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 .
Priority
Acknowledgment is made of applicant’s claim for domestic priority under 35 U.S.C. 119 (e)). For the purpose of examination, the priority date for claims 1-20 is 08/12/2021.
Claim Objections
Claims 3, 19 and 20 are objected to because of the following informalities:
Claims 3 and 19
Lines 2-3, "translations and rotations is derived", should be corrected to, "translation and rotations are derived".
Claim 20
lines 1-2 state, “…wherein to receive the malocclusion, the processor is configured to receive a final stage for the planned setup position.” Examiner recommends correction of these lines to, “…wherein to receive the
Claim 3 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 1. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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, 3-10, 12-17, and 19-20 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)
The claim(s) recite(s) a method for generating intermediate stages of orthodontic aligners, which falls into the category of a process, and a system for executing this process, which 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 step of “generating intermediate stages for aligners…using one or more deep learning methods” is an abstract idea, a mental process capable of being performed in the human mind. The use of a deep learning method also incorporates mathematical concepts that can be performed mentally. Generating intermediate stages or tooth arrangements between a maloccluded and desired final setup is process that can be performed by an orthodontist as they visually evaluate a patient’s dentition. Outputting the stages as 3D models does not amount to a practical application; a practical application of orthodontic aligners is a fabrication or manufacturing step.
Regarding claims 3-5, and 12-16, the steps of receiving tooth stages, outputting intermediate stages, post-processing the tooth stages and displaying the tooth stages further limit the abstract idea presented in claim 1 and do not introduce practical application of the method. Therefore, these dependent claims also fall into the category of an abstract idea.
Regarding claims 6-10, the specification of the deep learning method(s) used is a further limitation to the mathematical concepts of claim 1; therefore, these dependent claims also fall into the category of an abstract idea.
Regarding claim 17, the system is configured to perform the step of “generating intermediate stages for aligners…using one or more deep learning methods”, which is an abstract idea, a mental process capable of being performed in the human mind. The use of a deep learning method also incorporates mathematical concepts that can be performed mentally. This step being performed by a generic processor does not introduce a practical application of the step, as the actual step being performed by the processor is an abstract idea.
Regarding claims 19-20, the steps of receiving tooth stages further limit the abstract idea presented in claim 1 and do not introduce practical application of the method. Therefore, these dependent claims also fall into the category of an abstract idea.
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, 3-10, 12-17, and 19-20, the claimed invention does not recite additional elements that integrate the judicial exception into practical application because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea(s) to a particular technological environment or field of use (MPEP 2106.04(d)). The inclusion of a processor to perform the mental processes and mathematical calculations is insignificant extra solution activity and does not amount to an inventive concept, particularly when the activity is well-understood and conventional. For at least these reasons and as claims 1, 3-10, 12-17, and 19-20 do not recite additional elements which integrate the judicial exception into a practical application, the abstract mental processes and mathematical concepts identified for claims 1-20 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, 3-10, 12-17, and 19-20, 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, and judgements, which fall within the judicial exception. The claimed steps of 1, 3-10, 12-17, and 19-20 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 design 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 ( “generating" and “outputting”) with a standard computer.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. 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, 3-7, 9-10, 12-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anssari et al. (EP 3620130 A1), herein referred to as Anssari (refer to the provided translation), in view of Chekhonin et al. (US 20200306011 A1), herein referred to as Chekhonin.
Regarding claim 1, Anssari discloses a method for generating intermediate stages for orthodontic aligners (Fig. 6; refer to Paragraph [0062]), comprising steps [[of]] performed by a processor (refer to Paragraph [0026]; a processor is configured to determine a sequence of desired intermediate positions):
receiving a malocclusion of teeth (658; refer to Paragraphs [0057], [0062], [0063]; the start tooth positions (658) are received by the pathway determination system (619)) and a planned setup position of the teeth (659; refer to Paragraph [0065]; the final tooth positions (659) are received by the pathway determination system (619)) wherein the malocclusion of teeth is represented using translations and rotations (refer to Paragraphs [0063], [0121], [0129], Fig. 20; the CBCT and intra-oral scan (IOS) data sets are aligned in step 513, prior to being fused (step 514) and provided to the pathway determination system (619) as starting tooth positions (658); the aligned malocclusion data is represented by translation and rotation transformation parameters);
generating intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods (663; refer to Paragraph [0064]; a deep neural network is used to generate the final tooth positions (658) used for determining the intermediate tooth positions (663)); and
outputting the intermediate stages as digital 3D models (refer to Paragraph [0065]; the intermediate tooth positions (663) are output as part of the pathway determination step (619) as 3D models).
Anssari discloses wherein the one or more deep learning methods includes a dual arch method that considers both the upper and lower arches of a patient’s dentition (refer to Paragraphs [0102] -[0103]; the segmented data (507) subsequently used in the alignment step (513), represents the upper and lower jaws as separate data sets), but does not disclose wherein cross arch interference is avoided by analyzing an occlusal map for the intermediate stages.
Chekhonin discloses a method for generating intermediate stages for orthodontic aligners (Fig. 3; refer to Paragraph [0238]; stages of a treatment plan are generated) in the same field of endeavor using machine learning methods (refer to Paragraphs [0399], [0402]; the treatment settings for generating the treatment plan are automatically generated by a machine learning agent), wherein the method (Fig. 3) comprises analyzing an occlusal map for the intermediate stages (Fig. 22A) to avoid cross arch interference (refer to Paragraphs [0288], [0290], [0293]; inter-arch collisions in a treatment plan represented by stages, or intermediate positions, are minimized by quantifying occlusion between upper and lower arches as shown in Fig. 22A). Using the occlusal map limits collisions, ultimately improving the treatment plan (refer to Paragraph [0290]).
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 Anssari (Fig. 6) with the method of cross-arch interference using an occlusal map as taught by Chekhonin (refer to Paragraphs [0288], [0290], [0293], Fig. 22A) in order to limit collisions, ultimately improving the treatment plan (refer to Paragraph [0290]).
Claim 2 - Canceled
Regarding claim 3, Anssari and Chekhonin disclose the method of claim 1; Anssari further discloses, wherein the receiving step comprises receiving a digital 3D model of the teeth in a state of malocclusion (601; refer to Paragraph [0062]; 3D data sets of CBCT and IOS are obtained), and the translations and rotations is derived from the 3D model (refer to Paragraphs [0063], [0121], [0129], Fig. 20; the translation and rotation transformation parameters are derived from aligning the CBCT 3D data to the (IOS) data set in step 513).
Regarding claim 4, Anssari and Chekhonin disclose the method of claim 1; Anssari further discloses wherein the receiving step comprises receiving a final stage for the planned setup position (refer to Paragraph [0065]; the final tooth positions (659) are received by the pathway determination system (619)).
Regarding claim 5, Anssari and Chekhonin disclose the method of claim 1; Anssari further discloses wherein the outputting step comprises outputting the intermediate stages as digital 3D models (refer to Paragraph [0065]; the intermediate tooth positions (663) are output as part of the pathway determination step (619) as 3D models).
Regarding claims 6-7 and 9-10, Anssari and Chekhonin disclose the method of claim 1; Anssari is silent to the generating step further comprising using multilayer perceptron, time series forecasting, video interpolation models, and/or seq2seq models to generate the intermediate stages.
Chekhonin further discloses wherein generating intermediate stages (refer to Paragraph [0238]; stages are generated for each treatment plan) comprises using multilayer perceptron (refer to Paragraph [0405]), time series forecasting (refer to Paragraph [0405]; a time series forecasting approach uses past data to predict future outcomes; the treatment setting agent is trained on a historical database of previous treatment plans in order to predict the target functions for a future case thereby being a time series forecasting approach), video interpolation models (refer to Paragraph [0266], [0349], Fig. 14; by definition, video interpolation models generate new frames between existing frames to generate a video; the animation of the treatment plan is defined by key frames, wherein an intermediate stage that is not a key frame is interpolated between the two adjacent frames, meeting the definition of a video interpolation model), and/or seq2seq models (refer to Paragraphs [0355], [0357], Figs. 27, 57; by definition, seq2seq is a method of translating an input sequence into an output sequence; the user’s text or handwritten preferences are transformed or translated into a domain specific language used for determining the treatment settings or targets that are then used to generate the intermediate stages, thereby meeting the definition of a seq2seq method). These methods all function to simplify the treatment planning process by using an automated process; in the case of multilayer perceptron and time series forecasting the treatment settings are automated (refer to Paragraph [0405]), in the case of video interpolation key frames are used for automated treatment planning display (refer to Paragraph [0349]), and in the case of seq2seq, automatically translating the user’s text based preferences (refer to Paragraph [0355]).
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 generating intermediate stages (663) as taught by Anssari with the automated methods as taught by Chekhonin in order to simplify the treatment planning process (refer to Paragraphs [0349], [0355], [0405]).
Claim 11-Canceled
Regarding claim 12, Anssari and Chekhonin disclose the method of claim 1; Anssari further discloses performing post-processing of one or more of the intermediate stages (refer to Paragraph [0084]; the intermediate position models (663) undergo post-processing into surface mesh models for further analysis as part of determining an orthodontic treatment plan (621)).
Regarding claim 13, Anssari and Chekhonin disclose the method of claim 12; Anssari is silent to the post-processing step comprises resetting fixed teeth for the intermediate stages
Chekhonin further discloses wherein a post-processing step comprises resetting fixed teeth for the intermediate stages (refer to Paragraph [0262]; one of the treatment plan modifications includes a preference for not moving teeth at one or more of the displayed stages, thereby resetting the tooth). This step prevents the unwanted movement of selected teeth.
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 post-processing step of Anssari (refer to Paragraph [0084]) with the step of resetting teeth as taught by Chekhonin in order to prevent the unwanted movement of selected teeth (refer to Paragraph [0262]).
Regarding claim 14, Anssari and Chekhonin disclose the method of claim 12; Anssari further discloses wherein the post-processing step comprises removing collisions between teeth for the intermediate stages (refer to Paragraph [0084], Fig. 11; as part of the post-processing (621), detected tooth collisions are removed to create an adjusted STL (1107) of the intermediate tooth position model).
Regarding claim 15, Anssari and Chekhonin disclose the method of claim 1; Anssari is silent to the generating step comprises generating intermediate stages for a particular point in treatment by at least two different machine learning methods and displaying the intermediate stages for the particular point in treatment.
Chekhonin further discloses the generating step comprising generating intermediate stages for a particular point in treatment by at least two different machine learning methods (refer to Paragraphs [0237], [0303], [0399], [0405]; the treatment settings agent may apply one or more machine learning models to form the treatment targets used in the non-linear optimization problem of the treatment solver; this non-linear optimization problem is then solved as part of the treatment planning to generate a plurality of intermediate stages; a partial treatment plan, comprising a plurality of intermediate stages, may also be generated, where the partial treatment plan represents a predetermined point in treatment to address some of the treatment goals). The use of multiple machine learning models allows the user to view various treatment plans as a result of different treatment settings determined by the two different machine learning methods.
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 generating intermediate stages (663) as taught by Anssari with the method of using two or more machine learning methods (refer to Paragraphs [0237], [0303], [0399], [0405]) as taught by Chekhonin in order to view various treatment plan results from the different machine learning methods.
Chekhonin further discloses the outputting step comprising displaying the intermediate stages for the particular point in treatment (refer to Paragraph [0260], Figs. 10A-10C; a display of the teeth at each stage is shown based on the generated full or partial treatment plan ). This step allows the patient to view and select plans prior to consultation with the orthodontist (refer to Paragraph [0260]).
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 outputting intermediate stages (663) as taught by Anssari with displaying the intermediate stages for the particular point in treatment (refer to Paragraph [0260]) as taught by Chekhonin in order to view and select plans prior to consultation with the orthodontist (refer to Paragraph [0260]).
Regarding claim 16, Anssari and Chekhonin disclose the method of claim 15; Anssari is silent to wherein the displaying step comprises displaying the intermediate stages for the particular point in treatment side-by-side within a user interface.
Chekhonin further discloses wherein the displaying step comprises displaying the intermediate stages for the particular point in treatment side-by-side within a user interface (refer to Paragraphs, [0260], [0263], Fig. 12; the selected treatment plans, with a slider to move through each stage, are shown side by side on the user interface). This step allows the patient to easily view the treatment plan.
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 outputting intermediate stages (663) as taught by Anssari with displaying the intermediate stages for the particular point in treatment side-by-side within a user interface (refer to Paragraphs, [0260], [0263], Fig. 12) as taught by Chekhonin in order to easily view the treatment plan.
Regarding claim 17, Anssari discloses a system for generating intermediate stages for orthodontic aligners, comprising a processor configured to (refer to Paragraphs [0026], [0036]; a processor is configured to determine a sequence of desired intermediate positions as part of a hardware or software system embodiment):
receive a malocclusion of teeth (658; refer to Paragraphs [0057], [0062], [0063]; the start tooth positions (658) are received by the pathway determination system (619)) and a planned setup position of the teeth (659; refer to Paragraph [0065]; the final tooth positions (659) are received by the pathway determination system (619)) wherein the malocclusion of teeth is represented using translations and rotations (refer to Paragraphs [0063], [0121], [0129], Fig. 20; the CBCT and intra-oral scan (IOS) data sets are aligned in step 513, prior to being fused (step 514) and provided to the pathway determination system (619) as starting tooth positions (658); the aligned malocclusion data is represented by translation and rotation transformation parameters);
generate intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods (663; refer to Paragraph [0064]; a deep neural network is used to generate the final tooth positions (658) used for determining the intermediate tooth positions (663)); and
output the intermediate stages as digital 3D models (refer to Paragraph [0065]; the intermediate tooth positions (663) are output as part of the pathway determination step (619) as 3D models).
Anssari discloses wherein the one or more deep learning methods includes a dual arch method that considers both the upper and lower arches of a patient’s dentition (refer to Paragraphs [0102] -[0103]; the segmented data (507) subsequently used in the alignment step (513), represents the upper and lower jaws as separate data sets), but does not disclose wherein cross arch interference is avoided by analyzing an occlusal map for the intermediate stages.
Chekhonin discloses a method for generating intermediate stages for orthodontic aligners (Fig. 3; refer to Paragraph [0238] in the same field of endeavor) using machine learning methods (refer to Paragraphs [0399], [0402]; the treatment settings for generating the treatment plan are automatically generated by a machine learning agent), wherein the method (Fig. 3) comprises analyzing an occlusal map for the intermediate stages (Fig. 22A) to avoid cross arch interference (refer to Paragraphs [0288], [0290], [0293]; inter-arch collisions in a treatment plan represented by stages, or intermediate positions, are minimized by quantifying occlusion between upper and lower arches as shown in Fig. 22A). Using the occlusal map limits collisions, ultimately improving the treatment plan (refer to Paragraph [0290]).
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 Anssari (Fig. 6) with the method of cross-arch interference using an occlusal map as taught by Chekhonin (refer to Paragraphs [0288], [0290], [0293], Fig. 22A) in order to limit collisions, ultimately improving the treatment plan (refer to Paragraph [0290]).
Claim 18- Cancelled
Regarding claim 19, Anssari and Chekhonin disclose the system of claim 17; Anssari further discloses wherein to receive the malocclusion, the processor is configured to receive a digital 3D model of the teeth in a state of malocclusion (601; refer to Paragraph [0062]; 3D data sets of CBCT and IOS are obtained), and the translations and rotations is derived from the 3D model (refer to Paragraphs [0063], [0121], [0129], Fig. 20; the translation and rotation transformation parameters are derived from aligning the CBCT 3D data to the (IOS) data set in step 513).
Regarding claim 20, Anssari and Chekhonin disclose the system of claim 17; Anssari further discloses wherein to receive the malocclusion, the processor is further configured to receive a final stage for the planned setup position (refer to Paragraph [0065]; the final tooth positions (659) are received by the pathway determination system (619)).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anssari et al. (EP 3620130 A1), herein referred to as Anssari (refer to the provided translation), in view of Chekhonin et al. (US 20200306011 A1), herein referred to as Chekhonin, as applied to claim 1 above, and further in view of Zhang (WO 2020181972 A1); refer to provided translation for Zhang.
Regarding claim 8, Anssari and Chekhonin disclose the method of claim 1, but are both silent to the generating step using a generative adversarial network to generate the intermediate stages.
Zhang discloses a method for generating digital data sets for target tooth layout in the same field of endeavor (refer to Paragraph [0006]). This method comprises a receiving step, wherein the initial tooth layout is received, and a generating step, wherein the generating step comprises using a generative adversarial network (GAN) to generate the intermediate stage (refer to Paragraph [0060]). GANs have superior accuracy in predicting tooth positions (refer to Paragraph [0045]).
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 deep learning method of generating intermediate stages as taught by Anssari and Chekhonin with a GAN as taught by Zhang in order to accurately predict tooth positions.
Response to Arguments
The outstanding specification objection of the incorporation by reference is withdrawn in view of the newly submitted specification amendment.
The outstanding objection of claim 1 is withdrawn in view of the submitted claim amendments.
The outstanding objection of claim 20 remains, as adding “further” did not remedy the informality. Examiner has clarified the suggested language above.
Applicant's arguments filed 12/12/2025 have been fully considered but they are not persuasive.
Regarding the arguments under 35 U.S.C. 101, none of the claims incorporate a practical application of the method. Adding "3D models" and "processors" to the claim limitations does not amount to more than the abstract idea. Orthodontists have mapped a treatment plan for patient's using occlusal mapping, translations and rotations. The addition of using a deep learning method to do so and further, a computer do not amount to more than the abstract idea. This reasoning is in line with general guidance provided by TQAS in light of the referenced amendment on 08/04/2025 . Examiner recommends adding a clear manufacturing or fabrication step to the independent claims (1, 17) to overcome this rejection.
Regarding the arguments for amended claims 1 and 17, Chekhonin discloses using a dual arch method to avoid cross-arch interference as detailed in the rejection above (refer to Paragraphs [0288], [0290], [0293]). Further, the claim language is given its broadest reasonable interpretation in light of the specification; however, the specification is not read into the claims. The broadest reasonable interpretation of “inter-arch” is between the upper and lower arches.
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 10:00-7:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Edelmira Bosques can be reached at (571) 270-5614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADRIENA J WEBB LYTTLE/Examiner, Art Unit 3772
/EDELMIRA BOSQUES/Supervisory Patent Examiner, Art Unit 3772