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 .
Claim Objections
Claims 1 objected to because of the following informalities:
-Claim 1, line 1, “an polymeric” should be “a polymeric”.
-Original claim 4 is missing from the amended claim sheet, as such, claims 4-13 are misnumbered; for example, claim 4 is actually claim 5, claim 5 is actually claim 6 and so forth. Examiner has retained the numbering convention from the non-final rejection and has retained claim 4 for consistency, as it does not appear claim 4 is cancelled.
- Claim 4 (originally claim 5), line 2, “the aligner” should be correct to “the polymeric dental appliance” for consistency.
-Claim 13 (originally claim 14), lines 1-2, “an polymeric dental appliance” should be “a polymeric dental appliance”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12, and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 – Determination as to whether the claims are directed to a statutory category as specified in 35 U.S.C. 101 (MPEP 2106.03)
Claims 1-7 recite a method of use for additively manufacturing an orthodontic aligner, comprising the steps of receiving a digital model, processing the digital model for predicted deviations, determining if the predicted deviations are acceptable and outputting the updated digital model for fabrication. These methods fall into the category of a process.
Claims 8-10 recite a method of use for additively manufacturing an orthodontic aligner wherein the prediction model is used to generate the predicted deviations, with this model being a machine learning model. These methods fall into the category of a process.
Claims 11-12 recite a method of use for additively manufacturing an orthodontic aligner that further comprises a digital comparison of the physically fabricated slice and a model or image.
Claim 14 recites a system for additively manufacturing an orthodontic aligner, wherein the system comprises a processor and memory. This invention falls into the category of an apparatus.
Step 2A Prong 1 – Determination as to whether the claims recite a Judicial Exception including an abstract idea, law of nature, or natural phenomenon (MPEP 2106.04)
Regarding claim 1, the claimed invention is directed to an abstract idea, a mental process capable of being performed in the human mind, including observations, evaluations and judgements. The steps of updating a model based on predicted deviations and outputting said model is a mental process capable of being performed in the human mind as an operator analyzes and judges the aligner model for potential deviations. The additional step of modifying the first updated digital model during fabrication is not a physical implementation step, but rather a step of updating the digital model.
Regarding claims 2-7, the further steps of receiving fabrication parameters for predicting deviations is another mental process wherein the operator evaluates the model for potential deviations based on processing conditions.
Regarding claim 8-10, the steps of using a machine learning prediction model to generate the predicted deviations is a mathematical concept.
Regarding claims 11-12, the steps of comparing two models or images and updating the geometry of the aligner slice based on the comparison is a mental evaluation performed by an operator as they evaluate the quality via the models of the aligner.
Regarding claim 14, the processor and memory are merely performing the steps disclosed in claim 1, which is an abstract idea, a mental process capable of being performed in the human mind.
Step 2A, Prong Two – Determination as to whether the claims as a whole integrate the judicial exception into a practical application
This judicial exception is not integrated into a practical application because:
Regarding claims 1-12 and 14The computer implementation of this method is insignificant extra solution activity and does not amount to an inventive concept, particularly when the activity is well-understood and conventional. The incorporation of a processor is another example of insignificant extra solution activity that merely acts as a tool to perform the mental process. For at least these reasons and as claims 1-12, and 14 do not recite additional elements which integrate the judicial exception into a practical application, the abstract mental processes and mathematical concepts identified for claims 1-12, and 14 are not integrated into a practical application.
Step 2B – Determination as to whether the claims amount to significantly more than the judicial exception (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
Regarding claims 1-12, and 14, as set forth above with respect to Step 2A Prong One, the claimed method steps are all capable of being performed mentally and represent nothing more than concepts related to performing observations, evaluations, judgements, or mathematical concepts, which fall within the judicial exceptions. The claimed steps of processing based on predicted deviations, determining whether the predicted deviation is acceptable, using a machine learning prediction model and comparing the digital models require nothing more than a generic computer processor. The disclosure does not describe additional features to suggest these devices are beyond a generic component for the apparatus. Additionally, the method is not disclosed as improving the manner in which the apparatus operates. Mere recitation of generic conventional processing used in a conventional manner to perform conventional computer functions that are well understood and routine does not amount to “significantly more” than the judicial exception. The claims do not go beyond inputting data (“receiving”) and processing data ( “processing” and "outputting") with a standard computer.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Additional elements of claims 1-12, and 14 do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. The claims set forth do not require that the method be implemented by a particular machine and they do not require that the method particularly transforms a particular article. When viewed as a combination, the identified additional elements set forth a process of analyzing information of specific content and are not directed to any particularly asserted inventive technology for performing these functions. The disclosure and claims do not require anything beyond a generic computer to obtain and analyze the data according to mathematical algorithms. Therefore, the claimed method and apparatus fall within the judicial exception to patent eligible subject matter of an abstract idea without significantly more.
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) 1-11, and 13-14 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 Lection et al. (US 20180059644 A1), herein known as Lection.
Regarding claim 1, Wang discloses a method (100; refer to Paragraph [0009] and Fig. 1A) for use in additively manufacturing an polymeric dental appliance
receiving a digital model of a polymeric dental appliance (102; refer to Paragraph [0062] and 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 [0063]-[0065] and Fig. 1A; at block 104, processing logic uses a model, simulation or evaluator to analyze the digital design of the polymeric aligner; based on the analytics of block 104, blocks 106 predicts probable points of damage, with block 107 acting as the determining step of whether the points of damage were identified; if points of damage are identified, block 108 proceeds to a modified digital design of the aligner);
determining 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]; after modifying the initial digital design of the aligner, processing logic determines whether the modified aligner has one or more probable points of damage); 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 Paragraph [0061]; once the aligner is designed by the method (100) each aligner is manufactured which requires that the digital design is output prior to manufacturing the model);
modifying the first updated digital model based on the predicted deviations (refer to Paragraphs [0063], [0070]; a second corrective action is performed on the first modified model based on the processing logic identifying potential damage points).
Wang does not disclose modifying the first updated digital model during fabrication, based on predicted real-time deviations.
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 the step of modifying the digital model during fabrication, based on predicted real-time deviations (refer to Paragraphs [0059], [0076]; the partially completed 3D printed object is scanned; upon detection of a structural defect, the user is alerted to make changes to the 3D printed process to correct for the defect). 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 with the method of real time correction as taught by Lection in order to allow for correction of defects before the printing process is completed.
Regarding claim 2, Wang and Lection disclose the method of claim 1, with Wang further disclosing the method comprising: receiving the real-time fabrication parameters during fabrication of the physical polymeric dental appliance (refer to Paragraphs, [0296], [0300], [0302]; machine parameters for direct fabrication can be monitored and adjusted on a regular basis as part of the process control);
processing the first updated digital model to determine predicted deviations of the physical polymeric dental appliance based on the real-time fabrication parameters (refer to Paragraphs [0063], [0070], [0111]; processing logic performs an analysis on the modified digital design based on a machine learning model that is trained based on the manufacturing parameters); and
Regarding claim 3, Wang and Lection disclose the method of claim 2, with Wang further disclosing the method further comprising:
determining predicted deviations of a physical polymeric dental appliance exceed a threshold (refer to Paragraph [0070]; the process of the method (100) is repeated until only a threshold number of probable points of damage are present, if the threshold is exceeded further corrective modifications are required)
Regarding claim 4, Wang and Lection disclose the method of claim 3, with Wang further disclosing wherein the predicted deviations during fabrication are based on one or more parameters (refer to Paragraph [0111]; the machine learning models for processing the predicted deviations rely on manufacturing flows for parameters related to direct fabrication and material choice).
Regarding claim 5, Wang and Lection disclose the method of claim 4, with Wang further disclosing wherein the one or more parameters include angles of the polymeric dental appliance surfaces or thickness of the aligner at locations of the polymeric dental appliance (refer to Paragraphs [0242], [0244]; alternative embodiment (1000) block 1004 determines the parameters based on cutline angle and aligner thickness)
Regarding claim 6, Wang and Lection disclose the method of claim 4, with Wang further disclosing wherein the parameters include material type, exposure time, exposure power, or material (refer to Paragraph [0296]; machine parameters include curing time, curing power and material properties).
Regarding claim 7, Wang and Lection disclose the method of claim 1, with Wang further disclosing wherein real-time parameters include material temperature, ambient temperature, or ambient humidity (refer to Paragraph [0303]; process control parameters include environmental variables for temperature and humidity).
Regarding claim 8, Wang and Lection disclose the method of claim 1, with Wang further disclosing wherein a prediction model is used to generate the predicted deviations (refer to Paragraph [0063]; at block 104, processing logic uses a model, simulation or evaluator to analyze the digital design of the polymeric aligner for probable points of damage).
Regarding claim 9, Wang and Lection disclose the method of claim 8, with Wang further disclosing wherein the prediction model is a machine learning model (refer to Paragraph [0063]; at block 104, processing logic uses a trained machine learning model).
Regarding claim 10, Wang and Lection disclose the method of claim 9, with Wang further disclosing wherein the prediction model is a neural network (refer to Paragraph [0105]) 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 11, Wang and Lection disclose the method of claim 1, with Wang further disclosing wherein an image based quality control manufacturing flow is implemented during fabrication (refer to Paragraphs [0083], [0296]); however, Wang does not further disclose the process steps of the image based quality control manufacturing flow.
Lection further discloses 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 with the method of comparison as taught by Lection in order to allow for correction of defects before the printing process is completed.
Regarding claim 13, Wang and Lection disclose the method of claim 11; Wang does not disclose fabricating the next slice.
Lection further discloses fabricating the next slice as part of the method (refer to Paragraph [0076]; after the digital print is updated, the method (900) proceeds from the scanning step (904), printing the updated file and re-scanning for verification until the print is completed).
Regarding claim 14, Wang discloses a system for use in additively manufacturing an polymeric dental appliance (1400; refer to Paragraph [0260] and Fig. 14), the system comprising:
a processor (1402; refer to Paragraph [0262]); and
memory (1404) comprising instructions (1426) that when executed by the processor (1402) cause the system to carry out the method of claim 1, wherein the method of claim 1 is disclosed by the combination of Wang and Lection (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) 1 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20200100864 A1) in view of Cheverton (US 20150177158 A1).
Regarding claim 1, Wang discloses a method (100; refer to Paragraph [0009] and Fig. 1A) for use in additively manufacturing an polymeric dental appliance
receiving a digital model of a polymeric dental appliance (102; refer to Paragraph [0062] and 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 [0063]-[0065] and Fig. 1A; at block 104, processing logic uses a model, simulation or evaluator to analyze the digital design of the polymeric aligner; based on the analytics of block 104, blocks 106 predicts probable points of damage, with block 107 acting as the determining step of whether the points of damage were identified; if points of damage are identified, block 108 proceeds to a modified digital design of the aligner);
determining 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]; after modifying the initial digital design of the aligner, processing logic determines whether the modified aligner has one or more probable points of damage); 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 Paragraph [0061]; once the aligner is designed by the method (100) each aligner is manufactured which requires that the digital design is output prior to manufacturing the model);
modifying the first updated digital model based on the predicted deviations (refer to Paragraphs [0063], [0070]; a second corrective action is performed on the first modified model based on the processing logic identifying potential damage points).
Wang does not disclose modifying the first updated digital model during fabrication, based on predicted real-time deviations.
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]) wherein the method comprises the step of modifying the digital model during fabrication, based on predicted real-time deviations (refer to Paragraph [0062] and Fig. 4; during material based deposition of the layers, the structure is imaged; if the evaluation of those images determines a flaw has occurred, 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 as taught by Wang with the method of real time correction as taught by Cheverton in order to reduce manufacturing time, raw material waste and scrap
Regarding claim 12, Wang and Cheverton disclose the method of claim 1, wherein an image based quality control manufacturing flow is implemented during fabrication (refer to Paragraphs [0083], [0296]); however, Wang does not further disclose the process steps of the image based quality control manufacturing flow.
Cheverton further discloses generating a digital model of a physical fabricated slice a digital model (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 with the method of comparison as taught by Cheverton in order to reduce manufacturing time, raw material waste and scrap.
Response to Arguments
The outstanding drawing and specification objections are withdrawn in view of the newly submitted drawing and specification amendments.
The outstanding claim objection of claim 1 is withdrawn in view of the claim amendments.
The outstanding 35 USC112(b) rejections of claims 5-6, and 11-13 are withdrawn.
Applicant's arguments filed 11/07/2025 have been fully considered but they are not persuasive.
In response to the arguments against the 35 USC 101 rejection, Examiner maintains these rejections in light of the cited "August 4 Memo" by Applicant. Modifying digital models based on predicted deviations is at the core of the 3D printing. Users have long observed the printing in real time, predicted that the current or next slice will not be printed correctly, and as a result, modified the digital model, making this claimed process an abstract idea. Examiner recommends Applicant add a clear fabrication step, such as fabricating the updated digital model, not just outputting the updated digital model, to overcome the 101 rejection.
In response to the argument that Wang does not teach the subject matter of amended claim 1; amended claim 1 is now rejected in view of the combination of Wang and Lection and Wang and Cheverton as demonstrated above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Adriena J Webb Lyttle whose telephone number is (571)270-7639. The examiner can normally be reached Mon - Fri 8:00-5:00 EST.
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/ADRIENA J WEBB LYTTLE/Examiner, Art Unit 3772
/THOMAS C BARRETT/SPE, Art Unit 3799