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 .
Response to Arguments
Applicant’s arguments, see Remarks filed on 05/04/2026, with respect to claims 1-2, 4-14, and 17-28 have been fully considered. The Examiner withdraws the rejections of claim 20 under 35 U.S.C. 101 presented in the previous Office Action, since the amendments added a practical step to the claims (i.e. fabricating orthodontic appliances in accordance with the updated orthodontic treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated orthodontic treatment plan).
Upon careful consideration the Examiner finds that the claims are not patentable over the prior art of record. Applicant’s arguments against the rejections in view of the prior art of record have been fully considered, but are not persuasive, as they do not address the new grounds of rejection and/or interpretation below necessitated by Applicant’s amendments.
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C.
103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
Claim(s) 1-2, 4-14, 17-19 and 21-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuo (US 20150132708 A1) in view of Guotu LI (US 20200085546 A1), further in view of Cramer (US 20200022783 A1), further in view of Kopelman (US 11432908 B2).
Regarding claim 1, Kuo discloses a method for orthodontically treating a patient's teeth (Abstract), the method comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of the patient's teeth ([0102]); generating a first final 3D model of a first final arrangement of the patient's teeth based on the initial 3D model of the initial arrangement of the patient's teeth ([0104]); extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth (since digital models of entire teeth are produced from the raw data, including measured or extrapolated hidden surfaces and root structures [0103] and the desired final position of the teeth can be can be calculated from basic orthodontic principles, or can be extrapolated computationally from a clinical prescription (step 130) … The result of this step is a set of digital data structures that represents an orthodontically correct repositioning of the modeled teeth relative to presumed-stable tissue. The teeth and tissue are both represented as digital data [0104]).
Determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria based on the comparing (since the visual image interface can be presented to the user without any descriptions or labels to avoid any pre-conceived biases associated with the label and the user then identifies where the patient's dentition condition falls within a range of reference conditions depicting malocclusion and selects the image that either best represents the patient [0172]).
And responsive to determining that the first final arrangement of the patient's teeth satisfies the one or more outlier criteria (since the system is a data driven analyzers which may incorporate a number of models such as parametric statistical models, non-parametric statistical models, clustering models, nearest neighbor models, regression methods, and engineered (artificial) neural networks [0074] that are capable of determining that the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria). Additionally, the Examiner notes that the system enables statistically significant comparisons to be made between two or more populations of cases allowing a feedback mechanism that enables either the clinician or the manufacturer the ability to optimize the product/treatment design and usage based on performance data from a significantly large sample size using objective measurable data; therefore, allows the determination that the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria by statistically significant patterns of different treatment outcomes achieved by different clinicians for comparable patients (see [0087]-[0089]).
Performing the following comprising: classifying an orthodontic treatment plan comprising the first final 3D model of the first final arrangement of the patient's teeth as a clinical risk ([0089]); generating a second final 3D model of a second final arrangement of the patient's teeth; generating an updated orthodontic treatment plan based on the second final 3D model of the second final arrangement of the patient's teeth (since an updated orthodontic treatment plan is achieved based on the desired and intended end result of orthodontic treatment, see [0104] and [0105]). And fabricating one or more orthodontic appliances in accordance with the updated orthodontic treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated orthodontic treatment plan ([0109]).
However, Kuo fails to specifically disclose “tabularizing the extracted measurements to generate tabularized versions of the extracted measurements; performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements; generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements; comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from previous treatment plans of past patients”; “determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; comparing the path length for the first final arrangement of the patient's teeth to a path length threshold”, and “determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria based on the comparing of the dimensionally reduced tabular document with the set of the plurality of tabular documents and further based on the comparing of the path length to the path length threshold”.
Guotu LI discloses a method for orthodontically treating a patient's teeth (Abstract), the method comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of the patient's teeth ([0039]); generating a first final 3D model of a first final arrangement of the patient's teeth based on the initial 3D model of the initial arrangement of the patient's teeth ([0039]); extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth ([0051]).
Tabularizing the extracted measurements to generate tabularized versions of the extracted measurements (since a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising nine tooth measurement points [0060]); performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements ([0063]-[0064]); generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements (since principal component analysis is performed, a dimensionally reduced tabular document must be obtained); comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from previous treatment plans of past patients (since principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems; see Abstract).
Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo’s for orthodontically treating a patient's teeth to add the steps of tabularizing, performing principal component analysis on the tabularized versions of the extracted measurements, and additional comparing of the data (e.g. 3D scans/measurements), since such modification would advantageously aid the computer system to more accurate determined the precise geometry of the dental arch at every stage of the treatment plan.
The Examiner notes that Kuo and Guotu LI, as combined above, discloses determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria (see Kuo’s comparison analysis to determine whether condition falls within a range of reference conditions depicting malocclusion and selects the image that either best represents the patient [0172]) based on the comparing of the dimensionally reduced tabular document with the set of the plurality of tabular documents and further based on the comparing of the path length to the path length threshold (see Guotu LI’s principal component analysis (PCA) and comparing to other teeth, such as those obtained through automated machine learning systems; see Abstract).
Cramer discloses techniques for generating simulated images that show orthodontic treatment outcomes of the patients (Abstract). Cramer discloses determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; comparing the path length for the first final arrangement of the patient's teeth to a path length threshold ([0073]). Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo/Guotu LI’s method to add Isolation Forest analysis to the program, as taught by Cramer, since such modification would implement an appropriate machine learning model that would enhance automation.
Regarding claim 2, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the updated orthodontic treatment plan comprises tooth movement paths to move the patient's teeth from the initial arrangement towards the second final arrangement in a series of tooth movement stages since the patient's teeth are repositioned from an initial tooth arrangement to a final tooth arrangement by making a series of incremental position adjustments using appliances specified in accordance with the invention [0311]).
Regarding claim 4, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses further comprising: generating a set of parameters based on the extracted measurements ([0088]).
Regarding claim 5, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the set of parameters describe three-dimensional characteristics of the first final arrangement of the patient's teeth (since a three-dimensional (3-D) scan of the patient's teeth profile may be used to automatically capture the initial dental characteristics [0243] and a three-dimensional graphics model may be staged to represent the entire range of possible reference conditions, a user manipulates a slider to match a stage of the range which is closest to the actual patient condition [0214]) as represented in the dimensionally reduced tabular document (and Guotu LI discloses a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising e.g. nine tooth measurement points [0060] and the data is reduced via PCA ([0063]-[0064]).
Regarding claim 6, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the set of parameters include at least one of inclination, rotation, angulation, or prominence for each tooth of the first final arrangement of the patient’s teeth ([0065]) as represented in the dimensionally reduced tabular document (and Guotu LI discloses a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising e.g. nine tooth measurement points [0060] and the data is reduced via PCA ([0063]-[0064]).
Regarding claim 7, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the set of parameters include at least one of inclination-by-axes, angulation-by-axes, rotation-by-axes (Figure 1C and [0065]), whether or not a tooth is extracted, or a space between adjacent teeth for each tooth of the first final arrangement of the patient’s teeth (since the system recognizes and indexes the actual dentition conditions including missing teeth or treated with a root canal or an implant, these combinations may be represented with an indexing system for the initial dentition, target dentition (treatment goal), and final dentition which is the outcome of the treatment [0153]-[0154]) as represented in the dimensionally reduced tabular document (and Guotu LI discloses a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising e.g. nine tooth measurement points [0060] and the data is reduced via PCA ([0063]-[0064]).
Regarding claim 8, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Guotu LI discloses further comprising: selecting the set of the plurality of tabular documents each representing final arrangements of teeth ([0060]), but fails to specifically disclose “based on a geographic region of the past patients and an age of the past patients”.
However, Kuo discloses that its system is additionally able to determine the corresponding treatment plan based on, for example, previously treated cases that have similar or the same characteristics associated with the patient's condition and/or the desired treatment goal ([0301]) and Guotu LI further discloses that principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems; see Abstract. Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo/Guotu LI’s method for orthodontically treating a patient's teeth to add the steps/feature of selecting the set of the plurality of tabular documents each representing final arrangements of the teeth based on a geographic region of the past patients and an age of the past patients, since these feature (i.e. comparing saved files) are known capabilities of computer programs and systems, as the one taught by both Kuo and Guotu LI (see for instance Guotu LI’s system in [0048]).
Regarding claim 9, Kuo, Guotu LI and Cramer discloses wherein comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with the set of the plurality of tabular documents each representing final arrangements of teeth from the previous treatment plans of past patients includes: determining a cluster based local outlier factor for the first final arrangement of the patient's teeth with respect to the plurality of final arrangements of teeth from the previous treatment plans of the past patients (since Guotu LI discloses the tabularization of the data, said data being reduced via PCA ([0063]-[0064], the system Kuo and Guotu LI discloses comparing the tabular document representing the first final arrangement of the patient's teeth with tabular documents from past patients and Kuo discloses determining a cluster based local outlier factor for the first final arrangement of the patient's teeth; see [0068], [0074], [0085] and [0089], etc.).
Regarding claim 10, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein determining whether the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria comprises comparing the cluster based local outlier factor for the first final arrangement of the patient’s teeth to one or more thresholds related to deviation between the first final arrangement of the patient’s teeth and the plurality of final arrangements of teeth from the previous treatment plans (since the system models expected discrepancies between intended position and actual positions, the system uses revised expected position information where relevant, the system models risk for undesirable outcomes, the system also flags cases that require special attention and clinical constraints, the system iteratively collects data and loops back to identify/clusterize patient histories, clusters can be revised and reassigned and the system also continually identifies clusters without good representation for additional follow-up analysis [0092]).
Regarding claim 11, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses further comprising: generating clusters based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; and identifying the cluster based local outlier factor for the first final arrangement of the patient’s teeth based on the clusters, wherein the one or more outlier criteria comprise a local outlier factor threshold ([0087] and [0089]).
Regarding claim 12, Kuo, Guotu LI and Cramer discloses wherein comparing the dimensionally reduced tabular document (see Guotu LI’s principal component analysis) representing the first final arrangement of the patient's teeth with the set of the plurality of tabular documents each representing the final arrangements of teeth from the previous treatment plans of the past patients includes: determining an average k-nearest neighbor (k-NN) using a k-nearest neighbors algorithm for the first final arrangement of the patient's teeth with respect to the plurality of final arrangements of teeth from the previous treatment plans of the past patients (since the system uses nearest neighbor models, regression methods, and engineered (artificial) neural networks; see Kuo: [0074]).
Regarding claim 13, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein determining whether the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria comprises comparing the average k-NN for the first final arrangement of the patient’s teeth to a threshold (since the analyzer or model parameters is performed iteratively until the performance of the analyzer in classifying the test set reaches an optimal point [0074]).
Regarding claim 14, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses further comprising training the k-nearest neighbors algorithm by generating feature vectors and class labels based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients (since the nearest neighbor models are trained [0074] and the data could be feature vector series is processed the algorithm [0082]).
Regarding claim 17, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Cramer discloses further comprising: generating the isolation trees based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients ([0073]).
Regarding claim 18, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Cramer discloses wherein the comparing includes comparing based on application of two or more outlier detection methods including the isolation forest ([0073]) and at least one of: determining an average k-nearest neighbor (k-NN) using a k-nearest neighbors algorithm for the first final arrangement of the patient's teeth with respect to the plurality of final arrangements of teeth from the previous treatment plans of the past patients ([0074]).
Regarding claim 19, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the determining whether the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria comprises determining that a majority of the two or more outlier detection methods indicate that the first final arrangement of the patient’s teeth is an outlier (since the system enables statistically significant comparisons between populations of cases which allows the optimization of the treatment [0085]).
Regarding claim 21, Kuo discloses a method comprising: identifying an anticipated result of a treatment plan before implementing the treatment plan ([0104]), by: extracting measurements from a first final 3D model of a first final arrangement of a patient's teeth as reflected in the treatment plan (since digital models of entire teeth are produced from the raw data, including measured or extrapolated hidden surfaces and root structures [0103] and the desired final position of the teeth can be can be calculated from basic orthodontic principles, or can be extrapolated computationally from a clinical prescription (step 130) … The result of this step is a set of digital data structures that represents an orthodontically correct repositioning of the modeled teeth relative to presumed-stable tissue. The teeth and tissue are both represented as digital data [0104]).
Identifying whether the anticipated result of the treatment plan is an outlier result in comparison to one or more known results of one or more performed treatment plans by comparing the anticipated result of the treatment plan, including treatment plan-specific factors associated with the anticipated result and the one or more known results of the one or more performed treatment plans (see: [01072] the system is a data driven analyzers which may incorporate a number of models such as parametric statistical models, non-parametric statistical models, clustering models, nearest neighbor models, regression methods, and engineered, artificial, neural networks [0074] that are capable of determining that the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria).
And responsive to determining that the anticipated result of the treatment plan is an outlier result in comparison to the one or more known results (since the user then identifies where the patient's dentition condition falls within a range of reference conditions depicting malocclusion [0172]), performing the following comprising: classifying the treatment plan as a clinical risk ([0089]); generating a second final 3D model of a second final arrangement of the patient's teeth; generating an updated treatment plan based on the second final 3D model of the second final arrangement of the patient's teeth (since an updated orthodontic treatment plan is achieved based on the desired and intended end result of orthodontic treatment, see [0104] and [0105]); and fabricating one or more orthodontic appliances in accordance with the updated treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated treatment plan ([0109]).
However, Kuo fails to specifically disclose “tabularizing the extracted measurements to generate tabularized versions of the extracted measurements; performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements; and generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements”, “wherein the comparing comprises comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from the one or more performed treatment plans” and “determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the final arrangements of teeth from the one or more performed treatment plans; and comparing the path length for the first final arrangement of the patient's teeth to a path length threshold”.
Guotu LI discloses a method for orthodontically treating a patient's teeth (Abstract), the method comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of the patient's teeth ([0039]); generating a first final 3D model of a first final arrangement of the patient's teeth based on the initial 3D model of the initial arrangement of the patient's teeth ([0039]); extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth ([0051]).
Tabularizing the extracted measurements to generate tabularized versions of the extracted measurements (since a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising nine tooth measurement points [0060]); performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements ([0063]-[0064]); and generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements (since principal component analysis is performed, a dimensionally reduced tabular document must be obtained).
Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo’s for orthodontically treating a patient's teeth to add the steps of tabularizing, performing principal component analysis on the tabularized versions of the extracted measurements, and additional comparing of the data (e.g. 3D scans/measurements), since such modification would advantageously aid the computer system to more accurate determined the precise geometry of the dental arch at every stage of the treatment plan.
The Examiner notes that Kuo and Guotu LI, as combined above, discloses “wherein the comparing comprises comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from the one or more performed treatment plans (since principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems; as taught by Guotu LI, see Abstract).
Cramer discloses techniques for generating simulated images that show orthodontic treatment outcomes of the patients (Abstract). Cramer discloses determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; comparing the path length for the first final arrangement of the patient's teeth to a path length threshold ([0073]). Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo/Guotu LI’s method to add Isolation Forest analysis to the program, as taught by Cramer, since such modification would implement an appropriate machine learning model that would enhance automation.
Regarding claim 22, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the treatment plan and the performed treatment plans include digital treatment plans for treating one or more patients (claim 1).
Regarding claim 23, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the treatment plan-specific factors associated with the anticipated result and the one or more known results include characteristics of the anticipated result and the one or more known results (claim 1).
Regarding claim 24, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the treatment plan-specific factors associated with the anticipated result and the one or more known results include features in differences between the characteristics of the anticipated result and the one or more known results (claim 1).
Regarding claim 25, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses further comprising determining whether the anticipated result of the treatment plan is the outlier result based on deviation between the treatment plan-specific factors associated with the anticipated result and the treatment plan-specific factors associated with the one or more known results (the system implements statistical analysis that can determine outliers).
Regarding claim 26 Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses further comprising: applying a plurality of different outlier detection techniques to determine various degrees of deviation between the treatment plan-specific factors associated with the anticipated result and the treatment plan-specific factors associated with the one or more known results ([0074]); and determining that the anticipated result of the treatment plan is the outlier result responsive to determining that more than one of the different outlier detection techniques indicate that the anticipated result of the treatment plan is the outlier result based on the deviation between the treatment plan-specific factors associated with the anticipated result and the treatment plan-specific factors associated with the one or more known results ([0089]).
Regarding claim 27, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses the plurality of different outlier detection techniques include machine learning techniques for grouping values of the treatment plan-specific factors associated with the anticipated result and the one or more known results amongst the treatment plan-specific factors across the anticipated result and the one or more known results (nearest neighbor models are machine learning models [0074]).
Regarding claim 28, Kuo, Guotu LI and Cramer discloses the invention substantially as claimed. Kuo discloses wherein the treatment plan-specific factors associated with the anticipated result are identified by simulating the treatment plan without actually performing the treatment plan in its entirety (since the finite element models can be created using computer program application software such as computer aided engineering (CAE) or computer aided design (CAD) which can allow said simulations [0111]).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Guotu LI, in view of Cramer, further in view of Kopelman (US 11432908 B2).
Regarding claim 20, Kuo discloses a system comprising: a computing device comprising: a processor; and a memory ([0332]) including instructions that when executed by the processor cause the processor to perform operations comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of a patient's teeth ([0102]); generating a first final 3D model of a first final arrangement of the patient's teeth based on the initial 3D model of the initial arrangement of the patient's teeth ([0104]); extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth (since digital models of entire teeth are produced from the raw data, including measured or extrapolated hidden surfaces and root structures [0103] and the desired final position of the teeth can be can be calculated from basic orthodontic principles, or can be extrapolated computationally from a clinical prescription (step 130) … The result of this step is a set of digital data structures that represents an orthodontically correct repositioning of the modeled teeth relative to presumed-stable tissue. The teeth and tissue are both represented as digital data [0104]).
Determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria based on the comparing (since the visual image interface can be presented to the user without any descriptions or labels to avoid any pre-conceived biases associated with the label and the user then identifies where the patient's dentition condition falls within a range of reference conditions depicting malocclusion and selects the image that either best represents the patient [0172]).
And responsive to determining that the first final arrangement of the patient's teeth satisfies the one or more outlier criteria ([0074 and [0089]]), performing the following comprising: classifying an orthodontic treatment plan comprising the first final 3D model of the first final arrangement of the patient's teeth as a clinical risk ([0089]); generating a second final 3D model of a second final arrangement of the patient's teeth since the desired final position of the teeth can be extrapolated computationally from a clinical prescription (step 130) [0104]; and generating an updated orthodontic treatment plan based on the second final 3D model of the second final arrangement of the patient's teeth ([0104]).
However, Kuo fails to specifically disclose “tabularizing the extracted measurements to generate tabularized versions of the extracted measurements; performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements; generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements; comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from previous treatment plans of past patients; determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; comparing the path length for the first final arrangement of the patient's teeth to a path length threshold; “determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria based on the comparing of the dimensionally reduced tabular document with the set of the plurality of tabular documents and further based on the comparing of the path length to the path length threshold” and “and a 3D printer to fabricate one or more orthodontic appliances in accordance with the updated orthodontic treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated orthodontic treatment plan”.
Guotu LI discloses a method for orthodontically treating a patient's teeth (Abstract), the method comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of the patient's teeth ([0039]); generating a first final 3D model of a first final arrangement of the patient's teeth based on the initial 3D model of the initial arrangement of the patient's teeth ([0039]); extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth ([0051]).
Tabularizing the extracted measurements to generate tabularized versions of the extracted measurements (since a table with the data can be created by the extraction engine, e.g. 3D point cloud comprising nine tooth measurement points [0060]); performing principal component analysis on the tabularized versions of the extracted measurements to generate dimensionally reduced tabularized versions of the extracted measurements ([0063]-[0064]); generating a dimensionally reduced tabular document representing the first final arrangement of the patient's teeth based on the dimensionally reduced tabularized versions of the extracted measurements (since principal component analysis is performed, a dimensionally reduced tabular document must be obtained); comparing the dimensionally reduced tabular document representing the first final arrangement of the patient's teeth with a set of a plurality of tabular documents each representing final arrangements of teeth from previous treatment plans of past patients (since principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems; see Abstract).
Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo’s for orthodontically treating a patient's teeth to add the steps of tabularizing, performing principal component analysis on the tabularized versions of the extracted measurements, and additional comparing of the data (e.g. 3D scans/measurements), since such modification would advantageously aid the computer system to more accurate determined the precise geometry of the dental arch at every stage of the treatment plan.
The Examiner notes that Kuo and Guotu LI, as combined above, discloses determining whether the first final arrangement of the patient's teeth satisfies one or more outlier criteria (see Kuo’s comparison analysis to determine whether condition falls within a range of reference conditions depicting malocclusion and selects the image that either best represents the patient [0172]) based on the comparing of the dimensionally reduced tabular document with the set of the plurality of tabular documents and further based on the comparing of the path length to the path length threshold (see Guotu LI’s principal component analysis (PCA) and comparing to other teeth, such as those obtained through automated machine learning systems; see Abstract).
Cramer discloses techniques for generating simulated images that show orthodontic treatment outcomes of the patients (Abstract). Cramer discloses determining a path length in one or more isolation trees of an isolation forest for the first final arrangement of the patient's teeth based on isolation trees generated based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients; comparing the path length for the first final arrangement of the patient's teeth to a path length threshold ([0073]). Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo/Guotu LI’s method to add Isolation Forest analysis to the program, as taught by Cramer, since such modification would implement an appropriate machine learning model that would enhance automation.
Kopelman discloses a system comprising: a computing device comprising: a processor; and a memory ([0010]) including instructions that when executed by the processor cause the processor to perform operations comprising: 3D printer to fabricate one or more orthodontic appliances in accordance with the updated orthodontic treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated orthodontic treatment plan ([0126]). Therefore, it would have been obvious to one of ordinary skills in the art, before the effective filing date of the application, to modify Kuo/Guotu LI/Cramer’s method to add a 3D printer to fabricate one or more orthodontic appliances in accordance with the updated orthodontic treatment plan, wherein the one or more orthodontic appliances are applied to the patient's teeth to implement the updated orthodontic treatment plan, as taught by Kopelman, since such modification would implement an appropriate manufacturing method that would enhance automation and computer control.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS RUIZ whose telephone number is (571)270-0839. The examiner can normally be reached on M-F 8 Am - 5 PM (EST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Eric Rosen can be reached on (571) 270-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Luis Ruiz Martin/
Patent Examiner
Art Unit 3772
/ERIC J ROSEN/ Supervisory Patent Examiner, Art Unit 3772
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