”) a computing device (Vora 0027) configured to: receive the captured data from the topology monitoring system, determine an actual position of the surface of the as-deposited layer based on received data from the topology monitoring system (Vora 0020-0024 e.g. “the Defect Exploration System 23 controls sensors (e.g., image capture sensors that capture photographic, video graphics, optical, scanning electron microscope (SEM), tunneling electron microscope (TEM) systems as well as thermal profile capture sensors, and sensors that capture or detect distortion or dimensional profile data. The Defect Identification System 25 includes image or sensor data recognition systems (e.g., feature recognition). Embodiments also can perform comparison of captured thermal profile sensor data with a database of stored defect data or comparator data. The Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment) compare the actual position of the surface of the as-deposited layer to a modeled position of the surface of the as-deposited layer (Vora 0020, 0023, 0025-26, claim 1) determine a difference between the actual position and the modeled position of the as-deposited layer (Vora 0020, 0023, 0025-26, claim 1 e.g. “Embodiments also can perform comparison of captured thermal profile sensor data with a database of stored defect data or comparator data. The Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment). Defect exploration, identification and optically add defect operations can be repeated for each layer as they are produced by the AM/3D printer system 13 .”) control at least one of the energy delivery device or the powder delivery device based on the difference between the actual position and the modeled position of the as-deposited layer (Vora 0005-0006, 0013, Figure 4, 0019, 0024-28 e.g. “FIG. 4 shows exemplary corrective actions including strategies to address underprint and overprint defects; For example, an Over Print corrective action can include machine instructions which orient one or more lasers to execute a laser ablation operation on an over print or other defect condition on an AM build layer. An exemplary ablation laser can be mounted on a 7 axis system to provide precise orientations of the laser based on a stored library of machine instructions which select maneuvering of the laser(s) and operation of the laser to include power, pulse type, selection of laser type (e.g., ultrashort pulse laser, etc). The Corrective Actions 17 section further includes a library of corrective actions or machine instructions associated with various types of under print related defects where machine instructions operate the AM system 13 to perform AM deposition. Alternative embodiments can include a system which applies AM build material directly to an defect location, e.g., under print location, independently of the AM System 13 then operates a separate AM laser to perform layer touch up or spot build up on a particular defect location. Corrective actions are selected and executed (operation of various elements of the system) for every layer as defects are detected until end of AM printing operations. Removal of materials ablate material for dimensional accuracy, address distortions and other types of over prints. Add material operations fill voids, inclusions, reduce or eliminate dimensional inaccuracy, underprinted features, apply added material etc to mitigate this class of defects.”) One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of to the teachings of would achieve an expected and predictable result via combining said elements using known methods. is in the same field of endeavor and reasonably pertinent to controlling deposition layer quality based on monitoring powder delivery, ABSTRACT, summary of invention claim 12. The additive manufacturing system of claim 1, further comprising the component, wherein the component is a gas turbine engine component (Nelson, 0057 e.g. “Further, computing device 12 may control adjustable z-stage 40 to move PFMS 18 vertically and out of the way to allow powder delivery device 16 and energy delivery device 16 access to physically constrained areas, e.g., between vanes of a doublet or triplet of a nozzle guide vane for a gas turbine engine.”) Claim 13. The cited prior art teaches a method for additive manufacturing, comprising: delivering, via an energy delivery device of an additive manufacturing system, energy to a build surface of a component to form a melt pool in the build surface of the component; supra claim 1 delivering, via a powder delivery device of the additive manufacturing system, a powder stream toward the melt pool to form an as-deposited layer on the build surface, supra claim 1 receiving, by a computing device, data indicative of a position of a surface of the as-deposited layer from a topology monitoring system; supra claim 1 determining, by the computing device, an actual position of the surface of as-deposited layer based on the received data; comparing, by the computing device, the actual position of the surface of the as-deposited layer to a modeled position of the surface of the as-deposited layer; determining, by the computing device, a difference between the actual position and the modeled position of the as-deposited layer; supra claim 1 controlling, by the computing device and based on the determined difference between the actual position and the modeled position of the as-deposited layer, at least one of the powder delivery device or the energy delivery device, supra claim 1 claim 14. The method of claim 13, wherein comparing the actual position of the surface of the as-deposited layer to the modeled position of the layer comprises storing a model of the component, wherein the component includes a plurality of layers, and wherein the model includes positions of each as-deposited layer and deposition parameters for each layer of the plurality of layers (Vora 0020, 0023, 0025-26, claim 1 e.g. “e Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment).”) claim 18. The method of claim 13, further comprising generating, by the computing device, a three-dimensional scan of the surface of the as-deposited layer (Vora 0020, 0024 e.g. “e Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment). claim 19. The method of claim 17, wherein the topology monitoring system includes at least one of a computed tomography device, a structured-light device, a LIDAR device, or a time-of-flight camera device (Vora 0020, 0024 e.g. “(e.g., optical, x-rays, etc) and/or thermal images, the sensing system further can include one or more of a group comprising a scanning electron microscope (SEM), a transmission electron microscope (TEM), thermal profile, and distortion/dimensional profile sensing systems;” claim 20. The method of claim 15, wherein the computing device is configured to determine, based on output of a machine learning model that takes captured data from the topology monitoring system as input, the one or more deposition parameters (0021, 0025 e.g. “A real time, in-situ monitoring and feedback control loop (ISM FCL) system 15 is provided which communicates and operates a Corrective Strategies Section 17 and a Build Feedback and Machine Learning Platform 21 . The Corrective Strategies Section 17 includes machine instructions for altering operation of the AM 3D printer system 13 in response to detected defects which include Over Print and Under Print defects. The Build Feedback and Machine Learning Platform 21 system includes a Defect Exploration System 23 , a Defect Identification System 25 and an Add New Defect Type and Definition in a system database 27 . The ISM FLC System 15 operates the Defect Exploration System which receives sensor inputs (See FIG. 1A for example) and compares stored data or 3D Model Input File data 21 with the received sensor inputs to determine if a defect then identifies a specific type of defect via the Defect Identification system 25”) claim 3. The additive manufacturing system of claim 2, wherein, to adjust the one or more deposition parameters, the computing device is configured to adjust the one or more deposition parameters to reduce a distance between the actual position and the modeled position of the as-deposited layer (Vora, 0005-0006, 0013, Figure 4, 0019, 0024-28 e.g. “A real time, in-situ monitoring and feedback control loop (ISM FCL) system 15 is provided which communicates and operates a Corrective Strategies Section 17 and a Build Feedback and Machine Learning Platform 21 . The Corrective Strategies Section 17 includes machine instructions for altering operation of the AM 3D printer system 13 in response to detected defects which include Over Print and Under Print defects. The Build Feedback and Machine Learning Platform 21 system includes a Defect Exploration System 23 , a Defect Identification System 25 and an Add New Defect Type and Definition in a system database 27 . The ISM FLC System 15 operates the Defect Exploration System which receives sensor inputs (See FIG. 1A for example) and compares stored data or 3D Model Input File data 21 with the received sensor inputs to determine if a defect then identifies a specific type of defect via the Defect Identification system 25) claim 4. The additive manufacturing system of claim 1, wherein the computing device is configured to generate a three-dimensional scan of the surface of the as-deposited layer (Vora 0023, 0025-26, Figure 6A The Defect Identification 25 also can perform inspection or comparison of captured distortion/dimensions (x, y, z) sensor data outputs with data base of defects. The Add New Defect Type and Definition system 27 further operates to take image or sensor data, thermal profile data, and/or inspection of distortion/dimensional profile data to a data storage system 29 (or another database in an alternative embodiment) claim 5. The additive manufacturing system of claim 4, wherein the topology monitoring system includes at least one of a computed tomography device, a structured-light device, a LIDAR device, or a time-of-flight camera device. (Vora 0023, 0025-26, Figure 6A e.g. “an orienting system for the plurality of lasers; a sensing system that includes a sensor or imager system that selectively orients towards and generates sensor outputs or sensor data captures e.g., image captures of each AM build layer during or after printing, the sensor outputs or sensor data captures include electromagnetic (e.g., optical, x-rays, etc) and/or thermal images, the sensing system further can include one or more of a group comprising a scanning electron microscope (SEM), a transmission electron microscope (TEM), thermal profile, and distortion/dimensional profile sensing systems; a measuring system that receives inputs from at least one of the plurality of lasers and measures the AM build layer during and after the AM system prints or produced the AM build layers; a data storage or hard drive storing a plurality of in-situ monitoring and corrective action system machine instructions to operate the system comprising”) claim 6. The additive manufacturing system of claim 1, wherein the powder delivery device and the energy deliver device are parts of a common deposition head (Nelson, Figure 1-16/14) claim 7. The additive manufacturing system of claim 1, wherein the computing device is configured to control the powder delivery device according to a set of deposition parameters that includes one or more deposition parameters controllable by the powder delivery device, wherein the set of deposition parameters controllable by the powder delivery device include one or more of a carrier gas flow rate, a powder mass flow rate, and a delivery nozzle angle (Nelson, Figure 1-30, 0021-23, Figure 2-58 e.g. see angled deposition) claim 8. The additive manufacturing system of claim 7, wherein the computing device is further configured to: determine one or more deposition parameters controllable by the powder delivery device; and control, based on the one or more deposition parameters, the powder delivery device (Nelson, Figure 15, 0057 e.g. “Powder delivery device 52 includes a deposition head 54 that carries a plurality of powder nozzles 56 . Plurality of powder nozzles 56 output a powder stream 58 toward the build surface. As shown in FIG. 2 , the powder stream 58 may be focused at a focal plane, such that powder stream 58 is converging toward the focal plane and diverging away from the focal plane. claim 9. The additive manufacturing system of claim 8, wherein the computing device is configured to determine, based on output of a machine learning model that takes captured data from the topology monitoring system as input, the one or more deposition parameters (Vora 0020-21, 0025-26 claim 10. The additive manufacturing system of claim 1, wherein the computing device is configured to control the energy delivery device according to a set of deposition parameters that includes one or more deposition parameters controllable by the energy delivery device, wherein the set of deposition parameters controllable by the energy delivery device include one or more of a focus of the energy delivery device, a scan speed of the energy delivery device, and a power supplied to the energy delivery device. (Vora 0006 0024-25) claim 11. The additive manufacturing system of claim 8, wherein the computing device is further configured to: determine one or more deposition parameters controllable by the energy delivery device; and control, based on the one or more deposition parameters, the energy delivery device. (Vora 0006 0024-25, see also 0020-21 e.g. “A real time, in-situ monitoring and feedback control loop (ISM FCL) system 15 is provided which communicates and operates a Corrective Strategies Section 17 and a Build Feedback and Machine Learning Platform 21 . The Corrective Strategies Section 17 includes machine instructions for altering operation of the AM 3D printer system 13 in response to detected defects which include Over Print and Under Print defects. The Build Feedback and Machine Learning Platform 21 system includes a Defect Exploration System 23 , a Defect Identification System 25 and an Add New Defect Type and Definition in a system database 27 . The ISM FLC System 15 operates the Defect Exploration System which receives sensor inputs (See FIG. 1A for example) and compares stored data or 3D Model Input File data 21 with the received sensor inputs to determine if a defect then identifies a specific type of defect via the Defect Identification system 25 . The Defect Identification system 25 data is then correlated by the ISM FCL 15 system with Corrective Strategies machine instructions and database 17 associated with the identified specific type of defect; then stored instructions for operating a laser (e.g., see FIG. 1A) to ablate an overprint or add additional additive manufacturing material to an under print condition or defect (or another type of defect). Note various software systems, e.g., ISM FLC 15 , Defect Identification System 25 , Corrective Strategies System, etc, can be hosted on the computer that is included in the AM system 13 or on another system in communication with the computer coupled with the 3D printer AM system 13 . Multiple lasers can be used to increase effectiveness or precision of removal effects where the lasers can be oriented in a variety of ways to reduce damage to adjacent sections of a build layer with an overprint or another type of defect (e.g., texture, etc)….FIG. 1B shows another simplified system architecture of one embodiment of the invention. In particular, A machine learning stack 31 can be provided which implements or hosts, e.g., the FIG. 1A Build Feedback and Machine Learning Platform 21 (or the Platform 21 can be hosted on the local computer 39 . The machine learning stack 31 can include a Predictive Analytic Section, a Descriptive Analytics Section, and a Prescriptive Analytics Section which collectively communicate with a cloud based system with a big data environment (optional) and a Local Computer 13 hosting a Real Time Feedback Loop Control System (FLC System) 15 (e.g., the FIG. 1A ISM FLC System 15 hosted on computer 13 ). “ claim 16. The method of claim 15, wherein adjusting the deposition parameters of the powder delivery device or the energy delivery device reduces a distance between the actual position and the modeled position of the as-deposited layer (0022, 0024 e.g. “FIG. 4 shows exemplary corrective actions including strategies to address underprint and overprint defects; For example, an Over Print corrective action can include machine instructions which orient one or more lasers to execute a laser ablation operation on an over print or other defect condition on an AM build layer. An exemplary ablation laser can be mounted on a 7 axis system to provide precise orientations of the laser based on a stored library of machine instructions which select maneuvering of the laser(s) and operation of the laser to include power, pulse type, selection of laser type (e.g., ultrashort pulse laser, etc). The Corrective Actions 17 section further includes a library of corrective actions or machine instructions associated with various types of under print related defects where machine instructions operate the AM system 13 to perform AM deposition. Alternative embodiments can include a system which applies AM build material directly to an defect location, e.g., under print location, independently of the AM System 13 then operates a separate AM laser to perform layer touch up or spot build up on a particular defect location.”) claim 17. The method of claim 16, wherein adjusting the deposition parameters of the powder delivery device or the energy delivery device comprises adjusting the deposition parameters of both the powder delivery device and the energy delivery device (supra claim 1, Vota, 0022-0024, see Nelson, Figure 1-30, 0021-23, Figure 2-58 e.g. see angled deposition) 07-21-aia AIA Claim (s) 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vora et al. (PG/PUB 20210016509) in view over Nelson (PG/PUB 20230092671) in view over Yang (PG/PUB 20170368753) claim 2. The additive manufacturing system of claim 1 but does not expressly teach the threshold limitations described below. Yang teaches the threshold limitations described below, wherein, to control at least one of the energy delivery device or the powder delivery device, the computing device is configured to: compare the determined difference between the actual position and the modeled position of surface to a threshold difference (Yang, 0017 e.g. “Then the measurements from the real-life physical model may be compared to the measurements from the CAD image to determine one or more geometrical differences between the real-life component and the CAD image. If the difference(s) is within a tolerance threshold, no further compensation is needed. If the difference is outside the tolerance threshold a geometric compensation field for the deviation may be determined based on the high accuracy, high density measurement data,” see also Vora for corrective actions responsive to deviations between an image and model, supra claim 1) responsive to determining that the determined difference exceeds the threshold difference, adjust one or more deposition parameters of the energy delivery device or the powder delivery device ((Yang, 0017 e.g. “Then the measurements from the real-life physical model may be compared to the measurements from the CAD image to determine one or more geometrical differences between the real-life component and the CAD image. If the difference(s) is within a tolerance threshold, no further compensation is needed. If the difference is outside the tolerance threshold a geometric compensation field for the deviation may be determined based on the high accuracy, high density measurement data,” see also Vora for corrective actions responsive to deviations between an image and model, supra claim 1) claim 15. The method of claim 13, wherein controlling at least one of the energy delivery device or the powder delivery device comprises comparing the determined difference between the actual position and the modeled position of surface to a threshold difference; and responsive to determining that the determined difference exceeds the threshold difference, adjusting one or more deposition parameters of the energy delivery device or the powder delivery device, supra claim 2 Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. 10857738 - Systems and methods for real-time defect detection, and automatic correction in additive manufacturing environment Systems and methods of monitoring solidification quality and automatic correcting any detected defect in additive manufacturing are described. The present disclosure includes a build station for manufacturing one or more parts and a controller having one or more computer-vision based system coupled to the build station. One or more camera is provided to obtain a plurality of images of the solidified parts at predetermined settings. The present disclosure introduces a predictive model trained by machine learning algorithm, the predictive model calculates level of solidification quality of a manufactured part and build parameters value to be adjusted. The present disclosure introduces a plurality of validation coupons having various shapes to enhance more accuracy in manufacturing, wherein the validation coupons further include block data which is distributed to electronic ledger system. see claim 1 relevancy as adjusting AM parameters based on defect detection Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARRIN D DUNN whose telephone number is (571)270-1645. The examiner can normally be reached M-Sat (10-8) PST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DARRIN D DUNN/Patent Examiner, Art Unit 2117 Application/Control Number: 18/593,417 Page 2 Art Unit: 2117 Application/Control Number: 18/593,417 Page 3 Art Unit: 2117 Application/Control Number: 18/593,417 Page 4 Art Unit: 2117 Application/Control Number: 18/593,417 Page 5 Art Unit: 2117 Application/Control Number: 18/593,417 Page 6 Art Unit: 2117 Application/Control Number: 18/593,417 Page 7 Art Unit: 2117 Application/Control Number: 18/593,417 Page 8 Art Unit: 2117 Application/Control Number: 18/593,417 Page 9 Art Unit: 2117 Application/Control Number: 18/593,417 Page 10 Art Unit: 2117 Application/Control Number: 18/593,417 Page 11 Art Unit: 2117 Application/Control Number: 18/593,417 Page 12 Art Unit: 2117 Application/Control Number: 18/593,417 Page 13 Art Unit: 2117 Application/Control Number: 18/593,417 Page 14 Art Unit: 2117 Application/Control Number: 18/593,417 Page 15 Art Unit: 2117 Application/Control Number: 18/593,417 Page 16 Art Unit: 2117
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