DETAILED ACTION
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
Claim 1 is objected to because of the following informalities:
Claim 1, lines 16-19 state, “comprising an expansion metric that measures an amount of expansion associated with the second one or more tooth positions and an expansion weight configured to penalize movement associated with the second one or more tooth positions less than a threshold amount”. The specification (page 4, lines 10-15) states that the penalty term penalizes movements that are less than a threshold amount by measuring how much less the current movement is compared to an ideal amount. The current wording does not connect the function of the expansion weight to the expansion metric. Examiner recommends amending the language to state,“ comprising an expansion metric that measures an amount of expansion movement associated with the second one or more tooth positions less than a threshold amount and an expansion weight configured to penalize expansion movement associated with the second one or more tooth positions less than a threshold amount”
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 and 3-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 3-16 have been analyzed to determine if the claims are directed to a judicial exception.
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 and 3-12 recite(s) a method of generating stages of orthodontic aligner treatment, with method steps comprising: "receiving a digital 3D model of teeth"; "and generating a subset of stages of setups among a complete set of stages of setups for aligner treatment of teeth". These methods fall into the category of a "process" (MPEP 2106.03).
Claims 13-16 recite(s) a method of generating a setup of orthodontic aligner treatment, with method steps comprising: "receiving a digital 3D model of teeth"; " training a machine learning model that calculates loss", "calculating a mean square error", "computing a score" and "outputting the proposed final or intermediate setup". These methods fall into the category of a "process" (MPEP 2106.03).
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 and mathematical concepts. The step of “receiving, based on one or more intra-oral 3D scans of teeth, a digital 3D model of the teeth in malocclusion” as recited in claim 1 is performed by an orthodontist mentally observing the patient’s dentition. The step of “generating a subset of stages of setups among a complete set of stages of setups for aligner treatment of teeth” is performed by an orthodontist as they mentally evaluate the patient’s dentition for subsequent treatment planning. The perturb function, constrain function, penalty terms and score computation steps are merely mathematical calculations that can be performed mentally as part of developing the treatment plan and restricting the treatment plan to constrained tooth movements. Translating or rotating tooth positions of a tooth model to form a result of the treatment plan have long been performed as part of the planning activities required for orthodontia treatments, without the use of a computer. The modification of the limitation “3D model” with “digital” does not direct the claim away from being a mental process capable of being performed in the human mind (refer to MPEP 2106.04(a)(2)(III)). Further, the outputting step is not a step of actually manufacturing the devices, but merely a step of digitally generating the final setups for a manufacturing step that is not completed until claim 2.
Regarding dependent claim 3, the method steps of “generating a set of stages of setups from the digital 3D model…to a target intermediate setup” and “selecting the subset of stages from the set of stages” are further mental processes directed toward evaluating the data from the patient’s dentition in claim 1 to plan treatment, generating more data, including intermediary teeth arrangements.
Regarding dependent claim 4, the method step of “receiving the target intermediate setup from a user” is a mental process directed to a judgement that the orthodontist (user) is capable of performing.
Regarding dependent claims 5-7, the method step of “generating the target intermediate setup” is another mental process related to observation, evaluation and judgement. An orthodontist is capable of “generating a target intermediate setup” based upon “ a set of movements”, “metrics, constraints, or both”, and data from “previously treated patients”, and have historically done so without the implementation of software as part of orthodontic planning.
Regarding dependent claims 8-11, the method step of “generating each stage of the subset of stages, wherein each stage of the subset is generated based upon a most-recent previous stage” is a mental process capable of being performed by an orthodontist. Iterative treatment planning based on patient data received at different time points throughout the treatment can be performed by an orthodontist without additional “digital” limitations by using evaluations of “tooth movement limits” and “previously treated patients”, both forms of observable data.
Regarding dependent claim 12, the “treatment guidelines” do not amount to more than judgements capable of being performed mentally by an orthodontist in developing a treatment plan for correction of malocclusions.
Regarding claim 13, the method steps of "receiving a digital 3D model of teeth"; "training a machine learning model that has been trained using historic setups to generate a proposed final or intermediate setup" are mental process capable of being performed in the human mind, including observations, evaluations and judgements. The step of “receiving a digital 3D model of teeth in malocclusion” as recited in claim is performed by an orthodontist mentally observing the patient’s dentition. The step of generating “a proposed final or intermediate setup” is performed by an orthodontist as they mentally observe the patient’s dentition through treatment, predicting intermediate and final tooth arrangements. Further, the training of the machine learning model, calculating loss, mean square error, and a score under the broadest reasonable interpretation, are simply a set of mathematical calculations comparing a fixed group of teeth and non-fixed group of teeth. This calculation can be performed mentally by an orthodontist comparing the positions of the first group of teeth and second group of teeth, without the use of a computer. The addition of weighting factors to the step of calculating loss are merely variables as part of the mathematical calculations. The modification of the limitation “3D model” with “digital” does not direct the claim away from being a mental process capable of being performed in the human mind. Similarly, the limitation of “machine learning model” does not inhibit a human from performing the method step associated with this limitation – proposing teeth arrangements or setups throughout treatment (refer to MPEP 2106.04(a)(2)(III)). Further, the outputting step is not a step of actually manufacturing the devices, but merely a step of digitally generating the final setups for a manufacturing step.
Regarding dependent claim 14, the method step of “generating features from the digital 3D model” is a mental process performed by the orthodontist in labeling or marking certain teeth for the planned treatment prior to initialization of treatment. Again, the modification of the limitation “3D model” with “digital” does not direct the claim away from being a mental process capable of being performed in the human mind.
Regarding dependent claims 15-16, the method steps of using the machine learning model to generate “the proposed setup with one or more fixed” or “pinned teeth” is a mental process an orthodontist can perform mentally, without the addition of a specific software algorithm. An orthodontist is capable of mentally creating a proposed tooth arrangement while pinning or fixing certain teeth based on their evaluation of the treatment stages.
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 and 3-16, 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 to a particular technological environment or field of use (MPEP 2106.04(d)). The “computer-implemented” and “digital 3D model” limitations of claim 1, 3 and 12 are mere instruction to apply the judicial exceptions of these claims to a computer. The addition of the digital model as being from an intra-oral scan is merely extra-solution activity. Similarly, the inclusion of “machine learning” into claims 13-16 is using a generic computer to implement the process. The “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 data manipulation steps incorporated into the machine learning model that has been trained using historic setups is a method of data manipulation according to mathematical algorithms, transforming the data obtained from the patient’s dentition. The additional step of outputting orthodontic setups in claims 1 and 13 is insignificant extra-solution activity that does not amount to a practical step. Furthermore, the “generating” steps of dependent claims 3-12 are simply a transformation of the data received from the patient dentition model of claim 1. For at least these reasons and as claims 1 and 3-16 do not recite additional elements which integrate the judicial exception into a practical application, the abstract mental processes and mathematical algorithms identified for claims 1 and 3-16 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 and 3-16, as set forth above with respect to Step 2A Prong One, the claim 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 “receiving a digital 3D model”, “generating a set of stages of setups from the digital 3D model”, “receiving treatment guidelines for the digital 3D model” and using a “machine learning model” 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”) 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 and 3-16 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 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20130317800 A1), herein referred to as Wu.
Regarding claim 1, Wu discloses a computer-implemented method for generating stages for a portion of orthodontic aligner treatment (Fig. 10; refer to Paragraphs [0123], [0126]-[0127]; a method of adjusting tooth positions in a virtual dental model to generate treatment steps is disclosed), the method (refer to Fig, 10) comprising:
receiving, based on one or more intra-oral 3D scans of teeth, a digital 3D model of the teeth in malocclusion, the digital 3D model comprising a first one or more tooth positions of the teeth (refer to Paragraph [0007]; the patient’s mouth is images with intra-oral scanning technology);
generating a subset of stages of setups among complete set of stages of setups for aligner treatment of the teeth (refer to Paragraphs [0086], [0122], [0127]; after a tooth has moved to a new position according to optimization of the energy function, (interproximal reduction) IPR can be computed; once IPR values are assigned, the teeth are again adjusted in the arch form to reach a desirable result based on a different energy function; the stages generated as part of setup to the initial final position computed before IPR are a subset of stages) wherein generating the subset of stages comprises:
applying a perturb function to the first one or more tooth positions to generate a second one or more tooth positions, wherein applying the perturb function comprises translating or rotating the first one or more tooth positions (1040; refer to Paragraphs [0054], [0085]; per Applicant’s specification, a perturb function is an operation performed to move one or more teeth (refer to Table 1); to move the tooth from the first position to the second position, a transformation (R,T) based on an object function is computed; the transformation (R,T) is a series of translation and rotations);
identifying a first penalty term comprising a root movement metric that measures an amount of root movement associated with a second one or more tooth positions and a root movement weight (1038, 1039; refer to Paragraphs [0077], [0083], [0124]; per Applicant’s specification, a penalty term is understood as a weighted function which computes a value of a metric (refer to Table 1 and Page 4, lines 3-5); the root movement term measures the root movement due to the transformation from a first to second position and weighs this term);
identifying a second penalty term comprising an expansion metric that measures an amount of expansion associated with the second one or more tooth positions and an expansion weight (1038, 1039; refer to Paragraphs [0035] [0049], [0054], [0077],[0084], [0124]; per Applicant’s specification, a penalty term is understood as a weighted function which computes a value of a metric(refer to Table 1 and Page 4, lines 3-5); the tooth is moved from a first position to second position, where the second position corresponds to the expanded archform (316); the transformation (R,T) of the align points computes this expansion movement (metric), which is then weighted as shown below)
applying a constrain function to adjust the second one or more tooth positions (refer to Paragraph [0056]; per Applicant’s specification, a constrain function is an operation performed to adjust tooth positions to meet a defined constraint (refer to Table 1); after teeth are aligned to the archform using the energy function (J), automatic additional adjustment of the second positions are performed based on the defined range or constraint of tooth angulation or upper lateral incisor leveling); and
computing a score (J) of the second one or more tooth positions using the first penalty term and the second penalty term (refer to Paragraph [0077], [0126]; per Applicant’s specification, “computing a score” is understood as an optimization function based on the summation of the penalty terms (refer to Table 1); the energy function (J) sums the penalty terms to compute a score of the final computed target position); and
outputting the subset of stages of setups as a sequence of digital setups for fabrication of orthodontic appliances configured to transition the teeth according to the subset of stages of setups
PNG
media_image1.png
307
877
media_image1.png
Greyscale
Wu does not explicitly disclose the first penalty term as comprising a root torque metric and a root torque weight.
Although this is not explicitly disclosed, Wu discloses in an alternative embodiment that a tooth root can be used as a stabilization point, and in this case anisotropic penalties can be used to allow for selective control of translation and rotation such that undesired tooth rotation can suppressed (refer to Paragraph [0029]). This allows for selective control of the tooth movement (refer to Paragraph [0029]).
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 first penalty term of root movement to be separately weighted root translation and root torque terms to allow for anisotropic penalties to selectively control of rotational tooth movement (refer to Paragraph [0029]).
Further, Wu does not disclose the root movement torque coefficient as configured to penalize root torque and an expansion weight configured to penalize movement associated with the second one or more tooth positions less than a threshold amount (Examiner understands penalizing movement less than a threshold amount as promoting or encouraging ideal expansion in line with page 4, lines 10-15 of the specification).
Although, Wu does not explicitly disclose the root torque weight coefficient and expansion weight coefficient; Wu does disclose the weights to be result effective variables in that the weights can be adjusted to affect the movement of teeth (refer to Paragraphs [0055]), with greater weights indicating that a parameter will be complied with more (refer to Paragraph [0112]). Specific examples are given as assigning a larger weight to teeth where root movement (intrusion and extrusion are not desired) (refer to Paragraph [0055]) and angulation is not desired (refer to Paragraph [0029]), analogous to penalizing root torque, and assigning weights so that tooth movement within a defined treatment step is within a specified tolerance (refer to Paragraph [0031]), analogous to penalizing movement more than or less than a specified amount.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the root torque weight and expansion weight coefficient as taught by Wu, to penalize root torque, and encourage ideal expansion as a matter of routine optimization, as Wu teaches adjusting the weights to effect desired movements on teeth. Further, it has been held that “where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation”. In re Aller, 220 F.2d 454, 456, 105, USPQ 233, 235 (CCPA 1955).
Regarding claim 12, Wu discloses the method of claim 1; Wu discloses, receiving treatment guidelines for the digital 3D model of teeth in malocclusion (refer to Paragraph [0049]; a desired change of an archform is received as part of the treatment plan).
Claim(s) 2-6, and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20130317800 A1), herein referred to as Wu, in view of Akopov et al. (US 20190175303 A1), herein referred to as Akopov.
Regarding claims 2,-3 Wu discloses the method of claim 1, wherein generating the subset of stages (refer to Paragraphs [0086], [0122], [0127]) comprises:
generating the complete set of stages of setups (refer to Paragraphs [0086], [0122]; the complete set of stages of setups are generated from the first (J) and second (K) energy functions);
manufacturing only the subset of stages into corresponding aligners (refer to Paragraph [0127]; a set of appliances from the converged energy function can be fabricated); and
generating a set of stages of setups from the digital 3D model of teeth in malocclusion to a target intermediate setup representing desired movement of the teeth for the portion of a complete treatment (refer to Paragraph [0127]; a number of treatment steps between the preliminary target and final position are generated).
Wu is silent to selecting the subset of stages from the complete set of stages teeth.
Akopov discloses a method of orthodontic treatment planning in the same field of endeavor (refer to Paragraph [0225], Fig. 3), comprising the step of selecting the subset of stages from the set of stages (refer to Paragraph [0225]; the appliances for a treatment plan with a plurality of appliances, which includes a partial treatment plan, can be generated in sets or batches which requires selecting the set or batch of stages to be generated). This allows for generating appliances corresponding to partial treatment plans to address only some of the treatment goals (refer to Paragraph [0225]).
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 a stage of setups as taught by Wu (refer to Paragraphs [0086], [0122], [0127]), with selecting the subset of stages from the set of stages (refer to Paragraph [0225]) as taught by Akopov in order to generate appliances corresponding to partial treatment plans (refer to Paragraph [0225]).
Regarding claim 4, Wu and Akopov disclose the method of claim 3; Wu discloses receiving the target intermediate setup from a user (refer to Paragraph [0123]; the preliminary target position can be manually defined by the user).
Regarding claim 5, Wu and Akopov disclose the method of claim 3; Wu discloses generating the target intermediate setup based upon a desired set of movements of the teeth (refer to Paragraph [0127]; a number of treatment steps between the preliminary target and final position are generated, where the final position represents the desired set of movements).
Regarding claim 6, Wu and Akopov disclose the method of claim 3; in the original embodiment for the energy function (J) , Wu does not disclose generating the target intermediate setup based upon metrics, constraints, or both metrics and constraints relating to movement of the teeth.
In an alternative embodiment, Wu discloses defining tooth movement within a defined treatment step as being within a specified tolerance as part of the tooth movement trajectory to improve patient comfort during treatment (refer to Paragraph [0031]).
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 step of generating the target intermediate setup (refer to Paragraph [0127]), to include metrics or constraints to improve patient comfort (refer to Paragraph [0031]).
Regarding claims 8-10, Wu and Akopov disclose the method of claim 1; Wu is silent to wherein generating the subset of stages comprises sequentially generating the subset of the stages and wherein each stage of the subset is generated based applying a set of desired movements and per-stage tooth movement limits to the a most-recent previous stage.
Akopov further discloses wherein generating the subset of stages comprises sequentially generating the subset of the stages (refer to Paragraphs [0261], [0262]; the generated treatment plan, which is comprised of the subset of stages, determines the stages through a sequential solving process), wherein each stage of the subset is generated based upon applying a set of desired movements and per-stage tooth movement limits to the most-recent previous stage (refer to Paragraphs [0010], [0287]; at each stage of the generated treatment plan, one or more teeth are moved relative to the prior stage; staging constraints are used for tooth movements over a single stage). This method allows for simplifying the leading tooth movement (refer to Paragraph [0261]).
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 a stage of setups as taught by Wu (refer to Paragraphs [0086], [0122], [0127]), with sequentially generating the subset of stages from the set of stages based upon applying a set of desired movements and per-stage tooth movement limits to the most-recent previous stage (refer to Paragraphs [0261], [0262]) as taught by Akopov in order to simplify the leading tooth movements (refer to Paragraph [0261]).
Claim(s) 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ansarri et al. (EP 3620130 A1), herein referred to as Ansarri, in view of Wu et al. (US 20130317800 A1), herein referred to as Wu.
Regarding claim 13, Ansarri discloses a computer-implemented method for generating a setup for orthodontic aligner treatment (Fig. 6; refer to Paragraph [0062]), the method (Fig. 6) comprising:
training a machine learning model using historic setups (refer to Paragraphs [0053]-[0054], Fig. 5; a deep neural network is trained on already treated patient data, which includes their original scans (initial setup) and final setups in the form of tooth transformations or final positions from a scan after treatment), wherein training the machine learning model comprises calculating loss by (refer to Paragraph [0059]; a loss function is employed during training of the deep neural network):
calculating a second mean-squared error (MSE) of a difference in tooth positions between ground-truth positions of a second group of teeth and positions of the first group of teeth generated by the machine learning model (refer to Paragraphs [0059]-[0060]; the loss function calculates an error between the desired output (training target) and predicted output (neural network output at the specific moment in time during training) using mean squared error) ;
the second group of teeth comprising one or more non-fixed or one or more non-pinned teeth (refer to Paragraph [0004]; the orthodontic treatment results in a patient’s teeth being moved from an initial position to a desired position for alignment);
receiving, based on one or more intra-oral 3D scans of teeth, a digital 3D model of teeth in malocclusion (633) (601; refer to Paragraph [0062], Fig. 5; 3D data sets derived from an intraoral scan (633) are input);
using the machine learning model to generate a proposed final (659) or intermediate setup (663) for the digital 3D model of teeth in malocclusion (633) (617, 619; refer to Paragraphs [0064]-[0065]; step 617 determined final tooth position (633) using the deep neural network trained with the method of Fig. 5; the intermediate tooth positions (633) are determined in step 619 based on the final positions (659)); and
outputting the proposed final (659) or intermediate setup (663) as a digital setup for fabrication of orthodontic appliances configured to transition the teeth according to the proposed final (659) or intermediate setup (663) (621; refer to Paragraphs [0026], [0066], Figs. 5 and 11; 3D models of the aligners are determined for manufacturing)
Ansarri further discloses using dento-physical properties for a custom loss function in training the machine learning model which includes defining maximum movement of individual teeth (refer to Paragraphs [0016], [0018], [0061); however, Ansarri does not disclose defining the maximum movement as zero, indicating the teeth are not to be moved. Further, Ansarri is silent to calculating a first mean-squared error (MSE) of a difference in tooth positions between ground-truth positions of a first group of teeth and positions of the first group of teeth generated by the machine learning model, the first group of teeth comprising a first one or more fixed teeth or a first one or more pinned teeth and calculating a total loss by adding the second MSE to a product of the first MSE and a weighting factor;
Ansarri can be modified to meet these limitation(s) as follows:
Modify the MSE loss function to calculate MSE loss separately for two groups of teeth, movable (non-fixed, non-movable) and non-movable (fixed or pinned) teeth, summing these two losses together in the same manner as Wu, as Wu discloses a method of adjusting tooth position in a virtual tooth dental model using a weighted energy function (refer to Paragraph [0024]). The weighted energy function separately calculates cost or loss of fixed or pinned teeth and non-fixed/non-pinned teeth and sums them together for a total cost function (K) (refer to Paragraphs [0096]-[0100], [0113]; Applicant’s specification states a pinned tooth is a tooth that cannot be moved from a certain position, and a fixed tooth cannot be moved from its original position (se Page 7, lines 9-11); an anchorage or pinned tooth can be moved manually, but will not automatically follow adjustments to the jaw, a non-movable tooth equates to a fixed tooth, and a normal tooth is a tooth that is non-fixed/non-pinned; the weighted movements of the non-movable, anchorage and normal teeth are summed in a cost function).
A person of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to make the above modification(s) because separately calculating loss allows the different types of teeth to be weighted differently, while still calculating a total loss (K) representative of the final target position (refer to Paragraphs [0112]-[0113], [0126]).
Modify the MSE loss equation to weight the first MSE in the same manner as Wu, as Wu further discloses that the non-movable and anchorage teeth are assigned a high weighting factor (refer to Paragraphs [0112]-[0113]).
A person of ordinary skill in the art prior to the effective filing date of the claimed invention would have been motivated to make the above modification(s) to ensure the designated unmoved teeth remain unmoved when generating the final target position (refer to Paragraphs [0112]-[0113], [0126]), the same purpose of setting different weights as disclosed by Applicant (refer to page 7, lines 13-15 of specification).
Regarding claim 14, Ansarri and Wu disclose the method of claim 13; Ansarri discloses generating features from the digital 3D model of teeth (633) before using the machine learning model to generate the proposed final (659) or intermediate setup (663) (505; refer to Paragraphs [0055]-[0056], Fig. 6; the IOS scans are segmented in step 505 to compute data of interest).
Regarding claims 15-16, Ansarri and Wu disclose the method of claim 13; Ansarri discloses wherein using the machine learning model comprises generating the proposed final or intermediate setup.
Ansarri is silent to the generating step including a second one or more fixed and a second one or more pinned teeth.
Wu further discloses generating a proposed final setup for a second one or more fixed and pinned teeth (refer to Paragraphs [0096]-[0100], [0102], [0126]; the energy function converges to define a final target position based on the cost/energy equation that accounts for non-movable and anchorage teeth, equivalent to fixed and pinned teeth). This presents final target positions for the patient that accurately account for teeth that are not to be moved during treatment.
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 generating step of Ansarri to include fixed and pinned teeth as taught by Wu in order to present final target positions for the patient that accurately account for teeth that are not to be moved during treatment.
Claim(s) 7 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20130317800 A1), herein referred to as Wu, in view of Akopov et al. (US 20190175303 A1), herein referred to as Akopov, as applied to claims 3 and 8 above, and further in view of Salah et al. (US 20190125274 A1), herein referred to as Salah.
Regarding claims 7 and 11, Wu and Akopov disclose the method of claims 3 and 8, but are silent to generating the target intermediate setup based upon a set of target intermediate setups from previously treated patients.
Salah discloses a method of dental treatment planning in the same field of endeavor for the correction of malocclusions (refer to Paragraph [0001]) comprising the step of generating a subset of stages of setup from the digital 3D model of teeth in malocclusion to a target intermediate setup representing desired movement of the teeth (refer to Paragraphs [0057], [0058]; potential orthodontic treatments which consist of dental situations or setup stages are determined as part of the method, including a target intermediate setup), with the target intermediate setup based upon a set of target intermediate setups from previously treated patients (refer to Paragraphs [0211], [0212], [0222]; the intermediate tooth setup of a current patient is generated based on the method of statistical analysis utilizing all historical patient data at previous time points, including previous target intermediate setup). Generating the current patient intermediate target setup based on previously treated patients is advantageous in that it allows a dental practitioner to understand a future dental situation (tooth arrangement) beyond what is visually apparent to improve the efficacy of orthodontic treatment (refer to Paragraphs [0044]-[0045]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to have modified the method of generating the target intermediate setup of current patients as disclosed by Wu and Akopov with the method of using intermediate setups from historical patient data as disclosed by Salah to improve the efficacy of the orthodontic treatment (refer to Paragraphs [0044]-[0045]).
Response to Arguments
Applicant's arguments filed 12/30/2025 have been fully considered but they are not persuasive.
The 112(a) rejection of new matter is withdrawn in view of the newly submitted claim amendments.
Regarding the arguments that the amended claim 1 overcomes the 101 rejection, the step in claims 1 and 13 of "outputting the subset of stages of setups to a manufacturing process for tray manufacturing" is not a manufacturing step, but merely insignificant extra solution activity. An orthodontist manually sends the models to be manufactured as part of the treatment process. Further, the claims merely tie the data to a technical process, without improving the manner in which the computer operates. Performing a manual process on a computer does not amount to more than the judicial exception. Examiner recommends amending the claims to include the manufacturing step as demonstrated in claim 2.
Applicant’s arguments with respect to claim(s) 1 -13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new grounds of rejection rely on Wu for independent claim 1 and a combination of Ansarri and Wu for independent claim 13.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ADRIENA J WEBB LYTTLE/Examiner, Art Unit 3772
/EDELMIRA BOSQUES/Supervisory Patent Examiner, Art Unit 3772