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 .
Status of Amendments
Claims 1-5, 8-14, 16-19, and 21-28 are currently pending in this case and have
been examined and addressed below. This communication is a Final Rejection in
response to the Amendment to the Claims and Remarks filed on 07/02/2026.
Claims 1, 4, 10, 16-18, and 25 are amended claims.
Claims 2, 11, and 13-14 are original claims.
Claims 3, 5, 8-9, 12, 19, 21-24, and 26-28 are previously presented claims.
Claims 6-7, 15, and 20 have been cancelled and will not be considered at this time.
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 1-5, 8-14, 16-19, and 21-28 are rejected under 35 U.S.C. § 101
because the claimed invention is directed to a judicial exception (i.e. an abstract
idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 16, and 17 are drawn to methods, which are statutory categories of
invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claims 1 recites a method comprising receiving, from a dental
practitioner, a practitioner-specific subset of textual rules for generating a treatment plan
specific to a patient, wherein the practitioner- specific subset of textual rules are
selected from with a menu of textual rules specific to orthodontic treatment planning,
wherein the textual rules refer to a collection of instructions in a domain-specific
treatment language, wherein the practitioner-specific subset of textual rules relate to
one or more clinical conditions; encoding the practitioner-specific subset of textual rules
into a set of instructions in the domain-specific treatment language; applying the set of
instructions to a test dental data set to generate a test treatment plan, wherein the set of instructions include instructions for moving one or more teeth of a digital model
according to the practitioner-specific subset of textual rules; modifying, based on input
from the dental practitioner, the practitioner-specific subset of textual rules, wherein the modifying comprises appending one or more modifications as a higher layer to predefined treatment parameters; storing the practitioner-specific subset of textual rules to apply to one or more sets of patient data; and generating a treatment plan specific to the patient by applying the practitioner-specific subset of textual rules to the patient's dental data, wherein the treatment plan specific to the patient is configured to treat the patient's teeth malocclusions and includes the use of a series of aligners.
Independent claim 16 recites a method comprising receiving, from a dental
practitioner, a practitioner-specific subset of textual rules for generating a treatment plan
specific to a patient, wherein the practitioner- specific subset of textual rules are
selected from with a menu of textual rules specific to orthodontic treatment planning,
wherein the textual rules refer to a collection of instructions in a domain-specific
treatment language, wherein the practitioner-specific subset of textual rules relate to
one or more clinical conditions; encoding the practitioner-specific subset of textual rules
into a set of instructions in the domain-specific treatment language; applying the set of
instructions to a test dental data set to generate a test treatment plan, wherein the set of
instructions include instructions for moving one or more teeth of a digital model
according to the practitioner-specific subset of textual rules; modifying, based on input
from the dental practitioner, the practitioner-specific subset of textual rules, wherein the modifying comprises appending one or more modifications as a higher layer to predefined treatment parameters; storing the practitioner-specific subset of textual rules to apply to one or more sets of patient data; and generating a treatment plan specific to the patient by applying the practitioner-specific subset of textual rules to the patient's dental data, wherein the treatment plan includes the use of series of aligners.
Independent claim 17 recites a method comprising receiving a data structure and
a digital model of a patient's teeth at a treatment plan generating module, wherein the
data structure is in a dental protocol language, wherein the data structure includes predefined treatment parameters and one or more clinician modifications appended as a higher layer, wherein the treatment plan generating module is configured to generate an orthodontic treatment plan based on the higher layer, wherein the one or more clinician modifications are incorporated into a practitioner-specific subset of
textual rules that relate to one or more clinical conditions; generating the orthodontic
treatment plan for treating the patient's teeth malocclusions; and receiving approval of
the orthodontic treatment plan from the clinician.
These steps amount to methods of organizing human activity which includes
functions relating to interpersonal and intrapersonal activities, such as managing
relationships or transactions between people, social activities, and human behavior;
satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or
behaviors; and managing human activity (MPEP § 2106.04(a)(2)(II)(C) citing the
abstract idea grouping for methods of organizing human activity for managing personal
behavior or relationships or interactions between people – also note October 2019
Update: Subject Matter Eligibility on p. 5 and MPEP § 2106.04(a)(2)(II) stating certain
activity between a person and a computer may fall within the “certain methods of
organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the
additional elements within the claims only amount to instructions to implement the
judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 and 16 recite providing instructions to physically fabricate the series of
aligners that are shaped and sized specifically to treat the patient's teeth malocclusions
based on the generated treatment plan. Claim 17 recites fabricating the series of
aligners that are shaped and sized specifically for treating the patient's teeth
malocclusions according to the approved orthodontic treatment plan. When determining
if a particular treatment and prophylaxis as a practical application under Step 2A Prong
Two, Examiner considered the factors presented in the MPEP 2106.04(d)(2).
Factor A: The Particularity Or Generality Of The Treatment Or Prophylaxis. The
generating a treatment plan the abstract idea is not "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). Here, the fabricating of the series of aligners is not specified to a specific malocclusion
and the claim limitations do not recite any particular special techniques or
consideration(s) in the fabrication of aligners, such as the material(s) utilized to fabricate
the aligners or the specifics of how the aligners are fabricated. Therefore, the claims
recite a high-level recitation of a treatment without explicitly providing a particular
treatment for a particular disease or medical condition.
Factor B. Whether the Limitation(s) Have More Than a Nominal or Insignificant
Relationship to the Exception. The treatment limitation does not have a significant
relationship to the judicial exception – that is it does not integrate the law of nature into a practical application. As stated above, because the specific treatment and the
particular disease or medical condition fails to be explicitly recited and the particular
special techniques or consideration(s) in the fabrication of aligners, such as the
material(s) utilized to fabricate the aligners or the specifics of how the aligners are
fabricated is not explicitly recited, any possible treatment combination could not
reasonably be considered known in the art as a treatment for any disease.
Factor C. Whether the Limitation(s) Are Merely Extra-Solution Activity or A Field
of Use. The treatment or prophylaxis limitation does not impose meaningful limits on the
judicial exception and is only extra-solution activity or a field-of-use (see MPEP §2106.05(g))). The fabricating the series of aligners for treating the patient's teeth
malocclusions according to the approved orthodontic treatment plan is well known
(which is further addressed in analysis of Step 2B). The step does not add a meaningful
limitation to the process of determining treatment to be fabricated for a patient.
Therefore, the claims only recite the prophylactic step as a tool which does not meet the criteria for effecting a particular treatment or prophylaxis for a disease or medical condition and is therefore not a practical application of the recited judicial
exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract
idea on a computer.
The use of fabricating the series of aligners for treating the patient's teeth
malocclusions according to the approved orthodontic treatment plan is well-understood,
routine, and conventional. This position is supported by: (1) Javaid et al., Current status
and applications of additive manufacturing in dentistry: A literature-based review (2019),
teaching on major additive manufacturing applications in dentistry, which include
designing an orthodontic appliance and printing a customized aligner for the patient to
help with correcting teeth alignment (Pg. 1, I. Introduction and Pg. 5 Fig.2 Major
addictive manufacturing application in dentistry.), (2) Giannatsis et al., Additive
fabrication technologies applied to medicine and health care: a review (2009), teaching
on in the cases of dental implants, rapid prototyping technologies have been used either
as a direct method for manufacturing the scaffolds themselves or indirectly as a
manufacturing tool of the moulds required for the casting of scaffolds (Pg. 6 6 Tissue
and organism manufacturing engineering), and (3) Abduo et al, Trends in Computer - Aided Manufacturing in Prosthodontics: A Review of the Available Streams (2014),
teaching on that in prosthodontics today, utilizing computerized technologies to fabricate
prostheses is an acceptable treatment modality (Pg. 2 2. Digital Prosthodontic
Treatments)(treated as a review under MPEP 2106.07(a)(III)(C) that describes the state
of the art and discusses what is well-known and in common use in the relevant
industry). Therefore, fabricating dental appliances based on the treatment plan and
forming a series of aligners based on the approved treatment plan is not sufficient to
amount to significantly more than the recited judicial exception.
The above-identified judicial exception. Looking at the limitations as an ordered
combination adds nothing that is not already present when looking at the elements
taken individually. Their collective functions merely provide conventional computer
implementation.
For the reasons stated, these claims are consequently rejected under 35 U.S.C.
§ 101.
Dependent Claim Analysis:
Claim 2 recites wherein the menu of textual rules comprises rules specific to
tooth leveling, gap spacing, crowding, extraction, interproximal reduction, overjet,
overbite, cross bite, attachments, and anterior-to-posterior correction.
Claim 3 recites further comprising reconciling any conflict between the textual
rules of the practitioner-specific subset of textual rules.
Claim 4 recites wherein modifying comprises receiving one or more modifications to the test treatment plan and modifying the practitioner-specific subset of textual rules
accordingly.
Claim 5 recites wherein modifying comprises receiving dental practitioner edits to
the practitioner-specific subset of textual rules directly.
Claim 9 recites wherein the menu of textual rules specific to orthodontic
treatment planning comprises a curated list of textual rules organized by rank.
Claim 11 recites wherein the characteristic of the dental practitioner comprises a
size of the dental practitioner's practice, a number of dental aligner cases performed by the dental practitioner, and a geographic location of the dental practitioner.
Claim 12 recites wherein applying the set of instructions to the test dental data
set to generate the test treatment plan comprises generating a plurality of dental
aligners corresponding to each of a plurality of stages of the test treatment plan.
Claim 13 recites further comprising providing a patient data set from the dental
practitioner as the test dental data set.
Claim 14 recites wherein the test dental data set comprises a standardized
dental data set.
Claim 18 recites the one or more clinician modifications are encoded in the dental protocol language.
Claim 19 recites further comprising repeating the steps of presenting the clinician
with the predefined treatment parameters and accepting one or more modifications
following the display of the treatment plan to the clinician.
Claim 21 recites wherein presenting the clinician with the predefined treatment
preferences comprises generating or receiving the predefined treatment preferences
from a library of clinician treatment preferences indexed by the clinician.
Claim 22 recites wherein the predefined treatment preferences are based on
prior treatment plans approved by the clinician for other patients.
Claim 25 recites wherein the practitioner- specific subset of textual rules are conditional based on whether the one or more clinical conditions exist in the patient's teeth.
Claim 26 recites wherein the practitioner-specific subset of textual rules are
further conditional based on whether one or more specific orthodontic movements are to
be performed.
Claim 27 recites wherein the one or more specific orthodontic movements
include one or more of: performing tooth intrusion movement, performing tooth extrusion
movement, performing distalization, and performing tooth rotation.
Claim 28 recites wherein the one or more clinical conditions include one or more
of: open bite, deep bite, tooth crowding, tooth space, tooth rotation, and tooth inclination
Each of these steps of the preceding dependent claims 2-5, 9, 11-14, 18-19, 21-
22, and 25-28 only serve to further limit or specify the features of independent claims 1,
16, or 17 accordingly, and hence are nonetheless directed towards fundamentally the
same abstract idea as the independent claim and utilize the additional elements
analyzed below in the expected manner.
Claim 8 recites wherein the graphical user interface comprises a user dialog box
including a selectable list of the textual rules specific to orthodontic treatment planning.
The user dialog box is an additional element, which is mere instructions to apply the
exception and does not provide a practical application or significantly more for the same
reasons.
Claim 10 recites wherein the rules are taken from a curated rules database and
selected based on one or more characteristics of the dental practitioner. The curated
rules database is an additional element, which is mere instructions to apply the
exception and does not provide a practical application or significantly more for the same
reasons.
Claims 23 and 24 recite fabricating a series of aligners for treating the patient's
teeth malocclusions according to the approved treatment plan specific to the patient.
The limitation of receiving approval of the treatment plan specific to the patient from the dental practitioner is part of the abstract idea, which further specifies and limits the
independent claims. The limitation of fabricating a series of aligners for treating the
patient's teeth malocclusions according to the approved treatment plan specific to the
patient merely recites the prophylactic step as a tool which does not meet the criteria for effecting a particular treatment or prophylaxis for a disease or medical condition and is therefore not a practical application of the recited judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-5, 8-10, 12, 17-19, 21-23, and 25-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akopov (US 20190175303 A1) in view of Tsai (US 20200405445 A1).
As per Claim 1, Akopov teaches a method comprising:
receiving, from a dental practitioner, a practitioner-specific subset of textual rules for generating a treatment plan specific to a patient, wherein the practitioner- specific subset of textual rules are selected from a menu of textual rules specific to orthodontic treatment planning, wherein the textual rules refer to a collection of instructions in a domain-specific treatment language, wherein the practitioner-specific subset of textual rules relate to one or more clinical conditions; ([Para. 0047] The digital model of the patient's teeth, as well as any of the user's patient-specific or user-specific preferences and/or the dental product(s) to be used may be used as inputs (e.g., sent to a remote site) to generate the array of treatment plans. Each treatment plan generated may include a final position, staging (e.g., a description of tooth movement directions along with a speed associated with each stage) and (optionally) a set of aligner features placed on each tooth to improve predictability of the treatment and ensure teeth movements occur. In generating each of these treatment plans, the final position of the teeth may be determined so as to address all or some of the patient's clinical conditions (e.g., malocclusions) such a crowding, bite issues, etc., and/or may approximate, as closely as possible, an ideal tooth position that may be achieved for the patient's teeth. [Para. 0087] Treatment preferences may refer to the treatment preferences of the dental practitioner (e.g., which teeth not to move, etc.) and may be specific to the patient, or may be specific to the user and applied to all of the user's patient's. Thus, treatment details may include details about the product(s) that may be used to achieve the treatment, including the number and type of aligners, and any properties of the aligners themselves. [Para. 0340] There may be two sources of dental professional's preferences on how to prepare treatment plans. One source of treatment preferences which may be essentially a structured input where for a set of questions, the user provides answers, where each answer is a selection from a set of predefined answers. The second source of information may be represented as a text-based comments which defines the user's personal rules to follow when preparing a treatment plan for a doctor. Domain specific language may be used to store user's non-structure input (e.g., text comments describing his treatment preferences) which may enable full automation of treatment planning as well as aggregation of rules from multiple sources (for example, structured preferences and non-structured treatment preferences). [Para. 0341] While setting up a treatment plan, a technicians uses both structured treatment preferences and non-structured treatment preferences. Text-based comments expressing doctors treatment planning style may be converted into a domain-specific language (manually or automatically) and the methods or apparatus (e.g., software) may interpret this domain-specific language to automatically apply doctors preferences for treatment planning preparation.)
encoding the practitioner-specific subset of textual rules into a set of instructions in the domain-specific treatment language ([Para. 0016] A set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional. [Para. 0019] Updating the domain-specific language specific to the specified dental professional, and/or storing the domain-specific language specific to the specified dental professional in a remote database accessible by the treatment plan optimizing engine. Any of these methods may include automatically generating the domain-specific language specific to the specified dental professional, or manually converting textual instructions into the domain-specific language specific to the specified dental professional.)
applying the set of instructions to a test dental data set to generate a test treatment plan, wherein the set of instructions include instructions for moving one or more teeth of a digital model according to the practitioner-specific subset of textual rules; ([Para. 0226] In FIG. 3, the method starts by collecting from the patient (and by the user, e.g., dental professional), a model of the patient's teeth 303, as well as any conditions (e.g., tooth movement restrictions) or preferences for treatment 301; the method or apparatus may also optionally identify any general preferences that are specific to the dental processional 302, and that may be applied to all of that dental professional's patients. In addition (and optionally) the method or apparatus may also provide an indication of the type of dental product (e.g., the type of dental/orthodontic products to be used to treat the patient 304. The treatment plan optimizing generator, may use this information to generate a plurality of treatment plans. For example, the treatment plan optimizing generator may first prepare the model 305, e.g., by digitizing it if it is not already a digital model, and by segmenting the digital model into individual teeth, gingiva, etc. Once prepared, the automated treatment planning 307 may be performed to automatically generate multiple treatment plans using, e.g., different numbers of stages; for each stage multiple variations including different treatment properties (e.g., IPR, attachments, etc.) may also be generated 311. Each treatment plan is complete, and may be used to build an aligner series. For example, each treatment plan may include a new (and potentially unique) final position for the patient's teeth at the end of the treatment, staging showing the tooth movement and speed of movement for each stage (e.g., key frames) and a set of aligner features 313. The corresponding aligner features may include the location of the attachments, etc.)
modifying, based on input from the dental practitioner, the practitioner-specific subset of textual rules; ([Para. 0343] The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. Once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.)
storing the practitioner-specific subset of textual rules to apply to one or more sets of patient data; ([Para. 0340] Domain specific language may be used to store user's non-structure input (e.g., text comments describing his treatment preferences) which may enable full automation of treatment planning as well as aggregation of rules from multiple sources (for example, structured preferences and non-structured treatment preferences). [Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.)
and generating a treatment plan specific to a patient by applying the practitioner-specific subset of textual rules to the patient's dental data, wherein the treatment plan specific to the patient is configured to treat the patient's teeth malocclusions and includes the use of a series of aligners; ([Para. 0009] The model may be transmitted along with one or more of: treatment preferences from the dental professional specific to the patient, treatment preferences specific for the particular dental professional that may be applied to all patient's associated with that dental professional, and/or an indication of what clinical product(s) (e.g., orthodontic product) should be used to move the patient's teeth. This data may be used as inputs to generate the plurality of optional treatment plans. [Para. 0010] Creation of a large number of full treatment plans specific and customized to a patient setting froth an orthodontic and/or dental plan for beneficially modifying the subject's dentition, including in particular, moving (e.g., aligning, straightening, etc.) the patients teeth and/or resolving orthodontic issues specific to the patient. A treatment plan may include a series of patient-removable appliances to reposition the teeth, and some indication of the duration of time each appliance (“aligner”) is to be worn. [Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.)
and providing instructions to physically fabricate the series of aligners that are shaped and sized specifically to treat the patient's teeth malocclusions based on the generated treatment plan. ([Para. 0012] An automated method of creating a plurality of variations of treatment plans to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages may include: (a) specifying a set of treatment preferences and a set of treatment details (the treatment preferences and treatment details may be automatically or manually specified); (b) automatically determining a treatment plan based on the specified treatment preferences and treatment details. [Para. 0197] manufacture of a series or sequence of orthodontic aligner appliances that maybe worn sequentially to correct malocclusion(s). For example, FIG. 1 illustrates an exemplary tooth repositioning appliance or aligner 100 that can be worn by a patient in order to achieve an incremental repositioning of individual teeth 121 in the jaw. [Para. 0197] An appliance or portion(s) thereof may be indirectly fabricated using a physical model of teeth. For example, an appliance (e.g., polymeric appliance) can be formed using a physical model of teeth and a sheet of suitable layers of polymeric material. In some embodiments, a physical appliance is directly fabricated, e.g., using additive manufacturing techniques, from a digital model of an appliance. An appliance can fit over all teeth present in an upper or lower jaw, or less than all of the teeth. The appliance can be designed specifically to accommodate the teeth of the patient (e.g., the topography of the tooth-receiving cavities matches the topography of the patient's teeth), and may be fabricated based on positive or negative models of the patient's teeth generated by impression, scanning, and the like.)
Akopov does not explicitly teach, however Tsai teaches
wherein the modifying comprises appending one or more modifications as a higher layer to predefined treatment parameters; ([Para. 0052] One or more smile templates that are referred to herein as “crown rulers.” The crown ruler 100 is a virtual 3-D model of an ideal smile (i.e., a 3-D smile template). [Para.0029] (i) retrieve a first data structure that defines a digital 3-D smile template of at least two teeth arranged in accordance with predetermined criteria (i.e. predefined treatment parameters) from a memory, (ii) retrieve a second data structure that defines an orthodontic setup of a patient's teeth from the memory, and (iii) superimpose the digital 3-D smile template of the at least two teeth (i.e. higher layer) on the digital orthodontic setup of a patient's teeth using the first and second data structures. [Para. 0063] An exemplary user interface 120 is shown in FIG. 3 by which the practitioner may adjust one or more of the predetermined criteria (i.e. one or more modifications) by way of settings 122 to account for deviations between the patient's anatomical characteristics and the selected crown ruler 100. As the clinician adjusts any single one of the settings 122, the crown ruler 100 is automatically visually updated. [Para. 0068] Once any single one of the settings 122 is modified, the crown ruler 100 is visually updated according to the new value for that setting 122. Advantageously, the settings 122 may be adjusted while the crown ruler 100 is superimposed (i.e. higher layer) with the patient's digital orthodontic setup.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov and incorporate an orthodontic appliance for dental treatment as taught by Tsai, with the motivation of providing orthodontic appliances, computer modeling tools and systems, and treatment preparation methods that incorporate cosmetic dentistry methods and cosmetic treatment aspects of smile design in orthodontic appliance manufacturing (Tsai Para. 0009).
As per Claim 2, Akopov/ Tsai teach the method of claim 1, Akopov further teaches wherein the menu of textual rules comprises rules specific to tooth leveling, gap spacing, crowding, extraction, interproximal reduction, overjet, overbite, cross bite, attachments, and anterior-to-posterior correction. ([Para. 0060] Different filters/switches may toggle between treatment plans showing one or more tooth/treatment modifications. Virtually any modification may be used, including, for example: one or more of: extractions (of one or more teeth), adjusting of tooth overjet, adjusting for overbites, adjusting for cross bites, interproximal reductions (IPR), including one o or more aligner attachments on the teeth, and anterior to posterior (A-P) correction. These filters may correspond to treatment preferences.)
As per Claim 4, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches wherein modifying comprises receiving the one or more modifications to the test treatment plan and modifying the practitioner-specific subset of textual rules accordingly. ([Para. 0227] The user (e.g., dental professional) may then, using the interactive display, in real time, toggle between the multiple plans, and select one or a subset of treatment plans 319. Optionally, the user may modify one or more plans 319; if the user modifies a treatment plan in a manner that exceed the pre-calculated plurality of treatment plans 321, then the modifications may be transmitted to back to the automated treatment planning subsystem (including the treatment plan optimizing generator) to generate additional treatment plans including the user's modifications 337. These new treatment plans may replace or supplement the plans already pre-calculated. [Para. 0343] The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. This may initially be performed manually or semi-automatically, and may initially include manual review and checking (including checking with the user). However, once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.)
As per Claim 5, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches wherein modifying comprises receiving dental practitioner edits to the practitioner-specific subset of textual rules directly. ([Para. 0343] The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. This may initially be performed manually or semi-automatically, and may initially include manual review and checking (including checking with the user). However, once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.)
As per Claim 8, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches further comprising a graphical user interface that comprises a user dialog box including a selectable list of the textual rules specific to orthodontic treatment planning. ([Para. 0187] FIG. 44 is another example of a user interface display, illustrating the use of treatment plan filters allowing the user to switch or toggle between different treatment plans for display. In FIG. 44, a control configured as a drop-down menu or filter allows the user to switch between the type of attachments (e.g., all attachments, no attachments or poster-only attachments.)
As per Claim 9, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches wherein the menu of textual rules specific to orthodontic treatment planning comprises a curated list of textual rules organized by rank. ([Para. 0348] A module may convert structured input (e.g., answers given by a doctor on a set of questions) into additional set of rules. These rules may be combined via a rules aggregation module which combines rules from multiple sources into a single rules list. The language interpretation module may take any of these rules files as an input and interpret it to control the flow of FiPos, Staging and Aligner Features modules in order to create a treatment plan fully automatically [Para. 0351] The methods and systems described herein may determine how comprehensive each treatment plan is by comparing to the ideal final position and/or by applying ranking logic in which the each of one or more characteristics (also referred to herein as criterion) are used to determine the weighting. For example, treatment plans with interproximal reduction (IPR) may be weighted more than plans without IPR. [Para. 0352] The methods and apparatuses described herein may order the treatment plans based on one or more alternative or additional criterion, such as: the duration of the treatment plan, the number of stages, the amount of tooth movement achieved, etc. The criterion may be user selected or automatically selected. In some variations, the criterion may include, for example, a prediction of a user preference; the user's preference may be determined by machine learning, and may be specific to the user (e.g., based on prior/past preferences or selections for that user))
As per Claim 10, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches wherein the textual rules are taken from a curated rules database and selected based on one or more characteristics of the dental practitioner. ([Para. 0016] A set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional. [Para. 0019] Updating the domain-specific language specific to the specified dental professional, and/or storing the domain-specific language specific to the specified dental professional in a remote database accessible by the treatment plan optimizing engine. Any of these methods may include automatically generating the domain-specific language specific to the specified dental professional, or manually converting textual instructions into the domain-specific language specific to the specified dental professional.)
As per Claim 12, Akopov/ Tsai teaches the method of claim 1, Akopov further teaches wherein applying the set of instructions to the test dental data set to generate the test treatment plan comprises generating a plurality of dental aligners corresponding to each of a plurality of stages of the test treatment plan. ([Para. 0097] Creating a treatment plan to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages, may include: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a processor: a digital model of a patient's teeth; accessing (by the processor) a set of treatment preferences, a comprehensive final position of the patient's teeth, and a set of treatment details [Para. 0009] The model (i.e. digital model of patient’s teeth) may be transmitted along with one or more of: treatment preferences from the dental professional specific to the patient, treatment preferences specific for the particular dental professional that may be applied to all patient's associated with that dental professional, and/or an indication of what clinical product(s) (e.g., orthodontic product) should be used to move the patient's teeth. This data may be used as inputs to generate the plurality of optional treatment plans. [Para. 0010] Creation of a large number of full treatment plans specific and customized to a patient setting forth an orthodontic and/or dental plan for beneficially modifying the subject's dentition, including in particular, moving (e.g., aligning, straightening, etc.) the patients teeth and/or resolving orthodontic issues specific to the patient. [Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.) [Para. 0193] Manufacturing a series of aligners for a patient's teeth that may include generating multiple treatment plans that are limited various specified stages (e.g., 5 stages, 6 stages, 7 stages, 8 stages, 9 stages, 10 stages, 10 stages, 12 stages, 14 stages, 15 stages, 16 stages, 17 stages, 18 stages, 19 stages, 20 stages, 21 stages, 22 stages, 23 stages, 24 stages, 25 stages, 26 stages, 27 stages, 28 stages, 29 stages, 30 stages, etc.) and variations of these fixed-stage treatment plans in which one or more features are included to a predetermined degree (e.g., interproximal reduction, use of some number of aligner attachments, etc.).)
As per Claim 17, Akopov teaches a method of generating a series of aligners a treatment plan for treating a patient's teeth malocclusions an orthodontic treatment, the method comprising:
generating the orthodontic treatment plan for treating the patient's teeth malocclusions; ([Para. 0009] The model may be transmitted along with one or more of: treatment preferences from the dental professional specific to the patient, treatment preferences specific for the particular dental professional that may be applied to all patient's associated with that dental professional, and/or an indication of what clinical product(s) (e.g., orthodontic product) should be used to move the patient's teeth. This data may be used as inputs to generate the plurality of optional treatment plans. [Para. 0010] Creation of a large number of full treatment plans specific and customized to a patient setting froth an orthodontic and/or dental plan for beneficially modifying the subject's dentition, including in particular, moving (e.g., aligning, straightening, etc.) the patients teeth and/or resolving orthodontic issues specific to the patient. [Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.)
receiving approval of the orthodontic treatment plan from the clinician; ([Para. 0230] The user interface configured to allow interactive display of a plurality of different alternative treatment plans (“CCWeb”) may be used to review and select, and in some variations, modify, the treatment plans in the array of treatment plans. The patient may be consulted, as discussed above. Once the user selects a single treatment plan, and is satisfied with the treatment plan, the user may then transmit the selected treatment plan to the manufacturer (technician) who may (optionally) review and send a finalized version of the treatment plan for final approval. [Para. 0367] Finally, if the treatment plan looks good, the user may indicate approval 4615.)
and fabricating the series of aligners that are shaped and sized specifically for treating the patient's teeth malocclusions according to the approved orthodontic treatment plan. (([Para. 0012] An automated method of creating a plurality of variations of treatment plans to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages may include: (a) specifying a set of treatment preferences and a set of treatment details (the treatment preferences and treatment details may be automatically or manually specified); (b) automatically determining a treatment plan based on the specified treatment preferences and treatment details. [Para. 0197] manufacture of a series or sequence of orthodontic aligner appliances that maybe worn sequentially to correct malocclusion(s). For example, FIG. 1 illustrates an exemplary tooth repositioning appliance or aligner 100 that can be worn by a patient in order to achieve an incremental repositioning of individual teeth 121 in the jaw. An appliance or portion(s) thereof may be indirectly fabricated using a physical model of teeth. For example, an appliance (e.g., polymeric appliance) can be formed using a physical model of teeth and a sheet of suitable layers of polymeric material. In some embodiments, a physical appliance is directly fabricated, e.g., using additive manufacturing techniques, from a digital model of an appliance. An appliance can fit over all teeth present in an upper or lower jaw, or less than all of the teeth. The appliance can be designed specifically to accommodate the teeth of the patient (e.g., the topography of the tooth-receiving cavities matches the topography of the patient's teeth), and may be fabricated based on positive or negative models of the patient's teeth generated by impression, scanning, and the like. [Para. 0230] Once approved, the treatment plan, including all of the stages of aligners, may be fabricated using the treatment plan either directly or converting it into a manufacturing format. [Para. 0238] manufacturing a series of aligners for a patient's teeth. In particularly, this method may allow the real-time analysis and review of a huge number of treatment plans, selection of one of these treatment plans, and fabrication of a series or sequence of aligners based on these treatment plans.)
Akopov does not explicitly teach, however Tsai teaches
receiving a data structure and a digital model of a patient's teeth at a treatment plan generating module, wherein the data structure is in a dental protocol language, wherein the data structure includes predefined treatment parameters and one or more clinician modifications appended as a higher layer, ([Para.0029] (i) retrieve a first data structure that defines a digital 3-D smile template) of at least two teeth arranged in accordance with predetermined criteria (i.e. data structure includes predefined treatment parameters) from a memory, (ii) retrieve a second data structure that defines an orthodontic setup of a patient's teeth (i.e. digital model) from the memory, and (iii) superimpose the digital 3-D smile template of the at least two teeth (i.e. higher layer) on the digital orthodontic setup of a patient's teeth using the first and second data structures. [Para. 0063] An exemplary user interface 120 is shown in FIG. 3 by which the practitioner may adjust one or more of the predetermined criteria (i.e. data structure includes one or more clinician modifications) by way of settings 122 to account for deviations between the patient's anatomical characteristics and the selected crown ruler 100. As the clinician adjusts any single one of the settings 122, the crown ruler 100 is automatically visually updated. [Para. 0068] Once any single one of the settings 122 is modified, the crown ruler 100 is visually updated according to the new value for that setting 122. Advantageously, the settings 122 may be adjusted while the crown ruler 100 is superimposed (i.e. higher layer) with the patient's digital orthodontic setup.)
wherein the treatment plan generating module is configured to generate an orthodontic treatment plan based on the higher layer, wherein the one or more clinician modification are incorporated into a practitioner-specific subset of textual rules that relate to one or more clinical conditions; ([Para. 0054] The practitioner (e.g., a dental technician) may access each of the patient information (e.g., the digital imagery) and one or more crown rulers 100 through the web 12 according to arrow 14. At 16, the practitioner applies the crown ruler 100 to the patient information in a digital environment. By way of example only, the practitioner may apply the crown ruler 100 to set up a T2 model (also referred to as “T2” herein). The T2 model is a digital 3-D model that represents one possible after-treatment arrangement of the virtual teeth shown in the T1 model. That is, T2 is a virtual representation of the patient's teeth that corresponds exactly to one proposed outcome of dental treatment. From T2, a treatment plan is produced that may ultimately be used to instruct the orthodontic manufacturer on the design of orthodontic appliances, such as orthodontic brackets and aligners. [Para. 0056] Orthodontic techniques include movement of individual teeth relative to the patient's jaw, such as rotation and tipping of individual teeth, and arch expansion. Restorative techniques may include gingival alteration, such as re-contouring, and hard tissue build-up or removal on individual teeth.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov and incorporate an orthodontic appliance for dental treatment as taught by Tsai, with the motivation of providing orthodontic appliances, computer modeling tools and systems, and treatment preparation methods that incorporate cosmetic dentistry methods and cosmetic treatment aspects of smile design in orthodontic appliance manufacturing (Tsai Para. 0009).
As per Claim 18, Akopov/ Tsai teach the method of claim 17, Akopov further teaches wherein the one or more clinician modifications are encoded in the dental protocol language. ([Para. 0016] A set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional. [Para. 0019] Updating the domain-specific language specific to the specified dental professional, and/or storing the domain-specific language specific to the specified dental professional in a remote database accessible by the treatment plan optimizing engine. Any of these methods may include automatically generating the domain-specific language specific to the specified dental professional, or manually converting textual instructions into the domain-specific language specific to the specified dental professional.)
As per Claim 19, Akopov/ Tsai teach the method of claim 17, Akopov further teaches further comprising receiving a modification to the data structure at the treatment plan generating module from the clinician. ([Para. 0227] The user (e.g., dental professional) may then, using the interactive display, in real time, toggle between the multiple plans, and select one or a subset of treatment plans 319. Optionally, the user may modify one or more plans 319; if the user modifies a treatment plan in a manner that exceed the pre-calculated plurality of treatment plans 321, then the modifications may be transmitted to back to the automated treatment planning subsystem (including the treatment plan optimizing generator) to generate additional treatment plans including the user's modifications 337. These new treatment plans may replace or supplement the plans already pre-calculated. [Para. 0343] The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. This may initially be performed manually or semi-automatically, and may initially include manual review and checking (including checking with the user). However, once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.)
As per Claim 21, Akopov/ Tsai teach the method of claim 17, Akopov further teaches wherein the predefined treatment parameters are from a library of clinician treatment parameters preferences indexed by the clinician. ([Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.)
As per Claim 22, Akopov/ Tsai the method of claim 17, Akopov further teaches wherein the predefined treatment parameters preferences are based on prior treatment plans approved by the clinician for other patients. ([Para. 0016] Collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a treatment plan optimizing engine (e.g., a processor), a set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional, further wherein the textual instructions comprise unscripted instructions, and a second set of rules converted from a set of scripted instructions from the specified dental professional, wherein the scripted instructions comprise responses from a script of predefined choices. ([Para. 0017] The scripted instructions from the specified dental professional may be specific to the patient's teeth. The textual instructions may be specific to the patient's teeth, and/or the textual instructions may be extracted from a plurality of different prior cases by the specified dental professional.)
As per Claim 23, Akopov/ Tsai teach the method of claim 1, Akopov further teaches wherein the treatment plan specific to the patient is configured to treat the patient's teeth malocclusions, wherein the method further comprises:
receiving approval of the treatment plan specific to the patient from the dental practitioner; ([Para. 0230] The user interface configured to allow interactive display of a plurality of different alternative treatment plans (“CCWeb”) may be used to review and select, and in some variations, modify, the treatment plans in the array of treatment plans. The patient may be consulted, as discussed above. Once the user selects a single treatment plan, and is satisfied with the treatment plan, the user may then transmit the selected treatment plan to the manufacturer (technician) who may (optionally) review and send a finalized version of the treatment plan for final approval. [Para. 0367] Finally, if the treatment plan looks good, the user may indicate approval 4615.)
and fabricating a series of aligners for treating the patient's teeth malocclusions according to the approved treatment plan specific to the patient. ([Para. 0230] Once approved, the treatment plan, including all of the stages of aligners, may be fabricated using the treatment plan either directly or converting it into a manufacturing format. [Para. 0238] manufacturing a series of aligners for a patient's teeth. In particularly, this method may allow the real-time analysis and review of a huge number of treatment plans, selection of one of these treatment plans, and fabrication of a series or sequence of aligners based on these treatment plans.)
As per Claim 25, Akopov/ Tsai teach the method of claim 1, Akopov further teaches wherein the practitioner-specific subset of textual rules are conditional based on whether the one or more clinical conditions exist in the patient's teeth. ([Para. 0047] The digital model of the patient's teeth, as well as any of the user's patient-specific or user-specific preferences and/or the dental product(s) to be used may be used as inputs (e.g., sent to a remote site) to generate the array of treatment plans. The process of generating the treatment plans may be automated and may be fast (e.g., within a few seconds, minutes, or hours). Each treatment plan generated may include a final position, staging (e.g., a description of tooth movement directions along with a speed associated with each stage) and (optionally) a set of aligner features placed on each tooth to improve predictability of the treatment and ensure teeth movements occur. In generating each of these treatment plans, the final position of the teeth may be determined so as to address all or some of the patient's clinical conditions (e.g., malocclusions) such a crowding, bite issues, etc., and/or may approximate, as closely as possible, an ideal tooth position that may be achieved for the patient's teeth.)
As per Claim 26 , Akopov/ Tsai teach the method of claim 25, Akopov further teaches wherein the practitioner-specific subset of textual rules are further conditional based on whether one or more specific orthodontic movements are to be performed. ([Para. 0046] At the start of any of the methods described herein the dental professional may provide input, including patient-specific preferences or preferences specific to the dental professional (which may be applied to all of that dental professionals patients). Such preferences may include tooth movement restrictions (e.g., indicating which teeth should not move as part of the treatment).)
As per Claim 27, Akopov/ Tsai teach the method of claim 26, Akopov further teaches wherein the one or more specific orthodontic movements include one or more of: performing tooth intrusion movement, performing tooth extrusion movement, performing distalization, and performing tooth rotation. ([Para. 0244] The user interface may display the characteristics and/or user preferences that went into designing the treatment plan, such as the number or range of stages (e.g., a comprehensive plan having >21 stages), the amount of tooth movement (minimal or not), a description of the clinical goals (e.g., improving overbite, posterior cross bite, etc.), and aligner/staging features (e.g., pre-restorative spaces, IPR, expansion, proclination, extractions, elastic or surgical, distalization, attachments, etc.). [Para. 0285] Tooth movement limits may include rotation (e.g., tooth rotation along z axis); tip (e.g., tooth rotation along x axis), torque (e.g., tooth rotation along y axis); crown movement, including horizontal crown movement (e.g., translation along z axis ignored), buccal-lingual crown movement (crown center translation along x axis), mesial-distal crown movement (e.g., crown center translation along y axis); mesial-distal root apex movement (e.g., root apex translation along y axis); buccal-lingual root apex movement (e.g., root apex translation along x axis); extrusion/intrusion (e.g., tooth translation along z axis); and relative extrusion.)
As per Claim 28, Akopov/ Tsai teach the method of claim 25, Akopov further teaches wherein the one or more clinical conditions include one or more of: open bite, deep bite, tooth crowding, tooth space, tooth rotation, and tooth inclination. ([Para. 0047] In generating each of these treatment plans, the final position of the teeth may be determined so as to address all or some of the patient's clinical conditions (e.g., malocclusions) such as crowding, bite issues. [Para. 0142] In FIG. 12 12, the patient (Joe Smith) is being shown two options, a treatment plan having 26 stages with IPR and aligner attachments, and a second treatment plan having 14 stages without either IPR or aligners. The user interface (display) also indicates that the 26 stage treatment plan resolves the therapeutic goal of reducing crowding and open bite; the 14 stage treatment plan resolves the crowding (but not the open bite). The user interface also indicates the treatment goals upper and lower crowding and open bite) as the malocclusion analysis.)
Claim(s) 3, 16, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akopov (US 20190175303 A1) in view of Tsai (US 20200405445 A1) in view of Upadhyay (US 20210196428 A1).
As per Claim 3, Akopov/ Tsai teach the method of claim 1, however Upadhyay teaches further comprising reconciling any conflict between the textual rules of the practitioner-specific subset of textual rules. ([Abstract] The method compares the expert system treatment options to the multi-component model-based treatment options to determine disagreement or agreement between each other, enabling a suitable treatment decision to be arrived at. [Para. 0003] The method further comprises applying a computer-implemented multi-component model to a given set of feature variables to produce multi-component model-based treatment options that include primary and secondary model-based treatment options and comparing the expert system treatment options to the multi-component model-based treatment options to determine disagreement or agreement between each other. If disagreement, the method further comprises enabling an expert to review the expert system treatment options and the multi-component model-based treatment options, and adapting at least one of the given feature variable, rules-based expert system analysis, or multi-component model based on feedback from the expert. If agreement, the method further comprises outputting the primary and secondary model-based treatment options to a clinician.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov, an orthodontic appliance for dental treatment as taught by Tsai, and incorporate addressing the agreement or disagreement between user preferences as taught by Upadhyay, with the motivation of verifying treatment plans, minimizing human error, training orthodontists, and improving reliability (Upadhyay Abstract).
As per Claim 16, Akopov teaches a method comprising:
receiving, from a dental practitioner, a practitioner-specific subset of textual rules for generating a treatment plan specific to a patient, wherein the textual rules refer to a collection of instructions in a domain-specific treatment language, wherein the practitioner-specific subset of textual rules relate to one or more clinical conditions; ([Para. 0047] The digital model of the patient's teeth, as well as any of the user's patient-specific or user-specific preferences and/or the dental product(s) to be used may be used as inputs (e.g., sent to a remote site) to generate the array of treatment plans. Each treatment plan generated may include a final position, staging (e.g., a description of tooth movement directions along with a speed associated with each stage) and (optionally) a set of aligner features placed on each tooth to improve predictability of the treatment and ensure teeth movements occur. In generating each of these treatment plans, the final position of the teeth may be determined so as to address all or some of the patient's clinical conditions (e.g., malocclusions) such a crowding, bite issues, etc., and/or may approximate, as closely as possible, an ideal tooth position that may be achieved for the patient's teeth. [Para. 0087] Treatment preferences may refer to the treatment preferences of the dental practitioner (e.g., which teeth not to move, etc.) and may be specific to the patient, or may be specific to the user and applied to all of the user's patient's. Thus, treatment details may include details about the product(s) that may be used to achieve the treatment, including the number and type of aligners, and any properties of the aligners themselves. [Para. 0340] There may be two sources of dental professional's preferences on how to prepare treatment plans. One source of treatment preferences which may be essentially a structured input where for a set of questions, the user provides answers, where each answer is a selection from a set of predefined answers. The second source of information may be represented as a text-based comments which defines the user's personal rules to follow when preparing a treatment plan for a doctor. Domain specific language may be used to store user's non-structure input (e.g., text comments describing his treatment preferences) which may enable full automation of treatment planning as well as aggregation of rules from multiple sources (for example, structured preferences and non-structured treatment preferences). [Para. 0341] While setting up a treatment plan, a technicians uses both structured treatment preferences and non-structured treatment preferences. Text-based comments expressing doctors treatment planning style may be converted into a domain-specific language (manually or automatically) and the methods or apparatus (e.g., software) may interpret this domain-specific language to automatically apply doctors preferences for treatment planning preparation.)
encoding the practitioner-specific subset of textual rules into a set of instructions in the domain-specific treatment language; ([Para. 0016] A set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional. [Para. 0019] Updating the domain-specific language specific to the specified dental professional, and/or storing the domain-specific language specific to the specified dental professional in a remote database accessible by the treatment plan optimizing engine. Any of these methods may include automatically generating the domain-specific language specific to the specified dental professional, or manually converting textual instructions into the domain-specific language specific to the specified dental professional.)
applying the set of instructions to a test dental data set to generate a test treatment plan, wherein the set of instructions include instructions for moving one or more teeth of the digital model according to the practitioner-specific subset of textual rules; ([Para. 0226] In FIG. 3, the method starts by collecting from the patient (and by the user, e.g., dental professional), a model of the patient's teeth 303, as well as any conditions (e.g., tooth movement restrictions) or preferences for treatment 301; the method or apparatus may also optionally identify any general preferences that are specific to the dental processional 302, and that may be applied to all of that dental professional's patients. In addition (and optionally) the method or apparatus may also provide an indication of the type of dental product (e.g., the type of dental/orthodontic products to be used to treat the patient 304. The treatment plan optimizing generator, may use this information to generate a plurality of treatment plans. For example, the treatment plan optimizing generator may first prepare the model 305, e.g., by digitizing it if it is not already a digital model, and by segmenting the digital model into individual teeth, gingiva, etc. Once prepared, the automated treatment planning 307 may be performed to automatically generate multiple treatment plans using, e.g., different numbers of stages; for each stage multiple variations including different treatment properties (e.g., IPR, attachments, etc.) may also be generated 311. Each treatment plan is complete, and may be used to build an aligner series. For example, each treatment plan may include a new (and potentially unique) final position for the patient's teeth at the end of the treatment, staging showing the tooth movement and speed of movement for each stage (e.g., key frames) and a set of aligner features 313. The corresponding aligner features may include the location of the attachments, etc.)
modifying, based on input from the dental practitioner, the practitioner-specific subset of textual rules; ([Para. 0343] The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. Once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.)
generating a treatment plan specific to the patient by applying the practitioner-specific subset of textual rules to the patient's dental data, wherein the treatment plan includes the use of series of aligners. ([Para. 0009] The model may be transmitted along with one or more of: treatment preferences from the dental professional specific to the patient, treatment preferences specific for the particular dental professional that may be applied to all patient's associated with that dental professional, and/or an indication of what clinical product(s) (e.g., orthodontic product) should be used to move the patient's teeth. This data may be used as inputs to generate the plurality of optional treatment plans. [Para. 0010] Creation of a large number of full treatment plans specific and customized to a patient setting froth an orthodontic and/or dental plan for beneficially modifying the subject's dentition, including in particular, moving (e.g., aligning, straightening, etc.) the patients teeth and/or resolving orthodontic issues specific to the patient. A treatment plan may include a series of patient-removable appliances to reposition the teeth, and some indication of the duration of time each appliance (“aligner”) is to be worn. [Para. 0344] When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning.)
and providing instructions to physically fabricate the series of aligners that are shaped and sized specifically to treat the patient's teeth malocclusions based on the generated treatment plan. ([Para. 0012] An automated method of creating a plurality of variations of treatment plans to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages may include: (a) specifying a set of treatment preferences and a set of treatment details (the treatment preferences and treatment details may be automatically or manually specified); (b) automatically determining a treatment plan based on the specified treatment preferences and treatment details. [Para. 0197] manufacture of a series or sequence of orthodontic aligner appliances that maybe worn sequentially to correct malocclusion(s). For example, FIG. 1 illustrates an exemplary tooth repositioning appliance or aligner 100 that can be worn by a patient in order to achieve an incremental repositioning of individual teeth 121 in the jaw. An appliance or portion(s) thereof may be indirectly fabricated using a physical model of teeth. For example, an appliance (e.g., polymeric appliance) can be formed using a physical model of teeth and a sheet of suitable layers of polymeric material. In some embodiments, a physical appliance is directly fabricated, e.g., using additive manufacturing techniques, from a digital model of an appliance. An appliance can fit over all teeth present in an upper or lower jaw, or less than all of the teeth. The appliance can be designed specifically to accommodate the teeth of the patient (e.g., the topography of the tooth-receiving cavities matches the topography of the patient's teeth), and may be fabricated based on positive or negative models of the patient's teeth generated by impression, scanning, and the like.)
Akopov does not explicitly teach, however Tsai teaches
wherein the modifying comprises appending one or more modifications as a higher layer to predefined treatment parameters; ([Para. 0052] One or more smile templates that are referred to herein as “crown rulers.” The crown ruler 100 is a virtual 3-D model of an ideal smile (i.e., a 3-D smile template). [Para.0029] (i) retrieve a first data structure that defines a digital 3-D smile template of at least two teeth arranged in accordance with predetermined criteria (i.e. predefined treatment parameters) from a memory, (ii) retrieve a second data structure that defines an orthodontic setup of a patient's teeth from the memory, and (iii) superimpose the digital 3-D smile template of the at least two teeth (i.e. higher layer) on the digital orthodontic setup of a patient's teeth using the first and second data structures. [Para. 0063] An exemplary user interface 120 is shown in FIG. 3 by which the practitioner may adjust one or more of the predetermined criteria (i.e. one or more modifications) by way of settings 122 to account for deviations between the patient's anatomical characteristics and the selected crown ruler 100. As the clinician adjusts any single one of the settings 122, the crown ruler 100 is automatically visually updated. [Para. 0068] Once any single one of the settings 122 is modified, the crown ruler 100 is visually updated according to the new value for that setting 122. Advantageously, the settings 122 may be adjusted while the crown ruler 100 is superimposed (i.e. higher layer) with the patient's digital orthodontic setup.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov and incorporate an orthodontic appliance for dental treatment as taught by Tsai, with the motivation of providing orthodontic appliances, computer modeling tools and systems, and treatment preparation methods that incorporate cosmetic dentistry methods and cosmetic treatment aspects of smile design in orthodontic appliance manufacturing (Tsai Para. 0009).
Akopov/ Tsai do not explicitly teach, however Upadhyay teaches
reconciling any conflict between the textual rules of the practitioner-specific subset of textual rules; ([Abstract] The method compares the expert system treatment options to the multi-component model-based treatment options to determine disagreement or agreement between each other, enabling a suitable treatment decision to be arrived at. [Para. 0003] The method further comprises applying a computer-implemented multi-component model to a given set of feature variables to produce multi-component model-based treatment options that include primary and secondary model-based treatment options and comparing the expert system treatment options to the multi-component model-based treatment options to determine disagreement or agreement between each other. If disagreement, the method further comprises enabling an expert to review the expert system treatment options and the multi-component model-based treatment options, and adapting at least one of the given feature variable, rules-based expert system analysis, or multi-component model based on feedback from the expert. If agreement, the method further comprises outputting the primary and secondary model-based treatment options to a clinician.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov, an orthodontic appliance for dental treatment as taught by Tsai, and incorporate addressing the agreement or disagreement between user preferences as taught by Upadhyay, with the motivation of verifying treatment plans, minimizing human error, training orthodontists, and improving reliability (Upadhyay Abstract).
As per Claim 24, Akopov/ Tsai/ Upadhyay teach the method of claim 16, Akopov further teaches wherein the treatment plan specific to the patient is configured to treat the patient's teeth malocclusions, wherein the method further comprises:
receiving approval of the treatment plan specific to the patient from the dental practitioner; ([Para. 0230] The user interface configured to allow interactive display of a plurality of different alternative treatment plans (“CCWeb”) may be used to review and select, and in some variations, modify, the treatment plans in the array of treatment plans. The patient may be consulted, as discussed above. Once the user selects a single treatment plan, and is satisfied with the treatment plan, the user may then transmit the selected treatment plan to the manufacturer (technician) who may (optionally) review and send a finalized version of the treatment plan for final approval. [Para. 0367] Finally, if the treatment plan looks good, the user may indicate approval 4615.)
and fabricating a series of aligners for treating the patient's teeth malocclusions according to the approved treatment plan specific to the patient. ([Para. 0230] Once approved, the treatment plan, including all of the stages of aligners, may be fabricated using the treatment plan either directly or converting it into a manufacturing format. [Para. 0238] manufacturing a series of aligners for a patient's teeth. In particularly, this method may allow the real-time analysis and review of a huge number of treatment plans, selection of one of these treatment plans, and fabrication of a series or sequence of aligners based on these treatment plans.)
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akopov (US 20190175303 A1) in view of Tsai (US 20200405445 A1) in view of Brown (US 20220198573 A1).
As per Claim 11, Akopov/ Tsai teach the method of claim 10, however Brown teaches wherein the characteristic of the dental practitioner comprises a size of the dental practitioner's practice, a number of dental aligner cases performed by the dental practitioner, and a geographic location of the dental practitioner. ([Abstract] The application accesses a plurality of entries on providers, the entries including the provider's name, location, offered dental services, pricing, hours, and contact information. [Para. 0037] The Matching Tool comprises a dynamic matching algorithm that accounts for pricing, customer reviews of a dentist or dental practice, location of a dental practice (i.e. a geographic location of the dental practitioner), hours of operation, types of services provided (i.e. number of dental aligner cases performed by the dental practitioner), and dentist or appointment availability (i.e. size of the dental practitioner's practice). The weight provided to each category may be set by algorithm, adjusted by a user, adjusted by an administrator, or may be customized for a particular geographic area, market, employer, or plan.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov, an orthodontic appliance for dental treatment as taught by Tsai, and incorporate information about dentist and dental practices as taught by Brown, with the motivation of optimizing outreach to potential customers and helping drive business to their practices (Brown Para. 0004).
Claim(s) 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akopov (US 20190175303 A1) in view of Tsai (US 20200405445 A1) in view of Cunliffe (US 20230218371 A1).
As per Claim 13, Akopov/ Tsai teach the method of claim 1, however Cunliffe further teaches further comprising providing a patient data set from the dental practitioner as the test dental data set. ([Para. 0009] A digital three-dimensional (3D) model of a patient's malocclusion (i.e. patient data set) is received input. [Para. 0048] When a new case is received by the system, each of these unique machine learning models can be applied to generate a treatment plan for each of these groups. [Para. 0053-0054] Provide the user an exercise in which multiple setups for the same case are presented, and have the user either rank the setups or select which setup is preferred. Use this exercise to train a model to predict one or more of a set of setups that are most preferable to this user, and display these setups to that user. User (e.g. doctor or technician) has submitted a case (i.e. patient data) and is offered the opportunity to participate in the exercise.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov, an orthodontic appliance for dental treatment as taught by Tsai, and incorporate user ranking as taught by Cunliffe, with the motivation of reducing the time associated with evaluating a variety of dental treatment options (Cunliffe Para. 0002).
As per Claim 14, Akopov/ Tsai teach the method of claim 1, however Cunliffe further teaches wherein the test dental data set comprises a standardized dental data set. ([Para. 0038-0048] Treatment plans from previously treated patients are collected (i.e. standardized dental data set). These plans are separated into groups according to specific characteristics. From each of these (non-exhaustive) groups of cases, a different machine learning model is developed. When a new case is received by the system, each of these unique machine learning models can be applied to generate a treatment plan for each of these groups.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of automatic treatment planning as taught by Akopov, an orthodontic appliance for dental treatment as taught by Tsai, and incorporate using historical data of patients as test cases for generating a variety of different treatment plans as taught by Cunliffe, with the motivation of reducing the time associated with evaluating a variety of dental treatment options (Cunliffe Para. 0002).
Response to Arguments
Applicant's arguments, see pgs. 8-9 “Claim Rejections under 35 U.S.C. 101” filed 07/02/2026 have been fully considered but they are not persuasive.
Applicant submits that the claims are not directed to an abstract idea, but instead to a specific technological improvements in a computer-implemented orthodontic treatment planning and appliance fabrication systems. The claims recite a particular rule-based and data-structure-driven architecture that governs how treatment logic is defined, represented, refines, and executed by a computer. This layered rule and data structure architecture constitutes a specific improvement in how treatment planning information is structured and processed by a computer, enabling preservation of baseline parameters while allowing incremental, practitioner-specific refinements. Such functionality is not a mere organization of human activity, but a concrete improvement to computer functionality in the orthodontic planning domain. Examiner respectfully disagrees.
The claims recite (Claim 17 being representative) receiving, from a dental
practitioner, a practitioner-specific subset of textual rules for generating a treatment plan
specific to a patient, wherein the practitioner- specific subset of textual rules are
selected from with a menu of textual rules specific to orthodontic treatment planning,
wherein the textual rules refer to a collection of instructions in a domain-specific
treatment language, wherein the practitioner-specific subset of textual rules relate to
one or more clinical conditions; encoding the practitioner-specific subset of textual rules
into a set of instructions in the domain-specific treatment language; applying the set of
instructions to a test dental data set to generate a test treatment plan, wherein the set of
instructions include instructions for moving one or more teeth of a digital model
according to the practitioner-specific subset of textual rules; modifying, based on input
from the dental practitioner, the practitioner-specific subset of textual rules; storing the
practitioner-specific subset of textual rules to apply to one or more sets of patient data;
and generating a treatment plan specific to a patient by applying the practitioner-specific subset of textual rules to the patient's dental data, wherein the treatment plan includes
the use of series of aligners. These functions recite a series of rules and instruction that a user would follow to generate custom orthodontic treatment and plan for a patient. The method of organizing human activity includes the grouping of managing personal behavior and interactions between people, which includes following rules or instructions (see MPEP 2106.04 (a)(2)(II)(C)). Therefore, the claims is directed towards a certain method of organizing human activity. Furthermore, certain methods of organizing human activity encompasses certain activity between a person and a computer (see MPEP § 2106.04(a)(2)(II)).
The steps of representing treatment logic as practitioner-specific textual rules encoded in a domain-specific treatment language; applying those rules to digital models of teeth to generate treatment plans; performing iterative validation; and maintaining a layered data structure in which clinician modifications are appended as a higher layer to predefined treatment parameters have been previously identified as being part of the abstract idea. An improvement to the abstract idea of improving how treatment planning information is structured and processed does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG,921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to data structure and processing. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Merely reciting particular rule-based and data-structure-driven architecture does not amount to a particular technological improvement.
Applicant further submits that even if the claims were characterized as involving an abstract idea, they recite significantly more by integrating that idea into a practical application. The claims require: specialized data representations (textual rules and dental protocol language); specific processing modules configured to operate on layered treatment data; generation of treatment plans through execution of those rules on digital tooth models; and providing instructions to physically fabricate, or fabrication of series of aligners based on the computer treatment plan. These elements, taken together, reflect a specific technological implementation that enables automated, consistent, and scalable treatment planning and direct manufacture of orthodontic appliance, rather than routine or conventional use of generic computer components. Examiner respectfully disagrees.
Examiner has identified specialized data representations (textual rules and dental protocol language); specific processing modules configured to operate on layered treatment data; and the generation of treatment plans through execution of those rules on digital tooth models as being part of the abstract idea. The providing instructions to physically fabricate, or fabrication of series of aligners based on the computer treatment plan has been identified as additional elements that do not meet the criteria of effecting a particular treatment or prophylaxis for a disease or medical condition because the claims merely recite a high-level recitation of a treatment without explicitly providing a particular treatment for a particular disease or medical condition or the specific process or materials needed to carry out the fabrication/ manufacturing process. Furthermore, the limitations do not specifically claim how the fabricating the series of aligners is going to treat a specific malocclusion, nor does the claims recite the particular special techniques or consideration in the fabrication of aligners, such as the material(s) utilized to fabricate the aligners or the specifics of how the aligners are fabricated. The treatment limitation does not have a significant relationship to the judicial exception – that is it does not integrate the law of nature into a practical application. The specific treatment, the particular disease or medical condition, and the particular special techniques or considerations fail to be explicated recited, any possible treatment combination could not reasonably be considered known in the art as a treatment for any disease. The providing instructions to physically fabricate, or fabrication of series of aligners based on the computer treatment plan merely provides a recommendation of steps to follow in order to manufacture aligners to help treat the patient’s malocclusions and does not indicate the physical manufacture of the aligners. Therefore, this limitation is a practical application of the recited judicial exception.
An improvement to the abstract idea of improving automated, consistent, and scalable treatment planning does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG,921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to treatment planning. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
In regards to the improvement being to the direct manufacture of orthodontic appliance, the limitation(s) of providing instruction(s) is/ are not the same as the physical steps of undergoing the manufacturing process of fabricating aligners. The steps of providing instructions to physically fabricate, or fabrication of a series of aligners based on the computed treatment plan merely provides a recommendation of steps to follow in order to manufacture aligners to help treat the patient’s malocclusions and does not indicate the physical manufacture of the aligners. These limitations do not meet the criteria for effecting a particular treatment or prophylaxis for a disease or medical condition and is therefore not a practical application of the recited judicial exception.
Applicant's arguments, see pgs. 9-10 “Claim Rejections under 35 U.S.C. 102” filed 07/02/2026 have been fully considered are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Tsai, as per the rejection above.
Applicant's arguments, see pgs. 10-12 “Claim Rejections under 35 U.S.C. 103” filed 07/02/2026, have been fully considered but they are not persuasive.
Applicant submits that Tsai does not describe or suggest a data structure that includes predefined treatment parameters and one or more clinician modification appended as a higher layer. Examiner respectfully disagrees.
Tsai teaches at Para. 0052 that one or more smile templates that are referred to herein as “crown rulers.” The crown ruler 100 is a virtual 3-D model of an ideal smile (i.e., a 3-D smile template). Para.0029 teaches (i) retrieve a first data structure that defines a digital 3-D smile template) of at least two teeth arranged in accordance with predetermined criteria (i.e. data structure includes predefined treatment parameters) from a memory, (ii) retrieve a second data structure that defines an orthodontic setup of a patient's teeth (i.e. digital model) from the memory, and (iii) superimpose the digital 3-D smile template of the at least two teeth (i.e. higher layer) on the digital orthodontic setup of a patient's teeth using the first and second data structures. Para. 0063 further teaches an exemplary user interface 120 is shown in FIG. 3 by which the practitioner may adjust one or more of the predetermined criteria (i.e. data structure includes one or more clinician modifications) by way of settings 122 to account for deviations between the patient's anatomical characteristics and the selected crown ruler 100. As the clinician adjusts any single one of the settings 122, the crown ruler 100 is automatically visually updated. Para. 0068 teaches that once any single one of the settings 122 is modified, the crown ruler 100 is visually updated according to the new value for that setting 122. Advantageously, the settings 122 may be adjusted while the crown ruler 100 is superimposed (i.e. higher layer) with the patient's digital orthodontic setup.
Tsai teaches that the 3D smile template is an ideal smile template, that is arranged based off predetermined criteria (i.e. predefined treatment parameters), that is superimposed on the digital orthodontic setup of the patient’s teeth. This is indicative of the 3D smile template being appended as a higher level on top of the patient’s teeth. The 3D smile template is further adjusted by input from a clinician and these changes are then reflected through visual updates while the crown ruler (i.e. 3D smile template) is superimposed on the patient’s digital orthodontic setup. This is indicative of a data structure that includes predefined treatment parameters and one or more clinician modification appended as a higher layer.
Independent claims 1, 16, and 17 remain rejected under 35 U.S.C. 103, therefore the rejections of dependent claims 2-5, 8-14, 18-19, and 21-28 are maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patricia K Edouard whose telephone number is (571)272-6084. The examiner can normally be reached Monday - Friday 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at 469-295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/P.K.E./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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