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 .
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 1-8, 10-18, and 20-22 is 02/22/2023.
Drawings
The drawings are objected to because:
Figs. 12A-12E do not meet the drawing standards under 37 C.F.R. 1.84(l) .
Fig. 7 has "732" twice
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 1422, see Paragraph [0214]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0049], "patient 150's teeth" should be "patient's (150) teeth".
Appropriate correction is required.
Claim Objections
Claims 14-15, and 20 are objected to because of the following informalities:
Claim 14, lines 2-3 state "a threshold score value" as a limitation that is later recalled in Claim 15, line 4. Examiner recommends modifying "a threshold score value" to "a first threshold score value".
Claim 20, line 8 is missing “to” between “relative” and “teeth”.
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 1-8, 10-18, and 20-22 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 of determining a teeth setup score and a system for scoring a teeth aligner setup, which fall into the categories of a process and product.
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 steps of generating a teeth aligner setup, generating a movement report and determining a setup score using a machine learning model are abstract ideas, steps capable of being performed in the human mind. An orthodontist is capable of generating a setup of teeth in a desired position for the aligner with a movement report, and using the movement report to assign a score to the proposed setup. The use of a machine learning model to perform the abstract idea is simply a means of using mathematical algorithms to perform the abstract idea.
Regarding claims 2-7, 10-16, the abstract ideas from claim 1 are further defined and therefore also fall into the category of abstract ideas.
Regarding claim 8, the step of generating instructions to cause a machine to manufacture a dental appliance is not a practical manufacturing step, but a mental process of generating data for the fabrication machine, and therefore is also an abstract idea.
Regarding claim 17, the step of training the machine learning model is another process using mathematical algorithms to execute the abstract idea of scoring the teeth setup.
Regarding claim 18, the system for scoring a teeth aligner setup comprises a computing device with processors and memory configured to execute the abstract ideas of generating a teeth aligner setup, generating a movement report and determining a setup score using a machine learning model. While a processor and memory are recited as a part of the system, the actual steps being performed by the processor are merely abstract ideas, mental processes and mathematical concepts, while the memory is acting as a data storage for the processor.
Regarding claims 20-22, the abstract ideas from claim 18 are further defined and therefore also fall into the category of abstract ideas.
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 1-8, 10-18, and 20-22, 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)). Using a computer system, impression system, imaging system or motion capture system, and outputting data in a graphical user interface (GUI) are steps that amount to insignificant extra solution activity and do not amount to an inventive concept, particularly when the activity is well-understood and conventional. For at least these reasons and as claims 1-8, 10-18, and 20-22 do not recite additional elements which integrate the judicial exception into a practical application, the abstract mental processes and mathematical concepts identified for claims 1-8, 10-18, and 20-22 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 1-8, 10-18, and 20-22, 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 generating a teeth aligner setup, generating a movement report and determining a setup score 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. 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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, and 10-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nguyen et al. (US 20210259808 A1).
Regarding claim 1, Nguyen et al. discloses a method (Fig. 2) for determining a teeth setup score for a teeth aligner setup of a patient (refer to Paragraph [0004]; a scoring function generates and rates final setups), the method comprising:
receiving, by a computer system, oral scan data for a patient (12), wherein the oral scan data includes at least one of (i) a dental impression generated by a dental impression station (refer to Paragraph [0012]; 3D models of teeth are generated from scans of impressions of teeth; it is inherent that an impression based system is used to form a tooth impression), (ii) image data of the patient's mouth generated by an image capture system (refer to Paragraph [0012]; the digital 3D models of teeth are from intraoral scans), and (iii) motion data of the patient's jaw movement generated by a motion capture system;
generating, by the computer system, a teeth aligner setup for the patient based on the oral scan data (refer to Paragraph [0013]; the algorithm used to generate the final setup of the teeth takes the segmented digital 3D model of the teeth in the maloccluded position as an input);
simulating, by the computer system, movement of one or more teeth of the patient using the teeth aligner setup (refer to Paragraph [0059]; the final setup design is used to generate intermediate setups that incrementally move the teeth from an initial maloccluded state to the final state);
generating, by the computer system, a movement report based on the simulated movement (refer to Paragraphs [0024], [0035]; the perturb state (27) applies translations and rotations to a tooth or set of teeth, equivalent to a movement report);
determining, by the computer system, a teeth setup score for the teeth aligner setup based on providing the movement report as input to a machine learning model (refer to Paragraphs [0031], [0035], Fig. 2; the output (25) of the perturbed state (27), which represents the physical transformations of the teeth, equivalent to the movement report, is input into the scoring function (23), a machine learning classifier); and
returning, by the computer system, the teeth setup score for presentation in a graphical user interface (GUI) display of a computing device of a relevant user (refer to Paragraph [0162]; scores for machine learning classifier output is displayed).
Regarding claim 5, Nguyen et al. discloses the method of claim 1, further comprising generating, by the computer system, a digital dental model for the patient based on the oral scan data (refer to Paragraph [0012]; the digital images are processed to generate a digital 3D model representing the scanned teeth).
Regarding claim 10, Nguyen et al. discloses the method of claim 1, wherein simulating, by the computer system, movement of one or more teeth of the patient using the teeth aligner setup comprises simulating movement of the patient's teeth from a pre-treatment state to a post-treatment state (refer to Paragraphs [0059], [0162]; the final setup design is used to generate intermediate setups that incrementally move the teeth from an initial maloccluded state to the final, ideal, state), wherein the pre-treatment state is a current state of the patient's teeth according to the oral scan data (refer to Paragraphs [0012], [0013]; the initial, maloccluded, state is obtained by segmenting the digital 3D model obtained from the scan data) and the post-treatment state is achieved using the teeth aligner setup (refer the Paragraph [0059]; the final state (28) is generated from the scored final setup which meets the termination criteria).
Regarding claim 11, Nguyen et al. discloses the method of claim 1, wherein the movement report includes at least one of: mesial- distal translation data, buccal-lingual translation data, occlusal-gingival translation data, rotation change data (refer to Paragraphs [0035], [0036]; many perturbations can be applied to the original state, by rotation in multiple directions), tip change data, and torque change data.
Regarding claim 12, Nguyen et al. discloses the method of claim 1, wherein the movement report includes data about at least one degree of freedom of movement for each tooth in the oral scan data for the patient (refer to Paragraph [0035]; perturbations allow physical transformations for a set of teeth including rotation and translation, where rotation and translation each represent at least one degree of freedom).
Regarding claim 13, Nguyen et al. discloses the method of claim 12, further comprising applying, by the computer system, a coordinate system to each tooth in the oral scan data for the patient (refer to Paragraph [0062], Fig. 3; each tooth has a coordinate system), wherein the at least one degree of freedom of movement for each tooth is determined relative the coordinate system applied to the tooth (refer to Paragraph [0035]; the perturbation state (27) is based on 3D translations and rotations of the individual tooth, wherein that individual tooth is defined by its own coordinate system).
Regarding claim 14, Nguyen et al. discloses the method of claim 1, further comprising:
determining, by the computer system, whether the teeth setup score exceeds a threshold score value (refer to Paragraph [0145]; a score with an absolute value above 1 indicates that the metric used to compute the score for the teeth setup lies outside the ideal range);
generating, by the computer system and based on a determination that the teeth setup score exceeds the threshold score value, at least one recommendation for adjusting the teeth aligner setup (refer to Paragraph [0140]; the scores used to rate final setups inform the clinician or automated system about which metrics are currently in agreement with good final setups, suggesting that they do not need to be improved further, and which metrics are not in agreement with good final setups, suggesting that they need to be further refined), wherein adjusting the teeth aligner setup causes at least one change in teeth movement that results in a lower teeth setup score than the teeth setup score that exceeds the threshold score value (refer to Paragraphs [0035], [0038]; in response to the automated system being informed of the metrics that need to be improved further, the state (25) is updated through perturbing, which is the physical transformation through translation and rotation of the teeth to decrease the value of the scoring function); and
returning, by the computer system, the at least one recommendation for presentation in the GUI display of the computing device (refer to Paragraphs [0162], [0164]; the machine learning classifier alerts the clinician on a display interface of which metrics need further refinement).
Regarding claim 15, Nguyen et al. discloses the method of claim 14, further comprising automatically adjusting, by the computer system and responsive to determining that the teeth setup score exceeds a second threshold score value, the teeth aligner setup based on the at least one recommendation, wherein the second threshold score value is higher than the threshold score value (refer to Paragraphs [0025], [0038]; the final state algorithm functions to decrease the value of the scoring function through selecting scoring functions, perturbing, and selecting states that decrease the value of the scoring function; in response to the score of a proposed final setup exceeding the previous setup’s score (a second threshold value), where the previous setup’s score exceeded the threshold value, the system automatically adjusts the updated state (25) to ultimately reduce the score).
Regarding claim 16, Nguyen et al. discloses the method of claim 1, wherein generating, by the computer system, a teeth aligner setup for the patient based on the oral scan data comprises:
segmenting each tooth in the oral scan data (refer to Paragraph [0014]; tooth positions can be obtained by segmenting a digital 3D model);
identifying critical landmarks for each segmented tooth (refer to Paragraph [0015]);
generating the teeth aligner setup based on the critical landmarks (refer to Paragraph [0015]; the critical landmarks are input to the final setup algorithm);
defining a wire plane through the segmented teeth in the teeth aligner setup (refer to Paragraphs [0009], [0014], Fig. 4; the arch form coordinate system is defined from the segmented tooth positions);
applying a coordinate system to each segmented tooth in the teeth aligner setup (refer to Paragraph [0062], Fig. 3; each tooth has a defined coordinate system); and
aligning each coordinate system with a base of each segmented tooth, wherein the base of the segmented tooth is defined by the wire plane (refer to Paragraphs [0014], [0061], Figs. 3-4; tooth positions are represented using position and orientation in the arch form coordinate system; Fig. 4 shows alignment of the tooth (t) to the archform, where the base is defined by point (p)).
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) 2-4, 8, 18 and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (US 20210259808 A1) in view of Sporbert et al. (US 20050271996 A1).
Regarding claim 2, Nguyen et al. discloses the method of claim 1, wherein returning, by the computer system, the teeth setup score (refer to Paragraph [0162]; scores for machine learning classifier output is provided is displayed) further comprises generating instructions that, when executed by the computing device (10), cause the computing device (10) to:
output, in the GUI display (16), the teeth setup score and the teeth aligner setup (refer to Paragraph [0164]; the machine learning classifier output is displayed alongside the final setup);
receive user input indicating at least one modification to the teeth aligner setup (refer to Paragraph [0161]; the technician or clinician can manually adjust tooth movements); and
transmit the user input to the computer system (10) (refer to Paragraphs [0012], [0161]; an input device (18) receives user commands).
Nguyen et al. does not disclose wherein the teeth aligner setup is displayed as visually overlaying a digital dental model of the patient's mouth.
Sporbert discloses a method of orthodontic treatment planning in the same field of endeavor (refer to Paragraph [0003]), with the method comprising the step of the teeth aligner setup displayed as visually overlaying a digital dental model of the patient's mouth (refer to Paragraph [0176], Fig. 17; the display shows the original position of the teeth and the new position with color coding). Displaying both states of the tooth arrangement allows a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
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 Nguyen et al. with the step of overlaying the teeth aligner setup and digital dental model as taught by Sporbert et al. in order to allow a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
Regarding claim 3, the combination of Nguyen et al. and Sporbert et al. discloses the method of claim 2, with Nguyen et al. further disclosing the method comprising:
receiving, by the computer system (10) and from the computing device (10), the user input (refer to Paragraph [0012]; the input device (18) receives user commands);
adjusting, by the computer system (10), the teeth aligner setup based on the user input (refer to Paragraphs [0023], [0040], [0161], Fig. 2; the technician or clinician can manually adjust tooth movements, which are inputs to the final setup algorithm that automatically generates an optimized final setup based on the input data);
re-simulating, by the computer system (10), movement of the patient's teeth using the adjusted teeth aligner setup (refer to Paragraph [0059]; the final setup design is used to generate intermediate setups that incrementally move the teeth from an initial maloccluded state to the final state; by modifying the final setup, the intermediate set-ups are also modified); and
determining, by the computer system, an updated teeth setup score for the adjusted teeth aligner setup (refer to Paragraph [0038], Fig. 2; a score is computed for each updated state (25), including the updated state (25) that is ultimately output as the final state (28)).
Regarding claim 4, the combination of Nguyen et al. and Sporbert et al. discloses the method of claim 2, with Nguyen et al. further disclosing wherein outputting the teeth setup score comprises outputting information about at least one tooth according to the movement report that had a greatest impact on the determined teeth setup score (refer to Paragraphs [0031], [0060], [0145], [0162], Table 1; the overall setup score is a weighted summation of the individual metrics; scores for individual metrics are also output which describe the amount of deviation from an ideal setup for various characteristics of tooth movement; the metrics with the greatest amount of deviation have greater absolute values, thereby having the greatest impact on the determined setup score).
Regarding claim 8, Nguyen et al. discloses the method of claim 1, but is silent to generating, by the computer system (10), instructions that, when executed by a rapid fabrication machine, causes the rapid fabrication machine to manufacture a teeth aligner dental appliance.
Sporbert discloses a method of orthodontic treatment planning in the same field of endeavor (refer to Paragraph [0003]), with the method comprising generating, by the computer system (100), instructions that, when executed by a rapid fabrication machine, causes the rapid fabrication machine to manufacture a teeth aligner dental appliance (refer to Paragraph [0115], Fig. 1; the initial and final tooth positions are used to derive data sets representing intermediate tooth positions, which are used to fabricate aligners). Generating instructions for fabrication allows the treatment plan to be transmitted to one or more appliance manufacturers (refer to Paragraph [0143]).
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 Nguyen et al. with the step of generating fabrication instructions as taught by Sporbert et al. in order to allow the treatment plan to be transmitted to one or more appliance manufacturers (refer to Paragraph [0143]).
Regarding claim 18, Nguyen et al. discloses a system (10) for scoring a teeth aligner setup for a patient (refer to Paragraphs [0004], [0012], Fig. 1; a system (10) for rating final setups is disclosed), the system (10) comprising:
a computing device (10) having processors (20) and memory (refer to Paragraph [0012]; the system (10) receives digital models of teeth, meaning it must have a memory for storing the received data; further all computing devices inherently have a memory), wherein the computing device (10) is configured to:
a digital dental model of a patient, wherein the digital dental model represents a post-treatment teeth setup for the patient (refer to Paragraph [0026]; an initial final setup of the patient’s teeth is created during initialization (22));
generate a teeth aligner setup, wherein the teeth aligner setup represents a pre-treatment teeth setup for the patient (refer to Paragraph [0012]; the digital images are processed on the computing (10) device to generate a 3D model representing the scanned teeth in malocclusion (21));
generate for each tooth in the teeth aligner setup, a coordinate system (refer to Paragraph [0062], Fig. 3; each tooth has a coordinate system);
transmit, to a computing system (10), a request for a teeth setup score based on the difference between the teeth aligner setup the digital dental model (refer to Paragraphs [0014], [0135], Table 1, Fig. 2; the method of Fig. 2 computes a score based on the initial maloccluded state of the teeth (21) and the initialized state that represents the initial final setup (22); this score is computed based on the metrics, one such metric is the displacement between the maloccluded state and the given state representing a final setup);
receive, from the computing system (10), data indicating at least the teeth setup score (refer to Paragraph [0162]; scores for machine learning classifier output are displayed), wherein the teeth setup score is determined, by the computing system (10), based on (i) simulating movement of teeth in the teeth aligner setup to teeth in the digital dental model representing the post-treatment teeth setup for the patient to generate a teeth movement report (refer to Paragraphs [0024], [0035]; the perturb state (27) applies translations and rotations to a tooth or set of teeth, simulating the movement of the teeth to an updated state (25); the physical transformations produced by the perturb state are equivalent to a movement report) and (ii) providing the teeth movement report as input to a machine learning model that was trained to generate the teeth setup score (refer to Paragraphs [0031], [0035], [0146], Fig. 2; the output (25) of the perturbed state (27), which represents the physical transformations of the teeth, equivalent to the movement report, is input into the scoring function (23), a trained machine learning classifier); and
present, in the GUI display (16), a different portion of the digital dental model wherein the different portion of the digital dental model is determined based on the teeth movement report (refer to Paragraph [0038], [0164]; in light of Applicant’s specification (Paragraphs [0012]-[0013]), Examiner understands “a different portion of the digital dental model” as the part of the digital model that has changed according to the simulation; the state (22) is repeatedly updated by the perturb state (27), which represents the physical transformations of the teeth, equivalent to the movement report, to generate an optimized final setup, where that optimized setup is displayed); and
present, in the GUI display the teeth setup score (refer to Paragraph [0164]; the machine learning classifier output is displayed).
Nguyen et al. is silent to presenting the digital dental model in a graphical user interface (GUI) display (16) and presenting a coordinate system for each tooth in the teeth aligner setup in the GUI display (16). However, Nguyen et al. explicitly discloses generating the digital model (refer to Paragraph [0026]) and coordinate system (refer to Paragraph [0062]). Nguyen et al. also discloses the use of a display device (16) (refer to Paragraph [0012]). Therefore, it would have been obvious to one of ordinary skill in the art to display the digital dental model and coordinate systems using the provided display device. Furthermore, where the only difference between a prior art product and a claimed product is printed matter that is not functionally related to the product, the content of the printed matter will not distinguish the claimed product from the prior art. MPEP § 2112.01-III.
Nguyen et al. is silent to presenting in the GUI display (16) a teeth aligner setup as a graphical visual overlaying at least a portion of the digital dental model.
Sporbert discloses a method of orthodontic treatment planning in the same field of endeavor (refer to Paragraph [0003]), with the method comprising the step of the teeth aligner setup displayed as visually overlaying a digital dental model of the patient's mouth (refer to Paragraph [0176], Fig. 17; the display shows the original position of the teeth and the new position with color coding). Displaying both states of the tooth arrangement allows a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
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 Nguyen et al. with the step of overlaying the teeth aligner setup and digital dental model as taught by Sporbert et al. in order to allow a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
Further, Nguyen et al. is silent to the request for a teeth setup score being based on the overlay of the teeth aligner setup and the digital dental model, instead basing the teeth setup score on a coordinate point match difference (refer to Paragraphs [0014], [0135], Table 1, Fig. 2).
Sporbert discloses that the visual overlay of the digital model and teeth aligner setup represents the difference between the two models (refer to Paragraph [0176], Fig. 17; the tooth movement represents the difference between the initial and final tooth arrangements and is indicated by overlaying the two models). As Sporbert teaches the overlay of the two models as an equivalent method of modeling the difference between the initial and final tooth models, 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 measuring the difference between models as taught by Nguyen et al. with the method of overlaying the digital dental models as taught by Sporbert et al. to allow a user to visualize the tooth movement that will occur (refer to Paragraph [0176], Sporbert).
Nguyen et al. is silent to the teeth aligner setup visually overlaying a different portion of the digital dental model.
Sporbert et al. further discloses modifying the proposed set-up during the predetermined steps, wherein modifications made in any one of the one predetermined steps are carried over to subsequent steps (refer to Paragraph [0059]) and wherein the original position can be displayed with the a different portion of the digital dental model (refer to Paragraph [0176]; in light of Applicant’s specification (Paragraphs [0012]-[0013]), Examiner understands “a different portion of the digital dental model” as the part of the digital model that has changed according to the simulation; the original position is displayed with the new position at any time).
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 Nguyen et al. with the step of overlaying the teeth aligner setup with a different portion of the digital dental model as taught by Sporbert et al. in order to allow a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
Regarding claim 20, the combination of Nguyen et al. and Sporbert et al. discloses the system of claim 18, with Nguyen et al. further disclosing wherein the computing device (10) is further configured to:
receive second user input indicating at least one adjustment to the teeth aligner setup based on the teeth setup score (refer to Paragraph [0161]; the technician or clinician can manually adjust tooth movements based on the score which indicates which metrics require further improvement);
transmit the second user input to the computing system (10) (refer to Paragraphs [0012], [0161]; an input device (18) receives user commands);
receive, from the computing system (10), data indicating (i) an adjusted teeth aligner setup based on the user input (refer to Paragraphs [0023], [0040], [0161], Fig. 2; the technician or clinician can manually adjust tooth movements, which are inputs to the final setup algorithm that automatically generates an optimized final setup (28) based on the input data) and (ii) an updated teeth setup score based on re-simulating movement of the teeth in the adjusted teeth aligner setup relative the teeth in the digital dental model (refer to Paragraph [0038], Fig. 2; a score is computed for each updated state (25), including the updated state (25) that is ultimately output as the final state (28)); and
present, in the GUI display (16), the adjusted teeth aligner setup and the updated teeth setup score (refer to Paragraph [0164]; the machine learning classifier output is displayed alongside the final setup).
Nguyen et al. does not disclose wherein the teeth aligner setup is displayed as visually overlaying a digital dental model of the patient's mouth.
Sporbert discloses the teeth aligner setup displayed as visually overlaying a digital dental model of the patient's mouth (refer to Paragraph [0176], Fig. 17; the display shows the original position of the teeth and the new position with color coding). Displaying both states of the tooth arrangement allows a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
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 Nguyen et al. with the step of overlaying the teeth aligner setup and digital dental model as taught by Sporbert et al. in order to allow a user to visualize the tooth movement that will occur (refer to Paragraph [0176]).
Regarding claim 21, the combination of Nguyen et al. and Sporbert et al. discloses the system of claim 18, with Nguyen et al. disclosing wherein the teeth setup score is presented in at least one of (i) another GUI display (refer to Paragraph [0164]; the machine learning classifier output is displayed in a display interface alongside the final setup) and (ii) a pop-out window overlaying at least a portion of the GUI display.
Regarding claim 22, the combination of Nguyen et al. and Sporbert et al. discloses the system of claim 18, with Nguyen et al. further disclosing wherein the computing device (10) is further configured to present, in the GUI display (16), at least one of:
(i) a recommendation of whether the teeth aligner setup should be used for the patient, the recommendation being based on whether the teeth setup score satisfies threshold alignment criteria (refer to Paragraphs [0140], [0145], [0162], Table 1; the scores inform clinicians which metrics are in not in agreement with good final setups, thereby not meeting threshold criteria, wherein the metrics represent various movements of the patient’s teeth), (ii) a difficulty level associated with performing an alignment procedure with the teeth aligner setup, the difficulty level being a string value that corresponds to a numeric value of the teeth setup score, and (iii) a score explanation indicating at least one movement associated with at least one tooth of the teeth aligner setup that satisfied threshold movement criteria (refer to Paragraphs [0140], [0145], Table 1; the scores inform clinicians which metrics are in agreement with good final setups, thereby meeting threshold criteria, wherein the metrics represent various movements of the patient’s teeth).
Claim(s) 6-7, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al. (US 20210259808 A1) in view of Borovinskih et al. (US 20130231900 A1)
Regarding claims 6-7, Nguyen et al. discloses the method of claim 1, wherein the teeth setup score indicates an amount of translation that is in agreement or deviates from ideal population values (refer to Paragraphs [0134], [0140]), with a lower absolute value indicating greater alignment with the ideal value, and a higher absolute value indicating less alignment with the ideal value (refer to Paragraph [0145]); however, Nguyen et al. does not correlate the scores to a difficulty level wherein a higher teeth setup score indicates a greater difficulty level with performing an alignment procedure on the patient with the teeth aligner setup and a lower teeth setup score indicates a lower difficulty level with performing the alignment procedure on the patient with the teeth aligner setup.
Borovinskih et al. discloses a method of determining treatment difficulty in the same field of endeavor (refer to Paragraph [0001]), wherein the amount of translation indicates a difficulty level that corresponds to performing an alignment procedure on the patient with the teeth aligner setup (refer to Paragraphs [0030]- [0031]; the dental treatment difficulty is based on the average change in position) wherein a higher translation score indicates a greater difficulty level with performing an alignment procedure on the patient with the teeth aligner setup and a lower teeth setup score indicates a lower difficulty level with performing the alignment procedure on the patient with the teeth aligner setup (refer to Paragraphs [0032]-[0033]; the difficulty levels are assigned colors with a higher difficulty level associated with a greater average translation, and a lower difficulty level associated with a lower average translation). Estimating the treatment difficulty aids in predicting and/or adjusting a treatment plan (refer to Paragraph [0013]).
As Nguyen et al. teaches the teeth setup score indicating an amount of translation, and Borovinskih et al. teaches correlating an amount of translation to a difficulty level, 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 scoring a teeth setup based on translation as taught by Nguyen et al. with the method of using difficulty levels to indicate the amount of translation as taught by Borovinskih et al. in order to aid in predicting and/or adjusting a treatment plan (refer to Paragraph [0013]).
Regarding claim 17, Nguyen et al. discloses, the method of claim 1, wherein the machine learning model was trained, by the computer system (10), (refer to Paragraph [0146]; the machine learning classifier is trained to rate setups) using a process comprising:
receiving training data of teeth aligner setups for a plurality of patients (refer to Paragraph [0146]; the training data comprises a population of maloccluded and final setups);
annotating the training data with attributes that correspond to the scores (refer to Paragraph [0146], Table 1; the metrics described in Table 1 are used to calculate the score are annotated with the final setups);
training the model to score a teeth aligner setup based on the annotated training data (refer to Paragraph [0146]; the machine learning classifier is trained to rate setups); and
returning the model for runtime execution (refer to Paragraph [0031], Fig. 2; the trained machine learning classifier is used to compute the score (23)).
Nguyen et al. is silent to the annotated scores of the training data being difficulty scores.
Borovinskih et al. discloses a method of determining treatment difficulty in the same field of endeavor (refer to Paragraph [0001]), wherein metric based attributes correspond to a difficulty score (refer to Paragraphs [0030]- [0031]; the dental treatment difficulty is based on the average change in position). Estimating the treatment difficulty aids in predicting and/or adjusting a treatment plan (refer to Paragraph [0013]).
As Nguyen et al. teaches the teeth setup score being a metric based attribute, and Borovinskih et al. teaches correlating a metric based attribute to a difficulty level, 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 training a machine learning model as taught Nguyen et al. with the method of using difficulty levels as taught by Borovinskih et al. in order to aid in predicting and/or adjusting a treatment plan (refer to Paragraph [0013]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chekhonin et al. (US 20200306011 A1) discloses ranking teeth setups based on scoring (refer to Paragraph [0369]).
Li et al. (US 20200085546 A1) discloses a method of scoring teeth setups using a machine learning model (refer to Paragraph [0034]).
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/ADRIENA J WEBB LYTTLE/Examiner, Art Unit 3772
/THOMAS C BARRETT/SPE, Art Unit 3799