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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 1 of prior U.S. Patent No. 12,232,923 B2 Feb. 25, 2025. This is a statutory double patenting rejection.
Claim 1 of current application (claim 1 of US Patent 12,232,923):
A system comprising a deep neural network and at least one processor configured to: (A system comprising a deep neural network and at least one processor configured to:)
- obtain a plurality of training dental computed tomography scans which reflect a moment before respective successful orthodontic treatments (obtain a plurality of training dental computed tomography scans which reflect a moment before respective successful orthodontic treatments),
- identify individual teeth and jaw bone in each of said training dental computed tomography scans, and (identify individual teeth and jaw bone in each of said training dental computed tomography scans, and)
- train said deep neural network with training input data obtained from said plurality of training dental computed tomography scans and training target data per training dental computed tomography scan to determine a desired final position per tooth from input data obtained from a patient dental computed tomography scan (training target data per training dental computed tomography scan to determine a desired final position per tooth from input data obtained from a patient dental computed tomography scan), wherein training input data obtained from a training dental computed tomography scan represents all teeth and an entire alveolar process and identifies said individual teeth and said jaw bone (wherein training input data obtained from each training dental computed tomography scan represents all teeth and an entire alveolar process and identifies said individual teeth and said jaw bone),
wherein said input data comprises an image data set or a 3D data set along with information delineating said individual teeth and said jaw bone, said image data set representing an entire computed tomography scan, or multiple 3D data sets, said multiple 3D data sets comprising a 3D data set per tooth and a 3D data set for said jaw bone, and wherein (wherein said input data comprises an image data set or a 3D data set along with information delineating said individual teeth and said jaw bone, said image data set representing an entire computed tomography scan, or multiple 3D data sets, said multiple 3D data sets comprising a 3D data set per tooth and a 3D data set for said jaw bone, and wherein)
said training target data comprises an indicator indicating an achieved transformation per tooth for one or more of said plurality of training dental computed tomography scans, said transformation comprising a translation and/or a rotation per tooth, and/or (said training target data comprises an indicator indicating an achieved transformation per tooth for one or more of said plurality of training dental computed tomography scans, said transformation comprising a translation and/or a rotation per tooth, and/or)
said training target data comprises data obtained from one or more further training dental computed tomography scans which reflect a moment after a successful orthodontic treatment, each of said one or more further training dental computed tomography scans being associated with a training dental computed tomography scan of said plurality of training dental computed tomography scans (said training target data comprises data obtained from one or more further training dental computed tomography scans which reflect a moment after a successful orthodontic treatment, each of said one or more further training dental computed tomography scans being associated with a training dental computed tomography scan of said plurality of training dental computed tomography scans).
Claims 2 – 13 (due to their dependency on independent claim 1 of current application) are also rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 1 of prior U.S. Patent No. 12,232,923 B2 Feb. 25, 2025.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5 – 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over
Domeracheva US PGPub: US 2019/0148005 A1 May 16, 2019 and in view of
Maraj US PGPub: US 2018/0110590 A1 Apr. 26, 2018.
Regarding claim 1, Domracheva discloses,
a system comprising a neural network (the machine learning approach may be a fully convolutional neural network – FCN – paragraph 0062. A dental image processing protocol for the design of dental aligners. Specifically, the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment. Therefore, planned tooth movements reflect both crown movement and root movement within biological structures of the alveolar process – ABSTRACT, Figs. 1, 3, 5, 9, paragraph 0005) and at least one processor (CPU 1580 – Fig. 15/1580, paragraph 0093) configured to:
obtain a plurality of training dental (training, via the processing circuitry, a first neural network according to a first dataset, training, via the processing circuitry, a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network, and generating, via the processing circuitry, the training database based upon a plurality of classification predictions of the second neural network – paragraphs 0104, 0111, 0118) computed tomography scans (dental scanner of impressions or dental models, intraoral scanners for digital impressions, intraoral X-ray, ultrasound, and computed tomography can be used individually or in combination to acquire digital representations of the initial position of the patient's teeth – Fig. 3/s350, paragraph 0061) which reflect a moment before respective successful orthodontic treatments (an orthodontic treatment approach that considers an evaluation of the condition of the tissues surrounding the tooth and the alveolar process, in particular. Moreover, the evaluation of the condition of the tissues surrounding the tooth is patient-specific, reflecting the unique density and thickness of an individual patient's periodontal bone – paragraph 0058. Initially, digital representations of an initial position of a patient's teeth must be acquired. Here, an initial position of a patient’s teeth, reads on the claimed feature, which reflect a moment before respective successful orthodontic treatments – paragraph 0061. An orthodontist and dental technicians develop teeth movement plans based upon initial and ideal final crown positions – paragraph 0057),
identify individual teeth and jaw bone in each of said training dental computed tomography scans (the dental image processing protocol described herein can be appreciated in context of a full dental arch or an individual tooth – paragraph 0061. A heat map, overlaid on the 3D model, indicates local thicknesses of the alveolar process, the periodontal environment therein varying across individual teeth of the dental arches – Fig. 4, paragraph 0067. Following acquisition of a plurality of dental images of a patient via CBCT, various biological structures, including the teeth and the jaw, must be digitally identified so that they can be later incorporated into a holistic 3D model of the dental environment – paragraph 0062), and
train said neural network with training input data obtained from said plurality of training dental computed tomography scans (a training protocol of a classification approach of a dental image processing protocol – Fig. 5, paragraphs 0013, 0068) and training target data per training dental computed tomography scan to determine a desired final position per tooth from input data obtained from a patient dental computed tomography scan (varying densities of periodontal bone can impact potential root movements and realignment – paragraph 0057. In order to better define the periodontal environment of the simple 3D model and provide a realistic model of potential tooth and root movement, characteristics of the alveolar process is determined – paragraph 0064. The determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment – ABSTRACT, Fig. 1, paragraphs 0059),
wherein training input data obtained from a training dental computed tomography scan represents all teeth and an entire alveolar process and identifies said individual teeth and said jaw bone (an orthodontic treatment approach that considers an evaluation of the condition of the tissues surrounding the tooth and the alveolar process, in particular. Moreover, the evaluation of the condition of the tissues surrounding the tooth is patient-specific, reflecting the unique density and thickness of an individual patient's periodontal bone – paragraph 0058),
wherein said input data comprises an image data set (intraoral 3D imaging – Fig. 3/s341, paragraph 0061) or
a 3D data set along with information delineating said individual teeth and said jaw bone,
said image data set representing an entire computed tomography scan (dental scanner of impressions or dental models, intraoral scanners for digital impressions, intraoral X-ray, ultrasound, and computed tomography can be used individually or in combination to acquire digital representations of the initial position of the patient's teeth – paragraph 0061), or multiple 3D data sets (Fig. 3/s343),
said multiple 3D data sets comprising a 3D data set per tooth and a 3D data set for said jaw bone (a complex three-dimensional model generated from a plurality of processed dental images and annotated with a surface heat map. An acquisition and processing of intraoral 3D scans and radiographic images, surface mesh data may be integrated to create a 3D model of an initial position of the dental arches of a patient – Fig. 4, paragraphs 0012, 0067. An acquisition of a plurality of dental images of a patient via CBCT, various biological structures, including the teeth and the jaw – paragraph 0062), and wherein
said training target data comprises an indicator indicating an achieved transformation per tooth (a transformation matrix is computed and applied via translation and rotation or quaternion – paragraph 0063) for one or more of said plurality of training dental computed tomography scans, said transformation comprising a translation and/or a rotation per tooth (a transformation matrix is computed and applied via translation and rotation or quaternion – paragraph 0063), and/or
but, does not disclose, “a deep neural network” and
“said training target data comprises data obtained from one or more further training dental computed tomography scans which reflect a moment after a successful orthodontic treatment, each of said one or more further training dental computed tomography scans being associated with a training dental computed tomography scan of said plurality of training dental computed tomography scans”.
Maraj teaches, systems and methods for dental treatment utilizing mixed reality and deep learning. A patient's physical arch is scanned to produce a virtual arch that is then rendered in a computing environment for analysis and manipulation. Virtual targets, e.g., brackets, implants, etc., and/or a grid, are applied to the virtual arch to produce a virtual dental treatment template (ABSTRACT, Figs. 2 – 9B, paragraphs 0007 - 0009).
The system includes software for positioning a virtual target on the virtual arch to produce a virtual dental treatment template. In some embodiments, the virtual target includes a dental apparatus, e.g., a bracket, implant, reconstructed tooth, replacement tooth, alignment tray, etc.; a grid; or a combination thereof. In some embodiments, the positioning of the virtual target on the virtual arch is performed by Artificial Intelligence training (via deep learning, i.e., neural networks, Support Vector Machines, Decisions Trees, etc.) the system to recognize the optimal location, shape, color, etc. for the virtual target, e.g., clear versus metal brackets, various colors of elastomeric ties, alignment trays, crowns, restored teeth, replacement teeth, reconstructed teeth, alveolar ridge augmentations, etc., or a combination thereof (paragraph 0008).
The completed dental treatment and/or intermediate phase rendering is overlaid on a rendering of the patient, thus achieving a “before and after” demonstration. In these embodiments, the practitioner can simulate and show the patient aesthetic features, e.g., clear versus metal brackets; various colors of elastomeric ties; alignment trays; restorative options, e.g., crowns, restored tooth, replacement tooth, upper and/or lower jaw movement, etc.; alveolar ridge augmentation; or a combination thereof (paragraphs 0035 - 0040).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment of Domracheva (Domracheva, ABSTRACT, Figs. 1, 3 - 5, 9, paragraph 0005), wherein the system of Domracheva, would have incorporated, systems and methods for dental treatment utilizing mixed reality and deep learning of Maraj (Maraj, ABSTRACT, Figs. 2 – 9B, paragraphs 0007 – 0009, 0035 - 0040) for the virtual dental treatment template is rendered in the mixed reality device to provide a visual guide for a practitioner as they preview and/or install a physical dental apparatus as part of a patient's dental treatment (Maraj, paragraph 0007).
Regarding claim 2, Domracheva discloses,
the system as claimed in claim 1, wherein said at least one processor is configured to use said identification of said individual teeth and said jaw bone to determine dento-physical properties for each of said training dental computed tomography scans and facilitate an encoding of information reflecting said dento-physical properties in said neural network (a complex three-dimensional model generated from a plurality of processed dental images and annotated with a surface heat map. An acquisition and processing of intraoral 3D scans and radiographic images, surface mesh data may be integrated to create a 3D model of an initial position of the dental arches of a patient – Fig. 4, paragraphs 0012, 0067. An acquisition of a plurality of dental images of a patient via CBCT, various biological structures, including the teeth and the jaw – paragraph 0062),
but, does not disclose, “the deep neural network”.
Maraj teaches, systems and methods for dental treatment utilizing mixed reality and deep learning. A patient's physical arch is scanned to produce a virtual arch that is then rendered in a computing environment for analysis and manipulation. Virtual targets, e.g., brackets, implants, etc., and/or a grid, are applied to the virtual arch to produce a virtual dental treatment template (ABSTRACT, Figs. 2 – 9B, paragraphs 0007 - 0009).
The system includes software for positioning a virtual target on the virtual arch to produce a virtual dental treatment template. In some embodiments, the virtual target includes a dental apparatus, e.g., a bracket, implant, reconstructed tooth, replacement tooth, alignment tray, etc.; a grid; or a combination thereof. In some embodiments, the positioning of the virtual target on the virtual arch is performed by Artificial Intelligence training (via deep learning, i.e., neural networks, Support Vector Machines, Decisions Trees, etc.) the system to recognize the optimal location, shape, color, etc. for the virtual target, e.g., clear versus metal brackets, various colors of elastomeric ties, alignment trays, crowns, restored teeth, replacement teeth, reconstructed teeth, alveolar ridge augmentations, etc., or a combination thereof (paragraph 0008).
The completed dental treatment and/or intermediate phase rendering is overlaid on a rendering of the patient, thus achieving a “before and after” demonstration. In these embodiments, the practitioner can simulate and show the patient aesthetic features, e.g., clear versus metal brackets; various colors of elastomeric ties; alignment trays; restorative options, e.g., crowns, restored tooth, replacement tooth, upper and/or lower jaw movement, etc.; alveolar ridge augmentation; or a combination thereof (paragraphs 0035 - 0040).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment of Domracheva (Domracheva, ABSTRACT, Figs. 1, 3 - 5, 9, paragraph 0005), wherein the system of Domracheva, would have incorporated, systems and methods for dental treatment utilizing mixed reality and deep learning of Maraj (Maraj, ABSTRACT, Figs. 2 – 9B, paragraphs 0007 – 0009, 0035 - 0040) for the virtual dental treatment template is rendered in the mixed reality device to provide a visual guide for a practitioner as they preview and/or install a physical dental apparatus as part of a patient's dental treatment (Maraj, paragraph 0007).
Regarding claim 5, Domracheva discloses,
the system as claimed in claim 1, wherein one or more of said plurality of training dental computed tomography scans are each associated with an indicator indicating an attachment type per tooth, said indicator being included in said training target data (following acquisition and processing of intraoral 3D scans and radiographic images, surface mesh data may be integrated to create a 3D model of an initial position of the dental arches of a patient. A heat map, overlaid on the 3D model, indicates local thicknesses of the alveolar process, the periodontal environment therein varying across individual teeth of the dental arches. The canine 408 may be positioned closer to a buccal surface of an alveolar process 409, as indicated by a darker shade, intense red, while the premolar 407 may be positioned posteriorly with respect to the buccal surface of the alveolar process 409, proximate to a lingual surface of the alveolar process 409, as indicated by light shades of the heat map. This heat map feature allows a prescribing dental professional to visualize possible and impossible tooth movements and select appropriate intermediary movements within skeletal constraints – Fig. 4, paragraph 0067).
Regarding claim 6, Domracheva discloses,
the system as claimed in claim 1, wherein said at least one processor is configured to obtain at least one of said one or more training dental computer tomography scans by transforming data resulting from one of said further training dental computed tomography scans (training, via the processing circuitry, a first neural network according to a first dataset, training, via the processing circuitry, a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network, and generating, via the processing circuitry, the training database based upon a plurality of classification predictions of the second neural network – paragraphs 0104, 0111, 0118).
Regarding claim 7, Domracheva discloses,
The system as claimed in claim 1, wherein said at least one processor is configured to train said deep neural network with said training input data obtained from said plurality of training dental computed tomography scans (training, via the processing circuitry, a first neural network according to a first dataset, training, via the processing circuitry, a second neural network according to a second dataset, the second dataset comprising a plurality of classification predictions of the first neural network, and generating, via the processing circuitry, the training database based upon a plurality of classification predictions of the second neural network – paragraphs 0104, 0111, 0118) and said training target data per training dental computed tomography scan to determine said desired final position and an attachment type per tooth from said input data obtained from said patient dental computed tomography scan (The determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment – ABSTRACT, Fig. 1, paragraphs 0059).
Regarding claim 13, Domracheva discloses,
the system as claimed in claim 1, wherein said individual teeth and said jaw bone are identified from said computer tomography scan using a further neural network (dental scanner of impressions or dental models, intraoral scanners for digital impressions, intraoral X-ray, ultrasound, and computed tomography can be used individually or in combination to acquire digital representations of the initial position of the patient's teeth – Fig. 3/s350, paragraph 0061),
but, does not disclose, “a further deep neural network”.
Maraj teaches, systems and methods for dental treatment utilizing mixed reality and deep learning. A patient's physical arch is scanned to produce a virtual arch that is then rendered in a computing environment for analysis and manipulation. Virtual targets, e.g., brackets, implants, etc., and/or a grid, are applied to the virtual arch to produce a virtual dental treatment template (ABSTRACT, Figs. 2 – 9B, paragraphs 0007 - 0009).
The system includes software for positioning a virtual target on the virtual arch to produce a virtual dental treatment template. In some embodiments, the virtual target includes a dental apparatus, e.g., a bracket, implant, reconstructed tooth, replacement tooth, alignment tray, etc.; a grid; or a combination thereof. In some embodiments, the positioning of the virtual target on the virtual arch is performed by Artificial Intelligence training (via deep learning, i.e., neural networks, Support Vector Machines, Decisions Trees, etc.) the system to recognize the optimal location, shape, color, etc. for the virtual target, e.g., clear versus metal brackets, various colors of elastomeric ties, alignment trays, crowns, restored teeth, replacement teeth, reconstructed teeth, alveolar ridge augmentations, etc., or a combination thereof (paragraph 0008).
The completed dental treatment and/or intermediate phase rendering is overlaid on a rendering of the patient, thus achieving a “before and after” demonstration. In these embodiments, the practitioner can simulate and show the patient aesthetic features, e.g., clear versus metal brackets; various colors of elastomeric ties; alignment trays; restorative options, e.g., crowns, restored tooth, replacement tooth, upper and/or lower jaw movement, etc.; alveolar ridge augmentation; or a combination thereof (paragraphs 0035 - 0040).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the dental image processing protocol aids in the determination of tooth movements during realignment, based on an initial position and a final position, and on characteristics of the periodontal environment of Domracheva (Domracheva, ABSTRACT, Figs. 1, 3 - 5, 9, paragraph 0005), wherein the system of Domracheva, would have incorporated, systems and methods for dental treatment utilizing mixed reality and deep learning of Maraj (Maraj, ABSTRACT, Figs. 2 – 9B, paragraphs 0007 – 0009, 0035 - 0040) for the virtual dental treatment template is rendered in the mixed reality device to provide a visual guide for a practitioner as they preview and/or install a physical dental apparatus as part of a patient's dental treatment (Maraj, paragraph 0007).
Allowable Subject Matter
Claims 3, 4 and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, along with, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
The prior arts made of record and not relied upon are considered pertinent to applicants disclosure.
Azernikov US PGPub: US 2018/0058294 A1 Feb. 1, 2018.
A computer-implemented method of recognizing dental information associated with a dental model of dentition includes training a deep neural network to map a plurality of training dental models representing at least a portion of each one of a plurality of patients' dentitions to a probability vector including probability of the at least a portion of the dentition belonging to each one of a set of multiple categories. The category of the at least a portion of the dentition represented by the training dental model corresponds to the highest probability in the probability vector. The method includes receiving a dental model representing at least a portion of a patient's dentition and recognizing dental information associated with the dental model by applying the trained deep neural network to determine a category of the at least a portion of the patient's dentition represented by the received dental model (ABSTRACT, Figs. 1, 3)
Keustermans US PGPub: US 2019/0147666 A1 May 16, 2019.
A method is provided for obtaining an estimation of the shape, position and/or orientation of one or more existing teeth of a patient or of one or more teeth to be included in a dental restoration destined to replace one or more missing teeth in a partially edentulous patient. The method involves adapting a virtual teeth setup to the intra-oral anatomical situation of the patient, wherein said virtual teeth setup includes separated surface meshes of individual teeth positioned in a dental arch or segment thereof (ABSTRACT, Figs. 1, 3, 6, 12, paragraphs 0012 – 0015, 0018, 0025).
At each iteration, discriminator network 520 can output a loss function 540, which is used to quantify whether the generated sample 515 is a real natural image or one that is generated by generator 510. Loss function 540 can be used to provide the feedback required for generator 510 to improve each succeeding sample. In one embodiment, in response to the loss function, generator 510 can change one or more of the weights and/or bias variables and generate another output. In the recognition process, the discriminating deep neural network can generate a loss function based on comparison of a real dental restoration and the generated model of the dental restoration (Figs. 5B/540, 17/1720, paragraphs 0114, 0148).
Andreiko US PGPub: US 2014/0169648 A1 Jun. 19, 2016.
Shape data of a patient's crown and volumetric imagery of the patient's tooth are received. A determination is made of elements that represent one or more crowns in the shape data. A computational device is used to register the elements with corresponding voxels of the volumetric imagery. A tooth shape is determined from volumetric coordinates and radiodensities (ABSTRACT, Figs. 1, 2, paragraphs 0004, 0006 – 0009).
Alvarez US PGPub: US 2018/0005377 A1 Jan. 4, 2018.
A method for determining virtual articulation from dental scans. The method includes receiving digital 3D models of a person's maxillary and mandibular arches, and digital 3D models of a plurality of different bite poses of the arches. The digital 3D models of the maxillary and mandibular arches are registered with the bite poses to generate transforms defining spatial relationships between the arches for the bite poses. Based upon the digital 3D models and transforms, the method computes a pure rotation axis representation for each bite pose of the mandibular arch with respect to the maxillary arch. The virtual articulation can be used in making restorations or for diagnostic purposes (ABSTRACT, Figs. 1 – 3, 9, 23, paragraphs 0004, 0032).
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/NIMESH PATEL/Primary Examiner, Art Unit 2642
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