DETAILED ACTION
Claims 1-20 are pending in the present 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/23/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 18 describes a computer program, it appears that said claims, taken as a whole, read on computer listings per se.
Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical "things." They are neither computer components nor statutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program's functionality to be realized. In contrast, a claimed non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program's functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
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 limitation(s) is/are:
“generating, using the dental implant parameters and the three-dimensional digital jaw model, a three-dimensional digital drilling guide model, wherein the three-dimensional digital drilling guide model comprises means for achieving the dental implant parameters for the insertion of the one or more dental implants, wherein the means comprise one or more through holes defining drilling positions of drilling holes to be drilled into the patient’s jaw at the one or more dental implant positions, wherein the drilling positions are to be drilled into patient’s jaw for the insertion of the one or more dental implants” in claim 5.
“wherein the three-dimensional digital drilling guide model comprises means for achieving the dental implant parameters for the insertion of the one or more dental implants, wherein the means comprise one or more through holes defining drilling positions of drilling holes to be drilled into the patient’s jaw at the one or more dental implant positions” in claim 20.
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 § 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)(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.
Claim(s) 1-5, 8, 11, 14-15 and 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. PGPubs 2010/0151417 to Nilsson et al..
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Regarding claim 1, Nilsson et al. teach a computer-implemented method for determining one or more dental implant positions for one or more dental implants to be inserted into a patient’s jaw (abstract, “Methods are provided for computer-based planning of a dental restorative procedure of a patient having a craniooral space and/or of at least one dental component for the dental restorative procedure”), the method comprising:
receiving a three-dimensional digital jaw model of the patient’s jaw (par 0076-0078, “he craniooral space of a patient may be scanned by various data generating modalities or apparatuses. For instance, a dental impression of the patient's oral cavity or a part thereof may be produced. Imaging methods, like CT and MR or X-ray, may be used to provide data on deeper anatomical regions of the patient that are not obtainable by surface based data acquiring techniques …. The dental impression provides data for the topography of an oral cavity of a patient. The dental impression may directly be scanned by means of a three dimensional (3D) scanner system. Patient data may also be acquired from 3D scanning a plaster model produced from such a dental impression”, par 0081, “The dental space between the canines may be automatically filled with template front teeth from a teeth library. The teeth library is for instance provided in digital form in a database comprising at least one three-dimensional virtual template tooth object for each tooth in the maxilla and the mandible”, par 0119, “The template teeth are automatically or manually chosen from a library of virtual template teeth, dependent on the current dimensions of maxillary 200, the position of the teeth determined by the above describe mathematical calculations, the distance to occlusion line 900, bone density, each at the respective tooth position, etc. Also, adjacent teeth and the available total dental arch are considered when choosing suitable template teeth from the teeth library”);
receiving a number of dental implants to be inserted into the patient’s jaw (Fig 11, par 0125-0126, “A suggestion of the position of one or more dental implants, such as the plurality of dental implants 11a-11f in the illustrated example, may automatically be determined”, par 0144, “FIG. 12a is a schematic illustration in a view from below of the edentulous maxilla bone 200 showing the calculated occlusion line 900, the position and direction of implants 11a-11f, and automatically aligned standard teeth 10a-10m. The implants 11a-11f are illustrated by virtual markers 211a-211f going through the central longitudinal axis of each implant. The implants themselves are hidden under the illustrated standard teeth at the corresponding implant position, respectively. The virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure”);
receiving a preset indicator indicating an implant library preset selected from a plurality of implant library presets depending on the number of implants, wherein the implant library presets are assigned to respective numbers of implants, the plurality of implant library presets comprising area indicators of jaw areas for insertion of the respective numbers of dental implants (par 0081-0082, “ The dental space between the canines may be automatically filled with template front teeth from a teeth library. The teeth library is for instance provided in digital form in a database comprising at least one three-dimensional virtual template tooth object for each tooth in the maxilla and the mandible. In a similar way, the position of certain molars (posterior teeth) is determinable. The interspaces between teeth at determined positions are automatically filled with further teeth, e.g., template teeth from the teeth library”, par 0118-0119, “FIG. 10 is a schematic illustration in a view from below of the maxilla bone 200 showing the calculated occlusion line 900 and automatically aligned template teeth 10a-10m between the occlusion line 900 and the maxilla bone 200. The template teeth are automatically or manually chosen from a library of virtual template teeth, dependent on the current dimensions of maxillary 200, the position of the teeth determined by the above describe mathematical calculations, the distance to occlusion line 900, bone density, each at the respective tooth position, etc. Also, adjacent teeth and the available total dental arch are considered when choosing suitable template teeth from the teeth library.”, Fig 12b, par 0144, “The implants 11a-11f are illustrated by virtual markers 211a-211f going through the central longitudinal axis of each implant. The implants themselves are hidden under the illustrated standard teeth at the corresponding implant position, respectively. The virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure”, par 0158, “FIG. 18 is a schematic illustration of the virtually planned bridge framework 20, implants 11a-11f and occlusion line 900 in a perspective view …. Implants 11a-11e are provided with threads for threadibly mounting into pre-prepared bores in the jaw bone tissue. The virtual markers 211a-211f are shown more detailed in FIG. 18 and FIG. 19, indicating the central longitudinal axis of each implant, respectively. FIG. 20 is a schematic illustration of a detail of the bridge framework 20, implants 11a, 11b and the occlusion line 900 of FIG. 18 in an enlarged perspective view”, par 0160, “FIG. 21 is a schematic illustration of a detail of the bridge framework, implants and occlusion line of FIG. 18 in a perspective view from below. In FIG. 22 template teeth from the library of virtual teeth are added to the illustration, wherein it is shown how the teeth are positioned in relation to the occlusion line 900”); and
determining, using the selected implant library preset and the three-dimensional digital jaw model, a set of dental implant parameters defining one or more dental implant positions relative to the three-dimensional digital jaw model (par 0118-0124, “The template teeth are automatically or manually chosen from a library of virtual template teeth, dependent on the current dimensions of maxillary 200, the position of the teeth determined by the above describe mathematical calculations, the distance to occlusion line 900, bone density, each at the respective tooth position, etc. Also, adjacent teeth and the available total dental arch are considered when choosing suitable template teeth from the teeth library”, par 0143-0146, “FIG. 12a is a schematic illustration in a view from below of the edentulous maxilla bone 200 showing the calculated occlusion line 900, the position and direction of implants 11a-11f, and automatically aligned standard teeth 10a-10m. The implants 11a-11f are illustrated by virtual markers 211a-211f going through the central longitudinal axis of each implant. The implants themselves are hidden under the illustrated standard teeth at the corresponding implant position, respectively. The virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure ….Planning of the dental restoration may in the latter case be made visually on a display of a medical workstation, e.g., of the system described below with reference to FIG. 31, in an interactive way manipulated by user input. For instance the position and direction of dental implants in jaw bone is virtually presented on the display visualizing the jaw bone structure where a dental restoration is to be made. During planning care has to be taken that for instance no nerves are damaged or that the dental implant is positioned in as much dense bone as possible, in order to ensure a successful surgical installation of the dental implant. Hence, the user may virtually manipulate or accept placement of dental implants in advance of final placement. The implant's position, angulation, type of implant, length, in relation to final teeth restoration, may in an interactive manner be manually fine tuned”, par 0170-0171, “ FIG. 17 is a schematic illustration of the maxilla bone 200 and a surgical template 30 having drill guide bores 31. The surgical template 30 serves as a drill guide during a dental restorative procedure, during which a dental restoration is implanted in the oral cavity of a patient according to known methods. The surgical template is virtually planned from the final dental restoration data that is available at this stage. Guide sleeves 32 for guiding of drills are schematically illustrated. The guide sleeves 32 of the surgical template 30 are used for directing a drill through the soft tissue and into the jaw bone tissue of the patient. Guide sleeves 32 have a defined direction in line with the virtually planned position of an associated dental implant.” …. Thus precise bores are provided for subsequent implantation of dental implants that will have a defined orientation and position in bone tissue).
Regarding claim 2, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach wherein the implant library presets further comprise one or more definitions of distances between at least two implants (par 0119, “The template teeth are automatically or manually chosen from a library of virtual template teeth, dependent on the current dimensions of maxillary 200, the position of the teeth determined by the above describe mathematical calculations, the distance to occlusion line 900, bone density, each at the respective tooth position, etc. Also, adjacent teeth and the available total dental arch are considered when choosing suitable template teeth from the teeth library.”, par 0137, “The defined trace along which the plurality of vectors is traced may have different forms from the initial point of the center of gravity, e.g., cylindrical, conical, in one or more planes. This may be done in order to ensure e.g., a defined distance from anatomical structures and/or other dental restorations or implant components.”, Fig 21, par 0160, “FIG. 21 is a schematic illustration of a detail of the bridge framework, implants and occlusion line of FIG. 18 in a perspective view from below. In FIG. 22 template teeth from the library of virtual teeth are added to the illustration, wherein it is shown how the teeth are positioned in relation to the occlusion line 900”, par 0163, “The design of the dental veneering and of the bridge framework between these two locked boundary surfaces is automatically adapted to fit between the two boundary surfaces, while maintaining other requirements, such as distance to adjacent teeth etc. The spatial position of the boundary surface between the bride 20 and the dental veneering is determined relative the first and second spatial positions”).
Regarding claim 3, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach wherein the implant library presets further comprise one or more relative angulations between at least two implants, the determined dental implant parameters further defining one or more dental implant angles for the one or more dental implants relative to the three-dimensional digital jaw model (par 0147, “During planning care has to be taken that for instance no nerves are damaged or that the dental implant is positioned in as much dense bone as possible, in order to ensure a successful surgical installation of the dental implant. Hence, the user may virtually manipulate or accept placement of dental implants in advance of final placement. The implant's position, angulation, type of implant, length, in relation to final teeth restoration, may in an interactive manner be manually fine tuned”, Figs 18-19, par 0158-0159, “ The virtual markers 211a-211f are shown more detailed in FIG. 18 and FIG. 19, indicating the central longitudinal axis of each implant, respectively. FIG. 20 is a schematic illustration of a detail of the bridge framework 20, implants 11a, 11b and the occlusion line 900 of FIG. 18 in an enlarged perspective view”).
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Regarding claim 4, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach further comprising: generating, using the dental implant parameters, an output comprising a virtual implant positioning model, wherein the virtual implant positioning model visually represents the one or more dental implant positions relative to the three-dimensional digital jaw model of the patient’s jaw (par 0139, “The surface of the virtual jaw bone tissue may be defined by a plurality of polygons. The virtual jaw bone tissue may be modeled as a 3D object from these polygons”, Figs 21-22, par 0158-0160, “FIG. 21 is a schematic illustration of a detail of the bridge framework, implants and occlusion line of FIG. 18 in a perspective view from below. In FIG. 22 template teeth from the library of virtual teeth are added to the illustration, wherein it is shown how the teeth are positioned in relation to the occlusion line 900”).
Regarding claim 5, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach further comprising: generating, using the dental implant parameters and the three-dimensional digital jaw model, a three-dimensional digital drilling guide model, wherein the three-dimensional digital drilling guide model comprises means for achieving the dental implant parameters for the insertion of the one or more dental implants, wherein the means comprise one or more through holes defining drilling positions of drilling holes to be drilled into the patient’s jaw at the one or more dental implant positions, wherein the drilling positions are to be drilled into patient’s jaw for the insertion of the one or more dental implants (par 0144-0145, “he virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure”, par 0170-0171, “FIG. 17 is a schematic illustration of the maxilla bone 200 and a surgical template 30 having drill guide bores 31. The surgical template 30 serves as a drill guide during a dental restorative procedure, during which a dental restoration is implanted in the oral cavity of a patient according to known methods. The surgical template is virtually planned from the final dental restoration data that is available at this stage. Guide sleeves 32 for guiding of drills are schematically illustrated. The guide sleeves 32 of the surgical template 30 are used for directing a drill through the soft tissue and into the jaw bone tissue of the patient. Guide sleeves 32 have a defined direction in line with the virtually planned position of an associated dental implant”, par 0195-0196, “IG. 30B is a schematic illustration of implantation of a dental implant 312 in healed jaw bone tissue 314 at the position of a tooth gap 310 between adjacent teeth 315, 316. By means of a drill guide 311, which position may be controlled by the teeth 315, 316 adjacent the gap 310, or supported by the soft tissue, a bore may be provided in order to receive the dental implant 312”).
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Regarding claim 8, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach further comprising: determining a patient specific panoramic curve descriptive of the curved form of a ridge of the patient’s jaw extending along a patient’s jaw bow using the three-dimensional digital jaw model, wherein the area indicators of the jaw areas comprised by the implant library presets indicate jaw areas aligned on or alongside a generic panoramic curve, wherein the generic panoramic curve is descriptive of a generic curved form of a ridge of a generic patient’s jaw extending along a generic’s jaw bow, wherein the determining of the one or more dental implant positions comprises a mapping of the generic panoramic curve to the patient specific panoramic curve, the mapping resulting in a modification of the indicated jaw areas, the determining of the set of dental implant parameters being based on the modified indicated jaw areas (Fig 11, par 0125-0126, “FIG. 11 is a schematic illustration in a view from below of the maxilla bone 200 showing the calculated occlusion line 900 and a plurality of implants 11a-11f in the maxilla bone 200. A suggestion of the position of one or more dental implants, such as the plurality of dental implants 11a-11f in the illustrated example, may automatically be determined. This suggestion may be based on the data provided by the planned positions of the virtual teeth, as discussed above”, Fig 12a, par 0144, “FIG. 12a is a schematic illustration in a view from below of the edentulous maxilla bone 200 showing the calculated occlusion line 900, the position and direction of implants 11a-11f, and automatically aligned standard teeth 10a-10m. The implants 11a-11f are illustrated by virtual markers 211a-211f going through the central longitudinal axis of each implant. The implants themselves are hidden under the illustrated standard teeth at the corresponding implant position, respectively. The virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure”, Figs 21-22, “FIG. 21 is a schematic illustration of a detail of the bridge framework, implants and occlusion line of FIG. 18 in a perspective view from below. In FIG. 22 template teeth from the library of virtual teeth are added to the illustration, wherein it is shown how the teeth are positioned in relation to the occlusion line 900”).
Regarding claim 11, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach further comprising receiving planned positions of one or more artificial teeth relative to the three-dimensional digital jaw model (Fig 11, par 0125-0126, “FIG. 11 is a schematic illustration in a view from below of the maxilla bone 200 showing the calculated occlusion line 900 and a plurality of implants 11a-11f in the maxilla bone 200. A suggestion of the position of one or more dental implants, such as the plurality of dental implants 11a-11f in the illustrated example, may automatically be determined. This suggestion may be based on the data provided by the planned positions of the virtual teeth, as discussed above”, Fig 12a, par 0144, “FIG. 12a is a schematic illustration in a view from below of the edentulous maxilla bone 200 showing the calculated occlusion line 900, the position and direction of implants 11a-11f, and automatically aligned standard teeth 10a-10m. The implants 11a-11f are illustrated by virtual markers 211a-211f going through the central longitudinal axis of each implant. The implants themselves are hidden under the illustrated standard teeth at the corresponding implant position, respectively. The virtual markers 211a-211f illustrate the direction into which a surgical template will be provided with drill guiding bores for precision drilling of bores in the jaw bone tissue where the implants are to be installed during a surgical procedure subsequent to the virtual planning and production of dental components for the surgical procedure”).
Regarding claim 14, Nilsson et al. teach all the limitation of claim 1, and Nilsson et al. further teach wherein the set of dental implant parameters further define one or more dental implant sizes (par 0132, “sufficient bone tissue is provided for ensuring a secure fixation of the dental implant in the remaining bone tissue. Alternatively, if it is detected that sufficient bone tissue is not available, alternative implant sizes may be chosen”, par 0191, “The manually corrected position of the automatically planned and suggested dental restoration 27a may be based on a scaling of the template teeth which are provided by the above mentioned automatically planned suggestion of dental restoration 27a. Alternatively or additionally a manually corrected position may be based on different sized, larger or smaller, template teeth from the teeth library. The adapted bridge framework 273b, veneering 272b and implants 274b, 275b may automatically be chosen, based on the user input of the desired modified position and extension of the adjusted dental restoration 27b”, par 0195-0197, “Upon extraction of tooth 300, the gap 303 extends into jaw bone tissue 308. By means of a drill guide 304 a bore may be provided at that position in order to implant a dental implant 305. The dental implant may be provided with a healing cap 306 leading to an exact apposition of the surrounding soft tissue with simultaneous shaping of the gingiva upon healing of the extraction site”).
Regarding claim 15, Nilsson et al. teach all the limitation of claim 3, and Nilsson et al. further teach wherein the one or more relative angulations between at least two implants are determined such that at least two adjacent dental implants are arranged relative to each other with parallel orientations or mirrored orientations, wherein arranging the at least two adjacent dental implants relative to each other with mirrored orientations comprises a mirroring of a dental implant angles of one of the at least two adjacent dental implants using a mirror plane or mirror point arranged between the at least two adjacent dental implants (Figs 21-22, par 0160, “FIG. 21 is a schematic illustration of a detail of the bridge framework, implants and occlusion line of FIG. 18 in a perspective view from below. In FIG. 22 template teeth from the library of virtual teeth are added to the illustration, wherein it is shown how the teeth are positioned in relation to the occlusion line 900 “, par 0191, “FIGS. 27A-27C are schematic illustrations of a patient missing two teeth. A gap 271 has formed in the dental arch of the patient at the position of the two missing teeth. Remaining are teeth 270a-d. In FIG. 27B a suggestion of an automatically planned dental restoration 27a comprising two implants 274a, 274b, a bridge framework 273a and veneering 272a, is illustrated. The bridge framework 273a and veneering 272a have two bores 276, 277 for installation to dental implants 274a and 275a, respectively. The automatically planned suggestion of dental restoration 27a is based on two template teeth from a teeth library in order to fill the gap 271 along the dental arch of the patient and to fit an occlusion line, as calculated from the remaining teeth in the dental arch, and optionally also from anatomically fixed reference points. Due to the local situation of the patient, or due to esthetical reasons, it might be desired to manually adjust the suggested planning of the dental restoration 27a. This may for instance be due to esthetical reasons. In some embodiments, it may be desired to have larger teeth than the gap 271 allows when automatically interfitting library teeth therein. The result of a manually modified planning is shown in FIG. 27C, showing a modified virtually planned dental restoration 27b comprising adjusted dental implants 274b, 275b, an adjusted bridge framework 273b and veneering 272b. The manually corrected position of the automatically planned and suggested dental restoration 27a may be based on a scaling of the template teeth which are provided by the above mentioned automatically planned suggestion of dental restoration 27a. Alternatively or additionally a manually corrected position may be based on different sized, larger or smaller, template teeth from the teeth library. The adapted bridge framework 273b, veneering 272b and implants 274b, 275b may automatically be chosen, based on the user input of the desired modified position and extension of the adjusted dental restoration 27b. Locked surfaces that are fixed are the outer surface of the adjacent teeth 270a and 270c. When moving the virtual teeth past the occlusion line of the dental arch, automatic planning takes teeth that might be present in the opposed jaw into consideration, such that an occlusion is provided that is comfortable for the patient”).
Regarding claim 18, Nilsson et al. teach a computer program for determining one or more dental implant positions for one or more dental implants to be inserted into a patient’s jaw, the computer program comprising program instructions, the program instructions being executable by a processor of a computer device to cause the computer device to (par 0007, par 0203). The remaining limitations of the claim are similar in scope to claim 1 and rejected under the same rationale.
Regarding claim 19, Nilsson et al. teach a computer device for determining one or more dental implant positions for one or more dental implants to be inserted into a patient’s jaw (abstract, par 0009). The remaining limitations of the claim are similar in scope to claim 1 and rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2014/0272779 to Okay.
Regarding claim 6, Nilsson et al. teach all the limitation of claim 5, but keep silent for teaching further comprising: providing data for controlling a manufacturing of a physical drilling guide, the three-dimensional digital drilling guide model serving as a template for the physical drilling guide.
In related endeavor, Okay teaches further comprising: providing data for controlling a manufacturing of a physical drilling guide, the three-dimensional digital drilling guide model serving as a template for the physical drilling guide (par 0007-0009, “Creating the new paired surgical-prosthetic templates involves using a 3D imaging modality such as CT to image the patient's mouth with the denture in place, and using the resulting 3D image or images to form virtual templates that account for where implants should be placed and how they should be oriented relative to the patient's bone structure, soft tissue, and any existing teeth …. For each implant, a virtual abutment from the implant is registered in the denture with CAD software in the same planning file, and its position and angulation are projected for creating a matching guide hole in the new virtual prosthetic template that is designed over the external denture surfaces, preferably occlusal, palatal and facial surfaces ….. implant osteotomies are formed by drilling, through the guide holes in the surgical CAD-CAM template, into the bone stration and the implants can be placed through the connection holes of the denture which is in position over the tissue bearing surface. Due to using the same design and manufacture plan in planning and manufacturing each of the paired templates, the connection holes in the denture are in good registration with the implants. In one example, the paired surgical and prosthetic templates are manufactured by stereolithography, a laser driven polymerization process, and finished by hand with metal inserts into the desired diameter guide holes. In another example, the templates are fabricated by incorporating CAD-CAM rapid prototyping milling techniques into the process”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include further comprising: providing data for controlling a manufacturing of a physical drilling guide, the three-dimensional digital drilling guide model serving as a template for the physical drilling guide as taught by Okay to use the resulting three-dimensional (3D) image and computer-aided-design (CAD) software to develop a virtual guide that shows proposed locations and other information regarding the planned implant(s) as virtual template to fabricate the actual surgical template, which the dentist registers or fixates in the patient's mouth to prepare implant osteotomies by drilling through guide holes in the template.
Claim(s) 7 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2014/0272779 to Okay, further in view of U.S. PGPubs 2024/0148480 to Sanderson et al.
Regarding claim 7, Nilsson et al. as modified by Okay teach all the limitation of claim 6, but keep silent for teaching further comprising: controlling the manufacturing of the physical drilling guide using the data provided for controlling the manufacturing.
In related endeavor, Sanderson et al. teach further comprising: controlling the manufacturing of the physical drilling guide using the data provided for controlling the manufacturing (par 0099, “FIG. 1A is a block diagram of a system 100, that may be used to design and manufacture a dental implant using additive manufacturing techniques such as 3D laser printing. System 100 may include a clinician device 110, one or more imaging devices 115, a communication network 120, a computer 125, a dental implant fabrication tool 130, and/or a three-dimensional scanner 135 “, par 0102, “Dental implant fabrication tool 130 may be configured to receive instructions for the fabrication of one or more of the dental implants and/or dental implant components disclosed herein. Dental implant fabrication tool 130 may be, for example, a 3D printer, a computer-aided manufacturing (CAM) module, and/or a milling machine”, par 0160, “execution of step 385 may also include generation of one or more instructions for the manufacture of the dental implant based on the final model. In some cases, execution of step 385 includes translating the final model into CAM software for communication to a manufacturing device (e.g., a three-dimensional printer). In some embodiments, execution of step 385 may include receiving, or adapting, the instructions to generate the dental implant based on a material (e.g., titanium or other biocompatible material) and/or an additive manufacturing process used to manufacture the dental implant. In step 390, the formatted final model and/or instructions for manufacturing a root-analog dental implant based upon the final root-analog dental implant model may be communicated to an implant fabrication tool such as a 3D printer”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. as modified by Okay to include further comprising: controlling the manufacturing of the physical drilling guide using the data provided for controlling the manufacturing as taught by Sanderson et al. to provide a method for designing and fabricating facilitate dental implant insertion using an additive manufacturing process such as three-dimensional printing to provide for better engagement between the bone and dental implant after healing.
Regarding claim 20, Nilsson et al. teach all the limitation of claim 19, and further teach execution of the program instructions by the processor further causing the computer device to: generate, using the dental implant parameters and the three-dimensional digital jaw model, a three-dimension digital drilling guide model, wherein the three-dimensional digital drilling guide model comprises means for achieving the dental implant parameters for the insertion of the one or more dental implants, wherein the means comprise one or more through holes defining drilling positions of drilling holes to be drilled into the patient’s jaw at the one or more dental implant positions, wherein the drilling positions are to be drilled into patient’s jaw for the insertion of the one or more dental implants (see rejection claim 5 by Nilsson et al.); provide data for controlling the manufacturing of the physical drilling guide, the three-dimensional digital drilling guide model serving as a template for the physical drilling guide (see rejection claim 6 by Okay); control the manufacturing device to manufacture the physical drilling model using the data provided for controlling the manufacturing (see rejection claim 7 by Sanderson et al.), but keep silent for teaching a manufacturing system comprising the computer device, the manufacturing system further comprising a manufacturing device configured to manufacture a physical drilling template.
In related endeavor, Sanderson et al. teach a manufacturing system comprising the computer device, the manufacturing system further comprising a manufacturing device configured to manufacture a physical drilling template (par 0085, “An implant design process may use a three-dimensional scan, or other images and information, of the extracted tooth root as a base for designing a three-dimensional model of a dental implant to replace the extracted tooth that may then be fabricated using, for example, an additive manufacturing process such as three-dimensional printing. Once manufactured, a root-analog dental implant designed using one or more processes described herein may be inserted directly into the original, unmodified alveolar socket from which the scanned and/or imaged tooth/tooth root has been extracted prior to the jawbone reshaping itself to fill in the vacated alveolar socket.”, Fig 1A, par 0099-0102, “FIG. 1A is a block diagram of a system 100, that may be used to design and manufacture a dental implant using additive manufacturing techniques such as 3D laser printing. System 100 may include a clinician device 110, one or more imaging devices 115, a communication network 120, a computer 125, a dental implant fabrication tool 130, and/or a three-dimensional scanner 135.”). This would be obvious for the same reason given in the rejection for claim 7.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2024/0285379 to Saphier et al..
Regarding claim 9, Nilsson et al. teach all the limitation of claim 1, but keep silent for teaching using a first trained machine learning module to receive the preset indicator indicating the selected implant library preset, the first trained machine learning module being configured to provide the preset indicator as output in response to receiving the three-dimensional digital jaw model, the number of dental implants, and the plurality of implant library presets as input.
In related endeavor, Saphier et al. teach using a first trained machine learning module to receive the preset indicator indicating the selected implant library preset, the first trained machine learning module being configured to provide the preset indicator as output in response to receiving the three-dimensional digital jaw model, the number of dental implants, and the plurality of implant library presets as input (par 0219, “individual points on the 3D surface are scored and regions are determined based on groupings of similarly scored points. In some embodiments, data associated with a region of a 3D surface of a dental site is input into a trained machine learning model, which outputs one or more surface quality scores for the region. In some instances, a number of measured data points in a region is counted, and the surface quality score for the region is determined based at least on the number of points (e.g., number of points per mm.sup.2)”, par 0238-0240, “ a user manually marks a region of the 3D surface that is a preparation tooth. Alternatively, a user may indicate that the user is about to start scanning a preparation tooth, and a region scanned after receiving such user input may be marked as a preparation tooth. In one embodiment, the 3D surface is classified and/or segmented using one or more trained machine learning models. In some embodiments, processing logic uses one or more trained machine learning models (e.g., a neural network) trained to perform classification of dental sites, where at least one class is for a restorative object. Other classes of dental objects that may be identified include teeth, gums, preparation teeth (which may be considered a type of restorative object), margin line, upper palate, tongue, lips, gum-tooth line (referred to as emergent profile), and so on. In embodiments, the machine learning model receives data associated with a region of a 3D surface, which may include the 3D surface, one or more intraoral scans depicting the region, one or more 2D images depicting the region, one or more height maps of the region, etc. The machine learning model may process the data and output a dental object classification for the region. The trained machine learning model(s) may perform image level classification/scan level classification, may perform pixel-level classification, or may perform classification of groups of pixel”, par 0251, “processing logic inputs data from one or more intraoral scans (e.g., points from one or more intraoral scans and/or the intraoral scans themselves) into one or more trained machine learning models. At block 704, processing logic receives an output from the trained machine learning model (e.g., such as a trained neural network) comprising quality scores for each of the points of the one or more input intraoral scans”, par 0263, “once some or all of the above described parameters are determined for one or more points of an intraoral scan and/or a region of a 3D surface, the data for the one or more points may be input into a trained machine learning model such as a neural network, which may output final quality scores for the one or more points. In one embodiment, the regression analysis and/or machine learning model operates on a single point at a time to output a quality score for that point. In one embodiment, the regression analysis and/or machine learning model operates on multiple points in parallel to determine quality scores for the multiple points, where parameters (e.g., x,y,z position, distance, triangulation angle, surface angle, number of cameras capturing the point, etc.) for one point may affect not only the quality score for that point but also the quality scores for other nearby points”, par 0321, “The determined path may be a most efficient and/or a simplest path to follow from the current position/orientation of the intraoral scanner through each of the determined positions/orientations. The positions/orientations and/or the path may be determined using a trained machine learning model (e.g., a neural network) in embodiments. For example, the current position/orientation of the intraoral scanner and information on one or more regions of the dental site may be input into the trained machine learning model, which may output the determined positions/orientations and/or the determined path. Once the path is determined, an overlay may be generated that shows the path and/or the one or more target positions/orientations for the intraoral scanner that are included in the path”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include using a first trained machine learning module to receive the preset indicator indicating the selected implant library preset, the first trained machine learning module being configured to provide the preset indicator as output in response to receiving the three-dimensional digital jaw model, the number of dental implants, and the plurality of implant library presets as input as taught by Saphier et al. to use machine learning model to train the input dental data to generate higher quality virtual model to be able to design an optimal prosthesis or orthodontic treatment appliance(s).
Regarding claim 10, Nilsson et al. as modified by Saphier et al. teach all the limitation of claim 9, and Saphier et al. further teach further comprising: providing the first machine learning module to be trained; providing first training datasets for training the first machine learning module to be trained, each first training dataset comprising a three-dimensional digital training jaw model, a training number of dental implants, the plurality of implant library presets, and a training preset indicator; training the first machine learning module to be trained using the training datasets (par 0219, “individual points on the 3D surface are scored and regions are determined based on groupings of similarly scored points. In some embodiments, data associated with a region of a 3D surface of a dental site is input into a trained machine learning model, which outputs one or more surface quality scores for the region. In some instances, a number of measured data points in a region is counted, and the surface quality score for the region is determined based at least on the number of points (e.g., number of points per mm.sup.2)”, par 0238-0240, “ a user manually marks a region of the 3D surface that is a preparation tooth. Alternatively, a user may indicate that the user is about to start scanning a preparation tooth, and a region scanned after receiving such user input may be marked as a preparation tooth. In one embodiment, the 3D surface is classified and/or segmented using one or more trained machine learning models. In some embodiments, processing logic uses one or more trained machine learning models (e.g., a neural network) trained to perform classification of dental sites, where at least one class is for a restorative object. Other classes of dental objects that may be identified include teeth, gums, preparation teeth (which may be considered a type of restorative object), margin line, upper palate, tongue, lips, gum-tooth line (referred to as emergent profile), and so on. In embodiments, the machine learning model receives data associated with a region of a 3D surface, which may include the 3D surface, one or more intraoral scans depicting the region, one or more 2D images depicting the region, one or more height maps of the region, etc. The machine learning model may process the data and output a dental object classification for the region. The trained machine learning model(s) may perform image level classification/scan level classification, may perform pixel-level classification, or may perform classification of groups of pixel”, par 0246, “ the one or more trained machine learning models are trained to perform classification and/or segmentation of images, intraoral scans and/or 3D surfaces into dental classes. In embodiments, processing logic may input intraoral scans, 2D images, the 3D surface, projections of the 3D surface onto one or more planes, points from the 3D surface, and/or other data into the trained machine learning model(s). One implementation uses a deep neural network to learn how to map an input image, intraoral scan and/or 3D surface to human labeled dental classes, where the dental classes include regular teeth and one or more restorative objects. The result of this training is a trained machine learning model that can predict labels directly from input scan data and/or 3D surface data. Input data may be individual intraoral scans (e.g., height maps), 3D surface data (e.g., a 3D surface from multiple scans or a projection of such a 3D surface onto a plane) and/or or other images (e.g., color images and/or NIRI images)”, par 0250-0251, “processing logic inputs data from one or more intraoral scans (e.g., points from one or more intraoral scans and/or the intraoral scans themselves) into one or more trained machine learning models. At block 704, processing logic receives an output from the trained machine learning model (e.g., such as a trained neural network) comprising quality scores for each of the points of the one or more input intraoral scans”). This would be obvious for the same reason given in the rejection for claim 9.
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2022/0296344 to Lee et al..
Regarding claim 12, Nilsson et al. teach all the limitation of claim 11, but keep silent for teaching using a second trained machine learning module to receive the planned positions of the one or more artificial teeth, the second trained machine learning module being configured to provide the planned positions of the one or more artificial teeth as output in response to receiving the three-dimensional digital jaw model and the number of dental implants.
In related endeavor, Lee et al. teach using a second trained machine learning module to receive the planned positions of the one or more artificial teeth, the second trained machine learning module being configured to provide the planned positions of the one or more artificial teeth as output in response to receiving the three-dimensional digital jaw model and the number of dental implants (par 0041, “Once predicted by the computer-based ML system or method according to embodiments of the present invention, these parameters can be used to uniquely define the full 3D shape, position, and orientation of the restorative dental objects such as abutments and crowns. To calculate this shape the parameter values can be fed into an underlying 3D parametric model “,par 0183, “Embodiments of the present invention are based on the observation that the relationship between the design of a customized dental object and the input data from which the design is created can be learned by training a machine learning system or method by inputting (e.g. via an I/O port or an interface) and using many existing relevant examples of treatment plans and outcomes designed by human operators such as dental technicians. This could be considered a typical problem of Supervised Learning. Such an algorithm relates to a problem domain in which the input data is 1) the known implant type, position and orientation, 2) the 3D scan data of patient jaws (e.g. as point cloud), and 3) additional options (or customer preferences) on how the specific dental object needs to be designed. This information is complete and enough for a human designer to create a shape of a restorative dental object such as a customized crown and abutment shape. Human designers apply their dental knowledge and specific rules to design an abutment or crown that would fit to the dentition around the missing tooth in an optimal way based on the given input data. The output is a full restoration design, which could be further defined by a set of parameters that would uniquely define the shape, position, and orientation of an abutment or crown “, par 0196, “Embodiments of the present invention take advantage of the recent advance of Machine Learning (ML) and Artificial Intelligence (AI) and it is one of the aspects of embodiments of the present invention is to have a system learn underlying patterns between the input and output of a lot of actual examples, i.e. in an end-to-end manner. Embodiments of the present invention start from the existence of many actual dental clinical cases of restorative dental objects for implant-based restoration for which the starting position, e.g. a missing tooth, is known and the final treatment plan, treatment and result, i.e. the design of the restorative dental object, are known. The starting position, e.g. the missing tooth refers to one end of the process whereas the final treatment plan, treatment and result i.e. the design of the restorative dental object are the other end. Thus, methods according to embodiments of the present invention train a neural network such as a CNN with only the raw data and the final results, thus carrying out end-to-end machine learning. Traditionally end-to-end machine learning based on available information in the documentation of existing clinical cases has not been used“, par 0214, “a hierarchical training approach comprising training important individual parameters and/or dividing the remaining design parameters into different groups depending on their relative dependencies in terms of the coordinate system on which their values are interpreted. Thus, the hierarchical training approach refers to the ranking of the importance of one or more parameters (which are used for defining the overall shape of a structure such as a restorative dental object) with respect to other parameters of the same parametric model. Independent of the level within the hierarchical all of the parameters relate to the entire abutment, the entire crown, etc. The rotation parameter is the most important one, which can be trained first with the input scans in the implant coordinate system. Angle and position parameters can then be trained (e.g. each one on its own or within a group) based on the input scans normalized by the known rotation angles. Then the training of the rest shape parameters (e.g. each one on its own or within a group) can be done based on the input scans normalized by the known position and other angles”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include using a second trained machine learning module to receive the planned positions of the one or more artificial teeth, the second trained machine learning module being configured to provide the planned positions of the one or more artificial teeth as output in response to receiving the three-dimensional digital jaw model and the number of dental implants as taught by Lee et al. to use Machine Learning algorithms for the computer-implemented methods or systems of automatic restoration design for generating, reviewing, and accepting the planning for a customized single tooth replacement to achieve a clinically relevant design that meets the treatment solution expectations of the clinician and the patient.
Regarding claim 13, Nilsson et al. teach all the limitation of claim 1, but keep silent for teaching using a third trained machine learning module to receive the number of dental implants, the third trained machine learning module being configured to provide the number of dental implants as output in response to receiving the three-dimensional digital jaw model as input.
In related endeavor, Lee et al. teach using a third trained machine learning module to receive the number of dental implants, the third trained machine learning module being configured to provide the number of dental implants as output in response to receiving the three-dimensional digital jaw model as input (par 0041, “The outputs of the system or method are model parameters or representations of model parameters of the 3D shape of the dental object, e.g. parameter values which can be numbers defining the restorative dental objects such as abutments and crowns which are required for the patient. Once predicted by the computer-based ML system or method according to embodiments of the present invention, these parameters can be used to uniquely define the full 3D shape, position, and orientation of the restorative dental objects such as abutments and crowns. To calculate this shape the parameter values can be fed into an underlying 3D parametric model “,par 0183, “Embodiments of the present invention are based on the observation that the relationship between the design of a customized dental object and the input data from which the design is created can be learned by training a machine learning system or method by inputting (e.g. via an I/O port or an interface) and using many existing relevant examples of treatment plans and outcomes designed by human operators such as dental technicians. This could be considered a typical problem of Supervised Learning. Such an algorithm relates to a problem domain in which the input data is 1) the known implant type, position and orientation, 2) the 3D scan data of patient jaws (e.g. as point cloud), and 3) additional options (or customer preferences) on how the specific dental object needs to be designed. This information is complete and enough for a human designer to create a shape of a restorative dental object such as a customized crown and abutment shape. Human designers apply their dental knowledge and specific rules to design an abutment or crown that would fit to the dentition around the missing tooth in an optimal way based on the given input data. The output is a full restoration design, which could be further defined by a set of parameters that would uniquely define the shape, position, and orientation of an abutment or crown “, par 0196, “Embodiments of the present invention take advantage of the recent advance of Machine Learning (ML) and Artificial Intelligence (AI) and it is one of the aspects of embodiments of the present invention is to have a system learn underlying patterns between the input and output of a lot of actual examples, i.e. in an end-to-end manner. Embodiments of the present invention start from the existence of many actual dental clinical cases of restorative dental objects for implant-based restoration for which the starting position, e.g. a missing tooth, is known and the final treatment plan, treatment and result, i.e. the design of the restorative dental object, are known. The starting position, e.g. the missing tooth refers to one end of the process whereas the final treatment plan, treatment and result i.e. the design of the restorative dental object are the other end. Thus, methods according to embodiments of the present invention train a neural network such as a CNN with only the raw data and the final results, thus carrying out end-to-end machine learning. Traditionally end-to-end machine learning based on available information in the documentation of existing clinical cases has not been used“, par 0257, “ It is preferred that such auxiliary data is included into training only if the same information is available at the prediction time as well. The additional information can be tooth number, design preferences, and implant types and corresponding specifications. Among them, tooth number is useful to help increase the accuracies of the training and prediction. Then there are certain optional design preference information that the customers provide when they place an order, with which it would be useful to create some specific designs based on the options (e.g. how much sub-gingival depth of the abutment margin needs to be)”, par 0262, “As shown in the block diagram in the FIG. 6, the geometrical input (e.g. an occupancy grid) 31 is fed into the 3D CNN 30 and the auxiliary input 32 (e.g. tooth numbers, design preference etc.) is fed into separate densely connected layers 41, which are eventually merged in layer 37 with the dense layer 36 of the output of the 3D CNN 30”, par 0312-0314, “adapting the machine learning system to use a discriminative ML algorithm, using a patient's dentition which comprises an upper and/or lower jaw, prepared and opposing jaws, missing tooth, implant, and tooth numbers. performing the method with the restorative dental object being a crown, or an abutment for an implant”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include using a third trained machine learning module to receive the number of dental implants, the third trained machine learning module being configured to provide the number of dental implants as output in response to receiving the three-dimensional digital jaw model as input as taught by Lee et al. to use Machine Learning algorithms for the computer-implemented methods or systems of automatic restoration design for generating, reviewing, and accepting the planning for a customized single tooth replacement to achieve a clinically relevant design that meets the treatment solution expectations of the clinician and the patient.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2011/0287381 to Sanders.
Regarding claim 16, Nilsson et al. teach all the limitation of claim 14, but keep silent for teaching wherein the set of dental implant parameters are determined such that the one or more dental implants arranged within the patient’s jawbone according to the one or more dental implant parameters satisfy one or more of the following criteria: a vestibular minimum thickness of the patient’s jawbone in vestibular direction and an oral minimum thickness of the patient’s jawbone in oral direction being at least 1.5 mm, in particular at least 2 mm; a minimum implant distance between adjacent dental implants being at least 3 mm, in particular at least 4 mm; and a minimum implant-to-tooth distance between dental implants and roots of adjacent natural teeth being at least 1.5mm, in particular at least 2mm.
In related endeavor, Sanders teaches wherein the set of dental implant parameters are determined such that the one or more dental implants arranged within the patient’s jawbone according to the one or more dental implant parameters satisfy one or more of the following criteria: a vestibular minimum thickness of the patient’s jawbone in vestibular direction and an oral minimum thickness of the patient’s jawbone in oral direction being at least 1.5 mm, in particular at least 2 mm; a minimum implant distance between adjacent dental implants being at least 3 mm, in particular at least 4 mm; and a minimum implant-to-tooth distance between dental implants and roots of adjacent natural teeth being at least 1.5mm, in particular at least 2mm (par 0023, “To allow for a proper volume or thickness of jaw bone between the implant and the adjacent teeth so as to allow for a proper blood supply and health of the bone between the implant and the adjacent teeth, it has been accepted in the dental field to maintain a minimum distance of 2 millimeters between the implant and the adjacent teeth on either side of the implant. As noted above, this means that the head of the implant at the height of the crestal bone should not exceed a diameter of 6 to 8 millimeters in a mesio-distal dimension (the distance between the adjacent teeth where the missing tooth used to be), based on the formula: interdental space (space left by the missing tooth) minus 4 millimeters (2 millimeters on each side of the implant)=maximum diameter of implant head”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include wherein the set of dental implant parameters are determined such that the one or more dental implants arranged within the patient’s jawbone according to the one or more dental implant parameters satisfy one or more of the following criteria: a vestibular minimum thickness of the patient’s jawbone in vestibular direction and an oral minimum thickness of the patient’s jawbone in oral direction being at least 1.5 mm, in particular at least 2 mm; a minimum implant distance between adjacent dental implants being at least 3 mm, in particular at least 4 mm; and a minimum implant-to-tooth distance between dental implants and roots of adjacent natural teeth being at least 1.5mm, in particular at least 2mm as taught by Sanders to allow for a proper volume or thickness of jaw bone between the implant and the adjacent teeth so as to allow for a proper blood supply and health of the bone between the implant and the adjacent teeth.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2010/0151417 to Nilsson et al. in view of U.S. PGPubs 2023/0122558 to Aamodt
Regarding claim 17, Nilsson et al. teach all the limitation of claim 1, but keep silent for teaching further comprising: generating the three-dimensional jaw model, wherein generating the three-dimensional digital jaw model of the patient’s jaw comprises: receiving a computed tomography scan of the patient’s jaw; receiving an intraoral scan of the patient’s jaw; combining the computed tomography scan with the intraoral scan and using the combination for the generating of the three-dimensional jaw model.
In related endeavor, Aamodt teaches further comprising: generating the three-dimensional jaw model, wherein generating the three-dimensional digital jaw model of the patient’s jaw comprises: receiving a computed tomography scan of the patient’s jaw; receiving an intraoral scan of the patient’s jaw; combining the computed tomography scan with the intraoral scan and using the combination for the generating of the three-dimensional jaw model (par 0006, “three-dimensional intraoral surface scan data of the dentition may be received, and the intraoral surface scan data and the volumetric scan data may be overlayed to generate integrated scan data. In some variations, overlaying the intraoral surface scan data and the volumetric scan data may comprise registering the intraoral surface scan data with the volumetric scan data”, par 0033, “a method 100 of orthodontic treatment planning for a patient includes receiving three-dimensional volumetric scan data of a dentition of the patient 110. Optionally, three-dimensional intraoral surface scan data of the dentition of the patient 120 may be received. Optionally, the intraoral surface scan data and the volumetric scan data may be overlaid to generate integrated scan data 130. A mandibular condyle of a mandible of the patient may be determined 140. A mandibular rotation axis and a glenoid fossae of a temporal bone of the patient may be determined based on the mandibular condyle for use in planning an orthodontic treatment 150. Optionally, a jaw model of the patient may be generated based on the scan data, the rotation axis of the mandible, and the glenoid fossae 160. Optionally, a jaw movement of the patient may be predicted using the jaw model of the patient 170”, par 0039, “Combining volumetric scan data and intraoral surface data may reduce the error introduced by artifacts present in the volumetric scan data of the teeth such as those introduced from radio-opaque dental restorative materials like amalgam, metal, composites, and the like”, par 0041-0043, “ the overlaying of the intraoral surface scan data and the volumetric scan data may be performed automatically with suitable machine vision techniques (e.g., edge detection, corner finding, etc.). In yet other variations, the overlaying of the intraoral surface scan data and the volumetric scan data may be performed semi-automatically utilizing both manual and algorithmic techniques”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Nilsson et al. to include further comprising: generating the three-dimensional jaw model, wherein generating the three-dimensional digital jaw model of the patient’s jaw comprises: receiving a computed tomography scan of the patient’s jaw; receiving an intraoral scan of the patient’s jaw; combining the computed tomography scan with the intraoral scan and using the combination for the generating of the three-dimensional jaw model as taught by Aamodt to combine volumetric scan data and intraoral surface data to reduce the error introduced by artifacts present in the volumetric scan data of the teeth such as those introduced from radio-opaque dental restorative materials like amalgam, metal, composites, and the like.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jin Ge whose telephone number is (571)272-5556. The examiner can normally be reached 8:00 to 5:00.
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JIN . GE
Examiner
Art Unit 2619
/JIN GE/ Primary Examiner, Art Unit 2619
Prosecution Insights