DETAILED ACTION
This Office Action is in response for Application # 18/607,613 filed on March 18, 2024 in which claims 1-18 are presented for examination.
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 .
Status of claims
Claims 1-18 are pending, of which claims 1-18 are rejected under 35 U.S.C. 101 and also claims 1-18 are rejected under 35 U.S.C. 103.
Drawings
The drawings filed on March 18, 2024 are acceptable subject to correction of the informalities indicated below. In order to avoid abandonment of this application, correction is required in reply to the Office action. The correction will not be held in abeyance. Fig, 2, Fig, 3 and Fig. 9 contains empty rectangle boxes with missing text and applicant is requested to submitted the corrected drawings with missing text.
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 1-18 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-18 recite a method and apparatus respectively.
The analysis of claims 1 and 13 are as follows:
Step 2A, prong one: Does claims 1 and 13 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “receiving, in a computer memory, for a plurality of subjects:
a) one or more planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject;
b) first scan data obtained in a first scan of the respective subject before a tooth alignment process using the respective planned tooth movements has been performed; and
c) second scan data obtained in a second scan of said one or more teeth of the respective subject after the tooth alignment process has been performed;
wherein both the first and second scan data comprises scan data for said one or more teeth of the respective subject and scan data for one or more non-dental orthodontic reference points of the respective subject;
wherein the method comprises:
determining, for one or more of the subjects, actual tooth movements of the respective one or more teeth using the first and second scan data and the non-dental orthodontic reference points;
determining deviations between the planned tooth movements and the actual tooth movements; and
training one or more machine learning models at least in part using planned tooth movements of one or more subjects, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements;
wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process” as drafted, are mental steps based on various processes can be performed in a human mind of applying the model to planned tooth movements for a tooth alignment process (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science. Although the claim recites that the recited functionality includes “method”, “system” and “storage medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “receiving, in a computer memory, for a plurality of subjects:
a) one or more planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject;
b) first scan data obtained in a first scan of the respective subject before a tooth alignment process using the respective planned tooth movements has been performed; and
c) second scan data obtained in a second scan of said one or more teeth of the respective subject after the tooth alignment process has been performed;
wherein both the first and second scan data comprises scan data for said one or more teeth of the respective subject and scan data for one or more non-dental orthodontic reference points of the respective subject;
wherein the method comprises:
determining, for one or more of the subjects, actual tooth movements of the respective one or more teeth using the first and second scan data and the non-dental orthodontic reference points;
determining deviations between the planned tooth movements and the actual tooth movements; and
training one or more machine learning models at least in part using planned tooth movements of one or more subjects, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements;
wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process” are mere training machine learning model of training tooth alignment process (i.e., tracking actual and expected tooth movement); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process“, training by machine learning model also recited at a high level of generality and merely generally link to respective technological environments (e.g., training tooth alignment and movement by model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on decoding process (algorithm) is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the tooth alignment and movement are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1 and 13 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-12 and 14-18 are as follows:
Step 2A, prong one: Does claims 2-12 and 14-18 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “claim 2 recites wherein the one or more non-dental orthodontic reference points comprise at least one of: an anatomical landmark of the respective subject and a temporary anchorage point received by the respective subject.
Claim 3 recites wherein the one or more non-dental orthodontic reference points comprise one or more anatomical landmarks comprising at least one of: a feature or portion of the palate such as soft or hard tissue, a feature or portion of the median suture, a feature or portion of the incisive foramen, one or more anterior contours of the chin, one or more trabecular structures of the mandibular canal and symphysis, one or more inner cortical structures at the inferior border of the symphysis, and one or more lower contours of a molar germ.
Claim 4 recites wherein determining the actual tooth movements comprises forming a first model of the respective one or more teeth based on the first data; and forming a second model of the respective one or more teeth based on the second data.
Claim 5 recites wherein first and second models comprise the one or more non-dental orthodontic reference points.
Claim 6 recites wherein determining the actual tooth movements comprises superimposing or aligning the non-dental orthodontic reference points of the first model and the non-dental orthodontic reference points of the second model.
Claim 7 recites wherein determining the actual tooth movements comprises comparing at least one of a position and orientation of a tooth or teeth of first and second models.
Claim 8 recites wherein the planned tooth movements and the actual tooth movements comprise movements for a plurality of teeth, optionally at least 10 teeth, optionally at least 20 teeth, optionally all teeth of the respective subj ect.
Claim 9 recites wherein the planned tooth movements have been obtained using a computer-aided design CAD package for modelling virtual tooth movement.
Claim 10 recites wherein the or each planned tooth movement and / or the or each actual tooth movement comprises at least one of: a value representing the change in inclination, angulation, rotation, or translation of the respective tooth from an initial position to a final position.
Claim 11 recites wherein the machine learning model is a deep neural network DNN.
Claim 12 recites A method of training a machine learning model to determine tooth movements for a process of tooth alignment of a subject, the method comprising: receiving, in a computer memory, for a plurality of subjects: a) planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject; and b) actual tooth movements for said one or more teeth of the respective subject after a tooth alignment process has been performed; the method further comprising training one or more machine learning models at least in part using the planned tooth movements, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements; wherein the or each predicted deviations represent differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process.
Claims 14 and 18 recites A method of determining improved tooth movements for a process of tooth alignment of a subject, the method comprising: receiving, at a processor, one or more planned tooth movements of one or more teeth of the subject; extracting, using a machine learning model trained in accordance with the method as defined in claim 1, one or more predicted deviations based at least in part on the planned tooth movements; and determining, using a processor, improved planned tooth movements; wherein the or each predicted deviation represents one or more differences between the planned tooth movements and expected tooth movements if the initial planned tooth movements were used in a tooth alignment process; and wherein the improved planned tooth movements are based at least in part on the planned tooth movements and the predicted deviations.
Claim 15 recites communicating, using a communication device, data associated with the improved tooth movements to a fabrication device for manufacturing an aligner tray.
Claim 16 recites further comprising creating or updating a CAD model for manufacturing an aligner tray using the improved planned tooth movements.
Claim 17 recites comprising fabricating, using a fabrication device, one or more aligner trays in accordance with the improved tooth movements” as drafted, are mental steps based on various processes can be performed in a human mind of applying the model to planned tooth movements for a tooth alignment process (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science. Although the claim recites that the recited functionality includes “method”, “system” and “storage medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “claim 2 recites wherein the one or more non-dental orthodontic reference points comprise at least one of: an anatomical landmark of the respective subject and a temporary anchorage point received by the respective subject.
Claim 3 recites wherein the one or more non-dental orthodontic reference points comprise one or more anatomical landmarks comprising at least one of: a feature or portion of the palate such as soft or hard tissue, a feature or portion of the median suture, a feature or portion of the incisive foramen, one or more anterior contours of the chin, one or more trabecular structures of the mandibular canal and symphysis, one or more inner cortical structures at the inferior border of the symphysis, and one or more lower contours of a molar germ.
Claim 4 recites wherein determining the actual tooth movements comprises forming a first model of the respective one or more teeth based on the first data; and forming a second model of the respective one or more teeth based on the second data.
Claim 5 recites wherein first and second models comprise the one or more non-dental orthodontic reference points.
Claim 6 recites wherein determining the actual tooth movements comprises superimposing or aligning the non-dental orthodontic reference points of the first model and the non-dental orthodontic reference points of the second model.
Claim 7 recites wherein determining the actual tooth movements comprises comparing at least one of a position and orientation of a tooth or teeth of first and second models.
Claim 8 recites wherein the planned tooth movements and the actual tooth movements comprise movements for a plurality of teeth, optionally at least 10 teeth, optionally at least 20 teeth, optionally all teeth of the respective subj ect.
Claim 9 recites wherein the planned tooth movements have been obtained using a computer-aided design CAD package for modelling virtual tooth movement.
Claim 10 recites wherein the or each planned tooth movement and / or the or each actual tooth movement comprises at least one of: a value representing the change in inclination, angulation, rotation, or translation of the respective tooth from an initial position to a final position.
Claim 11 recites wherein the machine learning model is a deep neural network DNN.
Claim 12 recites A method of training a machine learning model to determine tooth movements for a process of tooth alignment of a subject, the method comprising: receiving, in a computer memory, for a plurality of subjects: a) planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject; and b) actual tooth movements for said one or more teeth of the respective subject after a tooth alignment process has been performed; the method further comprising training one or more machine learning models at least in part using the planned tooth movements, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements; wherein the or each predicted deviations represent differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process.
Claims 14 and 18 recites A method of determining improved tooth movements for a process of tooth alignment of a subject, the method comprising: receiving, at a processor, one or more planned tooth movements of one or more teeth of the subject; extracting, using a machine learning model trained in accordance with the method as defined in claim 1, one or more predicted deviations based at least in part on the planned tooth movements; and determining, using a processor, improved planned tooth movements; wherein the or each predicted deviation represents one or more differences between the planned tooth movements and expected tooth movements if the initial planned tooth movements were used in a tooth alignment process; and wherein the improved planned tooth movements are based at least in part on the planned tooth movements and the predicted deviations.
Claim 15 recites communicating, using a communication device, data associated with the improved tooth movements to a fabrication device for manufacturing an aligner tray.
Claim 16 recites further comprising creating or updating a CAD model for manufacturing an aligner tray using the improved planned tooth movements.
Claim 17 recites comprising fabricating, using a fabrication device, one or more aligner trays in accordance with the improved tooth movements” are mere training machine learning model of training tooth alignment process (i.e., tracking actual and expected tooth movement); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process“, training by machine learning model also recited at a high level of generality and merely generally link to respective technological environments (e.g., training tooth alignment and movement by model) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on decoding process (algorithm) is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the tooth alignment and movement are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 2-12 and 14-18 are rejected as being directed to non-patentable subject matter under §101.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chiu et al. US 2023/0190411 A1 (hereinafter ‘Chiu’) in view of Matov et al. US 2013/0230818 A1 (hereinafter ‘Matov’).
As per claim 1, Chiu disclose, A method of training a machine learning model (Chiu: paragraph 0008: disclose accurate and efficient in predicting tooth movement, because the trained prediction model ‘machine learning model’ (e.g., the Machine Learning agent or ML agent) may be consider not just movement of each individual tooth separately, but may consider multiple dimensions of movement of each individual tooth in combination with the effects of movement due to teeth that are adjacent to each individual tooth) to improve tooth movements for a process of tooth alignment of a subject (Chiu: paragraph 0057: disclose improve the treatment planning for dental aligner therapy by improving the efficacy of treatment plans), the method comprising:
receiving, in a computer memory, for a plurality of subjects (Chiu: paragraph 0009: disclose trained through analysis of historical patient data (training data) that may be generalized across patients ‘plurality of subjects’):
a) one or more planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject (Chiu: paragraph 0026: disclose initially be provided with a set of initial patient tooth positions, a treatment plan and/or a set of target tooth positions ‘alignment’);
b) first scan data obtained in a first scan of the respective subject before a tooth alignment process using the respective planned tooth movements has been performed (Chiu: paragraph 0026: disclose set of initial patient tooth positions ‘before a tooth alignment process’ include a digital model and/or digital scan of the patient's teeth); and
c) second scan data obtained in a second scan of said one or more teeth of the respective subject after the tooth alignment process has been performed (Chiu: paragraph 0114: disclose a patient has had previous dental treatment ‘scan after the alignment is performed’ moving one or more teeth, then the associated patient data regarding desired and/or achieved tooth movement associated with the previous dental treatment);
wherein both the first (Chiu: paragraph 0026: disclose set of initial patient tooth positions ‘before a tooth alignment process’ include a digital model and/or digital scan of the patient's teeth) and second scan data comprises scan data for said one or more teeth of the respective subject (Chiu: paragraph 0114: disclose a patient has had previous dental treatment ‘scan after the alignment is performed’ moving one or more teeth, then the associated patient data regarding desired and/or achieved tooth movement associated with the previous dental treatment) and scan data for one or more non-dental orthodontic reference points of the respective subject (Chiu: paragraph 0064: disclose scans from the patient's previous treatment (e.g., progress info) to account for the individual differences, for example, in wearing habits ‘non-dental orthodontic’, bone physiology ‘non-dental orthodontic’ etc., into the regression model, and may further improve the tooth movement prediction);
wherein the method comprises:
determining, for one or more of the subjects, actual tooth movements of the respective one or more teeth using the first and second scan data and the non-dental orthodontic reference points (Chiu: paragraph 0013: disclose determining a predicted tooth movement of the patient based at least in part on a biomechanical interaction between teeth described in historic tooth movement data, wherein the predicted tooth movement includes a predicted final position of the patient's teeth); and
training one or more machine learning models at least in part using planned tooth movements of one or more subjects, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements (Chiu: paragraph 0018: disclose training set of prior tooth movement data used to train the trained prediction model may include desired tooth movement data and achieved tooth movement data for a plurality of patients. Examiner would discuss deviation in view of secondary art below).
It is noted, however, Chiu did not specifically detail the aspects of
determining deviations between the planned tooth movements and the actual tooth movements;
wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process as recited in claim 1.
On the other hand, Matov achieved the aforementioned limitations by providing mechanisms of
determining deviations between the planned tooth movements and the actual tooth movements (Matov: paragraph 0061: disclose made more precise by allowing for the statistical deviation of targeted ‘planned’ from actual tooth position);
wherein the or each predicted deviation represents one or more predicted differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process (Matov: paragraph 0149: disclose each treatment stage of a treatment plan, and repeated if the result is closer to the target profile, and ignored if the results move away or deviate further from the target profile).
Chiu and Matov are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Machine Learning System”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Chiu and Matov because they are both directed to machine learning system and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Matov with the method described by Chiu in order to solve the problem posed.
The motivation for doing so would have been to allow the teeth sufficient time to adapt to the new position, before the process is repeated again as the teeth move progressively along the various treatment stages of a treatment plan (Matov: paragraph 0004).
Therefore, it would have been obvious to combine Matov with Chiu to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein the one or more non-dental orthodontic reference points comprise at least one of: an anatomical landmark of the respective subject and a temporary anchorage point received by the respective subject (Chiu: paragraph 0064: disclose scans from the patient's previous treatment (e.g., progress info) to account for the individual differences, for example, in wearing habits ‘non-dental orthodontic’, bone physiology ‘non-dental orthodontic’ etc., into the regression model, and may further improve the tooth movement prediction).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein the one or more non-dental orthodontic reference points comprise one or more anatomical landmarks comprising at least one of: a feature or portion of the palate such as soft or hard tissue, a feature or portion of the median suture, a feature or portion of the incisive foramen, one or more anterior contours of the chin, one or more trabecular structures of the mandibular canal and symphysis, one or more inner cortical structures at the inferior border of the symphysis, and one or more lower contours of a molar germ (Chiu: paragraph 0064: disclose scans from the patient's previous treatment (e.g., progress info) to account for the individual differences, for example, in wearing habits ‘non-dental orthodontic’, bone physiology ‘non-dental orthodontic’ etc., into the regression model, and may further improve the tooth movement prediction).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein determining the actual tooth movements comprises forming a first model of the respective one or more teeth based on the first data; and forming a second model of the respective one or more teeth based on the second data (Chiu: paragraph 0018: disclose training set of prior tooth movement data used to train the trained prediction model may include desired tooth movement data and achieved tooth movement data for a plurality of patients).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 4 above. In addition, Chiu disclose, wherein first and second models comprise the one or more non-dental orthodontic reference points (Chiu: paragraph 0064: disclose scans from the patient's previous treatment (e.g., progress info) to account for the individual differences, for example, in wearing habits ‘non-dental orthodontic’, bone physiology ‘non-dental orthodontic’ etc., into the regression model, and may further improve the tooth movement prediction).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1, 4 and 5 above. In addition, Chiu disclose, wherein determining the actual tooth movements comprises superimposing or aligning the non-dental orthodontic reference points of the first model and the non-dental orthodontic reference points of the second model (Chiu: paragraph 0011: disclose some cases adjacent to and/or superimposed over the patient's set of initial tooth positions. The target tooth position may also be displayed adjacent to and/or superimposed over an image (the same or a different image) of the patient's set of initial tooth positions).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claims 1 and 4 above. In addition, Chiu disclose, wherein determining the actual tooth movements comprises comparing at least one of a position and orientation of a tooth or teeth of first and second models (Chiu: paragraph 0009: disclose predicted treatment plans optimized by comparing target tooth positions (that may be specified by a user, such as a dental practitioner) with predicted tooth positions that may be provided by a trained prediction model).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein the planned tooth movements and the actual tooth movements comprise movements for a plurality of teeth, optionally at least 10 teeth, optionally at least 20 teeth, optionally all teeth of the respective subject (Chiu: paragraph 0094: disclose different numbers ‘can be 10 teeth and 20 teeth’ of teeth may be used to either side of the target tooth. For example, one tooth to the left of the target tooth and two teeth to the right of the target tooth may be used to predict the movement of the target tooth).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claim 1 above.
It is noted, however, Chiu did not specifically detail the aspects of
wherein the planned tooth movements have been obtained using a computer-aided design CAD package for modelling virtual tooth movement as recited in claim 9.
On the other hand, Matov achieved the aforementioned limitations by providing mechanisms of
wherein the planned tooth movements have been obtained using a computer-aided design CAD package for modelling virtual tooth movement (Matov: paragraph 0105: disclose solid geometry models, computer aided engineering (CAE) or computer aided design (CAD) programs).
As per claim 10, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein the or each planned tooth movement and / or the or each actual tooth movement comprises at least one of: a value representing the change in inclination, angulation, rotation, or translation of the respective tooth from an initial position to a final position (Chiu: paragraph 0025: disclose individual tooth of a dental arch, to include multiple translational and rotational directions (e.g., six rotational and translational directions, such as buccal/lingual, mesial distal, and intrusion/extrusion) for the individual tooth as well as reaction forces (e.g., multiple translational and rotational directions).
As per claim 11, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Chiu disclose, wherein the machine learning model is a deep neural network DNN (Chiu: paragraph 0025: disclose trained prediction model (e.g., a ML agent, ML model, neural network, etc.)).
As per claim 12, Chiu disclose, A method of training a machine learning model (Chiu: paragraph 0008: disclose accurate and efficient in predicting tooth movement, because the trained prediction model ‘machine learning model’ (e.g., the Machine Learning agent or ML agent) may be consider not just movement of each individual tooth separately, but may consider multiple dimensions of movement of each individual tooth in combination with the effects of movement due to teeth that are adjacent to each individual tooth) to determine tooth movements for a process of tooth alignment of a subject (Chiu: paragraph 0057: disclose improve the treatment planning for dental aligner therapy by improving the efficacy of treatment plans), the method comprising:
receiving, in a computer memory, for a plurality of subjects (Chiu: paragraph 0009: disclose trained through analysis of historical patient data (training data) that may be generalized across patients ‘plurality of subjects’):
a) planned tooth movements for a tooth alignment process for aligning one or more teeth of the respective subject (Chiu: paragraph 0010: disclose patient's dental appliances may be fabricated based on the treatment plan that generated the difference indicator); and
b) actual tooth movements for said one or more teeth of the respective subject after a tooth alignment process has been performed (Chiu: paragraph 0088: disclose actual tooth movement that was achieved in response to the patient's treatment plan);
the method further comprising training one or more machine learning models at least in part using the planned tooth movements, the one or more machine learning models being arranged to extract one or more predicted deviations based at least in part on the planned tooth movements (Chiu: paragraph 0018: disclose training set of prior tooth movement data used to train the trained prediction model may include desired tooth movement data and achieved tooth movement data for a plurality of patients. Examiner would discuss deviation in view of secondary art below).
It is noted, however, Chiu did not specifically detail the aspects of
wherein the or each predicted deviations represent differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process as recited in claim 12.
On the other hand, Matov achieved the aforementioned limitations by providing mechanisms of
wherein the or each predicted deviations represent differences between the planned tooth movements and expected tooth movements if the planned tooth movements were used in a tooth alignment process (Matov: paragraph 0149: disclose each treatment stage of a treatment plan, and repeated if the result is closer to the target profile, and ignored if the results move away or deviate further from the target profile).
As per claim 13, Chiu disclose, A system for determining improved tooth movements for a process of tooth alignment of a subject, the system comprising one or more processors and a memory comprising instructions which, when executed by the one or more processors (Chiu: paragraph 0151: disclose memory include instructions for performing all or a portion of the operations executed by the processor), cause the system to carry out the steps of the method of claim 1: remaining limitations in this claim 13 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 14, Chiu disclose, A method of determining improved tooth movements for a process of tooth alignment of a subject (Chiu: paragraph 0057: disclose improve the treatment planning for dental aligner therapy by improving the efficacy of treatment plans), the method comprising: receiving, at a processor, one or more planned tooth movements of one or more teeth of the subject (Chiu: paragraph 0011: determining the predicted tooth movement may be further based on the desired tooth movement of the patient);
extracting, using a machine learning model trained in accordance (Chiu: paragraph 0008: disclose accurate and efficient in predicting tooth movement, because the trained prediction model ‘machine learning model’ (e.g., the Machine Learning agent or ML agent) may be consider not just movement of each individual tooth separately, but may consider multiple dimensions of movement of each individual tooth in combination with the effects of movement due to teeth that are adjacent to each individual tooth) with the method as defined in claim 1: remaining limitations in this claim 14 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 15, most of the limitations of this claim have been noted in the rejection of claim 14 above. In addition, Chiu disclose, using a communication device, data associated with the improved tooth movements to a fabrication device for manufacturing an aligner tray (Chiu: paragraph 0019: disclose fabrication of dental appliances using, e.g., three-dimensional (3D) printing. Examiner argues that the teaching inherit the communication to 3D printing).
As per claim 16, most of the limitations of this claim have been noted in the rejection of claim 14 above.
It is noted, however, Chiu did not specifically detail the aspects of
creating or updating a CAD model for manufacturing an aligner tray using the improved planned tooth movements as recited in claim 16.
On the other hand, Matov achieved the aforementioned limitations by providing mechanisms of
creating or updating a CAD model for manufacturing an aligner tray using the improved planned tooth movements (Matov: paragraph 0105: disclose solid geometry models, computer aided engineering (CAE) or computer aided design (CAD) programs).
As per claim 17, most of the limitations of this claim have been noted in the rejection of claim 14 above. In addition, Chiu disclose, fabricating, using a fabrication device, one or more aligner trays in accordance with the improved tooth movements (Chiu: paragraph 0031: disclose fabricating the dental appliances (aligners, including polymeric aligners) of a treatment plan).
As per claim 18, Chiu disclose, A system for determining improved tooth movements for a process of tooth alignment of a subject, the system comprising one or more processors and a memory comprising instructions which, when executed by the one or more processors (Chiu: paragraph 0151: disclose memory include instructions for performing all or a portion of the operations executed by the processor), cause the system to carry out the steps of the method of claim 14: remaining limitations in this claim 18 are similar to the limitations in claim 14. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2022/0039919 A1 disclose “SYSTEMS AND METHODS FOR GENERATING 3D-REPRESENTATION OF TOOTH-SPECIFIC APPLIANCE”
US Pub. US 2023/0053428 A1 disclose “SYSTEMS AND METHODS FOR PROVIDING DIGITAL HEALTH SERVICES”
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/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159
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