Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/31/2026 has been entered.
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.
Claim(s) 1, 3-10, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200100864 A1), herein referred to as Wang, in view of Kumar et al. (US 20230315048 A1), herein referred to as Kumar.
Regarding claim 1, Wang discloses a method (100; refer to Paragraph [0061] , Fig. 1A) for use in additively manufacturing [[an]] a polymeric dental appliance (1512) (refer to Paragraph [0288]; the orthodontic appliances herein (or portions thereof) can be produced using direct fabrication, such as additive manufacturing techniques), the method (100) comprising:
receiving a digital model of a polymeric dental appliance (102) (refer to Paragraph [0062], Fig. 1A);
processing the digital model of the polymeric dental appliance to modify the digital model based on predicted deviations during fabrication to generate a first updated digital model of the polymeric dental appliance (104,108) (refer to Paragraphs [0055], [0063]-[0065], Fig. 1A; at block 104, processing logic may perform an analysis on the digital design of the polymeric aligner using a trained machine learning model trained to identify polymeric aligners having probable points of damage, where probable points of damage are based on manufacturing; at block 108, processing logic may perform one or more corrective actions based on the one or more probable points of damage, which includes modifying the digital design of the aligner to generate a modified digital design of the aligner);
determining whether the predicted deviations of a physical polymeric dental appliance to be additively manufactured based on the first updated digital model of the polymeric dental appliance are acceptable (refer to Paragraph [0070]; processing logic may determine whether the modified digital design of the aligner includes the one or more probable points of damage; if probable points of damage are below a threshold, the aligner is output without further corrective action, thus being labeled acceptable); and
outputting the first updated digital model of the polymeric dental appliance for fabrication of the physical aligner by additive manufacture of the physical polymeric dental appliance (refer to Paragraphs [0061]; once designed, each aligner may be manufactured by direct fabrication techniques, such as additive manufacturing);
beginning fabrication of the physical polymeric dental appliance (refer to Paragraphs [0061], [0288]);
receiving the real-time machine parameters during fabrication of the physical polymeric dental appliance (refer to Paragraph [0296]; process control can be achieved by including a sensor on the machine that measures power and other beam parameters every layer or every few seconds and automatically adjusts them with a feedback loop);
processing the first updated digital model to determine predicted deviations of the physical polymeric dental appliance (refer to Paragraph [0070]; processing logic may determine whether the modified digital design of the aligner includes probable points of damage exceeding a threshold); and
modifying the first updated digital model based on the predicted deviations (refer to Paragraph [0070]; a second corrective action is performed on the first modified model based on the processing logic identifying potential damage points exceeding a threshold).
While Wang does disclose processing and modifying the first updated digital model based on the predicted deviations (refer to Paragraph [0070]), Wang does not disclose processing the first updated digital model to determine predicted deviations of the physical polymeric dental appliance based on the real-time machine parameters, and modifying the first updated digital model during fabrication, based on predicted real-time deviations.
Kumar discloses methods for detecting anomalies in a print job in the analogous art of three-dimensional printing (refer to Paragraph [0017], Fig. 3). The system uses an anomaly detection engine (206) and real-time machine parameters of the layer (222) being printed to predict deviations based on a trained deep learning model (refer to Paragraphs [0021], [0023], [0030], [0032], [0034]), notifying the user for real time adjustment to data set of the layer being printed (refer to Paragraph [0037]). Thus, Kumar teaches adjusting the data associated with the 3D model to be printed based on predicted real-time deviations during fabrication. This process avoids wastage of print material by allowing a user to take appropriate action based on the predicted anomaly (refer to Paragraph [0024]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method updating digital models based on predicted deviations as taught by Wang with the method of using real time machine parameters to predict deviations as taught by Kumar to enable real time corrections based on the predicted anomalies (refer to Paragraph [0024]).
Claim 2-Cancelled
Regarding claim 3, Wang and Kumar disclose the method of claim [[2]] 1; Wang further discloses the method (100) further comprising:
determining that the predicted deviations of [[a]] the physical polymeric dental appliance exceed a threshold (refer to Paragraph [0070]; the process of modifying the virtual 3D model is performed based on the processing logic identifying potential damage points exceeding a threshold)
Regarding claims 4-5, Wang and Kumar disclose the method of claim 3; Wang further discloses wherein the predicted deviations areone or more appliance parameters including angles of the polymeric dental appliance surfaces or thickness of the polymeric dental appliance at locations of the polymeric dental appliance (refer to Paragraphs [0242], [0244]; processing logic may perform the analysis on the digital design of the aligner using a rules engine including one or more rules associated with parameters of the aligners indicative of points of damage, which includes at least one of an angle of a cutline at locations of the aligner and a thickness of the aligner).
Wang does not teach these predicted deviations as occurring during fabrication; however, based on the modification from claim 1 above, Kumar is relied upon for teaching the prediction of deviations during fabrication using an anomaly detection engine (refer to Paragraphs [0021], [0023], [0030], [0032], [0034]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method updating digital models based on predicted deviations (appliance parameters) as taught by Wang with the method of using real time machine parameters to predict deviations as taught by Kumar to enable real time corrections based on the predicted anomalies (refer to Paragraph [0024]).
Regarding claim 6, Wang and Kumar disclose the method of claim [[4]] 1; Wang further discloses wherein the real-time machine parameters include exposure time, exposure power, or material type (refer to Paragraph [0296]; the machine parameters can be monitored and adjusted on a regular basis, including curing parameters such as power and curing time).
Regarding claim 7, Wang and Kumar disclose the method of claim 1; Wang further discloses maintaining environmental variables (temperature, humidity) in tight range, but does not disclose these variables as real-time machine parameters.
Kumar further discloses using a plurality of sensors (216), including humidity sensors and ambient temperature sensors to communicate the real-time machine parameters, humidity and temperature, to the anomaly detection engine (206) (refer to Paragraphs [0028], [0030]). The sensor data related to humidity and ambient temperature allows the anomaly detection engine (206) to determine a root cause of the anomaly (refer to Paragraph [0039]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method of additive manufacturing of Wang with the real-time machine parameters as taught by Kumar in order to determine a root cause of the anomaly or deviation according to the humidity and ambient temperature sensors (refer to Paragraph [0039]).
Regarding claims 8-9, Wang and Kumar disclose the method of claim 1; Wang further discloses wherein a prediction model (a machine learning model) is used to generate the predicted deviations (refer to Paragraph [0063]; processing logic may perform an analysis on the digital design of the polymeric aligner using a trained machine learning model trained to identify polymeric aligners having probable points of damage).
Regarding claim 10, Wang and Kumar disclose the method of claim 9; Wang further discloses wherein the prediction model is a neural network (refer to Paragraph [0105]; the machine learning model may be a single level neural network or a deep neural network) and further comprising:
training the neural network based on previously fabricated physical parts (refer to Paragraphs [0084]-[0086]; a plurality of orthodontic aligners that have already been manufactured are used to train the model).
Regarding claim 14, Wang discloses a system (1400) for use in additively manufacturing [[an]] a polymeric dental appliance (refer to Paragraph [0260] and Fig. 14), the system (1400) comprising:
a processor (1402) (refer to Paragraph [0262]); and
memory (1404) comprising instructions (1426) that when executed by the processor (1402) cause the system (1400) to carry out the method of claim 1, wherein the method of claim 1 is disclosed by the combination of Wang and Kumar (refer to Paragraphs [0260]-[0264] of Wang; the machine (1400) is designed to carry out the disclosed methods (100) via the instructions(1426)).
Claim(s) 11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200100864 A1), herein referred to as Wang, in view of Kumar et al. (US 20230315048 A1), herein referred to as Kumar as applied to claim 1 above, and further in view of Lection et al. (US 20180059644 A1), herein referred to as Lection.
Regarding claim 11, Wang and Kumar disclose the method of claim 1; Wang further discloses an image based quality control manufacturing flow implemented during fabrication (refer to Paragraphs [0083], [0296]); however, Wang and Kumar do not further disclose generating a digital image of a physical fabricated slice of the first updated digital model, comparing the geometry of the physical fabricated slice of the first updated digital model depicted in the digital image and a geometry of a corresponding slice of the first updated digital model, and updating a next slice of the first updated digital model based on the comparing.
Lection discloses a method of modifying 3D printing of an object based on real-time information, in the analogous art of additive manufacturing processes (refer to Paragraph [0056]), wherein the method comprises generating a digital image of a physical fabricated slice a digital model (906) (refer to Paragraphs [0020], [0057], [0066], [0075] and Fig. 9; a holographic image of the partially completed 3D printed object is determined, which is a layer by layer process), comparing the geometry of the physical fabricated slice of the digital model depicted in the digital image and a geometry of a corresponding slice of the digital model (908) (refer to Paragraphs [0064]-[0066], [0075]; the holographic image is generated based on comparing the 3D object currently being printed and the digital file), and updating a next slice of the first updated digital model based on the comparing (912) (refer to Paragraph [0076]; the digital file is changed based on the holographic comparison). This process allows for correction of defects before the printing process is completed (refer to Paragraph [0017]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of image based quality control as taught by Wang and Kumar with the method of image comparison as taught by Lection in order to allow for correction of defects before the printing process is completed (refer to Paragraph [0017]).
Regarding claim 13, Wang, Kumar and Lection disclose the method of claim 11; Wang does not disclose fabricating the next slice.
Kumar further discloses an option to continue printing after an anomaly detection (refer to Paragraph [0053], Fig. 4B), where the printing process is a layer-by-layer process (refer to Paragraphs [0009], [0021]). Thus, Kumar teaches fabricating the next slice. This a necessary step when the deviations are above a pre-defined threshold, but deemed acceptable by the user (refer to Paragraph [0052]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method as taught by Wang, Kumar and Lection with the step of fabricating the next slice as taught by Kumar in order to allow the user to continue printing after the detection of an anomaly (refer to Paragraph [0052]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200100864 A1), herein referred to as Wang, in view of Kumar et al. (US 20230315048 A1), herein referred to as Kumar as applied to claim 1 above, and further in view of Cheverton (US 20150177158 A1).
Regarding claim 12, Wang and Kumar disclose the method of claim 1; Wang further discloses an image based quality control manufacturing flow implemented during fabrication (refer to Paragraphs [0083], [0296]); however, Wang and Kumar do not further disclose generating a digital model of a physical fabricated slice of the first updated digital model, comparing the geometry of the physical fabricated slice of the first updated digital model depicted in the digital model and a geometry of a corresponding slice of the first updated digital model, and updating a geometry of the corresponding slice of the first updated digital model based on the comparing to generate an updated corresponding slice.
Cheverton discloses a method of modifying 3D printing of an object based on real-time information, in the analogous art of additive manufacturing processes (refer to Paragraph [0061]), with the method including generating a digital model of a physical fabricated slice (406; refer to Paragraph [0062] and Fig. 4; a digital model is a computer based representation; therefore the computer generated images are digital models), comparing the geometry of the physical fabricated slice of the digital model depicted and a geometry of a corresponding slice of the digital model (refer to Paragraphs [0061]-[0062]; the printed materials are compared to the CAD specification to identify any operational flaws), and updating a geometry of the corresponding slice of the first updated digital model based on the comparing to generate an updated corresponding slice (refer to Paragraphs [0046], [0065]; remedial actions are taken to reprint a problem area by dynamic adjustment to the print process; printing of the repair slice requires data processing of a sliced 3D CAD model file, therefore reprinting inherently includes adjusting the 3D file geometry). Early detection of manufacturing flaws reduces manufacturing time, raw material waste and scrap (refer to Paragraph [0036]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of image based quality control as taught by Wang and Kumar with the method of slice comparison as taught by Cheverton in order to reduce manufacturing time, raw material waste and scrap (refer to Paragraph [0036]).
Response to Arguments
The outstanding objections of claims 1, and 4-13 are withdrawn in view of the newly submitted claim amendments.
The outstanding 35 U.S.C. 101 rejections are withdrawn in view of the newly submitted claim amendments incorporating a fabrication step in independent claim 1.
Applicant’s arguments with respect to claim(s) 1-14 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. The rejection of independent claim 1 now relies on a combination of Wang et al. (US 20200100864 A1) and Kumar et al. (US 20230315048 A1) for the teachings on predicted deviations based on real-time machine parameters.
Conclusion
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/ADRIENA J WEBB LYTTLE/Examiner, Art Unit 3772
/EDELMIRA BOSQUES/Supervisory Patent Examiner, Art Unit 3772
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