Office Action Predictor
Last updated: April 15, 2026
Application No. 18/325,797

MANUFACTURING DEVICE FOR MANUFACTURING A DENTAL OBJECT

Non-Final OA §103§112
Filed
May 30, 2023
Examiner
SHEN, YUZHEN
Art Unit
2623
Tech Center
2600 — Communications
Assignee
Ivoclar Vivadent AG
OA Round
3 (Non-Final)
70%
Grant Probability
Favorable
3-4
OA Rounds
2y 5m
To Grant
79%
With Interview

Examiner Intelligence

Grants 70% — above average
70%
Career Allow Rate
507 granted / 720 resolved
+8.4% vs TC avg
Moderate +8% lift
Without
With
+8.4%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
44 currently pending
Career history
764
Total Applications
across all art units

Statute-Specific Performance

§101
0.2%
-39.8% vs TC avg
§103
53.7%
+13.7% vs TC avg
§102
27.3%
-12.7% vs TC avg
§112
16.7%
-23.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 720 resolved cases

Office Action

§103 §112
Detailed Action 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/04/2025 has been entered. Claim Objections 3. Claim 15 is objected to because of the following informalities: Claim 15 depends on claim 12. Claim 12 is directed to a milling device. In claim 15, the limitations “a firing furnace” and “a 3D printer” should be deleted. Appropriate corrections are required. Claim Rejections - 35 USC § 112 4. The following is a quotation of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention. (FP 7.30.01) 5. Claims 12-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. 5. Claim 12 recites the limitations “determining control data … using a self-learning algorithm; and determining a milling tool … using a self-learning algorithm; wherein the self-learning algorithms for determining the control data and milling data …”. Applicant appears to claim at least two self-learning algorithms including a first self-learning algorithm determining control data and a second self-learning algorithm determining a milling tool. However, according to [0033]-[0035] of the specification and Fig. 2 of the drawing, applicant discloses only one self-learning algorithm. Nowhere in the specification discloses more than one self-learning algorithm. Therefore, the limitations “determining control data … using a self-learning algorithm; and determining a milling tool … using a self-learning algorithm; wherein the self-learning algorithms for determining the control data and milling data …” are not supported by the original disclosure and constitutes new matter (See also 37 C.F.R. 1.121(f), MPEP 608.04, 706.03(o)). Claims 13-15 are rejected as being dependent upon a rejected base claim. Claim Rejections - 35 USC § 103 6. 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 of this title, 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. 7. Claims 1-2, 5, 7, and 10 are rejected under 35 U.S.C. 103 as unpatentable over ROHNER (US 20210077232 A1) in view of AZERNIKOV (US 20180028294 A1). Regarding claim 1, ROHNER (Figs. 1-9) discloses a manufacturing device for manufacturing a dental object (Figs. 1-9; dental milling machine [0027]), comprising: an electronic camera (camera 64) for capturing an image data set of the dental object to be processed ([0025]-[0026] and [0055]; image capture of dental object); and a controller for determining the control data for manufacturing the dental object on the basis of the image data set ([0025]-[0026] and [0055]; control unit for manufacturing the dental object); wherein the controller is configured to determine a milling tool of the dental object to be processed based on the image data set ([0026] and [0055]; control unit determines a milling tool based on an image data). ROHNER does not disclose wherein the controller comprises a self-learning algorithm for determining the control data: wherein the self-learning algorithm for determining the control data is previously trained by a plurality of image data sets of different dental objects. However, AZERNIKOV (e.g., Figs. 1-5 and 10-17) discloses manufacturing of a dental object, wherein the controller comprises a self-learning algorithm for determining the control data (e.g., Fig. 5; self-learning model); wherein the self-learning algorithm for determining the control data is previously trained by a plurality of image data sets of different dental objects (e.g., Figs. 4-5; self-learning model or training module use the training data set containing a plurality of images or depth maps to determining control data of dental milling machine; [0077] and [0090]-[0118]). Therefore, it would be obvious to one skilled in the art to incorporate the teaching from AZERNIKOV to the manufacturing device of ROHNER to select and control milling tools using self-learning. Regarding claim 2, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, ROHNER (Figs. 1-9) discloses wherein the manufacturing device is configured to rotate the dental object to be processed in front of the camera (Figs. 8-9; the dental object 10 is rotated with respect to the camera 64). Regarding claim 5, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, AZERNIKOV discloses wherein the self-learning algorithm comprises an artificial neural network (e.g., Fig. 5; [0096]-[0114]). Regarding claim 7, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, ROHNER (Figs. 1-9) discloses wherein the controller is configured to determine a position and/or an orientation of the dental object to be processed based on the image data set (Figs. 8-9; orientation of the dental object 10). Regarding claim 10, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, ROHNER (Figs. 1-9) discloses wherein the manufacturing device comprises a milling device (Figs. 8-9; dental milling machine; [0027]). 7. Claims 3 and 6 are rejected under 35 U.S.C. 103 as unpatentable over ROHNER (US 20210077232 A1) in view of AZERNIKOV (US 20180028294 A1) and further in view of Benzinger (US 20210053169 A1). Regarding claim 3, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, but does not disclose discloses wherein the manufacturing device is configured to illuminate the dental object to be processed with light of one or more predetermined wavelengths. However, Benzinger (Figs. 1-6) discloses a manufacturing device (manufacturing machine 1) for manufacturing a dental object ([0049] and [0002]), comprising: an electronic camera (camera 13; [0051] and [0059]) for capturing an image data set of the dental object to be processed ([0054]-[0056]; image capture and processing); and a controller (computer 24) for determining the control data for manufacturing the dental object on the basis of the image data set (Figs. 4 and 6; [0055]-[0056] and [0062]-[0063]). Benzinger (Figs. 1-6) further discloses wherein the manufacturing device is configured to illuminate the dental object to be processed with light of one or more predetermined wavelengths (illumination light source 18; [0054]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from Benzinger to the manufacturing device of ROHNER in view of AZERNIKOV to control the manufacturing of dental objects. Regarding claim 6, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, AZERNIKOV discloses wherein the controller is configured to determine a size, a type, a material and/or a processing step of the dental object to be manufactured based on the image data set ([0026]-[0027] and [0055]). As another reference, Benzinger (Figs. 1-6) discloses wherein the controller is configured to determine a size, a type, a material and/or a processing step of the dental object to be manufactured based on the image data set ([0064]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from Benzinger to the manufacturing device of ROHNER in view of AZERNIKOV to control the manufacturing of dental objects. 8. Claim 17 is rejected under 35 U.S.C. 103 as unpatentable over ROHNER (US 20210077232 A1) in view of AZERNIKOV (US 20180028294 A1) and further in view of Feichtinger (US 20090180118 A1). Regarding claim 17, ROHNER in view of AZERNIKOV discloses the manufacturing device according to claim 1, ROHNER (e.g., Figs. 8-9) discloses wherein the image data sets are captured from different directions, but does not disclose the image data sets are captured under light having different wavelengths. However, Feichtinger (Figs. 1-13) discloses a manufacturing device for manufacturing a dental object ([0092], [0106], and [0111]; dental object), wherein the image data sets are captured from different directions and under light having different wavelengths ([0025]-[0026]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from Feichtinger to the manufacturing device of ROHNER in view of AZERNIKOV to control the manufacturing of dental objects. Allowable Subject Matter 9. Claims 16 and 9 are allowed. The following is an examiner’s statement of reasons for allowance: The present invention is directed to a manufacturing device for manufacturing a dental object. The closet prior arts, ROHNER (US 20140113237 A1), Feichtinger (US 20090180118 A1), Benzinger (US 20210053169 A1), ROHNER (US 20210077232 A1), Azernikov (US 20180028294 A1), and Noone (US 20200166909 A1), individually or in combination, discloses a manufacturing device for manufacturing a dental object, comprising: an electronic camera for capturing an image data set of the dental object to be processed; and a controller for determining control data for manufacturing the dental object on the basis of the image data set, wherein the manufacturing device comprises a firing furnace, wherein the control data comprises a firing temperature or a firing time, but fails to teach wherein the controller comprises a self-learning algorithm for determining the control data; wherein the self-learning algorithm for determining the control data is previously trained by a plurality of image data sets of different dental objects. Response to Arguments 10. Regarding claim 1, the references of ROHNER (US 20210077232 A1) and AZERNIKOV (US 20180028294 A1) have been used for rejection. Inquiry Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUZHEN SHEN whose telephone number is (571)272-1407. The examiner can normally be reached on 9:00-18:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chanh Nguyen can be reached on 571-272-7772. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /YUZHEN SHEN/Primary Examiner, Art Unit 2623
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Prosecution Timeline

May 30, 2023
Application Filed
Jun 09, 2025
Non-Final Rejection — §103, §112
Sep 05, 2025
Response Filed
Sep 21, 2025
Final Rejection — §103, §112
Nov 18, 2025
Response after Non-Final Action
Dec 04, 2025
Request for Continued Examination
Dec 17, 2025
Response after Non-Final Action
Jan 09, 2026
Non-Final Rejection — §103, §112
Apr 02, 2026
Response Filed

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
70%
Grant Probability
79%
With Interview (+8.4%)
2y 5m
Median Time to Grant
High
PTA Risk
Based on 720 resolved cases by this examiner. Grant probability derived from career allow rate.

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