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 .
Claim Rejections - 35 USC § 102
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, 2, 6-9, 15-16, 18-20 and 40 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Lucas (US 20190255778 A1).
As per claim 1, Lucas teaches,
A computer-implemented method for oral surgery planning comprising:
receiving, by a computing system, a patient profile (movement profile of a patient – Par. 12), wherein the patient profile comprises:
patient anatomy data, wherein the patient anatomy data comprises one or more models of a maxilla or mandible of the patient (“Gathering 3D information of the maxillary and mandibular teeth and arches”, “make models and convert to digital by scanning the models”, para 262); and kinematic data associated with movement of a jaw of the patient (“Jaw Motion Analyzer”, “mandibular range of motion data”, para 134); identifying, by the computing system based at least in part on the received patient profile, one or more candidate sites for dental implants; (“obtaining 3D arch data, obtaining or inputting TMJ data including range of motion of the mandible, determining movement parameters and collision (e.g., interference) detection in order to identify the movement restrictions, understand current patient situation (or obtaining this data using a JMA), establishing an envelope region (e.g., with or without virtual retentive pieces), establishing virtual point(s) based on an index position, parameterize a movement profile, and creating one or more of surgical, restorative and implant placement guides and retentive pieces (either virtually or through manufacturing) if/as needed (e.g., based on a desired solution)”; “index position” teaches candidate site, para 334); and generating, by the computing system based at least in part on the identified one or more candidate sites and the kinematic data, one or more dental implant parameters (para 365 “For example, it may be desirable to use an implant placement guide to create the appropriate angles of implant placement for a specific
curve of Spee and Wilson, respectively or relative to the angles of occlusion (See, e.g., FIGS. 44A-44B)”; claim 8 recites angle as dental implant parameter).
As per claim 2, Lucas teaches,
The computer-implemented method of claim l, wherein the patient profile includes any combination of one or more of bone volume, bone density, relative bone density, location of a nerve, or location of a sinus (a surgical guide involve major arch shape changes stents/guide for the surgeon to aid manipulation bone, sinus, gum and other tissues, para 365, Figs. 35E-35G and 35F, tooth structure, envelop region, calculating bone tissue, para 339).
As per Claim 6, Lucas teaches,
The computer-implemented method of claim 1, wherein identifying one or more candidate sites for dental implants comprises comparing the one or more models of the maxilla or mandible of the patient to one or more reference models (a guidance package configured to provide desired movement profile of for the mandibular and maxillary arches of recipient to each other. Generating a position based on the specified guidance package and determining a virtual representation with an articulator and then comparing and determining if the specified guidance package provides the desired movement profile, para 33 , (“obtaining 3D arch data, obtaining or inputting TMJ data including range of motion of the mandible, determining movement parameters and collision (e.g., interference) detection in order to identify the movement restrictions, understand current patient situation (or obtaining this data using a JMA), establishing an envelope region (e.g., with or without virtual retentive pieces), establishing virtual point(s) based on an index position, parameterize a movement profile, and creating one or more of surgical, restorative and implant placement guides and retentive pieces (either virtually or through manufacturing) if/as needed (e.g., based on a desired solution)”; “index position” teaches candidate site, para 334).
As per claim 7, Lucas teaches,
The computer-implemented method of claim 1, wherein identifying one or more candidate sites for dental implants comprises automatically analyzing a bone of the patient to determine any combination of one or more of: a dental arc, an inter-tooth separation, a bone volume, and a relative bone density (para 339-340, 3D envelop region involves virtual planar curve for both arches, Fig. 29, various types of arches).
As per claim 8, Lucas teaches,
The computer-implemented method of claim l , wherein the one or more dental implant parameters comprise any combination of one or more of: a location of the dental implant relative to a bone surface, an implant type, an implant material, a burial depth, an implant angle relative to the bone surface, an implant size, a crown size, and a crown geometry ( “For example, it may be desirable to use an implant placement guide to create the appropriate angles of implant placement for a specific curve of Spee and Wilson, respectively or relative to the angles of occlusion (See, e.g., FIGS. 44A-44B)”, para 365, geometry of the prosthetic teeth, para 389, generate guidance package derived restorative guides (for example, as illustrated in FIG. 35E) to modify those teeth with adjustments, crowns,”, para 351)).
As per claim 9, Lucas teaches,
The computer-implemented method of claim 8, wherein at least one of the crown size and the crown geometry is based at least in part on a prosthetic project, a prosthetic tooth, or an existing tooth of a patient (the retention piece includes prosthetic that may include crown bridge, removal partial denture, or removal prosthetics and combination of thereof, para 74, restoration with prosthetic teeth, para 158).
As per claim 15, Lucas teaches,
The computer-implemented method of claim l, further comprising: providing, to a user, an interface for modifying one or more implant parameters (para 59, user interface for specification or modification of the virtual points by the operator, para 59, para 141-142, a parameterized guidance package to modify).
As per claim 16, Lucas teaches,
The computer-implemented method of claim l, further comprising:
generating a surgical guide (para 365,Figs. 35E-35G, illustrate a guidance package derived a surgical guide comprising implant placement guide, a restorative guide), wherein the surgical guide comprises a 3D model of a guide that may be used during a surgical procedure (para 116, Figs. 31A-31E, illustrative patient arches that feature virtual planer curves for 3D region calculation, para 132, 3D Xray with scanning to create visual repetition of Mandibular and Maxilla helps create to guidance package, para 133, combination of 3D radiography and 3D scanning (planmeca) and Jaw motion analyzer to determine index position).
As per claim 18, Lucas teaches,
The computer-implemented method of claim 1, further comprising generating a surgical navigation plan (para 365, Figs. 35E-35G, illustrate a guidance package derived a surgical guide comprising implant placement guide, a restorative guide).
As per claim 19, Lucas teaches,
The computer-implemented method of claim 1, further comprising providing a visualization and interaction interface (para 97, Fig. 14, visualization of patient’s jaw movement in real time by Jaw motion analyzer, para 132, visual representation of patient’s mandibular and maxillary arches within a virtual articulator,).
Claim 20 has the same limitations as claim 1. Please refer to the analysis above.
As per claim 40, Lucas teaches,
The oral surgery planning system of claim 20, further comprising: a jaw motion tracking headset; and a jaw motion tracking detector (Fig. 14, para 97, A jaw motion analyzer for collecting TMJ and range of motion data from a patient and visualization of jaw movement, para 263, the jaw motion analyzer includes sensors 23-6, upper receiver 23-1 works for upper face detector, 23-4 for lower jaw detection).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claims 3, 4, 5, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lucas (US 20190255778 A1) in view of Barak et al. (US 20190209274 A1).
As per claim 3, Lucas teaches,
The patient profile and the kinematic data above.
However, Lucas does not teach
determining, by the computing system, a proposed crown geometry;
determining, by the computing system based at least in part on the kinematic data, an indication of a functional cone;
determining, by the computing system based at least in part on the patient profile and the proposed crown geometry, one or more crown contact points; generating, by the computing system based at least in part on the one or more crown contact points,
a constraint map;
selecting, by the computing system based at least in part on the constraint map, an implant model; and
generating, by the computing system based at least in part on the constraint map and the implant model, a modified implant model.
However, in the same field of endeavor, Barak et al. teach a proposed crown geometry (para 69, Fig. 1B); determining, an indication of a functional cone (para 69, “approximates a right frusto-conical cone”; Fig.2B #19); determining, by the proposed crown geometry, one or more crown contact points (Fig. 2B, contact points when crown sits on root, see para 56 also); generating, by one or more crown contact points, a constraint map; (para 56 “The target height of the crown 100 may be defined such as to provide adequate occlusion with the “working side” of the tooth and avoiding interfering contact between the crown and teeth of the opposite jaw when the crown is fixed onto the corresponding preparation 80 in the intraoral cavity.”); selecting, by the computing system based at least in part on the constraint map, an implant model; and generating, by the computing system based at least in part on the constraint map and the implant model (para 75), a modified implant model (Fig.6 #313 updating based on comparing model at #311 and #309). Barak et al. ‘s dental implant surgery process improvise the crown geometry for the crown placement.
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify Lucas’s teaching of generating guidance package by including Barak et al.’s crown geometry that contains crown contact points and dental constraint map. This would have been obvious because both Lucas and Barak et al. teach of dental models, and Lucas further teaches that the retention piece can include a crown including at least a portion of the guidance package (Pars. 71 and 161). Lucas’ design of the retentive pieces are to be precise (Para. 13). Barak et al.’s model of proposed crown geometry provides a method for calculating the target height of the crown to achieve adequate occlusion with the working side and prevents interfering contact between the crown and the teeth. By preventing the interfere between the crown and teeth, the crown placement process will be more successful, (paras 25, 26, 57, 73 - Barak et al.). The process of placing a precise crown, as taught by Barak et al., would be beneficial for Lucas’ when designing a retentive piece that is part of the crown.
As per claim 4, Lucas teaches,
The patient profile and the kinematic data above.
However, Lucas does not teach
A proposed crown geometry,
an indication of a functional cone;
automatically determining, by the computing system based at least in part on the patient profile, one or more crown contact points; and automatically selecting, by the computing system based at least in part on the crown contact points, an implant model.
In the same field of endeavor, Barak et al. teach,
determining, by the computing system (CAM module, computer aided manufacturing module, para 27), a proposed crown geometry (para 69);
an indication of a functional cone (para 69, approximation of right frusta-conical cone);
one or more crown contact points (para 115-117, Fig 2A); and automatically selecting, by the computing system based at least in part on the crown contact points, an implant model (para 73, Fig. 3, Fig. 4, obtaining images of patient’s teeth, three-dimensional solid geometry data using a computerized tomography device).
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify Lucas’s teaching of generating guidance package by including Barak et al.’s crown geometry that contains crown contact points and dental constraint map. This would have been obvious because both Lucas and Barak et al. teach of dental models, and Lucas further teaches that the retention piece can include a crown including at least a portion of the guidance package (Pars. 71 and 161). Lucas’ design of the retentive pieces are to be precise (Para. 13). Barak et al.’s model of proposed crown geometry provides a method for calculating the target height of the crown to achieve adequate occlusion with the working side and prevents interfering contact between the crown and the teeth. By preventing the interfere between the crown and teeth, the crown placement process will be more successful, (paras 25, 26, 57, 73 - Barak et al.). The process of placing a precise crown, as taught by Barak et al., would be beneficial for Lucas’ when designing a retentive piece that is part of the crown.
As per claim 5, Lucas teaches,
The computer-implemented method of claim 3, wherein generating the modified model comprises minimizing one or more stresses on the dental implant (para 200, preventing stress on the damaged area of one or both TMJs by using an oral appliance, para 7, applying a guidance package to neutralize posterior interferences and to reduce inappropriate muscle activity).
As per claim 11, the combination of Lucas and Barak et al. teach,
The computer-implemented method of claim 4, wherein determining one or more dental implant contact points comprises determining contact at one or more stages of jaw motion based at least in part on the indication of the functional cone (para 69, “approximates a right frusto-conical cone”; Fig.2B #19, Barak et al.) and the patient anatomy, wherein the jaw motion comprises recorded motion, simulated motion, or both (Fig. 14, para 97, Jaw motion analyzer, para 51, Virtual Articulation technology, para 261,Jaw Motion Analyzer technology and/or CAD-CAM methods with the unique attributes of the guidance package system and retentive piece technology, Lucas).
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lucas (US 20190255778 A1) in view of Karkar et al. (US 20100105011 A1).
As per claim 10, Lucas teaches,
The computer-implemented method of claim l, further comprising:
determining, by the computing system based on the patient profile (para 12, movement profile of a patient),
Lucas teaches a surgical guide that involves major arch shape changes stents/guide for the surgeon to aid manipulation of bone, sinus, gum and other tissues, para 365, Figs. 35E-35G and 35F, tooth structure, envelope region, calculating/modifying bone tissue, para 339.
However, Lucas does not specifically teach
that one or more candidate sites have insufficient bone volume or insufficient bone density for performing an implant procedure.
In the same field of endeavor, Karkar et al. teach
that one or more candidate sites have insufficient bone volume or insufficient bone density for performing an implant procedure (para 8, para 25, para 110, para 126).
Karkar et al. teach a bone grafting method of determine insufficient bone density before performing dental implant surgery or any adjustment to tissue (para 8). A systematic method is applied to generate anatomic model of teeth, root, jaw bones and tissue from patient data and a digital mathematical model is designed to calculate bone density, bone volume, and tissue shape etc. (para 108).
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify the teaching of Lucas’s dental implant guidance profile by adding Karkar et al.’s teaching of bone density measurement via bone grafting and mathematical modeling. This would have been obvious because both Lucas and Karkar et al. teach dental implant surgery with 3D modeling. By adding Karkar et al.’s method of insufficient bone density measurement into Lucas’s method, a surgeon can determine the treatment plant before modifying any tissue for dental implant surgery (para 108, Fig 2A and Fig. 2B, Karkar et al.).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lucas (US 20190255778 A1) in view of Matov et al. (US 20090191503 A1).
As per claim 12 although Lucas teaches,
The computer-implemented method of claim 1(a virtual 3D Model, para 29),
However, Lucas does not teach
wherein selecting an implant model comprises using an artificial intelligence engine to select a pre-configured model from a model database.
In the same field of endeavor, Matov et al. teach
wherein selecting an implant model comprises using an artificial intelligence engine to select a pre-configured model from a model database (para 51, A neural network is used to recognize a dental treatment pattern using a data mining software 3 (fig.1A), also, the neural network compares the input dental treatment plan with the stored treatment template plan and recognizes and selects the proper treatment plan from there. “The data mining software 3” using “neural network” teach the artificial intelligence engine, and the “stored template of treatment” teaches the database).
Matov et al. teach a dental plan recognition model where a data mining software uses a neural network and compares an input treatment plan with a stored treatment template and recognizes the correct treatment plan, para 51. Matov et al.’s model also includes a Hidden Markov Model (HMM) to determine the probability of the state of the treatment plan, para 66.
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify Lucas’s teaching of implant model by adding Matov et al.’s dental treatment recognition model to the system. This would have been obvious because both Lucas and Matov et al. teach a dental 3D model with patient’s profile. By adding Matov et al.’s recognition model database, patients’ treatment plan storage and retrieval process will be more convenient and by applying probability model the success of treatment plan can be predetermined, (para 51, para 66).
Claims 13 is rejected under 35 U.S.C. 103 as being unpatentable over Lucas (US 20190255778 A1) in view of Ciriello et al. (US 20220183789 A1),
As per claim 13. Lucas teaches
The computer-implemented method of claim l, (a virtual 3D model, para 29),
However, Lucas does not teach\
wherein generating implant parameters comprise:
providing patient data to an artificial intelligence model, the artificial intelligence
model configured to generate implant parameters.
In the same field of endeavor, Ciriello et al. teach
providing patient data to an artificial intelligence model, the artificial intelligence
model configured to generate implant parameters (machine learning algorithm comprise a neural network, para 93).
Ciriello et al teach a neural network machine learning model, where any missing surface patches can be patched by normalized tooth geometry applying a machine learning algorithm. Therefore, one or more missing surface can be generated by combining conventional dental scanning with optical coherence tomography of occluded or hidden surface, (para 94).
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify Lucas’s teaching of virtual 3D modelling with Ciriello et al.’s artificial intelligence method of 3D imaging. This would have been obvious because both Lucas and Ciriello et al. teach a dental 3D model. Ciriello et al.’s model provides a method for capturing 3D image even though some parts are missing or hidden. By scanning the missing parts with a generated scanning through a machine learning model, the 3D model will be able to capture a complete image, (para 93-95, Ciriello et al).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Lucas (US 20190255778 A1) in view of Ciriello et al. (US 20220183789 A1), and further in view of Matov et al. (US 20090191503 A1).
As per claim 14, the combination of Lucas and Ciriello et al. teach
The computer-implemented method of claim 13, (a virtual 3D model, para 29, Lucas).
However, Lucas does not teach
receiving, by the computing system, an indication of a surgical outcome; and retraining, by the computing system, the artificial intelligence model using the received indication of the surgical outcome.
In the same field of endeavor, Matov et al. teach
receiving, by the computing system, an indication of a surgical outcome; and retraining, by the computing system, the artificial intelligence model using the received indication of the surgical outcome (para 51, A neural network is used to recognize a dental treatment pattern using a data mining software 3 (fig.1A), also, the neural network compares the input dental treatment plan with the stored treatment template plan and recognizes and selects the proper treatment plan from there. Matov et al. teach a dental plan recognition model where a data mining software uses a neural network and compares an input treatment plan with a stored treatment template and recognizes the correct treatment plan, para 51. Matov et al.’s model also includes a Hidden Markov Model (HMM) to determine the probability of the state of the treatment plan, (para 66).
It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify Lucas’s teaching of implant model by adding the combination of Matov et al. and Ciriello et al.’s dental treatment recognition model to the system. This would have been obvious because the combination of Lucas, Ciriello et al. and Matov et al. teach a dental 3D model with patient’s profile. By adding Matov et al.’s recognition model database, patients’ treatment plan storage and retrieval process will be more convenient and by applying probability model the success of treatment plan can be predetermined, (para 51, para 66, Matov et al.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Please refer to the form PTO-892 Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rokeya Alam whose telephone number is (571)-272-0083. The examiner can normally be reached on 7:30am - 4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Scott Baderman can be reached at telephone number (571-272-3644). The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118