DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 1-30 are presented for examination.
2. The information disclosure statement (IDS) submitted on 06/22/23 was considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
4. Claims 1-13, 17 and 20-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. 11,471,251 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they are essentially similar in scope. They are not patentably distinct from each other because In view of the “obviousness-type” double patenting rational enunciated in Georgia Pacific Crop v United States Gypsum Co., 52 USPQ2d 1590, U.S. Court of Appeals Federal Circuit 1999, instant application claims 1-13, 17 and 20-30 and merely define an obvious variation of the invention claimed in the US Patent No. 11,471,251 B2.
5. After analyzing the language of the claims, initially it should be noted that the Patent No. 11,471,251 B2, having the same inventive entity. The Assignee in all applications is the same.
Claims 1-13, 17 and 20-30 of the instant application is anticipated by patent claims 1-12 in that claims 1-12 of the patent 11,471,251 B2 contains all the limitations of claims 1-13, 17 and 20-30 of the instant application. Claims 1-13, 17 and 20-30 of the instant application therefore is not patently distinct from the earlier patent claim and as such is unpatentable for obvious-type double patenting. In re Berg, 140 F.3d1428, 1431, 46 USPQ2d 1226, 1229 (Fed. Cir. 1998).
6. A comparison of the independent claims in the instant application and US patent application are shown in the table below.
Instant Application (18/212846)
US Patent No. 11,471,251
1. A system for creating a virtual model representing at least part of a dentition of a patient, comprising: at least one computing device which is configured to
execute in parallel a plurality of processes
at least one shared memory device which can be accessed by the at least one computing device at least one first interface for receiving a digital data record representing a 3d representation of at least part of a dentition of a patient and for storing the digital data record in the at least one shared memory device
at least one second interface for outputting data wherein the plurality of parallelly executed processes comprises a plurality of groups of computation modules, each computation module being configured to run at least one neuronal network in order to apply a machine learning technique and each group of computation modules comprising one or more computation modules wherein
different groups of computation modules represent different anatomical sub- structures that might be present in a
dentition of a patient, each anatomical sub-structure being represented in different configurational states, shapes and sizes, the different anatomical sub-structures being at least: visible parts of teeth and gingiva each group of computation modules is configured to apply the machine learning technique on at least part of the digital data record and to output the result to at least one
different group of computation modules and/or to the shared memory device and/or to the at least one second interface those anatomical sub-structures which are present in the at least part of a dentition represented by the digital data
record are identified by those groups of computation modules which represent these anatomical substructures and the virtual model is created based on the
identified anatomical sub-structures, said virtual model representing at least the visible parts of teeth, the visible parts of teeth being separated from each other
and the gingiva.
1. A system for creating a virtual model representing at least part of a dentition of a patient, comprising: at least one computing device which is configured to execute in parallel a plurality of processes
at least one shared memory device which can be accessed by the at least one computing device at least one first interface for receiving a digital data
record representing a 3d representation of at least part of a dentition of a patient and for storing the digital data record in the at least one shared memory device
at least one second interface for outputting data wherein the plurality of parallelly executed processes comprises
a plurality of groups of computation modules, each computation module being configured to run at least one neuronal network in order to apply a machine learning technique and each group of computation modules comprising one or more computation modules wherein different groups of computation modules represent different anatomical sub-structures that might be present in a
dentition of a patient, each anatomical sub-structure being represented in different configurational states, shapes and sizes, the different anatomical sub-structures being at least: visible parts of teeth and gingiva each group of computation modules is configured to apply the machine learning technique on at least part of the digital data record and to output the result to at least one
different group of computation modules or to the shared memory device or to the at least one second interface those anatomical sub-structures which are present in the at least part of a dentition represented by the digital data record are identified by those groups of computation
modules which represent these anatomical sub-structures and the virtual model is created based on the identified
anatomical sub-structures, said virtual model representing at least the visible parts of teeth, the visible parts of teeth being separated from each other and the gingiva and wherein different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of target dentitions different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of starting dentitions different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of intermediary dentitions for each starting dentition and for each target dentition,
there are connections, preferably represented by different groups of computation modules, between a starting dentition, different intermediary dentitions and a target dentition, thereby establishing at least one sequence of
intermediary dentitions leading from a starting dentition to a target dentition, wherein the plurality of parallelly executed processes further comprises at least
one data hub process, the at least one data hub process being connected to the shared memory device or to the at least one first interface and being configured to
segment the digital data record into data segments, if the digital data record is not already provided in form of data segments, and to provide each data segment with a key and wherein said computation modules are configured to:
check whether data segments provided with a specific key are present in the at least one shared memory device or are provided by at least one different group of computation modules; run idly if no data segment with the specific key is detected or provided; and if a data segment with the specific key is detected in the
at least one shared memory device or provided by at least one different group of computation modules, apply the machine learning technique on that data segment and output the result to at least one different group of computation modules or to the shared memory device or to the at least one output device.
20. A computer implemented method for creating a virtual model representing at least part of a dentition of a patient,
comprising running at least one computing device which receives a digital data record representing a 3d representation
of at least part of a dentition of a patient and stores the digital data record in at least one shared memory device and
which executes in parallel a plurality of processes comprising a plurality of groups of computation modules, wherein
each computation module runs at least one neuronal network in order to apply a machine learning technique and each
group of computation modules comprises one or more computation modules wherein different groups of computation modules represent different anatomical sub-structures that might be present in a
dentition of a patient, each anatomical sub-structure being represented in different configurational states, shapes and sizes, the different anatomical sub-structures being at least: visible parts of teeth and gingiva those anatomical sub-structure which are present in the digital data record are identified by those groups of computation modules which represent these anatomical sub-structures and the virtual model is created based on the identified anatomical sub-structures, said virtual model representing at least the visible parts of teeth, the visible parts of
teeth being separated from each other and the gingiva.
7. A computer implemented method for creating a virtual model representing at least part of a dentition of a patient,
comprising running at least one computing device which receives a digital data record representing a 3d representation of at least part of a dentition of a patient and stores the digital data record in at least one shared memory device and which executes in parallel a plurality of processes comprising a plurality of groups of computation modules, wherein each computation module runs at least one neuronal network in order to apply a machine learning technique and each
group of computation modules comprises one or more computation modules wherein different groups of computation modules represent different anatomical sub-structures that might be present in a
dentition of a patient, each anatomical sub-structure being represented in different configurational states, shapes and sizes, the different anatomical sub-structures being at least: visible parts of teeth and gingiva those anatomical sub-structure which are present in the digital data record are identified by those groups of computation modules which represent these anatomical sub-structures and the virtual model is created based on the identified anatomical sub-structures, said virtual model representing at least the visible parts of teeth, the visible parts of teeth being separated from each other and the gingiva and wherein different groups of computation modules represent at least parts of different dentitions which are cataloged as belonging to a catalog of target dentitions different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of starting dentitions different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of intermediary dentitions for each starting dentition and for each target dentition
there are connections, preferably represented by different groups of computation modules, between a starting
dentition, different intermediary dentitions and a target dentition, thereby establishing at least one sequence of
intermediary dentitions leading from a starting dentition to a target dentition, wherein the plurality of parallelly executed processes further comprises at least one data hub process, the at least one data hub process being connected to the shared memory device or to the at least one first interface and being configured to
segment the digital data record into data segments, if the digital data record is not already provided in form of data segments, and to provide each data segment with a key and wherein said computation modules are configured to:
check whether data segments provided with a specific key are present in the at least one shared memory device or are provided by at least one different group of computation modules; run idly if no data segment with the specific key is detected or provided; and if a data segment with the specific key is detected in the at least one shared memory device or provided by at least one different group of computation modules, apply the machine learning technique on that data segment and output the result to at least one different group of computation modules or to the shared memory device or to the at least one output device.
Claim Rejections - 35 USC § 101
7. 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.
8. Claim 29 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 29 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 29 recites “a computer-readable medium” However, the specification does not define the above medium, and the context the medium was used in the claim would fairly suggest to one of ordinary skill signals or other forms of propagation and transmission media, typewritten or handwritten text on paper, or other items failing to be an appropriate manufacture under 35 USC 101 in the context of computer-related inventions. Therefore, the broadest reasonable interpretation to the above medium would cover forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable storage medium (see MPEP 2111.01), particularly when the specification is silent, as in this case. A signal per se represents non-statutory subject matter because it is not tangible. In particular, the specification defines the term: “Such storage medium can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such storage media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media.” (see published specification 0016, 0023). Application is encouraged to clarify the medium by adding a phrase “non-transitory”, for example, a non-transitory tangible computer readable storage medium. Applicant’s attention is directed to MPEP §§2106(IV)(B), 2106.01 and 2111.01. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. $101, Aug. 24, 2009; p. 2.
9. Claim 30, “a data carrier signal carrying…”. A data carrier signal is not physical “things”. They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed data carrier signal 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. Also, the broadest reasonable interpretation of data carrier signal is transitory propagating signals per se in view of the ordinary and customary meaning of computer usable medium (see MPEP 2111.01), wherein the transitory propagating signals are non-statutory subject matter. The data carrier signal is a product that is presented in a tangible medium or carrier wave and modulated or otherwise encoded in the carrier wave, which is tangible, and transmitted according to any suitable transmission method.
Claim Rejections - 35 USC § 112
10. The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
11. Claims 27-30 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. A computer product (program), a process, a computer -readable medium and a data carrier signal claims are depended on a system claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
12. 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.
(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.
13. Claims 1, 2 and 4-30 and are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Martz et al. (Martz), US publication no. 2018/0303581.
As per claim 1, Martz discloses a system for creating a virtual model representing at least part of a dentition of a patient [figure 14; para 136], comprising: at least one computing device which is configured to execute in parallel a plurality of processes
at least one shared memory device which can be accessed by the at least one computing device at least one first interface for receiving a digital data record representing a 3d representation of at least part of a dentition of a patient and for storing the digital data record in the at least one shared memory device [para 37];
at least one second interface for outputting data wherein the plurality of parallelly executed processes comprises a plurality of groups of computation modules, each computation module being configured to run at least one neuronal network in order to apply a machine learning technique and each group of computation modules comprising one or more computation modules [para 92, 93]; wherein different groups of computation modules represent different anatomical sub- structures that might be present in a dentition of a patient, each anatomical sub-structure being represented in different configurational states, shapes and sizes, the different anatomical sub-structures being at least: visible parts of teeth and gingiva [para 91-94]; each group of computation modules is configured to apply the machine learning technique on at least part of the digital data record and to output the result to at least one different group of computation modules and/or to the shared memory device and/or to the at least one second interface [figure 2; para 61]; those anatomical sub-structures which are present in the at least part of a dentition represented by the digital data record are identified by those groups of computation modules which represent these anatomical substructures and the virtual model is created based on the identified anatomical sub-structures, said virtual model representing at least the visible parts of teeth, the visible parts of teeth being separated from each other and the gingiva [figure 5; para 97, 98, 139].
Martz teaches:
[0061] At operation 144, the digital dental model is segmented into component models. For example, the component models can represent individual teeth. In some embodiments, in addition to separating the individual tooth models from each other, the component models are also separated from gingival tissue. Examples techniques for segmenting the digital dental model are described herein.
[0092] At operation 268, each of the sub-models is segmented into a model region corresponding to the tooth at the identified location. For example, the portion of the submodel corresponding to the tooth at the identified location may be separated from the remainder of the sub-model. In some embodiments, the operation 268 is performed simultaneously for multiple of the sub-models ( e.g., using separate processors or separate processor cores). In some embodiments, operation 268 is performed within a loop to sequentially perform the operation 268 on at least some of the sub-models.
[0093] In some embodiments, the operation 268 performs automatic tooth segmentation using a neural network system. Although alternatives are possible, in this example operation 268 includes operations 270 and 272, which are performed on each sub-model.
[0097] At operation 274, the trimmed sub-models may be refined globally. For example, adjacent sub-models may be compared to resolve conflicts in which the same portion of a model is included in multiple models. Operation 274 may be performed similarly to operation 248, which has been previously described.
As per claim 2, Martz discloses the plurality of parallelly executed processes further comprises at least one data hub process, the at least one data hub process being connected to the shared memory device and/or to the at least one first interface and being configured to segment the digital data record into data segments, if the digital data record is not already provided in form of data segments, and to provide each data segment with a key [figure 13; para 104, 135].
As per claim 4, Martz discloses at least one digital data record is provided as a scan file and/or is provided in the form of at least one of the following group: CAD file
CBCT file, picture file, ASCII File, object file [para 37].
As per claim 5, Martz discloses at least one group of computation modules is configured to analyze spatial information regarding the anatomical sub-structures
contained in said at least one digital data record [para 72].
As per claim 6, Martz discloses at least one supplemental data record is provided to the system [figure 5; para 90-94].
As per claim 7, Martz discloses at least one group of computation modules analyzes the supplemental information represented in the at least one supplemental data record at least one group of computation modules transforms the anatomical sub-structures identified in the digital data record until they fit to their representation in the at least one supplemental data record, or vice-versa preferably, if supplemental information is present for at least one of the identified anatomical sub-structures in
the virtual model, said supplemental information is assigned to said at least one identified anatomical substructure in the virtual model [figure 5; para 90-94].
As per claim 8, Martz discloses different groups of computation modules represent at least parts of different dentitions which are cataloged as belonging to a catalog of target dentitions different groups of computation modules represent at least
parts of different dentitions which are cataloged as belonging to a catalog of starting dentitions different groups of computation modules represent at least parts of different dentitions which are cataloged as belonging to a catalog of intermediary dentitions
for each starting dentition and for each target dentition, there are connections between a starting dentition, different intermediary dentitions and a target dentition, thereby establishing at least one sequence of intermediary dentitions leading from a starting dentition to a target dentition [figure 5; para 90-94, 97, 98, 139].
As per claim 9, Martz discloses based on an inputted digital data record representing a (part of a) dentition showing misalignment, the system is configured:
to identify, in the catalog of starting dentitions, at least one dentition which is identical to or at least similar to the (part of a) dentition showing misalignment, and based on the identified at least one starting dentition, to find at least one established sequence of intermediary dentitions in the catalog of intermediary dentitions, such that an endpoint of the sequence of intermediary dentitions is a target dentition in the catalog of target
dentitions [figure 5; para 90-94, 97, 98, 139].
As per claim 10, Martz discloses the system is configured to determine a set of transformations necessary to transform the (part of a) dentition showing misalignment
into the at least one target dentition via at least one of the sequences of intermediary dentitions [figure 5; para 90-94, 97, 98, 139].
Ass per claim 11, Martz discloses a set of possible boundary conditions is provided based on the virtual model, in particular in case supplemental information is present in the virtual model, the system is configured to check whether at least one of
the boundary conditions is applicable if at least one of the boundary conditions is applicable, taking account of said at least one boundary condition when determining the sequence of intermediary dentitions [figure 5; para 55, 90-94, 97, 98].
As per claim 12, Martz discloses sequence of intermediary dentitions, preferably said set of transformations, is used to create at least one treatment plan, preferably a plurality of treatment plans, wherein, preferably, the at least one treatment plan is provided in form of: at least one CAD file, preferably an STL file and/or an object file, and/or at least one human-recognizable file, in particular an ASCII file or a graphic file [para 36, 53, 90-94].
As per claim 13, Martz discloses the at least one treatment plan comprises successive and/or iterative steps for arriving at the target dentition, using at least one
appliance, wherein said at least one appliance is in the form of a fixed and/or removable appliance [para 43, 133].
As per claim 14, Martz discloses at least one group of computation modules is configured to determine, for an appliance, the shape of a bonding surface of a virtual
model of the appliance such that the bonding surface is a fit to the part of the surface of a tooth to which it is to be bonded [para 43, 49, 64].
As per claim 15, Martz discloses at least some of the computation modules are configured to represent categorical constructs [para 90-94].
As per claim 16, Martz discloses the system is configured to do unsupervised learning by using categorical constructs [para 90-94].
As per claim 17, Martz discloses at least said at least one shared memory device
said at least one computing device are located on at least one server and at least said at least one first interface said at least one second interface are located in the form of at least one client program of the server on a computer which is connected to said at least one server by a communication network [figure 21; para 44, 57, 143].
As per claim 18, Martz discloses said at least one client program comprises a plugin in the form of an interface between at least one computer program running on said computer, and the at least one server, wherein the at least one client program is configured to translate and/or edit, user inputs and/or data of the at least one computer program relating to the at least part of a dentition of patient to create the at least one digital data record and/or translate and/or edit, the output of the at least one second
interface for the at least one computer program [para 44, 57, 99].
As per claim 19, Martz discloses the system is configured to attach description in written natural language to individual anatomical sub-structures of a dentition [para 36, 53].
As per claims 20-26, claims 1, 2 and 4-12 basically are the corresponding elements that are carried out the method of operating step in claims 20-26. Accordingly, claims 20-26 are rejected for the same reason as set forth in claims 1, 2 and 4-12.
As to claims 27 and 29, directed to a computer-readable medium storing the computer readable instructions to perform the method of steps executed by the system as set forth in claim 1. Therefore, it is rejected on the same basis as set forth hereinabove.
As per claim 28, Martz discloses a process for obtaining at least one appliance based on the at least one treatment plan obtained by a system according to claim 13, wherein the at least one appliance is chosen from a group comprising at least one of an aligner, an orthodontic bracket and a fixed lingual retainer [figure 5; para 47, 48, 90-94].
As per claim 30, Martz discloses a data carrier signal carrying: the at least one virtual model created by the system according to claim 1 [para 37].
Allowable Subject Matter
14. Claim 3 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
15. The following is a statement of reasons for the indication of allowable subject matter: the prior art of records do not teach of run idly if no data segment with the specific key is detected or provided if a data segment with the specific key is detected in the at least one shared memory device or provided by at least one different group of computation modules, apply the machine learning technique on that data segment and output the result to at least one different group of computation modules and/or to the shared memory device and/or to the at least one output device.
16. Examiner's note: Examiner has cited particular paragraphs and columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
17. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Davison, US 2016/0287340 teaches a method of pre-operatively forming a surgical splint configured to receive a patient's dentition the method comprising:
obtaining a 3-D facial computer model in a computer of at least the patient's maxilla, mandible, and dentition, the 3-D facial computer model including first virtual
dentition, wherein at least a portion of the first virtual dentition has a first virtual surface geometry that defines at least one first fiduciary marker, the at least one first fiduciary marker defining a first location that identifies a first anatomical feature of the first virtual
dentition; obtaining a 3-D optical scan of the patient's dentition in the computer, the 3-D optical scan including second virtual dentition, wherein at least a portion of the
second virtual dentition has a second surface geometry that defines at least one second fiduciary marker, the at least one second fiduciary marker defining a second location that identifies a second anatomical feature of the second virtual dentition; aligning the first fiduciary marker with the second fiduciary marker; and after the aligning step, replacing the first virtual dentition of the 3-D facial computer model with the second
virtual dentition to form a composite 3-D virtual model, the composite 3-D virtual model having third virtual dentition in a planned post-operative configuration.
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas Lee can be reached on 571-272-3667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sep. 19, 2025
/CHUN CAO/Primary Examiner, Art Unit 2115