DETAILED ACTION
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.
Allowable Subject Matter
Claims 8-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claims 1-7 and 14-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. In particular, Applicant’s arguments are directed to Putnam not teaching or suggesting a threshold for the additive manufacturing based, at least in part, on a correlation between input area energy density and emitted power, as recited in independent claims 1, 14, and 18. Claims 2-7, 15-17, and 19-20 depend, directly or indirectly, from independent claims 1, 14, and 18, respectively. In the current rejection, Milshtein is utilized to teach this claim limitation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 4, 6-7, 14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable
U.S. Patent Application Publication No. 2019/0299536 (Putman) (cited by Applicant) in view of
U.S. Patent Application Publication No. 2023/0415415 (Milshtein).
Claim 1:
The cited prior art describes a method comprising: (Putman: “In accordance with some embodiments, systems, methods, and media for artificial intelligence feedback control in additive manufacturing are provided.” Paragraph 0007)
obtaining a process model representative of an object to be generated through additive manufacture; and (Putman: “At 610, a production design specifying what a printed object should look like, as well as desired mechanical, optical and/or electrical properties for the printed object are provided to numerical control code generator 110. In some embodiments, some initial print parameters are entered by an operator. In some embodiments, a production design is provided to numerical control code generator 110, and image analyzer 180, using AIFC, determines desired mechanical, optical and/or electrical properties for the production design.” Paragraph 0078)
generating, based on the process model and using a hybrid machine-learning model, an instruction for generating the object through additive manufacture, (Putman: see code generation 620 based on the design and other parameters including AIFC as described in figures 0080, 0020 and as illustrated in figure 6; “At 620, numerical control code generator 110 can generate numerical control code for a layer of a printed object based on one or more of: input parameters entered by an operator, the print features of additive manufacturing printer 115; the specifications of the production design (including mechanical, optical and/or electrical properties); AIFC from one or more prior printed layers of the partially printed object and/or AIFC from other printed objects.” Paragraph 0080)
Putnam does not explicitly describe a threshold as described below. However, Milshtein teaches the threshold as described below.
the instruction comprising a threshold for additive manufacturing based, at least in part, on a correlation between input area energy density and emitted power. (Milshtein: see the threshold 1910 for the transformation of the material as illustrated in figure 19A and as described in paragraphs 0214, 0215; “Section 1903 in FIG. 19A shows an example in which the cumulative energy of the two beams is elevated to a value at or above the threshold 1910 for transformation of the pre-transformed material to a transformed material. Such alignment may allow (1) using greater spacing between energy beam trajectories (e.g., hatch lines), (2) lower an amount of energy required for building a 3D object, and/or (3) saving time (e.g., printing quicker) the 3D object, as compared to using the plurality of energy beam without taking advantage of the residual energy beam profiles (e.g., when each energy beam generates a different portion of the 3D object, regardless of their simultaneous operation).” Paragraph 0215)
One of ordinary skill in the art would have recognized that applying the known technique of Putnam, namely, artificial intelligence feedback control in additive manufacturing, with the known techniques of Milshtein, namely, processing field manipulation in 3D printing, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Putnam to correct layers in additive manufacturing using AI with the teachings of Milshtein to use various thresholds to control 3D manufacturing would have been recognized by those of ordinary skill in the art as resulting in an improved additive manufacturing system (i.e., the combination of the references provides for an additive manufacturing system that corrects layers during manufacturing using various thresholds based on the teachings of correcting layers during additive manufacturing using AI in Putnam and the teachings of using various thresholds during 3D manufacturing to in Milshtein).
Claim 4:
The cited prior art describes the method of claim 1, further comprising generating the process model based on a build file. (Putman: “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed. The production design can be received in any suitable format (e.g., standard tessellation language (.stl), drawing standard (DWS), or drawing (DWG) file formats) that can be processed by numerical control code generator 110.” Paragraph 0034)
Claim 6:
Putnam does not explicitly describe a correlation as described below. However, Milshtein teaches the correlation as described below.
The cited prior art describes the method of claim 1, wherein the instruction further comprises an adjustment for additive manufacture responsive to a crossing of the threshold, the adjustment, based at least in part, on the correlation between the input area energy density and the emitted power. (Putman: see the adjust print parameters using AIFC 680 as illustrated in figure 6; “If image analyzer 180 discovers that non-controllable variables are adversely affecting, beyond a threshold tolerance, a resulting print head motion and/or anomalies in a deposited layer, image analyzer 180 can send an alert to control module 160. Control module 160, upon receipt of an alert, can display a warning on a display of additive manufacturing system 100 and/or alert an operator via email, text or any other suitable electronic mechanism. In some embodiments, image analyzer 180 can be configured to alert an operator directly via email, text or any other suitable electronic mechanism. For example, in some embodiments, if image analyzer 180 determines that ambient humidity, temperature and/or light is negatively impacting a resulting print head motion or the number of anomalies in a layer is beyond a predetermined tolerance, then image analyzer 180 can send an alert to control module 160 and/or an operator. In some embodiments, if image analyzer 180 determines that wear and tear of additive manufacturing printer 115 and/or the total amount of filament available to print head 140 (e.g., low amount of filament) is negatively impacting a resulting print head motion or the number of anomalies in a layer is beyond a predetermined tolerance, then image analyzer 180 can send an alert to control module 160 and/or an operator to replace the additive manufacturing printer and/or to refill the filament. In some embodiments, if image analyzer 180 determines that a voltage variation is negatively impacting a resulting print head motion or the number of anomalies in a layer is beyond a predetermined tolerance, then image analyzer 180 can send an alert to control module 160 and/or an operator to check a voltage source.” Paragraph 0111) (Milshtein: “At times, the combination (e.g., selection) of the energy beams may change during formation of at least a portion of the 3D object (e.g., a layer or the entire 3D object). The selection of the energy beam combination may be made manual and/or in an automated fashion (e.g., using a controller). The selection of the energy beam for the combination may consider (i) an angle between the energy beam and the target surface, (ii) a distance of the position to be irradiated, (iii) a path length of the energy beam in the processing chamber, (iv) an energy density profile of the energy beam, (v) a power of the energy source generating the energy beam, and/or (vi) one or more characteristics of an optical system (e.g., scanner) that directs the energy beam across the target surface. The one or more characteristics of the optical system may comprise: response time, optical components, or optical setup.” Paragraph 0215; see the threshold 1910 for the transformation of the material as illustrated in figure 19A and as described in paragraphs 0214, 0215; “Section 1903 in FIG. 19A shows an example in which the cumulative energy of the two beams is elevated to a value at or above the threshold 1910 for transformation of the pre-transformed material to a transformed material. Such alignment may allow (1) using greater spacing between energy beam trajectories (e.g., hatch lines), (2) lower an amount of energy required for building a 3D object, and/or (3) saving time (e.g., printing quicker) the 3D object, as compared to using the plurality of energy beam without taking advantage of the residual energy beam profiles (e.g., when each energy beam generates a different portion of the 3D object, regardless of their simultaneous operation).” Paragraph 0215)
Putman and Milshtein are combinable for the same rationale as set forth above with respect to claim 1.
Claim 7:
The cited prior art describes the method of claim 1, further comprising generating the object through additive manufacture according to the instruction. (Putnam: see the print a layer based on the code 630 as illustrated in figure 6; “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed.” Paragraph 0034; “Numerical control code generator 110 can be configured to translate a production design into instructions for additive manufacturing printer 115 to print a physical representation of the production design.” Paragraph 0035)
Claim 14:
The cited prior art describes a method comprising: (Putman: “In accordance with some embodiments, systems, methods, and media for artificial intelligence feedback control in additive manufacturing are provided.” Paragraph 0007)
generating a layer of an object; (Putman: “At 630, print head 140 can deposit filament for a layer of a production design according to instructions provided by numerical control code generator 110 and/or control module 160.” Paragraph 0082)
taking a reading relative to the generation of the layer; (Putman: “At 650, image sensor 120 can capture an image of the illuminated printed layer.” Paragraph 0084)
Putnam does not explicitly describe a threshold as described below. However, Milshtein teaches the threshold as described below.
updating, based at least in part on a threshold corresponding to the reading and using a hybrid machine-learning model, a process model representative of the object, the threshold based, at least in part, on a correlation between input area energy density and emitted power; and (Putman: see the identify and record the anomalies 670 as illustrated in figure 6; “At 670, image analyzer 180 can use the generated topographical images, and/or other generated images, for the printed layer, as well as the generated numerical control code for the printed layer, to determine and record the anomalies (e.g., unintended gaps or curled edges, warped or uneven patterns, points of excessive extrusion, deviations from the print path specified in the numerical control code, unintended thread-like or other foreign artifacts and/or any other disruption in the printed layer) in the extruded layer.” Paragraph 0087; “Accordingly, it is desirable to provide artificial intelligence feedback control (AIFC) for each printed layer of an object so that timely corrective action can be taken during the printing process for the object. It is also desirable to provide AIFC to achieve the desired mechanical, optical and/or electrical properties of a printed object, as well as to achieve a printed object that closely resembles its production design, or improves upon the production design.” Paragraph 0006; “In some embodiments, image analyzer 180 can be configured to analyze the overall anomaly rate for the current and/or prior layers of a partially printed object, and based on AIFC from similar print jobs, provide instructions to the numerical control code generator 110 and/or control module 160 to adjust the print parameters for the next and/or any future layers of the partially printed object to obtain the desired mechanical, optical and/or electrical properties.” Paragraph 0073) (Milshtein: see the threshold 1910 for the transformation of the material as illustrated in figure 19A and as described in paragraphs 0214, 0215; “Section 1903 in FIG. 19A shows an example in which the cumulative energy of the two beams is elevated to a value at or above the threshold 1910 for transformation of the pre-transformed material to a transformed material. Such alignment may allow (1) using greater spacing between energy beam trajectories (e.g., hatch lines), (2) lower an amount of energy required for building a 3D object, and/or (3) saving time (e.g., printing quicker) the 3D object, as compared to using the plurality of energy beam without taking advantage of the residual energy beam profiles (e.g., when each energy beam generates a different portion of the 3D object, regardless of their simultaneous operation).” Paragraph 0215)
generating, based on the updated process model and using the hybrid machine-learning model, an instruction for generating a subsequent layer of the object through additive manufacture. (Putman: “Based on AIFC from other printed jobs, image analyzer 180 can determine whether any adjustments should be made to the print parameters of the next or subsequent layers of the partially printed object to achieve the desired mechanical, optical and/or electrical properties in view of the detected anomalies. For example, if, based on the detected anomalies, image analyzer 180 determines for a current and/or prior layers of a partially printed object that the mechanical properties for the completed printed object would be weaker than desired, then image analyzer 180 can instruct numerical control code generator 110 and/or control module 160 to adjust certain print parameters (e.g., increase infill density and/or change the infill pattern) on the next or any subsequent layers so that the desired mechanical properties can be achieved.” Paragraph 0090; “In some embodiments, image analyzer 180 can be configured to analyze the overall anomaly rate for the current and/or prior layers of a partially printed object, and based on AIFC from similar print jobs, provide instructions to the numerical control code generator 110 and/or control module 160 to adjust the print parameters for the next and/or any future layers of the partially printed object to obtain the desired mechanical, optical and/or electrical properties.” Paragraph 0073)
Putman and Milshtein are combinable for the same rationale as set forth above with respect to claim 1.
Claim 16:
The cited prior art describes the method of claim 14, further comprising, prior to updating the process model, generating the process model based on a build file. (Putman: “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed. The production design can be received in any suitable format (e.g., standard tessellation language (.stl), drawing standard (DWS), or drawing (DWG) file formats) that can be processed by numerical control code generator 110.” Paragraph 0034)
Claim 17:
The cited prior art describes the method of claim 14, further comprising generating the subsequent layer of the object according to the instruction. (Putnam: see the print a layer based on the code 630 as illustrated in figure 6; “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed.” Paragraph 0034; “Numerical control code generator 110 can be configured to translate a production design into instructions for additive manufacturing printer 115 to print a physical representation of the production design.” Paragraph 0035)
Claims 2-3, 5, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0299536 (Putman) (cited by Applicant) in view of
U.S. Patent Application Publication No. 2023/0415415 (Milshtein) and further in view of
U.S. Patent Application Publication No. 2020/0096970 (Mehr) (cited by Applicant).
Claim 2:
Putnam does not explicitly describe training as described below. However, Mehr teaches the training as described below.
The cited prior art describes the method of claim 1, wherein the hybrid machine-learning model was trained using simulated data and measured data. (Mehr: see the simulation data and the in-process data used to train the machine learning algorithm as illustrated in figure 1 and as described in paragraph 0028; “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to 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.” Paragraph 0007)
One of ordinary skill in the art would have recognized that applying the known technique of Putnam, namely, artificial intelligence feedback control in additive manufacturing, with the known techniques of Milshtein, namely, processing field manipulation in 3D printing, and the known techniques of Mehr, namely, real time adaptive control of additive manufacturing using machine learning, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Putnam to correct layers in additive manufacturing using AI with the teachings of Milshtein to use various thresholds to control 3D manufacturing and the teachings of Mehr to various configurations of AI to control additive manufacturing would have been recognized by those of ordinary skill in the art as resulting in an improved additive manufacturing system (i.e., the combination of the references provides for an additive manufacturing system that corrects layers during manufacturing using various training configurations of AI based on the teachings of correcting layers during additive manufacturing using AI in Putnam and the teachings of using various thresholds during 3D manufacturing to in Milshtein and the teachings of using various training configurations for AI during additive manufacturing to in Mehr).
Claim 3:
Putnam and Milshtein do not explicitly describe training as described below. However, Mehr teaches the training as described below.
The cited prior art describes the method of claim 1, further comprising training the hybrid machine-learning model using simulated data and measured data. (Mehr: see the simulation data and the in-process data used to train the machine learning algorithm as illustrated in figure 1 and as described in paragraph 0028; “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to 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.” Paragraph 0007)
Putman, Milshtein, and Mehr are combinable for the same rationale as set forth above with respect to claim 2.
Claim 5:
Putnam does not explicitly describe training or a threshold as described below. However, Mehr teaches the training and Milshtein teaches the threshold as described below.
The cited prior art describes the method of claim 1, wherein the hybrid machine-learning model was trained using data exhibiting the correlation between the input area energy density and the emitted power. (Mehr: see the simulation data and the in-process data used to train the machine learning algorithm as illustrated in figure 1 and as described in paragraph 0028; “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to 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.” Paragraph 0007) (Putman: “If image analyzer 180 discovers that non-controllable variables are adversely affecting, beyond a threshold tolerance, a resulting print head motion and/or anomalies in a deposited layer, image analyzer 180 can send an alert to control module 160.” Paragraph 0111; “In some embodiments, if the anomalies in a printed layer or layers exceed certain predetermined tolerances, then the print job for the printed object can be stopped prior to completion.” Paragraph 0075; “At 670, image analyzer 180 can use the generated topographical images, and/or other generated images, for the printed layer, as well as the generated numerical control code for the printed layer, to determine and record the anomalies (e.g., unintended gaps or curled edges, warped or uneven patterns, points of excessive extrusion, deviations from the print path specified in the numerical control code, unintended thread-like or other foreign artifacts and/or any other disruption in the printed layer) in the extruded layer.” Paragraph 0087) (Milshtein: see the threshold 1910 for the transformation of the material as illustrated in figure 19A and as described in paragraphs 0214, 0215; “Section 1903 in FIG. 19A shows an example in which the cumulative energy of the two beams is elevated to a value at or above the threshold 1910 for transformation of the pre-transformed material to a transformed material. Such alignment may allow (1) using greater spacing between energy beam trajectories (e.g., hatch lines), (2) lower an amount of energy required for building a 3D object, and/or (3) saving time (e.g., printing quicker) the 3D object, as compared to using the plurality of energy beam without taking advantage of the residual energy beam profiles (e.g., when each energy beam generates a different portion of the 3D object, regardless of their simultaneous operation).” Paragraph 0215)
Putman, Milshtein, and Mehr are combinable for the same rationale as set forth above with respect to claim 2.
Claim 15:
Putnam and Milshtein do not explicitly describe training as described below. However, Mehr teaches the training as described below.
The cited prior art describes the method of claim 14, wherein the hybrid machine-learning model was trained using simulated data and measured data. (Mehr: see the simulation data and the in-process data used to train the machine learning algorithm as illustrated in figure 1 and as described in paragraph 0028; “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to 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.” Paragraph 0007)
Putman, Milshtein, and Mehr are combinable for the same rationale as set forth above with respect to claim 2.
Claim 18:
The cited prior art describes a system for additive manufacture, the system comprising: (Putman: “In accordance with some embodiments, systems, methods, and media for artificial intelligence feedback control in additive manufacturing are provided.” Paragraph 0007)
a simulator configured to generate a process model according to a build file, the process model representative of an object to be generated through additive manufacture; (Putman: “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed. The production design can be received in any suitable format (e.g., standard tessellation language (.stl), drawing standard (DWS), or drawing (DWG) file formats) that can be processed by numerical control code generator 110.” Paragraph 0034; “At 620, numerical control code generator 110 can generate numerical control code for a layer of a printed object based on one or more of: input parameters entered by an operator, the print features of additive manufacturing printer 115; the specifications of the production design (including mechanical, optical and/or electrical properties); AIFC from one or more prior printed layers of the partially printed object and/or AIFC from other printed objects. The generated numerical control code can include a set of setpoints (e.g., a plurality of X-Y-Z coordinates) for print head 140 and/or build plate 150 to traverse. FIG. 7A shows an example simulation of a set of setpoints for a printed layer that might be included in the numerical control code. The generated numerical control code can also include instructions defining how the print head and/or build plate should traverse the individual setpoints. An example simulation of what a traversed print path might look like, based on the included instructions, is shown, for example, in FIG. 7B.” paragraph 0080)
Putnam does not explicitly describe training or a threshold as described below. However, Milshtein teaches the threshold and Mehr teaches the training as described below.
a hybrid machine-learning model trained using simulated data and measured data, the hybrid machine-learning model configured to generate, based on the process model, an instruction for generating the object; and (Putman: see code generation 620 based on the design and other parameters including AIFC as described in figures 0080, 0020 and as illustrated in figure 6; “At 620, numerical control code generator 110 can generate numerical control code for a layer of a printed object based on one or more of: input parameters entered by an operator, the print features of additive manufacturing printer 115; the specifications of the production design (including mechanical, optical and/or electrical properties); AIFC from one or more prior printed layers of the partially printed object and/or AIFC from other printed objects.” Paragraph 0080) (Mehr: see the simulation data and the in-process data used to train the machine learning algorithm as illustrated in figure 1 and as described in paragraph 0028; “a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, post-build inspection data, or any combination thereof, for a plurality of design geometries that are the same as or different from that of the object; b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties; c) providing a processor programmed to 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.” Paragraph 0007)
an object generator configured to generate an object through additive manufacture according to the build file and the instruction, the instruction comprising a threshold for the additive manufacturing based, at least in part, on a correlation between input area energy density and emitted power. (Putnam: see the print a layer based on the code 630 as illustrated in figure 6; “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed.” Paragraph 0034; “Numerical control code generator 110 can be configured to translate a production design into instructions for additive manufacturing printer 115 to print a physical representation of the production design.” Paragraph 0035; see the print a layer based on the code 630 as illustrated in figure 6; “FIG. 1 also shows numerical control code generator 110. In some embodiments, numerical control code generator 110 can be configured to receive a three-dimensional design (e.g., a Computer Aided Design (CAD) model) (referred to herein as a “production design”) of an object to be printed.” Paragraph 0034; “Numerical control code generator 110 can be configured to translate a production design into instructions for additive manufacturing printer 115 to print a physical representation of the production design.” Paragraph 0035) (Milshtein: see the threshold 1910 for the transformation of the material as illustrated in figure 19A and as described in paragraphs 0214, 0215; “Section 1903 in FIG. 19A shows an example in which the cumulative energy of the two beams is elevated to a value at or above the threshold 1910 for transformation of the pre-transformed material to a transformed material. Such alignment may allow (1) using greater spacing between energy beam trajectories (e.g., hatch lines), (2) lower an amount of energy required for building a 3D object, and/or (3) saving time (e.g., printing quicker) the 3D object, as compared to using the plurality of energy beam without taking advantage of the residual energy beam profiles (e.g., when each energy beam generates a different portion of the 3D object, regardless of their simultaneous operation).” Paragraph 0215)
Putman, Milshtein, and Mehr are combinable for the same rationale as set forth above with respect to claim 2.
Claim 19:
The cited prior art describes the system of claim 18,
wherein the object generator is further configured to take a reading relative to generation of a layer of the object; (Putman: “At 650, image sensor 120 can capture an image of the illuminated printed layer.” Paragraph 0084)
wherein the hybrid machine-learning model is further configured to update the process model based on the reading; and (Putman: “At 670, image analyzer 180 can use the generated topographical images, and/or other generated images, for the printed layer, as well as the generated numerical control code for the printed layer, to determine and record the anomalies (e.g., unintended gaps or curled edges, warped or uneven patterns, points of excessive extrusion, deviations from the print path specified in the numerical control code, unintended thread-like or other foreign artifacts and/or any other disruption in the printed layer) in the extruded layer.” Paragraph 0087; “If the print path for the printed layer as obtained from the generated images is the same as the extracted print path from the generated numerical control code, the difference between them will be zero or close to zero. A number greater than zero describes the amount of error detected between the actual print path and the print path specified in the generated numerical control code. A comparison of the print paths can also indicate where errors occurred along the print path.” Paragraph 0089)
wherein the hybrid machine-learning model is further configured to generate an updated instruction based on the updated process model. (Putman: “At 680, image analyzer 180 can analyze the number of anomalies and the pattern of the anomalies (including the deviations between the actual path and the print path in the generated numerical control code) that the image analyzer detected from the printed layer and/or prior layers. Based on AIFC from other printed jobs, image analyzer 180 can determine whether any adjustments should be made to the print parameters of the next or subsequent layers of the partially printed object to achieve the desired mechanical, optical and/or electrical properties in view of the detected anomalies.” Paragraph 0090)
Claim 20:
The cited prior art describes the system of claim 18,
wherein the object generator is further configured to take a reading relative to the generation of the object; and (Putman: “At 650, image sensor 120 can capture an image of the illuminated printed layer.” Paragraph 0084)
wherein the hybrid machine-learning model is configured to generate the instruction further based on the reading. (Putman: “At 680, image analyzer 180 can analyze the number of anomalies and the pattern of the anomalies (including the deviations between the actual path and the print path in the generated numerical control code) that the image analyzer detected from the printed layer and/or prior layers. Based on AIFC from other printed jobs, image analyzer 180 can determine whether any adjustments should be made to the print parameters of the next or subsequent layers of the partially printed object to achieve the desired mechanical, optical and/or electrical properties in view of the detected anomalies.” Paragraph 0090)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2022/0062997 describes defect detection and correction in an additive manufacturing process.
U.S. Patent Application Publication No. 2015/0331402 describes a 3D printing through print parameter optimization.
U.S. Patent Application Publication No. 2018/0243977 describes layer correction for a next layer in the production of an object.
U.S. Patent Application Publication No. 2019/0283333 describes real time error detection and correction in additive manufacturing environment.
U.S. Patent Application Publication No. 2020/0016883 describes monitoring and correcting defects in additive manufacturing.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117