DETAILED ACTION
The Applicant’s filing, received 05 June 2023, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
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 the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claims 1, 13, and 15 are objected to.
Priority
There are no domestic or foreign applications for which benefit is claimed.
Therefore, the effective filing date of the claimed invention is 05 June 2023.
Drawings
The drawings received 05 June 2023 have been objected to, for the reason noted below.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference # 404 in Fig. 4; and
Reference # 508 in Fig. 5.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
‘segmenting’/’segment,’ by an input resource, in claims 1, 13, and 15; and
‘proposing’/’propose’, using the restoration proposal module, in claims 1, 13, 15, and 19.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The specification discloses a corresponding structure for the non-structural generic placeholder:
‘proposing’/’propose’, using the restoration proposal module, in claims 1, 13, 15, and 19, e.g., in the specification at paras. [0056] & [0060] (may comprise a trained deep neural network/machine learning model, e.g., a convolutional neural network or Pointnet (i.e., software)) and at para. [0046] (processes may be performed by processing unit 206 in Fig. 2 (i.e., hardware)).
The specification does not disclose a corresponding structure for the non-structural generic placeholder:
‘segmenting’/’segment,’ by an input resource, in claims 1, 13, and 15.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claims 1, 13, and 15 are objected to because of the following informalities: The comma after the word omitting/omit in the first ‘responsive’ clause should be deleted.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 13, and 15 recite the limitation ‘segmenting’/’segment’, by an ‘input resource,’ however the disclosure does not clearly link any structure for the ‘input resource’ as required by MPEP 2181. For example, the specification describes a function of the ‘input resource’ (e.g., “An input resource may segment the 3D jaw model to obtain at least one segmented …” at para. [0004]) but does not link any structure to the function.
Claims 2-12 are rejected for depending from claim 1 and failing to remedy the failure of claim 1 to comply with the written description requirement.
Claim 14 is rejected for depending from claim 13 and failing to remedy the failure of claim 13 to comply with the written description requirement.
Claims 16-18 are rejected for depending from claim 15 and failing to remedy the failure of claim 15 to comply with the written description requirement.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 13, and 15 recite the limitation ‘segmenting’/’segment,’ by an input resource, which invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For example, the specification describes a function of the ‘input resource’ (e.g., “An input resource may segment the 3D jaw model to obtain at least one segmented …” at para. [0004]) but does not link any structure to the function. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
For purposes of examination and clarity of record, the limitation ‘segmenting’/’segment,’ by an input resource, has been interpreted to mean ‘instructions’ (e.g., an algorithm implemented as software) for dividing a digital object into different parts or regions.
Claims 1, 13, 15, and 19 are indefinite for reciting “proposing/propose, using the restoration proposal module,” because it is not clear as to whether the term ‘module’ is limited to instructions or alternatively comprises a hardware component (e.g., the term ‘engine’ with regard to machine learning could refer to the core software, algorithm, or hardware component that executes tasks, runs models, or automates functions). For the purpose of examination and clarity of record, the limitation “restoration proposal module” has been interpreted to mean instructions in an algorithm that is implemented as software.
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Claim 20 is indefinite for depending from claim 19 and failing to remedy the indefiniteness of claim 19.
Claim 1 is indefinite for reciting “segmenting, by an input resource …” because it is not clear as to whether the ‘input resource’ is data (e.g., input data, input features, etc.) or alternatively if the ‘input resource’ is a set of instructions and/or programs (e.g., software) that tell a computer or device how to perform a specific task (e.g., ‘segmenting’).
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 6 contains the trademark/trade name “Pointnet.” Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe a type of deep learning architecture and, accordingly, the identification/description is indefinite.
Claim 13 is indefinite for reciting “segmenting, by an input resource …” because it is not clear as to whether the ‘input resource’ is data (e.g., input data, input features, etc.) or alternatively if the ‘input resource’ is a set of instructions and/or programs (e.g., software) that tell a computer or device how to perform a specific task (e.g., ‘segmenting’).
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claim 15 is indefinite for reciting “segment, by an input resource …” because it is not clear as to whether the ‘input resource’ is data (e.g., input data, input features, etc.) or alternatively if the ‘input resource’ is a set of instructions and/or programs (e.g., software) that tell a computer or device how to perform a specific task (e.g., ‘segment’).
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Claim 1 recites the limitation “the at least one prepared tooth area” in line ten. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “segmenting …to obtain at least one segmented …prepared tooth area.”
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 1 recites the limitation "the modified 3D jaw model" in line ten. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites at lines two and three “receiving …the 3D jaw model including at least one missing or prepared tooth” and therefore the first ‘responsive’ clause reciting “responsive to …to obtain a modified 3D jaw model” is a contingent limitation, and therefore there is insufficient antecedent basis for this limitation in the claim.
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 1 recites the limitation "the restoration proposal module" in line eleven. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-12 are indefinite for depending from claim 1 and failing to remedy the indefiniteness of claim 1.
Claim 13 recites the limitation “the at least one prepared tooth area” in line twelve. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “segmenting …to obtain at least one segmented …prepared tooth area.”
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claim 13 recites the limitation "the modified 3D jaw model" in line twelve. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites at lines four and five “receiving …the 3D jaw model including at least one missing or prepared tooth” and therefore the first ‘responsive’ clause reciting “responsive to …to obtain a modified 3D jaw model” is a contingent limitation, and therefore there is insufficient antecedent basis for this limitation in the claim.
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claim 13 recites the limitation "the restoration proposal module" in line eleven. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 is indefinite for depending from claim 13 and failing to remedy the indefiniteness of claim 13.
Claim 14 is further indefinite for reciting the limitation “the modified training 3D jaw model” in line fifteen, because the claim previously recites “receiving a plurality of training 3D jaw models that each include at least one healthy and/or prepared training tooth” and therefore the first ‘responsive’ clause reciting “responsive to … to obtain a modified training 3D jaw model” is a contingent limitation, and therefore there is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation “the at least one healthy and/or prepared training segmented tooth” in lines 21-22. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “segmenting …to identify at least one healthy and/or prepared training segmented tooth area…” in lines seven and eight.
Claim 15 recites the limitation "the restoration proposal module" in line thirteen. There is insufficient antecedent basis for this limitation in the claim.
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Claim 15 recites the limitation “the at least one prepared tooth area” in line twelve. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “segment …to obtain at least one segmented …prepared tooth area…” in lines seven and eight.
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Claim 15 is further indefinite for reciting the limitation “the modified 3D jaw model” in line twelve, because the claim previously recites “receive … the 3D jaw model including at least one missing or prepared tooth” and therefore the first ‘responsive’ clause reciting “responsive to … to obtain a modified 3D jaw model” is a contingent limitation, and therefore there is insufficient antecedent basis for this limitation in the claim.
Claims 16-18 are indefinite for depending from claim 15 and failing to remedy the indefiniteness of claim 15.
Claim 16 is further indefinite for reciting the limitation “the modified training 3D jaw model” in line fourteen, because the claim previously recites “receiving a plurality of training 3D jaw models that each include at least one healthy and/or prepared training tooth” and therefore the first ‘responsive’ clause reciting “responsive to … to obtain a modified training 3D jaw model” is a contingent limitation, and therefore there is insufficient antecedent basis for this limitation in the claim.
Claim 17 is indefinite for depending from claim 16 and failing to remedy the indefiniteness of claim 16.
Claim 16 recites the limitation “the at least one healthy and/or prepared training segmented tooth” in lines 20-21. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “segmenting …to identify at least one healthy and/or prepared training segmented tooth area…” in lines six and seven.
Claim 17 is indefinite for depending from claim 16 and failing to remedy the indefiniteness of claim 16.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-12 recite a method (i.e., a process); claims 13-14 recite a non-transitory computer-readable storage medium (i.e., a machine and/or a manufacture); claims 15-18 recite a system comprising a processor and a memory storing instructions (i.e., a machine and/or a manufacture); and claims 19-20 recite a method (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
segmenting the 3D jaw model to obtain at least one segmented missing or prepared tooth area (i.e., mathematical concepts, e.g., a max-flow min-cut algorithm for 3D model segmentation is a mathematical process ground in graph theory and combinatorial optimization, wherein a 3D model or volumetric data (voxels) is converted into a mathematical graph (G = (V,E), where vertices (V) represent volume elements or pixels, and edges (E) represent boundaries between neighbors);
responsive to the 3D jaw model including at least one missing tooth, omitting the at least one segmented missing tooth area from the 3D jaw model to obtain a modified 3D jaw model (i.e., mathematical concepts);
responsive to the 3D jaw model including at least one prepared tooth, indicating a position of the at least one prepared tooth area to obtain the modified 3D jaw model (i.e., mental processes, e.g., evaluation and selection); and
proposing, using the restoration proposal module, and the modified 3D jaw model as input to the restoration proposal module, at least one output restoration for the at least one segmented missing or prepared tooth (i.e., mental processes, e.g., converting 3D coordinates of model vertices and edges into lists of numbers, vectors, or grids; and mathematical concepts, e.g., linear algebra, calculus, and statistics);
wherein the restoration proposal module is operated as a machine learning engine (i.e., mental processes and mathematical concepts, e.g., the simplest type of machine learning model could be a linear regression, which predicts a continuous numerical output by fitting a straight line (y = mx + b) to data, by using an input variable (x) and an output variable (y), making its mathematical relationship easy to calculate and interpret).
Independent claim 13 recites a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to carry out the abstract ideas recited by independent claim 1, as noted above.
Independent claim 15 recites a system comprising a processor and a memory storing instructions that, when executed by the processor, configure the system to perform the abstract ideas recited by independent claim 1, as noted above.
Independent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
proposing, using a restoration proposal module, and the 3D jaw model as input to the restoration proposal module, at least one output restoration for the at least one missing or prepared tooth (i.e., mental processes and mathematical concepts);
wherein the restoration proposal module is operated as a machine learning engine (i.e., mental processes and mathematical concepts).
Dependent claims 2-4, 6-11, 14, 16-18 and 20 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
one or more other input data are provided to the restoration proposal module comprising at least one of: an identification of tooth features, one or more tooth numbers identifying one or more teeth of the 3D jaw model, one or more color textures of the one or more teeth, one or more types of morphology of the one or more teeth (i.e., mental processes and mathematical concepts, e.g., information in the format of numbers, and entering those numbers into a mathematical function in a vector format).
Dependent claim 3 further recites:
the at least one output restoration is a surface representation or a volume representation (i.e., mental processes and mathematical concepts, e.g., an output of a mathematical function is the result of the mathematical operation, i.e., information (e.g., data)).
Dependent claim 4 further recites:
the restoration proposal module further proposes at least one tooth number corresponding to the at least one output restoration (i.e., mental processes and mathematical concepts, e.g., an output of a mathematical function is the result of the mathematical operation, i.e., information (e.g., data)).
Dependent claim 6 further recites:
the restoration proposal module is an artificial neural network that is at least one of a convolutional neural network, and a Pointnet (i.e., mathematical concepts, e.g., a neural network comprises a nested multivariate function, where inputs pass through layers of mathematical functions that are chained together).
Dependent claim 7 further recites:
the restoration proposal module is trained by (i.e., mathematical concepts, e.g., training a neural network is fundamentally a mathematical concept and process, comprising linear algebra (e.g., multiplying matrices of data), multivariable calculus (e.g., backpropagation, i.e., derivatives and the chain rule measure how much each individual weight contributes to errors), and statistics and probability (e.g., loss functions calculate the mathematical difference between what the network predicts and the correct answer): for each training 3D jaw model of the plurality of training 3D jaw models:
segmenting the training 3D jaw model to identify at least one healthy and/or prepared training segmented tooth area corresponding to the at least one healthy and/or prepared training tooth (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one healthy training segmented tooth, omitting at least one healthy training segmented tooth area from the training 3D jaw model to obtain a modified training 3D jaw model (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one prepared training tooth, indicating a position of the at least one prepared training segmented tooth area to obtain the modified training 3D jaw model (i.e., mental processes); and
providing the modified training 3D jaw model as input to the restoration proposal module (i.e., mental processes);
proposing, using the restoration proposal module, at least one corresponding training output restoration for the at least one healthy and/or prepared training segmented tooth area (i.e., mathematical concepts);
measuring a difference between the at least one healthy and/or prepared training segmented tooth and the at least one corresponding training output restoration (i.e., mathematical concepts); and
updating parameters of the restoration proposal module and repeating the proposing until the measured difference is minimized (i.e., mathematical concepts).
Dependent claim 8 further recites:
the restoration proposal module is trained in an ongoing fashion using at least data from a continuously growing dataset of jaw situations and/or tooth morphologies (i.e., mathematical concepts).
Dependent claim 9 further recites:
each training 3D jaw model is further preprocessed to obtain at least one other input for training the restoration proposal module by at least one of:
a) estimating a jaw line of the training 3D jaw model by computing a midline along a contour of training 3D jaw model (i.e., mathematical concepts);
b) extracting feature information from the training 3D jaw model (i.e., mental processes);
c) categorizing each tooth of the training 3D jaw model as healthy, unhealthy or prepared tooth (i.e., mental processes);
d) computing an orientation of the training 3D jaw model (i.e., mathematical concepts);
f) computing one or more colored surface texture of a tooth of the training 3D jaw model (i.e., mathematical concepts); and
g) identifying a tooth of the training 3D jaw model to be treated (i.e., mental processes).
Dependent claim 10 further recites:
providing the preprocessed training 3D jaw model as an input to train the restoration proposal module (i.e., mental processes and mathematical concepts).
Dependent claim 11 further recites:
training the restoration proposal module using a plurality of the at least one healthy and/or prepared training tooth one at a time until all healthy and/or prepared teeth in the training 3D jaw model have been used for training (i.e., mathematical concepts).
Dependent claim 14 further recites:
training the restoration proposal module by (i.e., mathematical concepts): for each training 3D jaw model of the plurality of training 3D jaw models:
segmenting the training 3D jaw model to identify at least one healthy and/or prepared training segmented tooth area corresponding to the at least one healthy and/or prepared training tooth (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one healthy training segmented tooth, omitting at least one healthy training segmented tooth area from the training 3D jaw model to obtain a modified training 3D jaw model (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one prepared training tooth, indicating a position of the at least one prepared training segmented tooth area to obtain the modified training 3D jaw model (i.e., mental processes); and
providing the modified training 3D jaw model as input to the restoration proposal module (i.e., mental processes);
proposing, using the restoration proposal module, at least one corresponding training output restoration for the at least one healthy and/or prepared training segmented tooth area (i.e., mathematical concepts);
measuring a difference between the at least one healthy and/or prepared training segmented tooth and the at least one corresponding training output restoration (i.e., mathematical concepts); and
updating parameters of the restoration proposal module and repeating the proposing until the measured difference is minimized (i.e., mathematical concepts).
Dependent claim 16 further recites:
trains the restoration proposal module by (i.e., mathematical concepts): for each training 3D jaw model of the plurality of training 3D jaw models:
segmenting the training 3D jaw model to identify at least one healthy and/or prepared training segmented tooth area corresponding to the at least one healthy and/or prepared training tooth (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one healthy training segmented tooth, omitting at least one healthy training segmented tooth area from the training 3D jaw model to obtain a modified training 3D jaw model (i.e., mathematical concepts);
responsive to the training 3D jaw model including at least one prepared training tooth, indicating a position of the at least one prepared training segmented tooth area to obtain the modified training 3D jaw model (i.e., mental processes); and
providing the modified training 3D jaw model as input to the restoration proposal module (mental processes);
proposing, using the restoration proposal module, at least one corresponding training output restoration for the at least one healthy and/or prepared training segmented tooth area (i.e., mathematical concepts);
measuring a difference between the at least one healthy and/or prepared training segmented tooth and the at least one corresponding training output restoration (i.e., mathematical concepts); and
updating parameters of the restoration proposal module and repeating the proposing until the measured distance is minimized (i.e., mathematical concepts).
Dependent claim 17 further recites:
trains the restoration proposal module in an ongoing fashion using at least data from a continuously growing dataset of jaw situations and/or tooth morphologies (i.e., mathematical concepts).
Dependent claim 18 further recites:
provides one or more other input data to the restoration proposal module comprising at least one of: an identification of tooth features, one or more tooth numbers identifying one or more teeth of the 3D jaw model, one or more colors of the one or more teeth, one or more types of morphology of the one or more teeth (i.e., mental processes and mathematical concepts).
Dependent claim 20 further recites:
the restoration proposal module is trained (i.e., mathematical concepts) by: for each training 3D jaw model of the plurality of training 3D jaw models:
proposing, using the restoration proposal module, at least one corresponding training output restoration for the at least one healthy and/or prepared training tooth (i.e., mathematical concepts);
measuring a difference between the at least one healthy and/or prepared training tooth and the at least one corresponding training output restoration (i.e., mathematical concepts); and
updating parameters of the restoration proposal module and repeating the proposing until the measured difference is minimized (i.e., mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., indicating a position of the at least one prepared tooth area), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., the restoration proposal module is operated as a machine learning engine) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the independent claims provide additional elements to receive data of a ‘3D jaw model’, however once the data is received, the subsequent steps only perform analysis and/or calculations using the data to generate ‘at least one output restoration,’ i.e., data. Thus, the claims do not recite any limitations (i.e., additional elements) to which the ‘output restoration’ (i.e., the result of the data analysis) is practically applied.
Dependent claims 2-4, 6, 8, 10, and 11 do not further recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
receiving a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth (i.e., receiving data).
The additional elements in independent claim 13 include:
a non-transitory computer-readable storage medium;
a computer; and
receiving a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth (i.e., receiving data).
The additional elements in independent claim 15 include:
a processor;
a memory storing instructions; and
receive a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth (i.e., receive data).
The additional elements in independent claim 19 include:
receiving a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth (i.e., receiving data).
The additional elements in dependent claims 5, 7, 9, 12, 14, 16-18, and 20 include:
the 3D jaw model is obtained by scanning a patient's teeth (claim 5);
e) receiving a tooth number for one or more teeth of the training 3D jaw model (i.e., receiving data) (claim 9 at step e));
providing parameters of the at least one output restoration to an operator (i.e., outputting data) (claim 12);
computer (claim 14);
receiving a plurality of training 3D jaw models that each include at least one healthy and/or prepared training tooth (i.e., receiving data) (claims 7, 14, 16, and 20); and
a processor (claims 16-18).
The additional elements of a computer (claims 13 and 14); a non-transitory computer-readable storage medium (claim 13); a processor (claims 15-18); and a memory storing instructions (claim 15); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of receiving/receive a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth (i.e., receiving/receive data) (claims 1, 13, 15, and 19); receiving a tooth number for one or more teeth of the training 3D jaw model (i.e., receiving data) (claim 9 at step e)); and receiving a plurality of training 3D jaw models that each include at least one healthy and/or prepared training tooth (i.e., receiving data) (claims 7, 14, 16, and 20); are merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of the 3D jaw model is obtained by scanning a patient's teeth (claim 5); is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional element of providing parameters of the at least one output restoration to an operator (i.e., outputting data) (claim 12); is merely a post-solution activity used in the claimed process to output data – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-4, 6, 8, 10, and 11 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 13, 15, and 19 and dependent claims 5, 7, 9, 12, 14, 16-18, and 20 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computer (claims 13 and 14); a non-transitory computer-readable storage medium (claim 13); a processor (claims 15-18); a memory storing instructions (claim 15); receiving data (claims 1, 7, 9, 13, 14, 15, 16, 19, and 20); and outputting data (claim 12); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional element of the 3D jaw model is obtained by scanning a patient's teeth (claim 5) is conventional. Evidence of conventionality is shown by:
Lee et al. (“Facial scanners in Dentistry: An overview.” Prosthesis, 2022, vol. 4, pp. 664-678).
Lee et al. reviews facial scanners in dentistry (Title) and explores the current status of facial scanning technology in the dental field, and outlines the history, mechanisms, and current evidence regarding its use and limitations within digital dentistry (Abstract). Lee et al. further shows a list of available facial scanners and software for obtaining a 3D model using facial scanning technology (Table 1).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Azernikov et al. (“Dental CAD Automation Using Deep Learning.” US 2022/0218449, hereinafter “Azernikov et al. ‘449”).
Independent claims 1, 13, and 15 recite a method, a non-transitory computer-readable storage medium, and a computing system, respectively, that encompass steps of receiving a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth; segmenting, by an input resource, the 3D jaw model to obtain at least one segmented missing or prepared tooth area; responsive to the 3D jaw model including at least one missing tooth, omitting, the at least one segmented missing tooth area from the 3D jaw model to obtain a modified 3D jaw model; responsive to the 3D jaw model including at least one prepared tooth, indicating a position of the at least one prepared tooth area to obtain the modified 3D jaw model; and proposing, using the restoration proposal module, and the modified 3D jaw model as input to the restoration proposal module, at least one output restoration for the at least one segmented missing or prepared tooth; wherein the restoration proposal module is operated as a machine learning engine.
Independent claim 19 recites a method that encompasses steps for receiving a three-dimensional (3D) jaw model of a patient, the 3D jaw model including at least one missing or prepared tooth; proposing, using a restoration proposal module, and the 3D jaw model as input to the restoration proposal module, at least one output restoration for the at least one missing or prepared tooth; wherein the restoration proposal module is operated as a machine learning engine.
Dependent claims 2, 4, 5, 9, 10, and 18 further define the data provided to the restoration proposal module (claims 2, 9, and 18), the output of the restoration proposal module (claim 4), how the 3D jaw model is obtained (claim 5), and the input data to train the restoration proposal module (claim 10).
Azernikov et al. ‘449 teaches computer-aided design (CAD) systems and methods for recognizing dental information or features from scan data of patients’ dentitions using deep neural networks (para. [0045]) to generate dental restoration models (para. [0055]).
Regarding independent claims 1, 13, and 15, Azernikov et al. ‘449 shows a processor and a non-transitory computer-readable storage medium (para. [0082]); receiving a 3D scan dental model attached with a client’s restoration request (para. [0109]); segmenting the dental model into different portions of the patient’s dentition (para. [0016]), e.g., segmenting one or more 3D dental models into one or more data set portions, for instance, for each dental feature (e.g., cusps, margin lines, tooth number, dental restoration, prepared tooth, etc.) (para. [0102]); determining locations of identified features dental features in the patient’s scan data, e.g., the location of a prepared tooth that is read to receive a crown (para. [0007]); a deep neural network to identify and characterize various dental features present and/or missing from training data, and one or more deep neural networks to predict the shape and size of a missing tooth, and the shape, size, and location of a prepared tooth (para. [0128]).
Regarding independent claim 19, Azernikov et al. ‘449 shows receiving a dentition data set of a patient, wherein the patient’s dentition data set is generated by scanning a 3D impression or model of the patient’s teeth (claim 20) and the digital dental model can be of a preparation tooth and its surrounding and opposing teeth (para. [0056]); selecting a deep neural network pre-trained by a group of training data sets designed to model a specific restoration type that matches the selected dental restoration type (claim 20); using the patient’s dentition data set as an input to the selected pre-trained deep neural network (claim 20); and generating an output restoration model using the selected pre-trained deep learning neural network based on the patient’s dentition data, where the restoration model is to be used on a preparation site (claim 20), e.g., to generate a 3D model of a crown using one or more of the patient’s dentition data sets (para. [0079]).
Regarding dependent claims 2, 9, and 18, Azernikov et al. ‘449 further shows that the dental information associated with a dental model comprises tooth numbers (para. [0073]).
Regarding dependent claim 4, Azernikov et al. ‘449 further shows a scan recognition module that may apply the trained one or more deep neural networks to scan data to recognize (i.e., identify, locate, and characterize) a prepared tooth (e.g., the tooth number of the prepared tooth) (para. [0135]).
Regarding dependent claim 5, Azernikov et al. ‘449 further shows a 3D digital image or file representing a portion of a patient’s dentition can be acquired by a direct intraoral scan of the patient’s teeth (para. [0085]).
Regarding dependent claim 10, Azernikov et al. ‘449 further shows that each dentition training data set can be a portion of a 3D dental model of one or more portions of a patient’s dentition (para. [0101]).
Thus, Azernikov et al. ‘449 anticipates claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 3, 6, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. ‘449 (“Dental CAD Automation Using Deep Learning.” US 2022/0218449) as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, and further in view of Azernikov et al. (“Neural Network Margin Proposal.” US 2022/0350936, hereinafter “Azernikov et al. ‘936”).
Dependent claims 3, 6, and 12 further define at least one output restoration (claim 3), the restoration proposal module type (claim 6), and providing parameters of the at least one output restoration to an operator (claim 12).
Azernikov et al. ‘449 teaches computer-aided design (CAD) systems and methods for recognizing dental information or features from scan data of patients’ dentitions using deep neural networks (para. [0045]) to generate dental restoration models (para. [0055]).
Azernikov et al. ‘936 teaches a computer-implemented method of automatic margin line proposal including steps of receiving a 3D digital model of at least a portion of a jaw, the 3D digital model including a digital preparation tooth; determining, using a first trained neural network, an inner representation of the 3D digital model; and determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model (Abstract).
Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, does not show the limitations of claims 3, 6, and 12.
Regarding dependent claim 12, Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, does not explicitly show the limitation ‘providing parameters of the at least one output restoration to an operator,’ but does show generating an output restoration model using the selected pre-trained deep neural network based on the patient’s dentition data, wherein the restoration model is to be used on a preparation site (claim 20).
Regarding dependent claim 3, Azernikov et al. ‘936 shows the deep neural networks used may output probabilities and/or a full 3D margin line proposal (e.g. a prediction of the boundary curve where a prepared tooth meets a dental crown or restoration, and thus comprises a surface and/or volume representation) (para. [0072]).
Regarding dependent claim 6, Azernikov et al. ‘936 shows that the deep neural network can also be a convolutional neural network (para. [0074]) and further shows that concatenated features can be passed through a unit PointNet (para. [0064]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, by incorporating methods for using a convolutional neural network, e.g., with an architecture for using point clouds for segmentation of a 3D model and outputting representations as shown by Azernikov et al. ‘936 and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, with the methods of Azernikov et al. ‘936, because Azernikov et al. ‘936 shows methods for using pointnet as a local feature learner to process a set of points that are unordered to perform feature extraction for model segmentation and outputting a surface and/or volume representation using a convolutional neural network. This modification would have had a reasonable expectation of success given that both Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, and Azernikov et al. ‘936 disclose methods for using neural networks for dental restoration modeling.
Claims 7, 8, 11, 14, 16, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. ‘449 (“Dental CAD Automation Using Deep Learning.” US 2022/0218449) as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, and further in view of Piche et al. (“Automatic Generation of Dental Restorations Using Machine Learning.” WO 2022/016294).
Dependent claims 7, 8, 11, 14, 16, 17, and 20 further define steps for training the restoration proposal module (claims 7, 14, 16, and 20), the restoration proposal module is trained in an ongoing fashion using at least data from a continuously growing dataset (claims 8 and 17), and training the restoration proposal module using a plurality of the at least one healthy and/or prepared training tooth one at a time (claim 11).
Piche et al. teaches a method and a system for generating a three-dimensional crown surface for replacing a missing tooth taking into account one or more of a dental preparation, margin line, occlusion, the gap between the preparation and adjacent and opposing teeth (Abstract).
Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, does not show the limitations of dependent claims 7, 8, 11, 14, 16, 17, and 20.
Regarding dependent claims 7, 14, 16, and 20, Piche et al. shows a method for training an AI model for segmenting a digital 3D representation of a mouth (para. [0020]; and Fig. 2); the AI model is the result of applying learning algorithms on a training dataset to label the teeth by giving each tooth a name, e.g., 17, 26, 14, etc. (i.e., position) (para. [0053]); the output of the AI model may be a probability distribution over a plurality of classes, where each tooth of the mouth may be considered as a class, while another class may be dental preparations, and at the end of the training, the AI model is able to recognize and classify teeth at their position, missing teeth and preparations (para. [0056]); the generalization error may be defined as the distance between the predicted classification and the classification provided by the labeler (i.e., a ground truth) (para. [0057]) and the AI model computes the parameters that best fit the training dataset by minimizing the training error to find values of parameters that make the prediction of the AI model optimal (para. [0034]).
Regarding dependent claims 8 and 17, Piche et al. shows that data augmentation is a technique used to reduce generalization error and is typically achieved by increasing the size of the training set by adding extra copies of the training examples that have been modified with transformations that do not change the original features of the data points, which allows for significantly increasing the diversity of data available for training (para. [0038]) and further shows that a person skilled in the art would recognize that the performance of AI models is greatly affected by the size of the training dataset (para. [0063]).
Regarding dependent claim 11, Piche et al. shows that during training, the AI model learns to label each tooth of a mouth and to detect teeth preparations (para. [0055]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, by incorporating methods for training a neural network for generating dental restoration models, e.g., dental crowns, as shown by Piche et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, with the methods of Piche et al., because Piche et al. shows methods for training a neural network for generating a three-dimensional crown surface. This modification would have had a reasonable expectation of success given that both Azernikov et al. ‘449 as applied to claims 1, 2, 4, 5, 9, 10, 13, 15, 18, and 19 under 35 U.S.C. 102 above, and Piche et al. disclose methods for using neural networks for dental restoration modeling.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KARLHEINZ SKOWRONEK can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEVEN W. BAILEY/Examiner, Art Unit 1687
