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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/20/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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: “determination module”; “reconstruction module”; registration module” and “generation module” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) 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 it/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 it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 10 and 17-20 are rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention because the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106(I). However, claims directed toward a non-transitory computer readable medium may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106(I). Therefore, amending the claims to recite a “non-transitory computer-readable medium” would resolve this issue.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chernov et al (“Chernov” hereinafter, U.S. Publication No. 2022/0165388 A1).
As per claim 1, Chernov discloses a tooth model generation method (abstract & figure 1A), wherein the method comprises: performing instance segmentation on acquired CBCT data of a target dental jaw, so as to determine voxel data of individual teeth forming the target dental jaw (paragraph [0090] & [0091] & figure 2: “segmenting a 3D scan of a patient's teeth, such as …, a CBCT scan, …”); performing reconstruction according to the voxel data of the individual teeth in the target dental jaw, so as to obtain reconstructed three dimensional data (paragraph [0092]: operation 204 resamples the 3D scan); registering the reconstructed three-dimensional data and scanned three-dimensional data which is acquired by an intraoral scanner scanning the target dental jaw (paragraph [0104]: “A digital 3D dental treatment plan may be generated during the course of a dental treatment for a patient. The dental treatment plan can comprise a three-dimensional model, such as a 3D mesh model or a 3D point cloud, that may be generated from a scan, such as an intraoral scan, of the patient's teeth” & paragraph [0105]: “coarse alignment” & “fine alignment”); and merging based on the registered reconstruction three-dimensional data and the registered scanned three-dimensional data, so as to generate a tooth model (paragraph [0104]: “The feature alignment engine 178 may implement one or more automated agents configured to align and merge segmented scan data from the scan segmentation engine(s) 160 with a digital 3D dental treatment plan”).
As per claim 2, Chernov discloses wherein the reconstructed three-dimensional data comprises three-dimensional data of the target dental jaw and first three-dimensional data of the individual teeth forming the target dental jaw; and the step of registering the reconstructed three-dimensional data and the scanned three-dimensional data which is acquired by an intraoral scanner scanning the target dental jaw comprises: performing coarse registration on the three-dimensional data of the target dental jaw and the scanned three-dimensional data which is acquired by the intraoral scanner scanning the target data dental jaw to obtain an initial pose of the individual teeth; and performing, based on the initial pose of the individual teeth, fine registration on the first three-dimensional data of the individual teeth and the scanned three-dimensional data (as explained above, the reconstructed three-dimension data was obtained through CBCT and the scanned three-dimensional data is obtained through an intraoral scanner, and the dental jaw and individual teeth is shown in figure 3C, and paragraphs [0019], [0104] & [0105] teaches coarse alignment and fine alignment between the segmented 3D data and the scanned 3D data; the pose of the dental model after the coarse alignment is the claimed “initial pose”).
As per claim 3, Chernov discloses wherein the step of performing coarse registration on the three-dimensional data of the target dental jaw and the scanned three-dimensional data which is acquired by the intraoral scanner scanning the target dental jaw to obtain an initial pose of the individual teeth comprises: performing coarse registration, based on a feature stitching algorithm, on the three-dimensional data of the target dental jaw and the scanned three-dimensional data which is acquired by the intraoral scanner scanning the target dental jaw to obtain an initial pose of the target dental jaw; and obtaining the initial pose of the individual teeth based on the initial pose of the target dental jaw (figure 5A; paragraphs [0019], [0044] & [0106] for stitching algorithm).
As per claim 4, Chernov discloses wherein the step of performing, based on the initial pose of the individual teeth, fine registration on the first three-dimensional data of the individual teeth and the scanned three-dimensional data comprises: performing instance segmentation on the scanned three-dimensional data to obtain second three-dimensional data of the individual teeth forming the target dental jaw; and performing, based on the initial pose of the individual teeth, fine registration on the first three-dimensional data of the individual teeth and the second three-dimensional data of the individual teeth through an iterative closed point algorithm (see paragraph [0013] for iterative closest point for fine alignment).
As per claim 5, Chernov discloses wherein the step of merging based on the registered reconstructed three-dimensional data and the registered scanned three-dimensional data, so as to generate a tooth model comprises: merging tooth root data and interproximal surface data of the registered first three-dimensional data of the individual teeth with tooth crown data of the second three-dimensional data of the individual teeth segmented from the scanned three-dimensional data to generate the tooth model (see paragraph [0019] for “stitching teeth crowns from the virtual treatment plan to corresponding teeth roots from the segmented 3D scan data” using both coarse and fine alignments).
As per claim 6, Chernov discloses wherein the method further comprises: obtaining three-dimensional data of gingiva in the target dental by segmenting the scanned three-dimensional data; and displaying the three-dimensional data of the gingiva in response to a triggering operation for a display mark (paragraph [0073]: “segmenting the scan or model into individual teeth, bones, interproximal spaces between teeth, and/or gingiva”; and the segmented model can be displayed in figures 12-13).
As per claim 7, Chernov discloses wherein the CBCT data comprises multiple CBCT images, and each image of the multiple CBCT images comprises information about at least one tooth forming the target dental jaw; and the step of performing instance segmentation on acquired CBCT data of a target dental jaw, so as to determine voxel data of individual teeth forming the target dental jaw comprises: classifying the multiple CBCT images using a pre-trained deep learning model, determining a category of the individual teeth comprised in each CBCT image of the multiple CBCT images, and segmenting based on the category of the individual teeth to obtain segmentation data of the individual teeth; and obtaining the voxel data of the individual teeth forming the target dental jaw according to the segmentation data corresponding to the individual teeth of a same category in the multiple CBCT images (paragraph [0086]: “The machine learning engine 172 may implement one or more automated agents configured to apply one or more machine learning engines to segment the processed scan data from the image processing engine. For example, the machine learning engine 172 can use, as an input, the original 2D/3D scan (e.g., a CT scan, CBCT scan, or MRI scan) and/or the one or more partitions, volumes, crops, or areas of the scan from the image processing engine”; and the machine learning engine is a pre-trained system as disclosed in paragraph [0008]).
As per claim 8, see explanation in claim 1, and see figure 1 for different hardware/software modules.
For claims 9-10, see figure 1A & paragraph [0028] for processor, memory and non-transitory computer-readable medium.
As per claim 11, see explanation in claim 2.
As per claim 12, see explanation in claim 3.
As per claim 13, see explanation in claim 4.
As per claim 14, see explanation in claim 5.
As per claim 15, see explanation in claim 6.
As per claim 16, see explanation in claim 7.
As per claim 17, see explanation in claim 2.
As per claim 18, see explanation in claim 3.
As per claim 19, see explanation in claim 4.
As per claim 20, see explanation in claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272 - 7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOM Y LU/ Primary Examiner, Art Unit 2667
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