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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered.
Response to Arguments
Applicant’s arguments, see Remarks filed on 12/08/2025, with respect to claims 1-2, and 4-28 have been fully considered. The Examiner withdraws the rejections of claims 1-19 and 21-28 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 dental appliances for use in the dental treatment). However, the 35 U.S.C. 101 rejection of claim 20 is maintained; claim 20 does not recite the active manufacturing of any particular appliance only the intention for some other method to occur (i.e. “causing fabrication”). According to the Specification it appears that a 3D printer can be used to fabricate the appliance. The Examiner suggests that if the system integrates said 3D printer to fabricate the one or more orthodontic appliance (i.e. “a system comprising, a processor, a memory, a 3D printer…processor configured to cause the 3D printer to fabricate the one or more orthodontic appliance”) said language would overcome the 35 U.S.C. 101 rejection.
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.
Rejections based 35 U.S.C. 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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance,” issued January 7, 2019 the pending claims are analyzed as follows —
Step 1 - In regard to claim 20 directed to “a system comprising: a processor; and a memory including instructions that when executed by the processor cause the system to perform operations comprising: receiving an initial three-dimensional (3D) model of an initial arrangement of a patient’s teeth; 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; comparing the first final arrangement of the patient’s teeth with a set of a plurality of final arrangements of teeth from previous treatment plans of past patients; determining whether the first final arrangement of the patient’s teeth satisfies one or more outlier criteria based on the comparing; and responsive to determining that the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria, 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”. The “system” is within the 35 U.S.C. 101 statutory category of a “process” (MPEP 2106.03), but falls into the judicial exception (MPEP 2106.04).
Even though a processor and a memory fall inside one of the four statutory categories, the claim is directed towards software or algorithm performing the method steps. For instance, the processor must comprise an algorithm in charge receiving an initial three-dimensional (3D) model of an initial arrangement of a patient’s teeth; 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; comparing the first final arrangement of the patient’s teeth with a set of a plurality of final arrangements of teeth from previous treatment plans of past patients, etc. The Examiner notes that practitioners were able to generate treatment objectives based on a comparison of the patient’s dental images and the practitioner expertise acquired during training and practice before the advent of computers. Moreover, the limitations are not directed to a distinct positive method step of manufacturing custom or appropriate orthodontic appliances for particular/specific patients — the limitation does not appear to positively limit the broad step of “obtaining and manipulating data”. The Examiner notes that the claim is directed solely to a digital virtual environment where data is input (“received/obtained”) and then processed (digitally/virtually determined” and “digitally/virtually compared”)—there are no additional elements integrating the judicial exception into a practical solution — the digital/virtual generation/comparison is not used to operate a manufacturing device, the digital/virtual generation/comparison/determination is not used to improve the functioning of a computer, the digital/virtual/generation/comparison/determination is not used to transform a particular article into a different state or thing — there is no meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Moreover, it is noted that the “digitally/virtually generating” step and the “digitally/virtually comparing” steps are quite broad failing to indicate what values or parameters are used for making such treatment generations, how the comparison of the treatment objectives are made with respect to the data set — the broad method steps cover all methods of digitally comparing patient’s data and generating diagnostics and treatment.
Step 2A —In regard to claim 20, the claimed invention is directed to an abstract idea (MPEP 2106.04(a)) without reciting additional elements that amount to significantly more than the judicial exception (MPEP 2106.05). The claimed invention is directed to a mental process — concepts that are capable of being performed in the human mind — including observations, evaluations and judgements. More particularly, the steps of “determining whether the first final arrangement of the patient’s teeth satisfies one or more outlier criteria based on the comparing; and responsive to determining that the first final arrangement of the patient’s teeth satisfies the one or more outlier criteria” and the steps of “extracting measurements from the first final 3D model of the first final arrangement of the patient's teeth; tabularizing the extracted measurements to generate tabularized versions of the extracted measurements”; etc. are capable of being done mentally (a dentist views a patient’s dentition envisioning a 3D image of the structure, and determines whether or not it satisfies one or more outlier criteria and can create tables with the resulting measurements); the steps of “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 and generating a second final 3D model of a second final arrangement of the patient’s teeth”; may be done mentally (the dentist mentally evaluates any a clinical risk that the proposed treatment could have). It is further noted that dentists have long practiced their trade/art of determining mentally what orthodontic treatment/appliances are needed for a patient and how they are to be shaped and designed—well before the advent of computers — and are most certainly capable of envisioning and mentally determining the shapes and arrangements of dental appliances and treatment steps necessary to accomplish a desired appearance. Additionally, there are no additional elements recited that integrate the exception into a practical application, i.e. manufacturing. For instance, manufacturing the first or second appliance based on the analysis.
Step 2B — In regard to claim 20, the claimed steps are all algorithms capable of being performed mentally and represent nothing more than concepts related to performing mathematical calculations which fall within the judicial exception. Implicit in the claimed invention is the intended use of a computing or data processing device, however, there is no disclosure in the written description that the processing unit is anything more than a generic component, nor is there any disclosure that the method improves the manner in which the processing unit operates. The mere recitation in the claim of a generic conventional processing unit that is 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 claim does not go beyond “determining” and “calculating” numerical values based on mathematical algorithms with a standard generic computer.
The analysis of data in a particular field and the stating those functions in general terms, without limiting them to technical means for performing the functions is an abstract idea and does not meet the requirements of 35 U.S.C. 101. The claim does not require that the method be implemented by a particular machine and they do not require that the method particularly transform a particular article. The claim sets forth a process of analyzing information of a specific content and are not directed to any particularly asserted inventive technology for performing those functions. Nothing in the claim or specification requires anything more than a conventional prior art computer for analyzing numbers according to a mathematical algorithm. The claimed system and method fall with the judicial exception to patent eligible subject matter of an abstract idea without significantly more. See Elec. Power Grp., LLC v. Alstom S.A., 119 USPQ2d 1739 (Fed. Cir. 2016) for further guidance.
Finally, the Examiner notes that Applicant’s disclosure appears to suggest that his/her invention is directed to a method of generating and manufacture or installation of orthodontic elements at an intraoral site within the oral cavity ([0115]) — the claimed method/system, however, eliminates the majority of these practical application steps (e.g. manufacturing) and reduces the method/system to an entirely virtual/digital method presenting numerous issues under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102
and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory
basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and
the rationale supporting the rejection, would be the same under either status.
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 and 20-28 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).
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”.
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.
Regarding claim 2, Kuo and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 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]).
Regarding claim 6, Kuo and Guotu LI 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 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]).
Regarding claim 7, Kuo and Guotu LI 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 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]).
Regarding claim 8, Kuo and Guotu LI 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 the 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 and Guotu LI discloses wherein comparing the 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, 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 20, Kuo discloses a system comprising: a processor; and a memory ([0332]) including instructions that when executed by the processor cause the system 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]); generating an updated orthodontic treatment plan based on the second final 3D model of the second final arrangement of the patient's teeth (0104); and causing fabrication of 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”.
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.
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 the one or more known results of the one or more performed treatment plans by comparing treatment plan-specific factors associated with the anticipated result and the one or more known results (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 orthodontic treatment plan ([0109]).
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); comparing the anticipated result of the treatment plan to one or more known results of one or more performed treatment plans based on 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; 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.
Regarding claim 22, Kuo and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI 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 and Guotu LI discloses the invention substantially as claimed. Kuo discloses wherein 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 and Guotu LI 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) 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Guotu LI, as applied to claim 1 above, and further in view of Roschin (US 20200297458 A1).
Regarding claim 15, Kuo and Guotu LI discloses wherein comparing the dimensionally reduced (see Guotu LI’s principal component analysis) final arrangement of the patient’s teeth with the set of the plurality of final arrangements of teeth from the previous treatment plans of the past patients includes: using cluster-based method and nearest neighbor algorithm (Kuo: [0074]). However Kuo and Guotu LI fail to specifically disclose wherein comparing the first tabular document 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 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.
Roschin discloses a method for orthodontically treating a patient’s teeth (Abstract), the method comprising: implementing an isolation trees of an isolation forest algorithms ([0095]). 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 Roschin, since such modification would implement an appropriate machine learning model that would enhance automation.
Therefore, Kuo, Guotu LI and Roschin, as combined above, discloses wherein comparing the first tabular document 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 (comparing the tabularized data as explained in claim 1): determining a path length in one or more isolation trees of an isolation forest (implementing an isolation trees of an isolation forest algorithms; see Roschin: [0095])) 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 (since raw dental features, 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 Guotu LI: Abstract).
Regarding claim 16, Kuo, Guotu LI and Roschin 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 path length the first final arrangement of the patient’s teeth to a threshold ([0105]).
Regarding claim 17, Kuo, Guotu LI and Roschin discloses the invention substantially as claimed. Further comprising: generating the isolation trees based on the plurality of final arrangements of teeth from the previous treatment plans of the past patients (Since Kuo discloses generating, analyzing and comparing data from the plurality of final arrangements of teeth from the previous treatment plans of the past patients ([0074]) and Roschin discloses implementing an isolation trees of an isolation forest algorithms [0095]).
Regarding claim 18, Kuo and Guotu LI discloses the invention substantially as claimed. Kuo discloses wherein the comparing includes comparing based on application of two or more outlier detection methods including: 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, 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 ([0074]), but fails to disclose 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 plurality of final arrangements of teeth from the previous treatment plans of the past patients.
Roschin discloses implementing an isolation trees of an isolation forest algorithms ([0095]). 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 Roschin, since such modification would implement an appropriate machine learning model that would enhance automation.
Therefore, Kuo, Guotu LI and Roschin, as combined above, 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.
Regarding claim 19, Kuo, Guotu LI and Roschin 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]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS MIGUEL RUIZ MARTIN whose telephone number is (571)270-0839. The examiner can normally be reached M-F 8 Am - 5 PM (EST).
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/LUIS RUIZ MARTIN/
Examiner, Art Unit 3772
/EDELMIRA BOSQUES/Supervisory Patent Examiner, Art Unit 3772