CTNF 18/593,575 CTNF 101488 DETAILED ACTION 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. Information Disclosure Statement The information disclosure statement (IDS) submitted on 4 June 2024 was considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 7, 17, 8 and 18 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. Claims 7 and 17 claim updating one or both of a layer thickness and build strategy . It is unclear which parameters would be controlled in order to update build strategy. It is unclear what properties would be affected by updating the build strategy. The specification does not provide clarity and one of ordinary skill in the art would not be apprised of the scope of the claim. Claims 8 and 18 claim that the machine learning model is trained on the same type of component. It is unclear what properties must be present in each component in order for them to be considered of the same type. The specification does not provide clarity and one of ordinary skill in the art would not be apprised of the scope of the claim. 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US11465240 of Liu in view of US11092983 of Cybulsky further in view of US20230202100 of Dave and US6149072 of Tseng . Claim 1 claims an additive manufacturing system 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; a powder delivery device configured to direct a powder stream toward the melt pool; a plurality of sensors, each mass sensor associated with a portion of the additive manufacturing system; a plurality of heat sensors; and one or more computing devices configured to: receive data from the plurality of mass sensors; determine an overall mass flux based on the data from the plurality of mass sensors; receive data from the plurality of heat sensors; determine an overall heat flux based on the data from the plurality of heat sensors; and input, into one or more machine learning models, the overall mass flux and the overall heat flux; and control, based at least in part on outputs from the one or more machine learning models, the powder delivery device and the energy delivery device. Liu teaches a method and apparatus for real time, in situ sensing and characterization of roughness, geometrical shapes, geometrical structures, composition, defects, and temperature in three-dimensional manufacturing systems in the same field of endeavor as the claimed invention. Liu discloses an energy delivery system, Para[0018], and a powder injection system, equivalent to the claimed powder delivery device, Para[0022]. Liu also teaches a non-destructive probing system to monitor the composition of the sample as well as an imager and processor configured to monitor structure , temperature , shape, defects, cracks, and roughness of the sample. Both the non-destructive probing system and the imager and processor are equivalent to the claimed mass sensors as they detect composition or structure. The imager and processor is also equivalent to the claimed heat sensors as Liu discloses that the imager and processor is configured to monitor temperature. Liu also teaches one or more computers that control the energy source and powder injection system based on the input from the sensors. While Liu teaches inputting data related to mass, i.e. composition and structure, and heat, i.e. temperature, Para[0003], Liu does not specifically teach determining a mass flux or a heat flux based on the input data. While Liu teaches a database reference for inputs, Liu does not specifically teach a machine learning model. Cybulsky teaches system control based on acoustic and image signals in the same field of endeavor as the claimed invention. Cybulsky discloses that the computing device includes a machine learning module configured to determine, based on at least the plurality of control parameters, the at least one time-dependent acoustic data signal, the at least one image data signal, and the plurality of process outputs, a relationship between the plurality of control parameters and the plurality of process outputs by machine learning, Para[0004]. Therefore, it would be obvious to one of ordinary skill in the art to use the machine learning module taught by Cybulsky in the system taught by Liu in order to determine the relationship between the control parameters and process outputs. Dave teaches defect detection for additive manufacturing in the same field of endeavor as the claimed invention. Dave discloses that considering an overall energy balance for the moving energy source, there will be radiated and conducted energy that also carries valuable signal and information content with respect to the key physical phenomena occurring in the thermally affected region. For example, heat conduction, indicated by a heat flux Q, will result in heat flow from the thermally affected region and the thermally cycled region. This flux will in general be normal to the contour of the profile of the thermally affected region below the surface of the substrate, Para[0044]. Therefore, based on the teachings of Dave, it would be obvious to one of ordinary skill in the art to determine the heat flux from the data collected by the sensors taught by Liu and input it into the machine learning model to control the output parameters as taught by Cybulsky because this carries valuable signal information related to key physical phenomena of the printed component. Tseng teaches droplet selection systems and methods for freeform fabrication of three-dimensional objects in the same field of endeavor as the claimed invention. Tseng teaches that the size and mass flux of the droplets are more precisely controlled according to the specific outline geometry and desired internal micro-structure of the three-dimensional object, Para[0010]. Therefore, based on the teachings of Tseng, it would be obvious to one of ordinary skill in the art to determine the mass flux from the data collected by the sensors taught by Liu and input it into the machine learning model to control the output parameters as taught by Cybulsky in order to obtain the desired internal microstructure of the three-dimensional object. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 1. Claim 2 further limits claim 1 by claiming that to control the powder delivery device and the energy delivery device, the one or more computing devices are configured to: control a plurality of operating parameters of the powder delivery device and the energy delivery device. Liu teaches that the computer is used to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, Para[0035]. Therefore, Liu teaches the additional limitation of claim 2. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 2. Claim 3 further limits claim 2 by claiming that to control the plurality of operating parameters, the one or more computing devices are configured to: adjust, in parallel, two or more operating parameters of the plurality of operating parameters. Liu teaches that the computer is used to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, etc., before either the next layer is additively manufactured or the current layer is repaired, Para[0035]. Therefore, Liu teaches controlling multiple parameters at once which covers the additional limitation of claim 3. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 3. Claim 4 further limits claim 3 by claiming that the two or more operating parameters have a non-linear impact on building of the component. Liu does not teach a non-linear relationship. Cybulsky teaches that some techniques may require prediction or guesses as to the nature of mathematical relationships between two or more parameters (for example, linear, exponential, logarithmic, periodic, non-linear , etc.), which may not be possible or feasible because of the complexity or number of parameters and outputs. Computing device may use machine learning may be used to more efficiently or accurately determine such relationships, even without a priori knowledge or information, Para[0065]. Therefore, it would be obvious to one of ordinary skill in the art, to include the machine learning module taught by Cybulsky in the additive manufacturing system disclosed by Liu and Cybulsky in order to determine the relationship between two or more non-linear parameters. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 4. Claim 5 claims that the plurality of operating parameters includes one or more powder delivery device operating parameters and one or more energy delivery device operating parameters. Liu teaches powder parameters such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, Para[0024]. Therefore, Liu teaches the additional limitation of claim 5. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 5. Claim 6 further limits claim 5 by claiming that the one or more powder delivery device operating parameters include one or more of a powder feed rate, a gas flow rate, and an agitator rate, and the one or more energy delivery device operating parameters include one or more of power, travel speed, pause time, dwell time, working distance, and spot size. Liu teaches that the computer will be used to process and analyze in about real-time the data gathered from the imaging and sensing and to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, etc, Para[0024]. Cybulsky teaches process control parameters for instance, including a primary gas flow rate, a secondary gas flow rate, a gun current, a carrier gas flow rate, a powder feed rate, a temperature, a pressure, a mass flow rate, a volumetric flow rate, a molecular flow rate, a molar flow rate, a composition, a velocity, or a concentration, or combinations thereof. Unsatisfactory coating characteristics may result from variances in process control parameters, or other causes such as component wear, Para[0021]. Therefore, based on the teachings of Liu and Cybulsky, it would be obvious to one of ordinary skill in the art to control the powder feed rate, gas flow rate, laser power, scanning speed, and spot size to avoid unsatisfactory characteristics in the printed product. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 6. Claim 7 further limits claim 1 by claiming that the one or more computing devices are further configured to update one or both of a layer thickness and build strategy of the component based at least in part on the outputs from the one or more machine learning models. The meaning of build strategy is unclear, see 112(b) above. Liu discloses that there are multiple parameters that control the microstructure and properties of additive manufactured parts, such as the powder size distribution; powder layer thickness ; powder flow rate; energy beam spot size; melt pool temperature profile; the cooling rate; melt flow dynamic characteristics; evaporation rates and many others, Para[0018]. Therefore, Liu covers the additional limitation of claim 7. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 7. Claim 8 further limits claim 1 by claiming that the one or more machine learning models are trained on components of a same type as the component. It is unclear what is meant by type, see 112(b) rejection above. Liu teaches that the computer sets the initial laser and powder injection parameters for additive manufacturing based on a database reference, Para[0035]. This implies references of the same type of component. Cybulsky teaches that the process parameters are controlled by the machine learning model based on the plurality of control parameters including composition, Para[0045]. This means that the machine learning model must be trained on components of the same composition or type. Cybulsky also teaches that unsatisfactory coating characteristics may result from variances in process control parameters, or other causes such as component wear, Para[0021]. Therefore, based on the teachings of Liu and Cybulsky, it would be obvious to use a machine learning model that is trained on components of the same type. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 8. Claim 9 further limits claim 8 by claiming that the one or more computing devices are further configured to: update, based on the build of the component, the one or more machine learning models. Liu does not specifically teach a machine learning model. Cybulsky teaches that the neural network may compare present data with past data on immediate, recent and long-term time scales to detect a variety of changes to the system. Based on the degree of change detected the neural net may permit the process to continue, alter the process to maintain coating characteristics, or halt the process for intervention, Para[0102]. Thus, Cybulsky teaches updating the machine learning model with the present data in real time. Therefore, it would be obvious to one of ordinary skill in the art to update the machine learning model as taught by Cybulsky in the additive manufacturing system taught by Liu and Cybulsky in order to continue, alter, or halt the process. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 9. Claim 10 further limits claim 8 by claiming that the component is a member of a gas turbine engine. Liu does not specifically teach a member of a gas turbine engine. Cybulsky teaches that the spray target may be a component used in any one or more mechanical systems, including, for example, a high temperature mechanical system such as a gas turbine engine , Para[0033]. Therefore, it would be obvious to one of ordinary skill in the art to use the additive manufacturing system disclosed by Liu and Cybulsky for a component used in a gas turbine engine as taught by Cybulsky. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 10. Claim 11 claims a method comprising: receiving, by one or more computing devices, data from a plurality of mass sensors of an additive manufacturing system, wherein the additive manufacturing system comprises 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 a component, a powder delivery device configured to direct a powder stream toward the melt pool, the plurality of mass sensors, each mass sensor associated with a portion of the additive manufacturing system, and a plurality of heat sensors; determining, by the one or more computing devices, a mass flux based on the data from the plurality of mass sensors; receiving, by the one or more computing devices, data from the plurality of heat sensors; determining, by the one or more computing devices, a heat flux based on the data from the plurality of heat sensors; inputting, by the one or more computing devices and into one or more machine learning models, the mass flux and the heat flux; and controlling, based at least in part on outputs from the one or more machine learning models, the powder delivery device and the energy delivery device. Liu teaches a method and apparatus for real time, in situ sensing and characterization of roughness, geometrical shapes, geometrical structures, composition, defects, and temperature in three-dimensional manufacturing systems in the same field of endeavor as the claimed invention. Liu discloses an energy delivery system, Para[0018], and a powder injection system, equivalent to the claimed powder delivery device, Para[0022]. Liu also teaches a non-destructive probing system to monitor the composition of the sample as well as an imager and processor configured to monitor structure , temperature , shape, defects, cracks, and roughness of the sample. Both the non-destructive probing system and the imager and processor are equivalent to the claimed mass sensors as they detect composition or structure. The imager and processor is also equivalent to the claimed heat sensors as Liu discloses that the imager and processor is configured to monitor temperature. Liu also teaches one or more computers that control the energy source and powder injection system based on the input from the sensors. While Liu teaches inputting data related to mass, i.e. composition and structure, and heat, i.e. temperature, Para[0003], Liu does not specifically teach determining a mass flux or a heat flux based on the input data. While Liu teaches a database reference for inputs, Liu does not specifically teach a machine learning model. Cybulsky teaches system control based on acoustic and image signals in the same field of endeavor as the claimed invention. Cybulsky discloses that the computing device includes a machine learning module configured to determine, based on at least the plurality of control parameters, the at least one time-dependent acoustic data signal, the at least one image data signal, and the plurality of process outputs, a relationship between the plurality of control parameters and the plurality of process outputs by machine learning, Para[0004]. Therefore, it would be obvious to one of ordinary skill in the art to use the machine learning module taught by Cybulsky in the method taught by Liu and Cybulsky in order to determine the relationship between the control parameters and process outputs. Dave teaches defect detection for additive manufacturing in the same field of endeavor as the claimed invention. Dave discloses that considering an overall energy balance for the moving energy source, there will be radiated and conducted energy that also carries valuable signal and information content with respect to the key physical phenomena occurring in the thermally affected region. For example, heat conduction, indicated by a heat flux Q, will result in heat flow from the thermally affected region and the thermally cycled region. This flux will in general be normal to the contour of the profile of the thermally affected region below the surface of the substrate, Para[0044]. Therefore, based on the teachings of Dave, it would be obvious to one of ordinary skill in the art to determine the heat flux from the data collected by the sensors taught by Liu and input it into the machine learning model to control the output parameters as taught by Cybulsky because this carries valuable signal information related to key physical phenomena of the printed component. Tseng teaches droplet selection systems and methods for freeform fabrication of three-dimensional objects in the same field of endeavor as the claimed invention. Tseng teaches that the size and mass flux of the droplets are more precisely controlled according to the specific outline geometry and desired internal micro-structure of the three-dimensional object, Para[0010]. Therefore, based on the teachings of Tseng, it would be obvious to one of ordinary skill in the art to determine the mass flux from the data collected by the sensors taught by Liu and input it into the machine learning model to control the output parameters as taught by Cybulsky in order to obtain the desired internal microstructure of the three-dimensional object. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 11. Claim 12 further limits claim 11 by claiming that controlling the powder delivery device and the energy delivery device comprises: controlling a plurality of operating parameters of the powder delivery device and the energy delivery device. Liu teaches that the computer is used to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, Para[0035]. Therefore, Liu teaches the additional limitation of claim 12. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 12. Claim 13 further limits claim 12 by claiming that controlling the plurality of operating parameters comprises: adjusting, in parallel, two or more operating parameters of the plurality of operating parameters. Liu teaches that the computer is used to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, etc., before either the next layer is additively manufactured or the current layer is repaired, Para[0035]. Therefore, Liu teaches controlling multiple parameters at once which covers the additional limitation of claim 13. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 13. Claim 14 further limits claim 13 by claiming that the two or more operating parameters have a non-linear impact on building of the component. Liu does not teach a non-linear relationship. Cybulsky teaches that some techniques may require prediction or guesses as to the nature of mathematical relationships between two or more parameters (for example, linear, exponential, logarithmic, periodic, non-linear , etc.), which may not be possible or feasible because of the complexity or number of parameters and outputs. Computing device may use machine learning may be used to more efficiently or accurately determine such relationships, even without a priori knowledge or information, Para[0065]. Therefore, it would be obvious to one of ordinary skill in the art, to include the machine learning module taught by Cybulsky in the method disclosed by Liu and Cybulsky in order to determine the relationship between two or more non-linear parameters. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 14. Claim 15 further limits claim 12 by claiming that the plurality of operating parameters includes one or more powder delivery device operating parameters and one or more energy delivery device operating parameters. Liu teaches powder parameters such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, Para[0024]. Therefore, Liu teaches the additional limitation of claim 15. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 15. Claim 16 further limits claim 15 by claiming that the one or more powder delivery device operating parameters include one or more of a powder feed rate, a gas flow rate, and an agitator rate, and the one or more energy delivery device operating parameters include one or more of power, travel speed, pause time, dwell time, working distance, and spot size. Liu teaches that the computer will be used to process and analyze in about real-time the data gathered from the imaging and sensing and to feedback to the three-dimensional manufacturing system to adjust the additive manufacturing laser, process, and powder parameters, such as laser power, pulse width, energy, pulse repetition rate, beam shape, temporal format, scanning speed, hatching space, scanning strategy/pattern, powder thickness, etc, Para[0024]. Cybulsky teaches process control parameters for instance, including a primary gas flow rate, a secondary gas flow rate, a gun current, a carrier gas flow rate, a powder feed rate, a temperature, a pressure, a mass flow rate, a volumetric flow rate, a molecular flow rate, a molar flow rate, a composition, a velocity, or a concentration, or combinations thereof. Unsatisfactory coating characteristics may result from variances in process control parameters, or other causes such as component wear, Para[0021]. Therefore, based on the teachings of Liu and Cybulsky, it would be obvious to one of ordinary skill in the art to control the powder feed rate, gas flow rate, laser power, scanning speed, and spot size to avoid unsatisfactory characteristics in the printed product. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 16. Claim 17 further limits claim 11 by further comprising further comprising updating one or both of a layer thickness and build strategy of the component based at least in part on the outputs from the one or more machine learning models. The meaning of build strategy is unclear, see 112(b) above. Liu discloses that there are multiple parameters that control the microstructure and properties of additive manufactured parts, such as the powder size distribution; powder layer thickness ; powder flow rate; energy beam spot size; melt pool temperature profile; the cooling rate; melt flow dynamic characteristics; evaporation rates and many others, Para[0018]. Therefore, Liu covers the additional limitation of claim 17. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 17. Claim 18 further limits claim 11 by claiming that the one or more machine learning models are trained on components of a same type as the component. It is unclear what is meant by type, see 112(b) above. Liu teaches that the computer sets the initial laser and powder injection parameters for additive manufacturing based on a database reference, Para[0035]. This implies references of the same type of component. Cybulsky teaches that the process parameters are controlled by the machine learning model based on the plurality of control parameters including composition, Para[0045]. This means that the machine learning model must be trained on components of the same composition or type. Cybulsky also teaches that unsatisfactory coating characteristics may result from variances in process control parameters, or other causes such as component wear, Para[0021]. Therefore, based on the teachings of Liu and Cybulsky, it would be obvious to use a machine learning model that is trained on components of the same type. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 18. Claim 19 further limits claim 18 by further comprising: updating, based on the build of the component, the one or more machine learning models. Liu does not specifically teach a machine learning model. Cybulsky teaches that the neural network may compare present data with past data on immediate, recent and long-term time scales to detect a variety of changes to the system. Based on the degree of change detected the neural net may permit the process to continue, alter the process to maintain coating characteristics, or halt the process for intervention, Para[0102]. Thus, Cybulsky teaches updating the machine learning model with the present data in real time. Therefore, it would be obvious to one of ordinary skill in the art to update the machine learning model as taught by Cybulsky in the method taught by Liu and Cybulsky in order to continue, alter, or halt the process. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 19. Claim 20 further limits claim 18 by claiming that the component is a member of a gas-turbine engine. Liu does not specifically teach a member of a gas turbine engine. Cybulsky teaches that the spray target may be a component used in any one or more mechanical systems, including, for example, a high temperature mechanical system such as a gas turbine engine, Para[0033]. Therefore, it would be obvious to one of ordinary skill in the art to use the method disclosed by Liu and Cybulsky for a component used in a gas turbine engine as taught by Cybulsky. Thus, Liu, Cybulsky, Dave, and Tseng cover all the limitations of claim 20. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB BENJAMIN STILES whose telephone number is (571)272-0598. The examiner can normally be reached Monday-Friday 7:30am - 5:00pm. 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, Keith Hendricks can be reached at (571) 272-1401. 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. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Keith D. Hendricks/Supervisory Patent Examiner, Art Unit 1733 /JACOB BENJAMIN STILES/Examiner, Art Unit 1733 Application/Control Number: 18/593,575 Page 2 Art Unit: 1733 Application/Control Number: 18/593,575 Page 3 Art Unit: 1733 Application/Control Number: 18/593,575 Page 4 Art Unit: 1733 Application/Control Number: 18/593,575 Page 5 Art Unit: 1733 Application/Control Number: 18/593,575 Page 6 Art Unit: 1733 Application/Control Number: 18/593,575 Page 7 Art Unit: 1733 Application/Control Number: 18/593,575 Page 8 Art Unit: 1733 Application/Control Number: 18/593,575 Page 9 Art Unit: 1733 Application/Control Number: 18/593,575 Page 10 Art Unit: 1733 Application/Control Number: 18/593,575 Page 11 Art Unit: 1733 Application/Control Number: 18/593,575 Page 12 Art Unit: 1733 Application/Control Number: 18/593,575 Page 13 Art Unit: 1733 Application/Control Number: 18/593,575 Page 14 Art Unit: 1733 Application/Control Number: 18/593,575 Page 15 Art Unit: 1733 Application/Control Number: 18/593,575 Page 16 Art Unit: 1733 Application/Control Number: 18/593,575 Page 17 Art Unit: 1733 Application/Control Number: 18/593,575 Page 18 Art Unit: 1733 Application/Control Number: 18/593,575 Page 19 Art Unit: 1733 Application/Control Number: 18/593,575 Page 20 Art Unit: 1733 Application/Control Number: 18/593,575 Page 21 Art Unit: 1733
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