DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 20, 2026 has been entered.
Response to Amendment
3. The amendment filed April 20, 2026 has been entered. Claims 1 and 3-14 remain pending in the application.
Response to Arguments
4. Applicant's arguments filed April 20, 2026 have been fully considered but they are not persuasive.
5. Applicant argues that in Azernikov, the dentition category is derived from the content of the scan data whereas the claimed type of 3D scan data is based on the acquisition condition of the data. Furthermore, the Applicant argues that Azernikov teaches operating on a single, fixed type of input data and merely analyzes that data to determine a dentition category for selecting a neural network. The Applicant argues that Azernikov “neither discloses nor suggests identifying a scan data type from among multiple predefined categories, nor distinguishing among different scan data availability scenarios”.
Examiner replies claim 1 as amended teaches the type of 3D scan data corresponds to a plurality of predefined categories “including”. The term “including” is open-ended and thus allows for other predefined categories aside from the ones listed to exist. Refer to MPEP 2111.03(I). Thus, the type of scan data can also be characterized by the dentition categories disclosed by Azernikov and choosing a neural network based on those dentition categories teaches identifying a scan data type from among multiple predefined categories and selecting a module based on the category. Furthermore, Saphier et al. (U.S. Patent Application Publication No. 2021/0321872 A1), hereinafter referred to as Saphier, a new ground of rejection, is used to teach the second and third categories.
In addition, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., ‘the claimed “type of 3D scan data” is defined based on the acquisition condition of the data’) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Thus, the type of 3D scan data does not have to be defined based on just the acquisition condition of the data.
Therefore, Azernikov in view of Saphier teaches the dentition categories as claimed in claim1 and operating on different categories to select a neural network or module.
6. Applicant argues that Azernikov does not contemplate or distinguish multiple scan data configurations and therefore lacks any decision logic that selects one of multiple mutually exclusive scan data scenarios. The Applicant clarifies that “different manner” refers to differences in design strategy or workflow depending on the availability and combination of scan data types.
Examiner replies that In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “different manner” refers to differences in design strategy or workflow depending on the availability and combination of scan data types) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, claim 1 as amended teaches the type of 3D scan data corresponds to a plurality of predefined categories “including”. The term “including” is open-ended and thus allows for other predefined categories aside from the ones listed to exist. Refer to MPEP 2111.03(I). Thus, the type of scan data can also be characterized by the dentition categories that Azernikov teaches and generating a prosthesis in different manners based on the neural network selected teaches the limitations as claimed in claim 1 under the broadest reasonable interpretation.
Furthermore, Saphier, a new ground of rejection, is used to teach the second and third categories where different modules or different manner of generating prostheses are taught.
7. Conclusion: The rejections set in the Office Action are shown to have been proper, and the claims are rejected below.
Claim Rejections - 35 USC § 112
8. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
9. Claim 1 and 3-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, lines 4-7 states “selecting one module from a plurality of modules according to a type of the 3D scan data of the object; and generating a prosthesis … using the selected module” and lines 18-19 state “selecting a module configured to design the prosthesis in a different manner according to an identified category to which the 3D scan data corresponds.” Is the module selected in line 4 the same as the module selected in line 18? The Examiner is unclear as to which module selected in line 4 or 18 is the one designing the prosthesis.
Line 18 “selecting a module to configured to design the prosthesis in a different manner” is also indefinite as to what the “different manner” refers to. The Examiner is unclear as to what the “different manner” is as claimed. Does the Applicant mean the manner of selecting the modules is different from the initially selecting of a module claimed in line 4 of Claim 1? Or does the different manner refer to the prosthesis being designed differently depending on the category? Thus, claim 1 in indefinite.
Regarding claim 14, claim 14 is rejected for similar rationale as to claim 1.
Claims 3-13 are rejected by dependency on claim 1.
Claim Rejections - 35 USC § 103
10. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
11. 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.
12. Claim(s) 1, 3-11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. (U.S. Patent Application Publication No. 2018/0028294 A1), hereinafter referred to as Azernikov, in view of Saphier et al. (U.S. Patent Application Publication No. 2021/0321872 A1), hereinafter referred to as Saphier.
13. Regarding claim 1, Azernikov teaches a data processing method using a data processing apparatus, the data processing method comprising: obtaining a 3D oral model comprising 3D scan data of an object (Paragraph 56 mentions scanning the patient’s dental or oral anatomy which can be assembled into a digital model; Paragraph 78 mentions uploading the patient’s dentition scan data set which can be in its original 3D format);
selecting one module from a plurality of modules according to a type of the 3D scan data of the object (Paragraph 12-13 mentions that the 3d scan data can be analyzed to determine a category of dentition or dental feature. The category can include restoration types such as crowns, inlays, bridge, and implants; Paragraph 78 mentions selecting one of the neural networks depending on selected ‘type of dental restoration’. Selecting the type of dental restoration and neural networks teaches selecting a module under broadest reasonable interpretation),
and generating a prosthesis for the object by designing the prosthesis using the selected module (Paragraph 78 teaches that the client can choose a dental or restoration to be modeled and choose the neural network based on the selected restoration type which then generates the prosthesis. This designs a prosthesis using the selected module; Paragraph 79 teaches using the selected trained neural network, they can generate a 3d model of a crown),
wherein the selecting one module from the plurality of modules, comprises identifying the type of the 3D scan data by determining to which one of a plurality of predefined categories the 3D scan data of the object corresponds, the plurality of predefined categories including: a first category in which the 3D scan data includes post-preparation 3D scan data and does not include pre-preparation 3D scan data (Paragraph 7 teaches scanning the prepared tooth and not the pre-prepared tooth. Thus, the 3D scan data is post-preparation 3D scan data; Paragraph 93 teaches the neural network identifies prepared teeth. Thus, this teaches identifying the type of 3D scan data to belong to the first category since prepared teeth are not pre-preparation 3D scan data; Paragraph 126 teaches “determining dental category of the dentition” and also teaches “recognizing prepared jaw and opposing jaws, identifying prepared tooth … recognizing restoration types”. This teaches identifying the type of the 3D scan data to be a first category which the 3D scan data includes post-preparation 3D scan data),
selecting a module configured to design the prosthesis in a different manner according to an identified category to which the 3D scan data corresponds (Paragraphs 77- 78 teaches selecting one of the neural networks depending on the category of the dentition identified from the post-preparation 3D scan. The category of dentition can be considered a type of 3D scan data. Thus, the module or neural network is selected differently according to the identified type of the 3D scan data; Figure 10B and Paragraph 131 teaches generating a dental restoration model at step 1080 depending on the identified dental features or identified category. It teaches it can create a crown model, bridge, or dental implant which teaches designing prostheses in different manners according to the identified category the 3D scan data corresponds to. Thus, Azernikov teaches selecting a module or network to design the prosthesis based on the identified category like a post-prepared tooth. Furthermore, the applicant states “the predefined categories including” which means the predefined categories or identified category can include more predefined categories other than the ones listed in Claim 1.).
However, Azernikov is not relied upon for the below claim language: identifying the type of the 3D scan data by determining to which one of a plurality of predefined categories the 3D scan data of the object corresponds, the plurality of predefined categories including a second category in which the 3D scan data includes pre-preparation 3D scan data and does not include post-preparation 3D scan data, and a third category in which the 3D scan data includes both post-preparation 3D scan data and pre-preparation 3D scan data.
Saphier teaches identifying the type of the 3D scan data by determining to which one of a plurality of predefined categories the 3D scan data of the object corresponds, the plurality of predefined categories including a second category in which the 3D scan data includes pre-preparation 3D scan data and does not include post-preparation 3D scan data (Paragraph 283 teaches scanning the dental arch before any treatment is performed and “may identify that saved scan/3D model as a pre-scan/pre-scan 3D model. In an example, a pre-scan 3D model may be generated before a tooth is ground to form a preparation tooth” This teaches identifying the type of 3D scan data as pre-preparation 3D scan data that does not include post-preparation 3D scan data; Figure 20 and Paragraph 592-593 teach in step 2006 determining “whether a restorative object was detected” and a restorative object includes a preparation tooth. It then teaches generating a prosthesis differently in steps 2016 and 2012 depending on if the 3D scan data has the prepared tooth or no prepared tooth. No restorative object being detected teaches the second category. This teaches identifying the 3D scan data by determining it corresponds to the second category in which the 3D scan data includes pre-preparation 3D scan data and does not include post-preparation 3D scan data).
and a third category in which the 3D scan data includes both post-preparation 3D scan data and pre-preparation 3D scan data (Paragraph 611-612 and Figure 23 teach using 3D scan data including both post-preparation 3D scan data and pre-preparation 3D scan data. Paragraph 611 teaches leveraging “previously generated 3D models that were generated before the preparation tooth was created” in addition to the scan of “an entire dental arch that contains a restorative object” in order to identify any differences “between a current 3D surface of a dental arch and a prior 3D surface of the dental arch”. This is used to then generate a dental prosthesis).
Azernikov and Saphier are considered analogous to the claimed invention as because both are in the same field of creating dental prosthesis using 3D scan data of the patient’s teeth. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of generating a dental prosthesis taught by Azernikov with the identification of the second and third categories taught by Saphier in order to automatically generate prescriptions and a restorative or orthodontic workflow based on the patient’s dental arch (Saphier Paragraph 201).
14. Regarding claim 3, Azernikov in view of Saphier teaches the limitations of claim 1. Azernikov further teaches the data processing method wherein the selecting of the module from the plurality of modules comprises selecting a first module on the basis that the 3D scan data of the object is determined to correspond to the first category (Paragraph 7 teaches scanning the prepared tooth and not the pre-prepared tooth. Thus, the 3D scan data is post-preparation 3D scan data; Paragraph 93 teaches the neural network identifies prepared teeth. Thus, this teaches identifying the type of 3D scan data to belong to the first category since prepared teeth are not pre-preparation 3D scan data; Paragraph 126 teaches “determining dental category of the dentition” and also teaches “recognizing prepared jaw and opposing jaws, identifying prepared tooth … recognizing restoration types”. This teaches identifying the type of the 3D scan data to be a first category which the 3D scan data includes post-preparation 3D scan data), wherein the generating of the prosthesis comprises:
obtaining a library tooth corresponding to the object based on the selecting of the first module (Paragraph 58 teaches that a tooth library can be searched; Paragraph 73 mentions the design software can include the library-based automatic dental restoration to design the dental restoration or prosthesis);
and generating a first prosthesis using the post-preparation 3D scan data of the object and the library tooth (Paragraph 73 teaches the design software can include the library-based automatic dental restoration along with the dental information from the post-preparation 3D scan to design the dental restoration or prosthesis).
15. Regarding claim 4, Azernikov in view of Saphier teaches the limitations of claim 1. Azernikov further teaches selecting a module based on the 3D scan data of the object (Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature. The category can include restoration types such as crowns, inlays, bridge, and implants; Paragraph 78 teaches using the category information identified from the 3D scan data to select one of the neural networks to generate the prosthesis).
However, Azernikov is not relied upon for the below claim language: selecting a second module on the basis that the 3D scan data of the object is determined to correspond to the second category, wherein the generating of the prosthesis comprises generating a second prosthesis based on the selecting of the second module by using the pre-preparation 3D scan data of the object.
Saphier teaches that wherein the selecting of the module from the plurality of modules comprises selecting a second module on the basis that the 3D scan data of the object is determined to correspond to the second category, wherein the generating of the prosthesis comprises generating a second prosthesis based on the selecting of the second module by using the pre-preparation 3D scan data of the object (Paragraph 283 teaches scanning the dental arch before any treatment is performed and “may identify that saved scan/3D model as a pre-scan/pre-scan 3D model. In an example, a pre-scan 3D model may be generated before a tooth is ground to form a preparation tooth” This teaches identifying the type of 3D scan data as pre-preparation 3D scan data that does not include post-preparation 3D scan data; Figure 20 and Paragraph 592-593 teach in step 2006 determining “whether a restorative object was detected” and a restorative object includes a preparation tooth. It then teaches generating a prosthesis differently in steps 2016 and 2012 depending on if the 3D scan data has the prosthesis or no prosthesis. If no restorative object is identified, it goes to step 2016 and step 2012. This teaches selecting the second module of step 2016 and creating a prosthesis explained in Paragraph 594 by determining it corresponds to the second category in which the 3D scan data includes pre-preparation 3D scan data and does not include post-preparation 3D scan data).
Azernikov and Saphier are considered analogous to the claimed invention as because both are in the same field of creating dental prosthesis using 3D scan data of the patient’s teeth. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of generating a dental prosthesis taught by Azernikov with the selecting of a second module based on the second category taught by Saphier in order to automatically generate prescriptions and a restorative or orthodontic workflow based on the patient’s dental arch (Saphier Paragraph 201).
16. Regarding claim 5, Azernikov in view of Saphier teaches the limitations of claim 1. Azernikov further teaches selecting a module based on the 3D scan data of the object (Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature. The category can include restoration types such as crowns, inlays, bridge, and implants; Paragraph 78 teaches using the category information identified from the 3D scan data to select one of the neural networks to generate the prosthesis).
However, Azernikov is not relied upon for the below claim language: selecting a third module on the basis that the 3D scan data of the object is determined to correspond to the third category, wherein the generating of the prosthesis comprises generating a third prosthesis based on the selecting of the third module by using the post-preparation 3D scan data of the object and the pre-preparation 3D scan data of the object.
Saphier teaches selecting a third module on the basis that the 3D scan data of the object is determined to correspond to the third category, wherein the generating of the prosthesis comprises generating a third prosthesis based on the selecting of the third module by using the post-preparation 3D scan data of the object and the pre-preparation 3D scan data of the object (Paragraph 611-612 and Figure 23 teach using 3D scan data including both post-preparation 3D scan data and pre-preparation 3D scan data. Paragraph 611 teaches leveraging “previously generated 3D models that were generated before the preparation tooth was created” in addition to the scan of “an entire dental arch that contains a restorative object” in order to identify any differences “between a current 3D surface of a dental arch and a prior 3D surface of the dental arch”. From the differences determined, the third module, as seen through the steps in Figure 23, is executed to generate a prosthesis using both the pre and post preparation 3D scan data).
Azernikov and Saphier are considered analogous to the claimed invention as because both are in the same field of creating dental prosthesis using 3D scan data of the patient’s teeth. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of generating a dental prosthesis by selecting a module taught by Azernikov with the selecting a third module based on the third category taught by Saphier in order to automatically generate prescriptions and a restorative or orthodontic workflow based on the patient’s dental arch (Saphier Paragraph 201).
17. Regarding claim 6, Azernikov in view of Saphier teaches the limitations of claim 1. Azernikov further teaches the data processing method further comprising receiving a selection of one module from the plurality of modules from a user (Paragraph 9 teaches that the user can decide which dental restoration type to be fabricated; Paragraph 78 teaches selecting one of the neural networks depending on selected restoration type).
18. Regarding claim 7, Azernikov in view of Saphier teaches the limitations of claim 1. Azernikov further teaches the data processing method further comprising identifying, from the 3D scan data included in the 3D oral model, 3D scan data required as mandatory input data by the selected module based on the identified category (Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature. The category can include restoration types such as crowns, inlays, bridge, and implants; Paragraph 79 teaches requiring the patient’s dentition data set in order to use the neural network or selected module to generate the 3D model of a crown which is a prosthesis. This discloses that the 3D scan data is mandatory input data; Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature. The category can include restoration types such as crowns, inlays, bridge, and implants; Paragraph 78 teaches using the category information identified from the 3D scan data to select one of the neural networks to generate the prosthesis).
19. Regarding claim 8, Azernikov in view of Saphier teaches the limitations of claim 7. Azernikov further teaches the data processing method wherein the 3D oral model comprises 3D scan data of a dental arch opposite to an arch including the object, and the dental arch comprises an antagonist tooth corresponding to the object (Paragraph 56 teaches taking a scan of the preparation and opposing jaws which inherently contains the antagonist tooth), wherein the identifying of the 3D scan data comprises identifying the 3D scan data of the dental arch as mandatory input data (Paragraph 79 teaches requiring the patient’s dentition data set in order to use the neural network or selected module to generate the 3D model of a crown which is a prosthesis. This discloses that the 3D scan data is mandatory input data).
20. Regarding claim 9, Azernikov in view of Saphier teaches the limitations of claim 8. Azernikov further teaches the data processing method wherein the generating of the prosthesis for the object comprises generating the prosthesis for the object by using both the 3D scan data of the object and the 3D scan data of the dental arch (Paragraph 56 teaches taking a scan of the preparation and opposing jaws which inherently contains the antagonist tooth. It also teaches using the dental model of those scans to design a dental restoration or prosthesis).
21. Regarding claim 10, Azernikov in view of Saphier teaches the limitations of claim 7. Azernikov further teaches the data processing method wherein the identifying of the 3D scan data required as mandatory input data comprises identifying the category of the 3D scan data from identification information on the 3D scan data (Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature; Paragraph 79 teaches requiring the patient’s dentition data set in order to use the neural network or selected module to generate the 3D model of a crown which is a prosthesis. This discloses that the 3D scan data is mandatory input data), wherein the identification information indicates presence or absence of pre-preparation 3D scan data and post-preparation 3D scan data (Paragraph 93 teaches the neural network identifies prepared teeth. Thus, this teaches identifying the presence of post-preparation 3D scan data which also teaches the absence of pre-preparation 3D scan data. The post-preparation 3D scan data means the teeth have been prepared which thus means there is an absence of pre-preparation scan data; Paragraph 126 teaches “determining dental category of the dentition” and also teaches “recognizing prepared jaw and opposing jaws, identifying prepared tooth … recognizing restoration types”. This teaches the presence of post-preparation 3D scan data and absence of pre-preparation 3D scan data).
22. Regarding claim 11, Azernikov in view of Saphier teaches the limitations of claim 10. Azernikov further teaches the data processing method wherein the category of the 3D scan data comprises at least one piece of information indicating whether the 3D scan data is about maxilla or mandible, or information indicating whether the 3D scan data is about a pre-preparation tooth or a post-preparation tooth (Paragraph 12-13 teaches that the 3D scan data can be analyzed to determine a category of dentition or dental feature; Paragraph 20 teaches that the data set has dental features identified which can comprise of a dental preparation, lower jaw, or upper jaw).
23. Regarding claim 14, claim 14 is the data processing apparatus claim (Azernikov Figure 2 teaches a processor at marker 202; Azernikov Paragraph 82 teaches the processor uses instructions and data from the memory) of method claim 1 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
24. Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. (United States Patent Application Publication No. 2018/0028294 A1), hereinafter referred to as Azernikov, in view of Saphier et al. (U.S. Patent Application Publication No. 2021/0321872 A1), hereinafter referred to as Saphier, as applied to claim 1 above, and further in view of Ryu (United States Patent Application Publication No. 2021/0244518 A1).
25. Regarding claim 12, Azernikov in view of Saphier teaches the limitations of claim 1. However, Azernikov and Saphier are not relied upon for the below claim language: the data processing method wherein, when the object comprises a plurality of objects, the generating of the prosthesis for the object comprises generating prostheses together for the plurality of objects.
Ryu teaches the data processing method wherein, when the object comprises a plurality of objects, the generating of the prosthesis for the object comprises generating prostheses together for the plurality of objects (Paragraph 178-179 and Figure 8 marker ‘C’ teaches that in the 3D scan model, we can mark two teeth to be the target of restoration to provide a plurality of prostheses to be generated; Paragraph 21, Figures 9A and 9B teach a user creating prosthesis for two objects).
Azernikov, Saphier, and Ryu are considered analogous to the claimed invention as because both are in the same field of creating dental prostheses based on 3D scan data of the patient’s teeth. it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the data processing method to generate a prosthesis in Azernikov in view of Saphier with the method of generating multiple prostheses in Ryu to reduce time for designing a prosthesis by designing multiple prostheses at a time (Ryu Paragraph 178).
26. Regarding claim 13, Azernikov in view of Saphier and Ryu teaches the limitations of claim 12. However, Azernikov and Saphier are not relied upon for the below claim language: the data processing method wherein, when a portion of the object is included in maxilla and another portion of the object is included in mandible, the generating of the prosthesis for the object comprises generating together a prosthesis for the portion of the object included in the maxilla and a prosthesis for the other portion of the object included in the mandible.
Ryu teaches the data processing method wherein, when a portion of the object is included in maxilla and another portion of the object is included in mandible, the generating of the prosthesis for the object comprises generating together a prosthesis for the portion of the object included in the maxilla and a prosthesis for the other portion of the object included in the mandible (Paragraph 178-179, Figure 8 marker ‘C’ teaches that a user can mark two teeth to be the target of restoration; Paragraph 21, Figures 9A and 9B show a user creating prostheses for two objects; Paragraph 82 teaches that you can take a scan of the upper and lower jaw which are the maxilla and mandible; Paragraph 96 teaches the design module can display both the oral image for the upper and lower jaw. Since the user can see both the upper and lower jaw, they can mark teeth in both the maxilla and mandible to create a prosthesis).
Azernikov, Saphier, and Ryu are considered analogous to the claimed invention as because both are in the same field of creating dental prostheses based on 3D scan data of the patient’s teeth. It would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the data processing method to generate a prosthesis in Azernikov in view of Saphier with the method of generating multiple prostheses in the maxilla and mandible taught by Ryu to reduce time for designing a prosthesis by designing multiple prostheses at a time (Ryu Paragraph 178).
Conclusion
27. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 9-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571)272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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